Tootfinder

This https://arxiv.org/abs/2402.18233 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Zero-Shot Aerial Object Detection with Visual Description Regularization
Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consuming and may require expert knowledge. Thus, it is desirable to study label-efficient object detection methods on aerial images. In this work, we propose a zero-shot method for aerial object detection named visual Description Regularization, or DescReg. Concretely, we identify the weak semantic-visual correlation of the …

Creator Hearts: Investigating the Impact Positive Signals from YouTube Creators in Shaping Comment Section Behavior
Frederick Choi, Charlotte Lambert, Vinay Koshy, Sowmya Pratipati, Tue Do, Eshwar Chandrasekharan
https://arxiv.org/abs/2404.03612

Creator Hearts: Investigating the Impact Positive Signals from YouTube Creators in Shaping Comment Section Behavior
Much of the research in online moderation focuses on punitive actions. However, emerging research has shown that positive reinforcement is effective at encouraging desirable behavior on online platforms. We extend this research by studying the "creator heart" feature on YouTube, quantifying their primary effects on comments that receive hearts and on videos where hearts have been given. We find that creator hearts increased the visibility of comments, and increased the amount of positive engage…

Emergent Elastic Surfaces from Two-Dimensional Dirac Materials
Pablo A. Morales, Pavel Castro-Villareal
https://arxiv.org/abs/2404.02741 https://

Emergent Elastic Surfaces from Two-Dimensional Dirac Materials
Temperature constraints are highly desirable in the experimental setup when seeking the synthesis of new carbon structures. Fluctuations of the Dirac field result in temperature-dependent corrections to the Helfrich-Canham formulation, which governs the classical elasticity of the membrane in equilibrium state. Here, we examine the emergent shapes allowed by the effective model up to quadratic order in Ricci curvature and discuss the constraints required to observe them. We determine the mechan…

Please try Session, SimpleX, or Threema before deciding to stay with Signal (or WhatsApp).
They do a lot of things well, but when deciding things like this that could greatly improve the user experience I've found the Signal developers / leadership are not making good decisions.
#QT

Deviant Ollam (@deviantollam@defcon.social)
Attached: 1 image Question: am i alone in being frustrated that @signalapp@mastodon.world does not allow you to be logged in on two mobile devices at the same time? I have more than one phone. (I have my main phone and a backup in case main phone dies or breaks) I'd *really* like to be logged in on both (or have one be the "primary" and the other device "linked" etc etc) Is there some reason why the developers have decided that this is not a desirable way to operate?

Ampere Analysis: Disney has the biggest library of desirable titles at 148, followed by Paramount Global's 72; Netflix would be Disney's best licensing partner (David Satin/The Streamable)
https://thestre…

Disney Has the Biggest Library of Desirable Titles as Licensing Becomes Increasingly Important in Streaming Survival
Disney’s catalog contains the highest number of titles that other streamers might find desirable, according to new data from Ampere Analysis.

Vestibular schwannoma growth_prediction from longitudinal MRI by time conditioned neural fields
Yunjie Chen, Jelmer M. Wolterink, Olaf M. Neve, Stephan R. Romeijn, Berit M. Verbist, Erik F. Hensen, Qian Tao, Marius Staring
https://arxiv.org/abs/2404.02614

Vestibular schwannoma growth_prediction from longitudinal MRI by time conditioned neural fields
Vestibular schwannomas (VS) are benign tumors that are generally managed by active surveillance with MRI examination. To further assist clinical decision-making and avoid overtreatment, an accurate prediction of tumor growth based on longitudinal imaging is highly desirable. In this paper, we introduce DeepGrowth, a deep learning method that incorporates neural fields and recurrent neural networks for prospective tumor growth prediction. In the proposed method, each tumor is represented as a si…

ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics
Georges DupretLeo, Konstantin SozinovLeo, Carmen Barcena GonzalezLeo, Ziggy ZacksLeo, Amber YuanLeo, Benjamin CarteretteLeo, Manuel MaiLeo, Shubham BansalLeo, Gwo LiangLeo, Lien, Andrey Gatash, Roberto Sanchis Ojeda, Mounia Lalmas
https://arxiv.org/abs/24…

ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics
Making ideal decisions as a product leader in a web-facing company is extremely difficult. In addition to navigating the ambiguity of customer satisfaction and achieving business goals, one must also pave a path forward for ones' products and services to remain relevant, desirable, and profitable. Data and experimentation to test product hypotheses are key to informing product decisions. Online controlled experiments by A/B testing may provide the best data to support such decisions with high c…

NPEPPS Is a Druggable Driver of Platinum Resistance
https://aacrjournals.org/cancerres/article/doi/10.1158/0008-5472.CAN-23-1976/741998/NPEPPS-Is-a-Druggable-Driver-of-Platinum

Modeling temporal dependency of longitudinal data: use of multivariate geometric skew-normal copula
Subhajit Chattopadhyay
https://arxiv.org/abs/2404.03420

Modeling temporal dependency of longitudinal data: use of multivariate geometric skew-normal copula
Use of copula for the purpose of modeling dependence has been receiving considerable attention in recent times. On the other hand, search for multivariate copulas with desirable dependence properties also is an important area of research. When fitting regression models to non-Gaussian longitudinal data, multivariate Gaussian copula is commonly used to account for temporal dependence of the repeated measurements. But using symmetric multivariate Gaussian copula is not preferable in every situati…

This https://arxiv.org/abs/2403.19581 has been replaced.
initial toot: https://mastoxiv.page/@a…

Tuning the intrinsic anomalous Hall effect from large to zero in the two ferromagnetic states of the layered SmMn2Ge2
The intrinsic anomalous Hall conductivity in a ferromagnetic metal is completely determined by its band structure. Since the spin orientation direction is an important band structure tuning parameter, it is highly desirable to study the anomalous Hall effect in a system with multiple spin reorientation transitions. We study a layered tetragonal room temperature ferromagnet SmMn2Ge2, which gives us the opportunity to measure magneto-transport properties where the long c-axis and the short a-axis…

Creator Hearts: Investigating the Impact Positive Signals from YouTube Creators in Shaping Comment Section Behavior
Frederick Choi, Charlotte Lambert, Vinay Koshy, Sowmya Pratipati, Tue Do, Eshwar Chandrasekharan
https://arxiv.org/abs/2404.03612

Creator Hearts: Investigating the Impact Positive Signals from YouTube Creators in Shaping Comment Section Behavior
Much of the research in online moderation focuses on punitive actions. However, emerging research has shown that positive reinforcement is effective at encouraging desirable behavior on online platforms. We extend this research by studying the "creator heart" feature on YouTube, quantifying their primary effects on comments that receive hearts and on videos where hearts have been given. We find that creator hearts increased the visibility of comments, and increased the amount of positive engage…

Lower numbers in Flensburg🇩🇪 are always desirable - whether it's points for German drivers (traffic fines) - or district heating temperatures. By reducing the need for 95°C heat from 10 > 1%, it's much easier to let heat pumps take over most of the heat supply. That's what the local utility will do.

Transformation in Flensburg: low temperatures for efficient heat pumps
As an innovative public utility company, Stadtwerke Flensburg is committed to decarbonisation and CO2 neutrality. To achieve this goal, the municipal utility company is working with Gradyent to reduce temperatures in the district heating system. As part of the partnership, the two companies have found a way to reduce the necessary annual heat share with a temperature above 95°C from around 10% to 1%. This means that heat pumps can be used to a higher extent and operated efficiently.

Please try Session, SimpleX, or Threema before deciding to stay with Signal (or WhatsApp).
They do a lot of things well, but when deciding things like this that could greatly improve the user experience I've found the Signal developers / leadership are not making good decisions.
#QT

Deviant Ollam (@deviantollam@defcon.social)
Attached: 1 image Question: am i alone in being frustrated that @signalapp@mastodon.world does not allow you to be logged in on two mobile devices at the same time? I have more than one phone. (I have my main phone and a backup in case main phone dies or breaks) I'd *really* like to be logged in on both (or have one be the "primary" and the other device "linked" etc etc) Is there some reason why the developers have decided that this is not a desirable way to operate?

FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality
Sai Li, Linjun Zhang
https://arxiv.org/abs/2404.01608

FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality
Machine learning methods often assume that the test data have the same distribution as the training data. However, this assumption may not hold due to multiple levels of heterogeneity in applications, raising issues in algorithmic fairness and domain generalization. In this work, we address the problem of fair and generalizable machine learning by invariant principles. We propose a training environment-based oracle, FAIRM, which has desirable fairness and domain generalization properties under …

This https://arxiv.org/abs/2404.02614 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Vestibular schwannoma growth_prediction from longitudinal MRI by time conditioned neural fields
Vestibular schwannomas (VS) are benign tumors that are generally managed by active surveillance with MRI examination. To further assist clinical decision-making and avoid overtreatment, an accurate prediction of tumor growth based on longitudinal imaging is highly desirable. In this paper, we introduce DeepGrowth, a deep learning method that incorporates neural fields and recurrent neural networks for prospective tumor growth prediction. In the proposed method, each tumor is represented as a si…

This https://arxiv.org/abs/2209.14128 has been replaced.
link: https://scholar.google.com/scholar?q=a

Generalizing Liquid Democracy to multi-agent delegation: A Voting Power Measure and Equilibrium Analysis
In this study, we propose a generalization of the classic model of liquid democracy that allows fractional delegation of voting weight, while simultaneously allowing for the existence of equilibrium states. Our approach empowers agents to partition and delegate their votes to multiple representatives, all while retaining a fraction of the voting power for themselves. We introduce a penalty mechanism for the length of delegation chains. We discuss the desirable properties of a reasonable general…

Iziko Museums of South Africa presents: "THEN I KNEW I WAS GOOD AT PAINTING: ESTHER MAHLANGU, A RETROSPECTIVE"
https://www.iziko.org.za/news/iziko-museums-of-south-africa-presentsthen-…

IZIKO MUSEUMS OF SOUTH AFRICA PRESENTS:THEN I KNEW I WAS GOOD AT PAINTING: ESTHERMAHLANGU, A RETROSPECTIVE - Iziko Museums
Esther Mahlangu. Ndebele Abstract, 2023, acrylic on canvas, 180cm diameter.Image courtesy of the artist and The Melrose Gallery Cape Town, South Africa, 16 January 2024 – Iziko Museums of South Africa is pleased to announce “Then I Knew I Was Good at Painting”: Esther Mahlangu, A Retrospective, is opening on February 17, 2024.Curated by Nontobeko […]

Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning
Greg d'Eon, Neil Newman, Kevin Leyton-Brown
https://arxiv.org/abs/2402.19420

Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning
Iterative combinatorial auctions are widely used in high stakes settings such as spectrum auctions. Such auctions can be hard to understand analytically, making it difficult for bidders to determine how to behave and for designers to optimize auction rules to ensure desirable outcomes such as high revenue or welfare. In this paper, we investigate whether multi-agent reinforcement learning (MARL) algorithms can be used to understand iterative combinatorial auctions, given that these algorithms h…

This https://arxiv.org/abs/2105.08419 has been replaced.
link: https://scholar.google.com/scholar?q=a

A sparse ADMM-based solver for linear MPC subject to terminal quadratic constraint
Model Predictive Control (MPC) typically includes a terminal constraint to guarantee stability of the closed-loop system under nominal conditions. In linear MPC this constraint is generally taken on a polyhedral set, leading to a quadratic optimization problem. However, the use of an ellipsoidal terminal constraint may be desirable, leading to an optimization problem with a quadratic constraint. In this case, the optimization problem can be solved using Second Order Cone (SOC) programming solve…

This https://arxiv.org/abs/2106.14969 has been replaced.
link: https://scholar.google.com/scholar?q=a

Hop-Constrained Metric Embeddings and their Applications
In network design problems, such as compact routing, the goal is to route packets between nodes using the (approximated) shortest paths. A desirable property of these routes is a small number of hops, which makes them more reliable, and reduces the transmission costs. Following the overwhelming success of stochastic tree embeddings for algorithmic design, Haeupler, Hershkowitz, and Zuzic (STOC'21) studied hop-constrained Ramsey-type metric embeddings into trees. Specifically, embedding $f:G(V,E…

Demistifying Inference after Adaptive Experiments
Aur\'elien Bibaut, Nathan Kallus
https://arxiv.org/abs/2405.01281 https://arxiv…

Demistifying Inference after Adaptive Experiments
Adaptive experiments such as multi-arm bandits adapt the treatment-allocation policy and/or the decision to stop the experiment to the data observed so far. This has the potential to improve outcomes for study participants within the experiment, to improve the chance of identifying best treatments after the experiment, and to avoid wasting data. Seen as an experiment (rather than just a continually optimizing system) it is still desirable to draw statistical inferences with frequentist guarante…

This https://arxiv.org/abs/2310.20426 has been replaced.
link: https://scholar.google.com/scholar?q=a

Dealing with Structure Constraints in Evolutionary Pareto Set Learning
In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own structure. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this wo…

This https://arxiv.org/abs/2402.15435 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Particle selection from an equilibrium DF
When starting an N-body simulation of an isolated galaxy, it is desirable to select particles from a distribution function to ensure that the model is in equilibrium. Random sampling from a DF is widely used, but results in a set of particles that differs by shot noise from that intended. This paper presents a method to reduce sampling noise that has been developed by the author in a many collaborations over a number of years. The technique has been partly described in past papers, though the i…

A support preserving homotopy for the de Rham complex with boundary decay estimates
Andrea N\"utzi
https://arxiv.org/abs/2404.18005 https://

A support preserving homotopy for the de Rham complex with boundary decay estimates
We study the de Rham complex of relative differential forms on compact manifolds with boundary. Chain homotopies for this complex are highly non-unique, and different homotopies can have different analytic properties, particularly near the boundary. We construct a chain homotopy that has desirable support propagation properties, and that satisfies estimates relative to weighted Sobolev norms, where the weights measure decay at the boundary. The estimates are optimal given the homogeneity proper…

This https://arxiv.org/abs/2307.16811 has been replaced.
link: https://scholar.google.com/scholar?q=a

DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures
Public figures receive a disproportionate amount of abuse on social media, impacting their active participation in public life. Automated systems can identify abuse at scale but labelling training data is expensive, complex and potentially harmful. So, it is desirable that systems are efficient and generalisable, handling both shared and specific aspects of online abuse. We explore the dynamics of cross-group text classification in order to understand how well classifiers trained on one domain …

This https://arxiv.org/abs/2402.04880 has been replaced.
link: https://scholar.google.com/scholar?q=a

Combining Cloud and Mobile Computing for Machine Learning
Although the computing power of mobile devices is increasing, machine learning models are also growing in size. This trend creates problems for mobile devices due to limitations like their memory capacity and battery life. While many services, like ChatGPT and Midjourney, run all the inferences in the cloud, we believe a flexible and fine-grained task distribution is more desirable. In this work, we consider model segmentation as a solution to improving the user experience, dividing the computa…

This https://arxiv.org/abs/2404.15764 has been replaced.
link: https://scholar.google.com/scholar?q=a

Assessment of the quality of a prediction
Shannon defined the mutual information between two variables. We illustrate why the true mutual information between a variable and the predictions made by a prediction algorithm is not a suitable measure of prediction quality, but the apparent Shannon mutual information (ASI) is; indeed it is the unique prediction quality measure with either of two very different lists of desirable properties, as previously shown by de Finetti and other authors. However, estimating the uncertainty of the ASI is…

This https://arxiv.org/abs/2209.14128 has been replaced.
link: https://scholar.google.com/scholar?q=a

Generalizing Liquid Democracy to multi-agent delegation: A Voting Power Measure and Equilibrium Analysis
In this study, we propose a generalization of the classic model of liquid democracy that allows fractional delegation of voting weight, while simultaneously allowing for the existence of equilibrium states. Our approach empowers agents to partition and delegate their votes to multiple representatives, all while retaining a fraction of the voting power for themselves. We introduce a penalty mechanism for the length of delegation chains. We discuss the desirable properties of a reasonable general…

This https://arxiv.org/abs/2311.03189 has been replaced.
link: https://scholar.google.com/scholar?q=a

Safe Control for Soft-Rigid Robots with Self-Contact using Control Barrier Functions
Incorporating both flexible and rigid components in robot designs offers a unique solution to the limitations of traditional rigid robotics by enabling both compliance and strength. This paper explores the challenges and solutions for controlling soft-rigid hybrid robots, particularly addressing the issue of self-contact. Conventional control methods prioritize precise state tracking, inadvertently increasing the system's overall stiffness, which is not always desirable in interactions with the…

Attacking LLM Watermarks by Exploiting Their Strengths
Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith
https://arxiv.org/abs/2402.16187 https://

Attacking LLM Watermarks by Exploiting Their Strengths
Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications. Watermarking, a technique that aims to embed information in the output of a model to verify its source, is useful for mitigating misuse of such AI-generated content. However, existing watermarking schemes remain surprisingly susceptible to attack. In particular, we show that desirable properties shared by existing LLM watermarking systems such as qu…

Data-Driven Knowledge Transfer in Batch $Q^*$ Learning
Elynn Chen, Xi Chen, Wenbo Jing
https://arxiv.org/abs/2404.15209 https://arxiv…

Data-Driven Knowledge Transfer in Batch $Q^*$ Learning
In data-driven decision-making in marketing, healthcare, and education, it is desirable to utilize a large amount of data from existing ventures to navigate high-dimensional feature spaces and address data scarcity in new ventures. We explore knowledge transfer in dynamic decision-making by concentrating on batch stationary environments and formally defining task discrepancies through the lens of Markov decision processes (MDPs). We propose a framework of Transferred Fitted $Q$-Iteration algori…

Hell yeah. I'm a landlord for tax reform. Tax me baby.
https://www.theguardian.com/australia-news/2024/feb/16/greens-are-targeting-tax-breaks-for-investors…

This https://arxiv.org/abs/2304.04506 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

A micro-founded comparison of fiscal policies between indirect and direct job creation
The purpose of this paper is to provide a micro-economic foundation for an argument that the direct employment by the government is more desirable than the government purchase of private goods to eliminate unemployment. A general equilibrium model with monopolistic competition is devised, and the effects of policies (government purchase, tax rate operation, and government employment) on macroeconomic variables (consumption, price, and profit) are investigated. It is shown that 1) the government…

This https://arxiv.org/abs/2311.06918 has been replaced.
link: https://scholar.google.com/scholar?q=a

Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks
Video caching can significantly improve backhaul traffic congestion by locally storing the popular content that users frequently request. A privacy-preserving method is desirable to learn how users' demands change over time. As such, this paper proposes a novel resource-aware hierarchical federated learning (RawHFL) solution to predict users' future content requests under the realistic assumptions that content requests are sporadic and users' datasets can only be updated based on the requested …

Following yesterday's interest rate announcement, it's worth a reminder that our collective fixation on inflation only reflects one aspect of the Bank's remit under the Bank of Canada Act (1985).
The BoC is tasked with mitigating "fluctuations in the general level of production, trade, prices and employment" - that is, using interest rates not only to influence inflation, but employment, etc. The obsession with 2% came later and isn't reflected in the law.

This https://arxiv.org/abs/2310.01236 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Mirror Diffusion Models for Constrained and Watermarked Generation
Modern successes of diffusion models in learning complex, high-dimensional data distributions are attributed, in part, to their capability to construct diffusion processes with analytic transition kernels and score functions. The tractability results in a simulation-free framework with stable regression losses, from which reversed, generative processes can be learned at scale. However, when data is confined to a constrained set as opposed to a standard Euclidean space, these desirable character…

Desirability and social rankings
Michele Aleandri, Felix Fritz, Stefano Moretti
https://arxiv.org/abs/2404.18755 https://arxiv.org/pd…

Desirability and social rankings
In coalitional games, a player $i$ is regarded as strictly more desirable than player $j$ if substituting $j$ with $i$ within any coalition leads to a strict augmentation in the value of certain coalitions, while preserving the value of the others. We adopt a property-driven approach to 'integrate' the notion of the desirability relation into a total relation by establishing sets of independent axioms leading to the characterization of solutionconcepts from the related literature. We focus on s…

This https://arxiv.org/abs/2205.09847 has been replaced.
link: https://scholar.google.com/scholar?q=a

Strict irreducibility of Markov chains and ergodicity of skew products
We consider a family of measure preserving transformations, which act on a common probability space and are chosen at random by a stationary ergodic Markov chain. This setting defines an instance of a random dynamical system (RDS), which may be described in terms of a step skew product. In many contexts it is desirable to know whether ergodicity of the family implies ergodicity of the skew product. Introducing the notion of strict irreducibility for Markov kernels we shall characterize the clas…

Low-mass dark sector searches with deuteron photodisintegration
Cornelis J. G. Mommers, Marc Vanderhaeghen
https://arxiv.org/abs/2404.16390 https://…

Low-mass dark sector searches with deuteron photodisintegration
Recent years have seen much activity in searches for dark-sector messenger particles in the 10-100 MeV mass range, especially in view of a potential new light boson conjectured by the ATOMKI collaboration, X17. Under the assumption that the messenger particle has definite parity and either zero or unit spin, quite stringent bounds already exist on its coupling to electrons and protons. Equally stringent bounds on the neutron coupling do not exist yet, but are nonetheless desirable. We explore h…

This https://arxiv.org/abs/2310.20426 has been replaced.
link: https://scholar.google.com/scholar?q=a

Dealing with Structure Constraints in Evolutionary Pareto Set Learning
In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own structure. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this wo…

Predictable Interval MDPs through Entropy Regularization
Menno van Zutphen, Giannis Delimpaltadakis, Maurice Heemels, Duarte Antunes
https://arxiv.org/abs/2403.16711

Predictable Interval MDPs through Entropy Regularization
Regularization of control policies using entropy can be instrumental in adjusting predictability of real-world systems. Applications benefiting from such approaches range from, e.g., cybersecurity, which aims at maximal unpredictability, to human-robot interaction, where predictable behavior is highly desirable. In this paper, we consider entropy regularization for interval Markov decision processes (IMDPs). IMDPs are uncertain MDPs, where transition probabilities are only known to belong to in…

Design and Validation of a Very Low-Power Phasor Measurement Unit
Zachary J. Lythgoe, Thomas F. Long, Michael J. Buchholz, Anthony R. Livernois, Kebba Kanuteh, David R. Allee, Anamitra Pal, Ian R. Graham, Zachary D. Drummond
https://arxiv.org/abs/2402.14306

Design and Validation of a Very Low-Power Phasor Measurement Unit
Phasor measurement units (PMUs) provide a high-resolution view of the power system at the locations where they are placed. As such, it is desirable to place them in bulk in low voltage distribution circuits. However, the power consumption of a PMU/micro-PMU is in the order of Watts (W) that results in them requiring an external power supply, which in turn increases the overall cost. This work details the hardware design of a PMU capable of measuring and reporting voltage and current phasors for…

Variants of the slacks-based measure with assurance region and zeros in input-output data
Atsushi Hori, Kazuyuki Sekitani
https://arxiv.org/abs/2404.14820 …

Variants of the slacks-based measure with assurance region and zeros in input-output data
Incorporating an assurance region (AR) into the slacks-based measure (SBM) improves practicality; however, its efficiency measure may not have desirable properties, such as monotonicity. We incorporate a closer target setting approach into the SBM with AR and a variant of the SBM with AR. We demonstrate that the efficiency measure with the hybrid approach has the same desirable properties as that without AR, and we also show that the efficiency scores can be computed by solving linear programmi…

This https://arxiv.org/abs/2310.19806 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Automated Verification of Equivalence Properties in Advanced Logic Programs -- Bachelor Thesis
With the increase in industrial applications using Answer Set Programming, the need for formal verification tools, particularly for critical applications, has also increased. During the program optimisation process, it would be desirable to have a tool which can automatically verify whether an optimised subprogram can replace the original subprogram. Formally this corresponds to the problem of verifying the strong equivalence of two programs. In order to do so, the translation tool anthem was d…

AdapTics: A Toolkit for Creative Design and Integration of Real-Time Adaptive Mid-Air Ultrasound Tactons
Kevin John, Yinan Li, Hasti Seifi
https://arxiv.org/abs/2404.19275 https://arxiv.org/pdf/2404.19275
arXiv:2404.19275v1 Announce Type: new
Abstract: Mid-air ultrasound haptic technology can enhance user interaction and immersion in extended reality (XR) applications through contactless touch feedback. Yet, existing design tools for mid-air haptics primarily support creating tactile sensations (i.e., tactons) which cannot change at runtime. These tactons lack expressiveness in interactive scenarios where a continuous closed-loop response to user movement or environmental states is desirable. This paper introduces AdapTics, a toolkit featuring a graphical interface for rapid prototyping of adaptive tactons-dynamic sensations that can adjust at runtime based on user interactions, environmental changes, or other inputs. A software library and a Unity package accompany the graphical interface to enable integration of adaptive tactons in existing applications. We present the design space offered by AdapTics for creating adaptive mid-air ultrasound tactons and show the design tool can improve Creativity Support Index ratings for Exploration and Expressiveness in a user study with 12 XR and haptic designers.

High-order accurate positivity-preserving and well-balanced discontinuous Galerkin schemes for ten-moment Gaussian closure equations with source terms
Jiangfu Wang, Huazhong Tang, Kailiang Wu
https://arxiv.org/abs/2402.15446

High-order accurate positivity-preserving and well-balanced discontinuous Galerkin schemes for ten-moment Gaussian closure equations with source terms
This paper proposes novel high-order accurate discontinuous Galerkin (DG) schemes for the one- and two-dimensional ten-moment Gaussian closure equations with source terms defined by a known potential function. Our DG schemes exhibit the desirable capability of being well-balanced (WB) for a known hydrostatic equilibrium state while simultaneously preserving positive density and positive-definite anisotropic pressure tensor. The well-balancedness is built on carefully modifying the solution stat…

This https://arxiv.org/abs/2308.01557 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models
Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as priors for a new planning problem is highly desirable. Prior works propose several ways on utilizing this prior to bootstrapping the motion planning problem. Either sampling the prior for initializations or using the prior distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we prop…

One-sided DI-QKD secure against coherent attacks over long distances
Michele Masini, Shubhayan Sarkar
https://arxiv.org/abs/2403.11850 https://

One-sided DI-QKD secure against coherent attacks over long distances
Quantum Key Distribution (QKD) is a technique enabling provable secure communication but faces challenges in device characterization, posing potential security risks. Device-Independent (DI) QKD protocols overcome this issue by making minimal device assumptions but are limited in distance because they require high detection efficiencies, which refer to the ability of the experimental setup to detect quantum states. It is thus desirable to find quantum key distribution protocols that are based o…

One concept that seems obvious to me, but seems to elude others, is you can't favor one individual or group without simultaneously disfavoring all the rest.
Whether that is fair, reasonable, or desirable is an entirely separate discussion.
#ScottThoughts

This https://arxiv.org/abs/2402.07896 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Suppressing Pink Elephants with Direct Principle Feedback
Existing methods for controlling language models, such as RLHF and Constitutional AI, involve determining which LLM behaviors are desirable and training them into a language model. However, in many cases, it is desirable for LLMs to be controllable \textit{at inference time}, so that they can be used in multiple contexts with diverse needs. We illustrate this with the \textbf{Pink Elephant Problem}: instructing an LLM to avoid discussing a certain entity (a ``Pink Elephant''), and instead discu…

Randomization-based confidence intervals for the local average treatment effect
P. M. Aronow, Haoge Chang, Patrick Lopatto
https://arxiv.org/abs/2404.18786

Randomization-based confidence intervals for the local average treatment effect
We consider the problem of generating confidence intervals in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens and Rosenbaum (2005) has desirable properties. Namely, we show that using a studentized Anderson-Rubin-type statistic as a test statistic yields confidence intervals that are finite-sample exact under treatment effect homogeneity, and remain asymptotically valid for the Local Average Treatment Effect when the tre…

This https://arxiv.org/abs/2401.14224 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the well-posedness of inverse problems under information field theory: application to model-form error detection
We derive conditions in which inverse problems posed under an information field theory (IFT) framework have unique solutions. While the theorems here apply to IFT inverse problems in general, we place a special focus on the problem of identifying model-form error. Due to the continued increase in popularity of physics-informed methods, tools which can validate the form of the physics chosen under a physics-informed framework are desirable. Using IFT, we pose the problem of identifying model-for…

The Spatial Whitham Equation
John D. Carter, Diane Henderson, Panayotis Panayotaros
https://arxiv.org/abs/2402.14643 https://arxiv.or…

The Spatial Whitham Equation
The Whitham equation is a nonlocal, nonlinear partial differential equation that models the temporal evolution of spatial profiles of surface displacement of water waves. However, many laboratory and field measurements record time series at fixed spatial locations. In order to directly model data of this type, it is desirable to have equations that model the spatial evolution of time series. The spatial Whitham equation, proposed as the spatial generalization of the Whitham equation, fills this…

This https://arxiv.org/abs/2301.04090 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems
Networked discrete dynamical systems are often used to model the spread of contagions and decision-making by agents in coordination games. Fixed points of such dynamical systems represent configurations to which the system converges. In the dissemination of undesirable contagions (such as rumors and misinformation), convergence to fixed points with a small number of affected nodes is a desirable goal. Motivated by such considerations, we formulate a novel optimization problem of finding a nontr…

This https://arxiv.org/abs/2303.14086 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Finite Field Multiple Access
In the past several decades, various techniques have been developed and used for multiple-access (MA) communications. With the new applications for 6G, it is desirable to find new resources, physical or virtual, to confront the fast development of MA communication systems. For binary source transmission, this paper proposes an element-pair (EP) coding scheme for supporting massive users with short packet traffic, which solves the finite block length of multiuser reliability transmission problem…

This https://arxiv.org/abs/2312.06242 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Expansion of hypergeometric functions in terms of polylogarithms with nontrivial variable change
Hypergeometric functions of one and many variables play an important role in various branches of modern physics and mathematics. Often we have hypergeometric functions with indices linear dependent on a small parameter with respect to which one needs to perform Laurent expansions. Moreover such expansions are desirable to be expressed in terms of well known functions which can be evaluated with arbitrary precision. To solve this problem we use the differential equation method and the reduction …

This https://arxiv.org/abs/2010.13442 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Purely Regular Approach to Non-Regular Core Spanners
The regular spanners (characterised by vset-automata) are closed under the algebraic operations of union, join and projection, and have desirable algorithmic properties. The core spanners (introduced by Fagin, Kimelfeld, Reiss, and Vansummeren (PODS 2013, JACM 2015) as a formalisation of the core functionality of the query language AQL used in IBM's SystemT) additionally need string-equality selections and it has been shown by Freydenberger and Holldack (ICDT 2016, Theory of Computing Systems 2…

WHAC: World-grounded Humans and Cameras
Wanqi Yin, Zhongang Cai, Ruisi Wang, Fanzhou Wang, Chen Wei, Haiyi Mei, Weiye Xiao, Zhitao Yang, Qingping Sun, Atsushi Yamashita, Ziwei Liu, Lei Yang
https://arxiv.org/abs/2403.12959

WHAC: World-grounded Humans and Cameras
Estimating human and camera trajectories with accurate scale in the world coordinate system from a monocular video is a highly desirable yet challenging and ill-posed problem. In this study, we aim to recover expressive parametric human models (i.e., SMPL-X) and corresponding camera poses jointly, by leveraging the synergy between three critical players: the world, the human, and the camera. Our approach is founded on two key observations. Firstly, camera-frame SMPL-X estimation methods readily…

Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Christopher Wiedeman, Chuang Niu, Mengzhou Li, Bruno De Man, Jonathan S Maltz, Ge Wang
https://arxiv.org/abs/2402.16212

Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing. Deep learning is a possible method for super-resolution (SR), but sourcing paired training data that adequately models the target task is difficult. Additionally, SR algorithms can distort noise texture, which is an important in many clinical diagnostic scenarios. Here, we train conditional denoising diffusion probabilistic models (DDPMs) for PCCT…

Gaussian process surrogate models for the properties of micro-tearing modes in spherical tokamaks
William Hornsby, Ander Gray, James Buchanan, Daniel Kenndy, Bhavin Patel, Francis Casson, Colin Roach, Mikkel Lykkegaard, Huy Nguyen, Nikolaos Papadimas, Ben Fourcin, Jordan Hart
https://arxiv.org/abs/2403.13006

Gaussian process surrogate models for the properties of micro-tearing modes in spherical tokamaks
Spherical tokamaks (STs) have many desirable features that make them a suitable choice for fusion power plants. To understand their confinement properties, accurate calculation of turbulent micro-instabilities is necessary for tokamak design. Presented is a novel surrogate model for Micro-tearing modes (MTMs), the micro-instability thought to be dominant in high beta STs. Direct numerical calculation of micro-instabilities is computationally expensive and is a significant bottleneck in integrat…

Tuning the intrinsic anomalous Hall effect from large to zero in the two ferromagnetic states of the layered SmMn2Ge2
Mahima Singh, Jyotirmoy Sau, Banik Rai, Arunanshu Panda, Manoranjan Kumar, Nitesh Kumar
https://arxiv.org/abs/2403.19581

Tuning the intrinsic anomalous Hall effect from large to zero in the two ferromagnetic states of the layered SmMn2Ge2
The intrinsic anomalous Hall conductivity in a ferromagnetic metal is completely determined by its band structure. Since the spin orientation direction is an important band structure tuning parameter, it is highly desirable to study the anomalous Hall effect in a system with multiple spin reorientation transitions. We study a layered tetragonal room temperature ferromagnet SmMn2Ge2, which gives us the opportunity to measure magneto-transport properties where the long c-axis and the short a-axis…

Particle selection from an equilibrium DF
J A SellwoodSteward Observatory
https://arxiv.org/abs/2402.15435 https://arxiv.org/pdf/2402…

Particle selection from an equilibrium DF
When starting an N-body simulation of an isolated galaxy, it is desirable to select particles from a distribution function to ensure that the model is in equilibrium. Random sampling from a DF is widely used, but results in a set of particles that differs by shot noise from that intended. This paper presents a method to reduce sampling noise that has been developed by the author in a many collaborations over a number of years. The technique has been partly described in past papers, though the i…

Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores
Wei Liu, Satyajit Mojumder, Wing Kam Liu, Wei Chen, Daniel W. Apley
https://arxiv.org/abs/2404.15207

Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores
A representative volume element (RVE) is a reasonably small unit of microstructure that can be simulated to obtain the same effective properties as the entire microstructure sample. Finite element (FE) simulation of RVEs, as opposed to much larger samples, saves computational expense, especially in multiscale modeling. Therefore, it is desirable to have a framework that determines RVE size prior to FE simulations. Existing methods select the RVE size based on when the FE-simulated properties of…

This https://arxiv.org/abs/2102.04363 has been replaced.
link: https://scholar.google.com/scholar?q=a

Efficient Data-Driven Optimization with Noisy Data
Classical Kullback-Leibler or entropic distances are known to enjoy certain desirable statistical properties in the context of decision-making with noiseless data. However, in most practical situations the data available to a decision maker is subject to a certain amount of measurement noise. We hence study here data-driven prescription problems in which the data is corrupted by a known noise source. We derive efficient data-driven formulations in this noisy regime and indicate that they enjoy …

This https://arxiv.org/abs/2311.15865 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A precise symbolic emulator of the linear matter power spectrum
Computing the matter power spectrum, $P(k)$, as a function of cosmological parameters can be prohibitively slow in cosmological analyses, hence emulating this calculation is desirable. Previous analytic approximations are insufficiently accurate for modern applications, so black-box, uninterpretable emulators are often used. We utilise an efficient genetic programming based symbolic regression framework to explore the space of potential mathematical expressions which can approximate the power s…

Microscale Morphology Driven Thermal Transport in Fiber Reinforced Polymer Composites
Sabarinathan P Subramaniyan, Jonathan D Boehnlein, Pavana Prabhakar
https://arxiv.org/abs/2403.17650

Microscale Morphology Driven Thermal Transport in Fiber Reinforced Polymer Composites
Fiber-reinforced polymer composite (FRPC) materials are used extensively in various industries, such as aerospace, automobiles, and electronics packaging, due to their remarkable specific strength and desirable properties, such as enhanced durability and corrosion resistance. The evolution of thermal properties in FRPCs is crucial for advancing thermal management systems, optimizing material performance, and enhancing energy efficiency across these diverse sectors. Despite significant research …

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan
https://arxiv.org/abs/2403.19605

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Decision-making pipelines are generally characterized by tradeoffs among various risk functions. It is often desirable to manage such tradeoffs in a data-adaptive manner. As we demonstrate, if this is done naively, state-of-the art uncertainty quantification methods can lead to significant violations of putative risk guarantees. To address this issue, we develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively. Our methodology supports mon…

Quantum Light Generation based on GaN Microring towards Fully On-chip Source
Hong Zeng, Zhao-Qin He, Yun-Ru Fan, Yue Luo, Chen Lyu, Jin-Peng Wu, Yun-Bo Li, Sheng Liu, Dong Wang, De-Chao Zhang, Juan-Juan Zeng, Guang-Wei Deng, You Wang, Hai-Zhi Song, Zhen Wang, Li-Xing You, Kai Guo, Chang-Zheng Sun, Yi Luo, Guang-Can Guo, Qiang Zhou
https://

Quantum Light Generation based on GaN Microring towards Fully On-chip Source
Integrated quantum light source is increasingly desirable in large-scale quantum information processing.~Despite recent remarkable advances, new material platform is constantly being explored for the fully on-chip integration of quantum light generation, active and passive manipulation, and detection. Here, for the first time, we demonstrate a gallium nitride (GaN) microring based quantum light generation in the telecom C-band, which has potential towards the monolithic integration of quantum l…

This https://arxiv.org/abs/2306.03045 has been replaced.
link: https://scholar.google.com/scholar?q=a

Designing Equilibria in Concurrent Games with Social Welfare and Temporal Logic Constraints
In game theory, mechanism design is concerned with the design of incentives so that a desired outcome of the game can be achieved. In this paper, we explore the concept of equilibrium design, where incentives are designed to obtain a desirable equilibrium that satisfies a specific temporal logic property. Our study is based on a framework where system specifications are represented as temporal logic formulae, games as quantitative concurrent game structures, and players' goals as mean-payoff ob…

This https://arxiv.org/abs/2402.07518 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Resilient Watermarking for LLM-Generated Codes
With the development of large language models, multiple AIs are now made available for code generation (such as ChatGPT and StarCoder) and are adopted widely. It is often desirable to know whether a piece of code is generated by AI, and furthermore, which AI is the author. For instance, if a certain version of AI is known to generate vulnerable codes, it is particularly important to know the creator. Existing approaches are not satisfactory as watermarking codes are more challenging compared to…

Reinforcement Learning from Reflective Feedback (RLRF): Aligning and Improving LLMs via Fine-Grained Self-Reflection
Kyungjae Lee, Dasol Hwang, Sunghyun Park, Youngsoo Jang, Moontae Lee
https://arxiv.org/abs/2403.14238

Reinforcement Learning from Reflective Feedback (RLRF): Aligning and Improving LLMs via Fine-Grained Self-Reflection
Despite the promise of RLHF in aligning LLMs with human preferences, it often leads to superficial alignment, prioritizing stylistic changes over improving downstream performance of LLMs. Underspecified preferences could obscure directions to align the models. Lacking exploration restricts identification of desirable outputs to improve the models. To overcome these challenges, we propose a novel framework: Reinforcement Learning from Reflective Feedback (RLRF), which leverages fine-grained feed…

This https://arxiv.org/abs/2203.02511 has been replaced.
link: https://scholar.google.com/scholar?q=a

Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments
Robots often face situations where grasping a goal object is desirable but not feasible due to other present objects preventing the grasp action. We present a deep Reinforcement Learning approach to learn grasping and pushing policies for manipulating a goal object in highly cluttered environments to address this problem. In particular, a dual Reinforcement Learning model approach is proposed, which presents high resilience in handling complicated scenes, reaching an average of 98% task complet…

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan
https://arxiv.org/abs/2403.19605

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Decision-making pipelines are generally characterized by tradeoffs among various risk functions. It is often desirable to manage such tradeoffs in a data-adaptive manner. As we demonstrate, if this is done naively, state-of-the art uncertainty quantification methods can lead to significant violations of putative risk guarantees. To address this issue, we develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively. Our methodology supports mon…

Drawing Competitive Districts in Redistricting
Gabriel Chuang, Oussama Hanguir, Clifford Stein
https://arxiv.org/abs/2404.10964 https://

Drawing Competitive Districts in Redistricting
In the process of redistricting, one important metric is the number of competitive districts, that is, districts where both parties have a reasonable chance of winning a majority of votes. Competitive districts are important for achieving proportionality, responsiveness, and other desirable qualities; some states even directly list competitiveness in their legally-codified districting requirements. In this work, we discuss the problem of drawing plans with at least a fixed number of competitive…

This https://arxiv.org/abs/2204.09791 has been replaced.
link: https://scholar.google.com/scholar?q=a

Robust Phase Retrieval via Reverse Kullback-Leibler Divergence
Robustness to noise and outliers is a desirable trait in phase retrieval algorithms for many applications in imaging and signal processing. In this paper, we develop novel robust phase retrieval algorithms based on the minimization of reverse Kullback-Leibler divergence (RKLD) within the Wirtinger Flow (WF) framework. We use RKLD over intensity-only measurements in two distinct ways: i) to design a novel initial estimate based on minimum distortion design of spectral estimates, and ii) as a los…

This https://arxiv.org/abs/2312.14998 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Synthetic images aid the recognition of human-made art forgeries
Previous research has shown that Artificial Intelligence is capable of distinguishing between authentic paintings by a given artist and human-made forgeries with remarkable accuracy, provided sufficient training. However, with the limited amount of existing known forgeries, augmentation methods for forgery detection are highly desirable. In this work, we examine the potential of incorporating synthetic artworks into training datasets to enhance the performance of forgery detection. Our investig…

This https://arxiv.org/abs/2306.05221 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Steering No-Regret Learners to Optimal Equilibria
A mediator observes no-regret learners playing an extensive-form game repeatedly across $T$ rounds. The mediator attempts to steer players toward some desirable predetermined equilibrium by giving (nonnegative) payments to players. We call this the steering problem. The steering problem captures problems several problems of interest, among them equilibrium selection and information design (persuasion). If the mediator's budget is unbounded, steering is trivial because the mediator can simply pa…

Gaussian process surrogate models for the properties of micro-tearing modes in spherical tokamaks
William Hornsby, Ander Gray, James Buchanan, Daniel Kenndy, Bhavin Patel, Francis Casson, Colin Roach, Mikkel Lykkegaard, Huy Nguyen, Nikolaos Papadimas, Ben Fourcin, Jordan Hart
https://arxiv.org/abs/2403.13006

Gaussian process surrogate models for the properties of micro-tearing modes in spherical tokamaks
Spherical tokamaks (STs) have many desirable features that make them a suitable choice for fusion power plants. To understand their confinement properties, accurate calculation of turbulent micro-instabilities is necessary for tokamak design. Presented is a novel surrogate model for Micro-tearing modes (MTMs), the micro-instability thought to be dominant in high beta STs. Direct numerical calculation of micro-instabilities is computationally expensive and is a significant bottleneck in integrat…

This https://arxiv.org/abs/2303.02812 has been replaced.
link: https://scholar.google.com/scholar?q=a

Frames for signal processing on Cayley graphs
The spectral decomposition of graph adjacency matrices is an essential ingredient in the design of graph signal processing (GSP) techniques. When the adjacency matrix has multi-dimensional eigenspaces, it is desirable to base GSP constructions on a particular eigenbasis (the `preferred basis'). In this paper, we provide an explicit and detailed representation-theoretic account for the spectral decomposition of the adjacency matrix of a (weighted) Cayley graph, which results in a preferred basis…

This https://arxiv.org/abs/2301.11984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning
The quest for optimal operation in environments with unknowns and uncertainties is highly desirable but critically challenging across numerous fields. This paper develops a dual control framework for exploration and exploitation (DCEE) to solve an auto-optimisation problem in such complex settings. In general, there is a fundamental conflict between tracking an unknown optimal operational condition and parameter identification. The DCEE framework stands out by eliminating the need for additiona…

Bound-Preserving Framework for Central-Upwind Schemes for General Hyperbolic Conservation Laws
Shumo Cui, Alexander Kurganov, Kailiang Wu
https://arxiv.org/abs/2403.13420

Bound-Preserving Framework for Central-Upwind Schemes for General Hyperbolic Conservation Laws
Central-upwind (CU) schemes are Riemann-problem-solver-free finite-volume methods widely applied to a variety of hyperbolic systems of PDEs. Exact solutions of these systems typically satisfy certain bounds, and it is highly desirable or even crucial for the numerical schemes to preserve these bounds. In this paper, we develop and analyze bound-preserving (BP) CU schemes for general hyperbolic systems of conservation laws. Unlike many other Godunov-type methods, CU schemes cannot, in general, b…

Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
Nur Yildirim, Susanna Zlotnikov, Deniz Sayar, Jeremy M. Kahn, Leigh A. Bukowski, Sher Shah Amin, Kathryn A. Riman, Billie S. Davis, John S. Minturn, Andrew J. King, Dan Ricketts, Lu Tang, Venkatesh Sivaraman, Adam Perer, Sarah M. Preum, James McCann, John Zimmerman

Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medici…

This https://arxiv.org/abs/2402.12661 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Dynamics of Symmetry-Protected Topological Matter on a Quantum Computer
Control of topological edge modes is desirable for encoding quantum information resiliently against external noise. Their implementation on quantum hardware, however, remains a long-standing problem due to current limitations of circuit depth and noise, which grows with the number of time steps. By utilizing recently developed constant-depth quantum circuits in which the circuit depth is independent of time, we demonstrate successful long-time dynamics simulation of bulk and surface modes in to…

Uniform-over-dimension convergence with application to location tests for high-dimensional data
Joydeep Chowdhury, Subhajit Dutta, Marc G. Genton
https://arxiv.org/abs/2403.16328 …

Uniform-over-dimension convergence with application to location tests for high-dimensional data
Asymptotic methods for hypothesis testing in high-dimensional data usually require the dimension of the observations to increase to infinity, often with an additional condition on its rate of increase compared to the sample size. On the other hand, multivariate asymptotic methods are valid for fixed dimension only, and their practical implementations in hypothesis testing methodology typically require the sample size to be large compared to the dimension for yielding desirable results. However,…

A Genetic Algorithm For Convex Hull Optimisation
Scott Donaldson, Robert A. Lawrence, Matt I. J. Probert
https://arxiv.org/abs/2404.14354 https://

A Genetic Algorithm For Convex Hull Optimisation
Computationally efficient and automated generation of convex hulls is desirable for high throughput materials discovery of thermodynamically stable multi-species crystal structures. A convex hull genetic algorithm is proposed that uses methodology adapted from multi-objective optimisation techniques to optimise the convex hull itself as an object, enabling efficient discovery of convex hulls for N >= 2 species. This method, when tested on a LiSi system utilising pre-trained machine learned pote…

Covariate-adjusted Group Sequential Comparisons of Survival Probabilities
Peter Zhang, Brent Logan, Michael Martens
https://arxiv.org/abs/2403.17117 https:…

Covariate-adjusted Group Sequential Comparisons of Survival Probabilities
In confirmatory clinical trials, survival outcomes are frequently studied and interim analyses for efficacy and/or futility are often desirable. Methods such as the log rank test and Cox regression model are commonly used to compare treatments in this setting. They rely on a proportional hazards (PH) assumption and are subject to type I error rate inflation and loss of power when PH are violated. Such violations may be expected a priori, particularly when the mechanisms of treatments differ suc…

Impact of regularization on achieved resolution in 3D tunable structured illumination microscopy (TSIM)
Arash Atibi, Abdulaziz Alqahtani, Mohammed Younis, Chrysanthe Preza
https://arxiv.org/abs/2403.14035

Impact of regularization on achieved resolution in 3D tunable structured illumination microscopy (TSIM)
We present a study that evaluates the impact of regularization on the achieved resolution in restorations from a novel three-dimensional (3D) Structured Illumination Microscopy (3D-SIM) system with desirable tunability properties. This contribution is the first performance evaluation of the Tunable SIM (TSIM) system through the restoration process. The study quantifies the achieved resolution in restorations, from simulated TSIM data of a 3D star-like object, at various expected resolution limi…

This https://arxiv.org/abs/2402.08711 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
A method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and Zygalakis in ``Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations". They analyze the UBU integrator which is strong order two and only requires one gradient evaluation per step, resulting in desirable non-asymptotic guarantees, in particular $\mathcal{O}(d^{1/4}ε^{-1/2})$…

Group Privacy Amplification and Unified Amplification by Subsampling for R\'enyi Differential Privacy
Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan G\"unnemann
https://arxiv.org/abs/2403.04867

Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy
Differential privacy (DP) has various desirable properties, such as robustness to post-processing, group privacy, and amplification by subsampling, which can be derived independently of each other. Our goal is to determine whether stronger privacy guarantees can be obtained by considering multiple of these properties jointly. To this end, we focus on the combination of group privacy and amplification by subsampling. To provide guarantees that are amenable to machine learning algorithms, we cond…

This https://arxiv.org/abs/2306.05221 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Steering No-Regret Learners to Optimal Equilibria
A mediator observes no-regret learners playing an extensive-form game repeatedly across $T$ rounds. The mediator attempts to steer players toward some desirable predetermined equilibrium by giving (nonnegative) payments to players. We call this the steering problem. The steering problem captures problems several problems of interest, among them equilibrium selection and information design (persuasion). If the mediator's budget is unbounded, steering is trivial because the mediator can simply pa…

Measuring Dependence between Events
Marc-Oliver Pohle, Timo Dimitriadis, Jan-Lukas Wermuth
https://arxiv.org/abs/2403.17580 https://a…

Measuring Dependence between Events
Measuring dependence between two events, or equivalently between two binary random variables, amounts to expressing the dependence structure inherent in a $2\times 2$ contingency table in a real number between $-1$ and $1$. Countless such dependence measures exist, but there is little theoretical guidance on how they compare and on their advantages and shortcomings. Thus, practitioners might be overwhelmed by the problem of choosing a suitable measure. We provide a set of natural desirable prop…

Group Privacy Amplification and Unified Amplification by Subsampling for R\'enyi Differential Privacy
Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan G\"unnemann
https://arxiv.org/abs/2403.04867

Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy
Differential privacy (DP) has various desirable properties, such as robustness to post-processing, group privacy, and amplification by subsampling, which can be derived independently of each other. Our goal is to determine whether stronger privacy guarantees can be obtained by considering multiple of these properties jointly. To this end, we focus on the combination of group privacy and amplification by subsampling. To provide guarantees that are amenable to machine learning algorithms, we cond…

This https://arxiv.org/abs/2308.13072 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model
Objective: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. Approach: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based met…

This https://arxiv.org/abs/2008.12927 has been replaced.
link: https://scholar.google.com/scholar?q=a

Broadcasted Nonparametric Tensor Regression
We propose a novel use of a broadcasting operation, which distributes univariate functions to all entries of the tensor covariate, to model the nonlinearity in tensor regression nonparametrically. A penalized estimation and the corresponding algorithm are proposed. Our theoretical investigation, which allows the dimensions of the tensor covariate to diverge, indicates that the proposed estimation yields a desirable convergence rate. We also provide a minimax lower bound, which characterizes the…

This https://arxiv.org/abs/2312.05119 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pM…

This https://arxiv.org/abs/2404.12696 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule
We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing. The proposed test clusters the data according to the 20/60/20 rule and confronts conditional covariance (or correlation) estimates on the obtained subsets. The corresponding test statistic has a natural practical interpretation, desirable statistical properties, and asymptotic …

Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule
Jakub Wo\'zny, Piotr Jaworski, Damian Jelitoa, Marcin Pitera, Agnieszka Wy{\l}oma\'nska
https://arxiv.org/abs/2404.12696

Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule
We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing. The proposed test clusters the data according to the 20/60/20 rule and confronts conditional covariance (or correlation) estimates on the obtained subsets. The corresponding test statistic has a natural practical interpretation, desirable statistical properties, and asymptotic …

Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule
Jakub Wo\'zny, Piotr Jaworski, Damian Jelitoa, Marcin Pitera, Agnieszka Wy{\l}oma\'nska
https://arxiv.org/abs/2404.12696

Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule
We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing. The proposed test clusters the data according to the 20/60/20 rule and confronts conditional covariance (or correlation) estimates on the obtained subsets. The corresponding test statistic has a natural practical interpretation, desirable statistical properties, and asymptotic …

A class of bootstrap based residuals for compositional data
Gustavo H. A. Pereira, Jianwen Cai
https://arxiv.org/abs/2403.13544 https://

A class of bootstrap based residuals for compositional data
Regression models for compositional data are common in several areas of knowledge. As in other classes of regression models, it is desirable to perform diagnostic analysis in these models using residuals that are approximately standard normally distributed. However, for regression models for compositional data, there has not been any multivariate residual that meets this requirement. In this work, we introduce a class of asymptotically standard normally distributed residuals for compositional d…

This https://arxiv.org/abs/2308.08346 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

RMST-based multiple contrast tests in general factorial designs
Several methods in survival analysis are based on the proportional hazards assumption. However, this assumption is very restrictive and often not justifiable in practice. Therefore, effect estimands that do not rely on the proportional hazards assumption are highly desirable in practical applications. One popular example for this is the restricted mean survival time (RMST). It is defined as the area under the survival curve up to a prespecified time point and, thus, summarizes the survival curv…