Tootfinder

AcademiaOS: Automating Grounded Theory Development in Qualitative Research with Large Language Models
Thomas \"Ubellacker
https://arxiv.org/abs/2403.08844

AcademiaOS: Automating Grounded Theory Development in Qualitative Research with Large Language Models
AcademiaOS is a first attempt to automate grounded theory development in qualitative research with large language models. Using recent large language models' language understanding, generation, and reasoning capabilities, AcademiaOS codes curated qualitative raw data such as interview transcripts and develops themes and dimensions to further develop a grounded theoretical model, affording novel insights. A user study (n=19) suggests that the system finds acceptance in the academic community and…

GrounDial: Human-norm Grounded Safe Dialog Response Generation
Siwon Kim, Shuyang Dai, Mohammad Kachuee, Shayan Ray, Tara Taghavi, Sungroh Yoon
https://arxiv.org/abs/2402.08968

GrounDial: Human-norm Grounded Safe Dialog Response Generation
Current conversational AI systems based on large language models (LLMs) are known to generate unsafe responses, agreeing to offensive user input or including toxic content. Previous research aimed to alleviate the toxicity, by fine-tuning LLM with manually annotated safe dialogue histories. However, the dependency on additional tuning requires substantial costs. To remove the dependency, we propose GrounDial, where response safety is achieved by grounding responses to commonsense social rules w…

ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation
Sayar Ghosh Roy, Jiawei Han
https://arxiv.org/abs/2403.08737 https://

ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation
Existing Machine Learning approaches for local citation recommendation directly map or translate a query, which is typically a claim or an entity mention, to citation-worthy research papers. Within such a formulation, it is challenging to pinpoint why one should cite a specific research paper for a particular query, leading to limited recommendation interpretability. To alleviate this, we introduce the evidence-grounded local citation recommendation task, where the target latent space comprises…

Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation
Daniel Honerkamp, Martin Buchner, Fabien Despinoy, Tim Welschehold, Abhinav Valada
https://arxiv.org/abs/2403.08605

Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation
To fully leverage the capabilities of mobile manipulation robots, it is imperative that they are able to autonomously execute long-horizon tasks in large unexplored environments. While large language models (LLMs) have shown emergent reasoning skills on arbitrary tasks, existing work primarily concentrates on explored environments, typically focusing on either navigation or manipulation tasks in isolation. In this work, we propose MoMa-LLM, a novel approach that grounds language models within s…

GrounDial: Human-norm Grounded Safe Dialog Response Generation
Siwon Kim, Shuyang Dai, Mohammad Kachuee, Shayan Ray, Tara Taghavi, Sungroh Yoon
https://arxiv.org/abs/2402.08968

GrounDial: Human-norm Grounded Safe Dialog Response Generation
Current conversational AI systems based on large language models (LLMs) are known to generate unsafe responses, agreeing to offensive user input or including toxic content. Previous research aimed to alleviate the toxicity, by fine-tuning LLM with manually annotated safe dialogue histories. However, the dependency on additional tuning requires substantial costs. To remove the dependency, we propose GrounDial, where response safety is achieved by grounding responses to commonsense social rules w…

This https://arxiv.org/abs/2403.07805 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Beyond Memorization: The Challenge of Random Memory Access in Language Models
Recent developments in Language Models (LMs) have shown their effectiveness in NLP tasks, particularly in knowledge-intensive tasks. However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive. In this paper, we investigate whether a generative LM (e.g., GPT-2) is able to access its memory sequentially or randomly. Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question an…

This https://arxiv.org/abs/2211.06410 has been replaced.
link: https://scholar.google.com/scholar?q=a

RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory and run-time requirements hinder their applicability to large datasets, many low-rank kernel approximations, such as random Fourier features, were recently developed to scale up such kernel methods. However, these scalable approaches are based on approximations of isotropic kernels, which cannot remove the influence of irrelevant features. In this work, we design random Fo…

Manifest V3 Unveiled: Navigating the New Era of Browser Extensions
Nikolaos Pantelaios, Alexandros Kapravelos
https://arxiv.org/abs/2404.08310 https://

Manifest V3 Unveiled: Navigating the New Era of Browser Extensions
Introduced over a decade ago, Chrome extensions now exceed 200,000 in number. In 2020, Google announced a shift in extension development with Manifest Version 3 (V3), aiming to replace the previous Version 2 (V2) by January 2023. This deadline was later extended to January 2025. The company's decision is grounded in enhancing three main pillars: privacy, security, and performance. This paper presents a comprehensive analysis of the Manifest V3 ecosystem. We start by investigating the adoption r…

I’m gonna decline that trade…
The #TESCREAL billionaire fascination with virtual immortality is grounded in juvenile cowardice & narcissism. Humans die. All of us. Humans age. All of us. Our *minds* age, both because of our brains aging & because we have experiences which include other humans close to us dying. Nothing can be forever.
It's a pharaonic impulse to build a monu…

Amazon's hexacopter drones, planned for use in Tolleson, Arizona, can't operate when temperatures top 104°F, less than the average summer highs in the city (Paresh Dave/Wired)
https://www.wired.com/story/amazon-delivery-drones-heat-grounded-summer/…

This https://arxiv.org/abs/2403.08605 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation
To fully leverage the capabilities of mobile manipulation robots, it is imperative that they are able to autonomously execute long-horizon tasks in large unexplored environments. While large language models (LLMs) have shown emergent reasoning skills on arbitrary tasks, existing work primarily concentrates on explored environments, typically focusing on either navigation or manipulation tasks in isolation. In this work, we propose MoMa-LLM, a novel approach that grounds language models within s…

This https://arxiv.org/abs/2403.05452 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
Radio-interferometric (RI) imaging entails solving high-resolution high-dynamic range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denois…

Grounded & Connected Empowerment
Great Australian Pods Podcast Directory: #GreatAusPods

Grounded & Connected Empowerment | Great Australian Pods Podcast Directory
Podcast: Grounded & Connected Empowerment Host: Jarmbi Category: Society & Culture Sub-Category: Culture & Identity, Philosophy First Episode: 2024 Grounded and Connected Empowerment…

This https://arxiv.org/abs/2403.07805 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Beyond Memorization: The Challenge of Random Memory Access in Language Models
Recent developments in Language Models (LMs) have shown their effectiveness in NLP tasks, particularly in knowledge-intensive tasks. However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive. In this paper, we investigate whether a generative LM (e.g., GPT-2) is able to access its memory sequentially or randomly. Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question an…

This https://arxiv.org/abs/2211.06410 has been replaced.
link: https://scholar.google.com/scholar?q=a

RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory and run-time requirements hinder their applicability to large datasets, many low-rank kernel approximations, such as random Fourier features, were recently developed to scale up such kernel methods. However, these scalable approaches are based on approximations of isotropic kernels, which cannot remove the influence of irrelevant features. In this work, we design random Fo…

I am rewatching Person of Interest and it strikes me just how much current "AI" discourse is just ... this show. It's grounded in a story. A clever, well-written story, but just a story.
It's not grounded in the systems of our world. It's not grounded in understandings of power.

This https://arxiv.org/abs/2401.01614 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

GPT-4V(ision) is a Generalist Web Agent, if Grounded
The recent development on large multimodal models (LMMs), especially GPT-4V(ision) and Gemini, has been quickly expanding the capability boundaries of multimodal models beyond traditional tasks like image captioning and visual question answering. In this work, we explore the potential of LMMs like GPT-4V as a generalist web agent that can follow natural language instructions to complete tasks on any given website. We propose SEEACT, a generalist web agent that harnesses the power of LMMs for in…

This https://arxiv.org/abs/2310.06518 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

21-cm foreground removal using AI and frequency-difference technique
The deep learning technique has been employed in removing foreground contaminants from 21 cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel foreground removal technique grounded in U-Net networks. The essence of this technique lies in introducing an innovative data preprocessing step specifically, utilizing the temperature difference between neighboring frequency bands as input, which can substantiall…

Even in critical perspectives "AI" increasingly gets displayed as "robots" ("I want AI to take out my trash"). The #AI systems we see today are as far away from that as possible, they are just symbol-mixing machines. I think it is relevant to keep criticism and talking about those systems grounded in reality.

“You little bastard! You are SO grounded!” 😂
https://press.coop/@CBCNews/112211079125122480

CBC News :press: (@CBCNews@press.coop)
13-year-old charged after crashing Lamborghini in ditch Police in West Vancouver say a "joyride'' by a 13-year-old in their parents' Lamborghini set off a single-vehicle crash that resulted in a total write-off by the insurance company. #press https://www.cbc.ca/news/canada/british-columbia/lamborghini-ditch-west-vancouver-1.7162865?cmp=rss&utm_source=press.coop

This https://arxiv.org/abs/2403.06811 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Weak form Shallow Ice Approximation models with an improved time step restriction
The Shallow Ice Approximation (SIA) model written on strong form is commonly used for inferring the dynamics of ice sheets and glaciers. The model describes non-Newtonian, viscous, and gravity driven flow of ice in grounded ice sheets. The solution to the SIA model is a closed-form expression for the velocity field. A disadvantage is that when using the SIA velocities to advance the ice surface in time, the time step restriction has a quadratic scaling in terms of the horizontal mesh size. In t…

🚁 Are Marines relying more on the CH-53E helicopters while their Ospreys are grounded?
#military

Are Marines relying more on the CH-53E helicopters while their Ospreys are grounded?
The Marines have few alternatives to their grounded Ospreys. The CH-53E is one of three vertical lift aircraft organic to marine aviation.

Like with many things in life, I really have the biggest issue with the centrists--these are the people who legitimize bad things.
If you're in the "I'm so grounded, and so very intelligent and everything must have balance and no technology can be good or bad and I urge decorum and let's hear everyone out" camp; please think about what you're doing harder.

Beyond Memorization: The Challenge of Random Memory Access in Language Models
Tongyao Zhu, Qian Liu, Liang Pang, Zhengbao Jiang, Min-Yen Kan, Min Lin
https://arxiv.org/abs/2403.07805

Beyond Memorization: The Challenge of Random Memory Access in Language Models
Recent developments in Language Models (LMs) have shown their effectiveness in NLP tasks, particularly in knowledge-intensive tasks. However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive. In this paper, we investigate whether a generative LM (e.g., GPT-2) is able to access its memory sequentially or randomly. Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question an…

Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue
Nhat Tran, Diane Litman
https://arxiv.org/abs/2405.04713 https://<…

Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue
Knowledge retrieval is one of the major challenges in building a knowledge-grounded dialogue system. A common method is to use a neural retriever with a distributed approximate nearest-neighbor database to quickly find the relevant knowledge sentences. In this work, we propose an approach that utilizes topic modeling on the knowledge base to further improve retrieval accuracy and as a result, improve response generation. Additionally, we experiment with a large language model, ChatGPT, to take …

«Five principles for building generative AI products» @…'s keynote title for #CollabW24 🙄
tl;dr:
1. no decisions: use "AI" as 'assistive technology’
2. design for failure, consider the cost of getting it wrong
3. have it be grounded in reality
4. make it invite only: opt-in to using it
5. leverage: “explain like it's me”: personalize responses to personal knowledge level
(My 6. obvious addition: as we say in German: “wes Brot ich ess, des Lied ich sing”)

Beyond Memorization: The Challenge of Random Memory Access in Language Models
Tongyao Zhu, Qian Liu, Liang Pang, Zhengbao Jiang, Min-Yen Kan, Min Lin
https://arxiv.org/abs/2403.07805

Beyond Memorization: The Challenge of Random Memory Access in Language Models
Recent developments in Language Models (LMs) have shown their effectiveness in NLP tasks, particularly in knowledge-intensive tasks. However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive. In this paper, we investigate whether a generative LM (e.g., GPT-2) is able to access its memory sequentially or randomly. Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question an…

Latest Mock Draft Has Raiders Going Cornerback (Not Quarterback) In The 1st Round https://www.yardbarker.com/nfl/articles/latest_mock_draft_has_raiders_going_cornerback_not_quarterback_in_the_1st_r…

Latest Mock Draft Has Raiders Going Cornerback (Not Quarterback) In The 1st Round
Las Vegas Raiders fans (mostly) desperately want a quarterback to come out of the first round for their team, but The Athletic believes otherwise. Looking through social media, the consensus really does appear to be Jayden Daniels or bust. Still, it’s best to remain grounded when it comes to the NFL draft. Trading up into the top three will be a monumental task for Raiders general manager Tom Telesco; to judge him solely on that (successful or not) will be highly shortsighted. The fact is, th…

This https://arxiv.org/abs/2403.06811 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Weak form Shallow Ice Approximation models with an improved time step restriction
The Shallow Ice Approximation (SIA) model written on strong form is commonly used for inferring the dynamics of ice sheets and glaciers. The model describes non-Newtonian, viscous, and gravity driven flow of ice in grounded ice sheets. The solution to the SIA model is a closed-form expression for the velocity field. A disadvantage is that when using the SIA velocities to advance the ice surface in time, the time step restriction has a quadratic scaling in terms of the horizontal mesh size. In t…

"Basing your opinion on factional loyalty and vibes is fine for a blogger or a pundit, but it does not make for a sound management strategy."
I beg to differ, Baldur Bjarnason. It's not fine if you're a blogger or a pundit, either. Seeking to profit from expediting the extinction of life on the planet ought to be treated as a crime of the highest order.
#ClimateCatastrophe

Facing reality, whether it's about Apple or the EU, is a core requirement for good management
You can’t be a good manager or executive, in any industry, if you operate in constant denial of the facts on the ground. Arguing from ideology or beliefs that aren’t grounded in observation, measurement, or study, is the hallmark of a politician or media personality, not a manager responsible for other people’s jobs.

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…

"""
The case of One Laptop per Child shows us why it is dangerous to ignore the origins of charisma: one risks being perpetually entranced by the newest charismatic technology. This is not to say that cultural change with a technology-centric project is impossible. Still, even more realistic reforms grounded in the realities of their intended beneficiaries sometimes have difficulty gaining broad popular support outside the school unless they add a charismatic gloss of rapid, revolutionary change.
This charismatic pressure can put even open-eyed reformers in a catch-22. They must promise dramatic results to gain the social and financial support for reforms, and then they must either admit to not achieving their goals or pretend that they did achieve them. Either way, funders will declare that the project is finished and withdraw financial support, and then researchers and other observers will begin to note the discrepancies between reformers’ promises and their own observations. Thus, projects that rely on charismatic technologies are often short lived; their resources are cut off before charisma recedes into the background and before the technology becomes part of everyday classroom experience. This catch-22 has dogged efforts for educational reform, development, and cultural change — especially those funded through grants or other short-term funding — for well over a century. As the technology community moves on to the next charismatic device without learning from its failures, this will continue to hamper the possibility of real, if incremental, change.
[…] After all, charisma is ultimately a conservative social force. Even when charismatic technologies promise to quickly and painlessly transform our lives for the better, they appeal precisely because they echo existing stereotypes, confirm the value of existing power relations, and reinforce existing ideologies. Meanwhile, they may divert attention and resources from more complicated, expensive, or politically charged reforms that do not promise a quick fix and are thus less charismatic.
"""
(Morgan G. Ames, The Charisma Machine)

Getting grounded in the opto-neural lobby... Time to take a few deep breaths and admire the wood grain.

FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning
Kaled M. Alshmrany, Mohannad Aldughaim, Chenfeng Wei, Tom Sweet, Richard Allmendinger, Lucas C. Cordeiro
https://arxiv.org/abs/2404.06031

FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning
We present FuSeBMC-AI, a test generation tool grounded in machine learning techniques. FuSeBMC-AI extracts various features from the program and employs support vector machine and neural network models to predict a hybrid approach optimal configuration. FuSeBMC-AI utilizes Bounded Model Checking and Fuzzing as back-end verification engines. FuSeBMC-AI outperforms the default configuration of the underlying verification engine in certain cases while concurrently diminishing resource consumption.

Determining cell population size from cell fraction in cell plasticity models
Yuman Wang, Shuli Chen, Jie Hu, Da Zhou
https://arxiv.org/abs/2405.04557 http…

Determining cell population size from cell fraction in cell plasticity models
Quantifying the size of cell populations is crucial for understanding biological processes such as growth, injury repair, and disease progression. Often, experimental data offer information in the form of relative frequencies of distinct cell types, rather than absolute cell counts. This emphasizes the need to devise effective strategies for estimating absolute cell quantities from fraction data. In response to this challenge, we present two computational approaches grounded in stochastic cell …

This https://arxiv.org/abs/2206.09514 has been replaced.
link: https://scholar.google.com/scholar?q=a

Ethics in AI through the Practitioner's View: A Grounded Theory Literature Review
The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems- the AI practitioners? We conducted a grounded theory literature review (GTLR) of 38 prim…

This https://arxiv.org/abs/2403.11003 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Extreme Treatment Effect: Extrapolating Dose-Response Function Into Extreme Treatment Domain
The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose-response function (also called the effect curve), denoted as (μ(t)), is typically of interest when dealing with a continuous treatment variable (exposure). The focus of this work is to determine the impact of an extreme level of treatment, potentially beyond the range of observed values--that is, estimating (μ(t)) for very large (t). Our approach is grounded in the field of statistic…

Statistical Equivalence of Metrics for Meteor Dynamical Association
Eloy Pe\~na-Asensio, Juan Miguel S\'anchez-Lozano
https://arxiv.org/abs/2405.03308 …

Statistical Equivalence of Metrics for Meteor Dynamical Association
We statistically evaluate and compare four orbital similarity criteria within five-dimensional parameter space ($D_{SH}$, $D_D$, $D_H$, and $\varrho_2$) to study dynamical associations using the already classified meteors (manually by a human) in CAMS database as a benchmark. In addition, we assess various distance metrics typically used in Machine Learning with two different vectors: ORBIT, grounded in heliocentric orbital elements, and GEO, predicated on geocentric observational parameters. A…

The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
Amir Aghabiglou, Chung San Chu, Arwa Dabbech, Yves Wiaux
https://arxiv.org/abs/2403.05452

The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
Radio-interferometric (RI) imaging entails solving high-resolution high-dynamic range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denois…

Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes
Chin Tseng, Arran Zeyu Wang, Ghulam Jilani Quadri, Danielle Albers Szafir
https://arxiv.org/abs/2404.03787

Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes
Existing guidelines for categorical color selection are heuristic, often grounded in intuition rather than empirical studies of readers' abilities. While design conventions recommend palettes maximize hue differences, more recent exploratory findings indicate other factors, such as lightness, may play a role in effective categorical palette design. We conducted a crowdsourced experiment on mean value judgments in multi-class scatterplots using five color palette families--single-hue sequential,…

Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts
Lotem Golany, Filippo Galgani, Maya Mamo, Nimrod Parasol, Omer Vandsburger, Nadav Bar, Ido Dagan
https://arxiv.org/abs/2405.01121

Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts
Existing methods for creating source-grounded information-seeking dialog datasets are often costly and hard to implement due to their sole reliance on human annotators. We propose combining large language models (LLMs) prompting with human expertise for more efficient and reliable data generation. Instead of the labor-intensive Wizard-of-Oz (WOZ) method, where two annotators generate a dialog from scratch, role-playing agent and user, we use LLM generation to simulate the two roles. Annotators …

#NutrientScience
1/6
In the ever-evolving landscape of nutritional supplements, it's rare to encounter a gem that truly revolutionizes our approach to wellness. Pantethine, a derivative of vitamin B5 (pantothenic acid), emerges as just that: an unsung hero in the vast world of health enhancement. Grounded in solid scientific research, its benefits are not merely anecdotal but backed by robust evidence, marking a significant stride in heart health, stress management, and dermatological care.

Good Morning #Canada
On this day in 1851 the fastest ship in the world was launched at Saint John, New Brunswick. Not the Bluenose, but a three-masted wooden clipper ship, christened the Marco Polo. Despite some early trouble, grounded twice before her maiden voyage, the ship was the first vessel to make the round trip from Liverpool to Australia in under six months. The Marco Polo broke apart in a storm off Cavendish PEI in 1883.
#CanadaIsAwesome #MaritimeHisrory
https://mysteriesofcanada.com/new-brunswick/the-marco-polo-the-worlds-fastest-clipper-ship/

This https://arxiv.org/abs/2309.17168 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Charge-parity switching effects and optimisation of transmon-qubit design parameters
Enhancing the performance of noisy quantum processors requires improving our understanding of error mechanisms and the ways to overcome them. In this study, we identify optimal ranges for qubit design parameters, grounded in comprehensive noise modeling. To this end, we also analyze a previously unexplored error mechanism that can perturb two-qubit gates due to charge-parity switches caused by quasiparticles. Due to the utilization of the higher levels of a transmon, where the charge dispersion…

This https://arxiv.org/abs/2310.04781 has been replaced.
link: https://scholar.google.com/scholar?q=a

Unifying Foundation Models with Quadrotor Control for Visual Tracking Beyond Object Categories
Visual control enables quadrotors to adaptively navigate using real-time sensory data, bridging perception with action. Yet, challenges persist, including generalization across scenarios, maintaining reliability, and ensuring real-time responsiveness. This paper introduces a perception framework grounded in foundation models for universal object detection and tracking, moving beyond specific training categories. Integral to our approach is a multi-layered tracker integrated with the foundation …

To phrase it in different terms, Blue and Red and Black philosophies are colonial philosophies. Especially in combination, and there's a bunch of permutations and more or less destructive ways.
Green is an indigenous philosophical mode. And I don't mean that in a sense of an indigenous Other, but in that fundamentally a green philosophy is grounded in body, time, place, history and _connection_. It is situated. It is the philosophy of observing in situ, in context.

Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
Yang Wu, Yao Wan, Hongyu Zhang, Yulei Sui, Wucai Wei, Wei Zhao, Guandong Xu, Hai Jin
https://arxiv.org/abs/2404.17136

Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capab…

This https://arxiv.org/abs/2403.19660 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Discrete Linear Canonical Transform on Graphs: Uncertainty Principle and Sampling
With an increasing influx of classical signal processing methodologies into the field of graph signal processing, approaches grounded in discrete linear canonical transform have found application in graph signals. In this paper, we initially propose the uncertainty principle of the graph linear canonical transform (GLCT), which is based on a class of graph signals maximally concentrated in both vertex and graph spectral domains. Subsequently, leveraging the uncertainty principle, we establish c…

Weak form Shallow Ice Approximation models with an improved time step restriction
Igor Tominec, Josefin Ahlkrona
https://arxiv.org/abs/2403.06811 https://<…

Weak form Shallow Ice Approximation models with an improved time step restriction
The Shallow Ice Approximation (SIA) model written on strong form is commonly used for inferring the dynamics of ice sheets and glaciers. The model describes non-Newtonian, viscous, and gravity driven flow of ice in grounded ice sheets. The solution to the SIA model is a closed-form expression for the velocity field. A disadvantage is that when using the SIA velocities to advance the ice surface in time, the time step restriction has a quadratic scaling in terms of the horizontal mesh size. In t…

Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
https://arxiv.org/abs/2402.14664

Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
In interactive systems, actions are often correlated, presenting an opportunity for more sample-efficient off-policy evaluation (OPE) and learning (OPL) in large action spaces. We introduce a unified Bayesian framework to capture these correlations through structured and informative priors. In this framework, we propose sDM, a generic Bayesian approach designed for OPE and OPL, grounded in both algorithmic and theoretical foundations. Notably, sDM leverages action correlations without compromis…

This https://arxiv.org/abs/2403.04460 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such…

Korovkin-type approximation for non positive operators
V. B. Kiran Kumar, M. N. N. Namboodiri, P. C. Vinaya
https://arxiv.org/abs/2403.03476 https://

Korovkin-type approximation for non positive operators
This article investigates the convergence properties of Grunwald-type operators defined using the Lagrange interpolation operator. In $C[0, π]$, we establish sufficient conditions under which alternate grid point selection yields a convergence result. Employing a quantitative approach grounded in the modulus of continuity, we derive a theorem that provides insightful estimates for the convergence of Grunwald operator, accompanied by illustrative numerical examples. Addressing challenges relate…

Air Gap: Protecting Privacy-Conscious Conversational Agents
Eugene Bagdasaryan, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage
https://arxiv.org/abs/2405.05175

Air Gap: Protecting Privacy-Conscious Conversational Agents
The growing use of large language model (LLM)-based conversational agents to manage sensitive user data raises significant privacy concerns. While these agents excel at understanding and acting on context, this capability can be exploited by malicious actors. We introduce a novel threat model where adversarial third-party apps manipulate the context of interaction to trick LLM-based agents into revealing private information not relevant to the task at hand. Grounded in the framework of contex…

This https://arxiv.org/abs/2402.18357 has been replaced.
initial toot: https://mastoxiv.page/@a…

BCS surrogate models for floating superconductor-semiconductor hybrids
Superconductor-semiconductor hybrid devices, involving quantum dots interfaced with floating and/or grounded superconductors, have reached a level of complexity which calls for the development of versatile and numerically efficient modelling tools. Here, we propose an extension of the surrogate model solver for sub-gap states [Phys. Rev. B 108, L220506 (2023)], which is able to handle floating superconducting islands with finite charging energy. Upon eliminating all finite-size effects of the c…

Higher-Order Analysis of Three-Dimensional Anisotropy in Imbalanced Alfv\'enic Turbulence
Nikos Sioulas, Themistocles Zikopoulos, Chen Shi, Marco Velli, Trevor Bowen, Alfred Mallet, Luca Sorriso-Valvo, Andrea Verdini, B. D. G. Chandran, Mihailo M. Martinovi\'c, S. S. Cerri, Nooshin Davis, Corina Dunn
https://arxiv.org/ab…

Higher-Order Analysis of Three-Dimensional Anisotropy in Imbalanced Alfvénic Turbulence
We analyze in-situ observations of imbalanced solar wind turbulence to evaluate MHD turbulence models grounded in "Critical Balance" (CB) and "Scale-Dependent Dynamic Alignment" (SDDA). At energy injection scales, both outgoing and ingoing modes exhibit a weak cascade; a simultaneous tightening of SDDA is noted. Outgoing modes persist in a weak cascade across the inertial range, while ingoing modes shift to a strong cascade at $λ\approx 3 \times 10^{4} d_i$, with associated spectral scalings d…

This https://arxiv.org/abs/2302.10128 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Leveraging the kernel trick in both the input and output spaces, surrogate kernel methods are a flexible and theoretically grounded solution to structured output prediction. If they provide state-of-the-art performance on complex data sets of moderate size (e.g., in chemoinformatics), these approaches however fail to scale. We propose to equip surrogate kernel methods with sketching-based approximations, applied to both the input and output feature maps. We prove excess risk bounds on the origi…

Bergman local isometries are biholomorphisms
Jihun Yum
https://arxiv.org/abs/2404.18792 https://arxiv.org/pdf/2404.18792

Bergman local isometries are biholomorphisms
We prove that a proper holomorphic local isometry between bounded domains with respect to the Bergman metrics is necessarily a biholomorphism. The proof relies on a new method grounded in Information Geometry theories.

DistriFS: A Platform and User Agnostic Approach to File Distribution
Julian Boesch
https://arxiv.org/abs/2402.13387 https://arxiv.org…

DistriFS: A Platform and User Agnostic Approach to File Distribution
In an age where the distribution of information is crucial, current file sharing solutions suffer significant deficiencies. Popular systems such as Google Drive, torrenting and IPFS suffer issues with compatibility, accessibility and censorship. This paper introduces DistriFS, a novel decentralized approach tailored for efficient and large-scale distribution of files. The architecture of DistriFS is grounded in three foundational pillars: scalability, security, and seamless integration. The pro…

This https://arxiv.org/abs/2312.00849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback
Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction. However, existing MLLMs prevalently suffer from serious hallucination problems, generating text that is not factually grounded in associated images. The problem makes existing MLLMs untrustworthy and thus impractical in real-world (especially high-stakes) applications. To address the challenge, we present RLHF-V, which enhances MLLM trustworthiness…

I find it ironic that an aversion to tribalism can be essentially grounded in an unmitigated sense of alienation, distrust, and differentness.
Not “we are all one big happy human family” but rather “we are 7 billion teams of one and our only way to hold our own against chaos, ignorance, and thermodynamics is to find common causes with people we find alien and unpleasant.”

This https://arxiv.org/abs/2308.01123 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Planar Friction Modelling with LuGre Dynamics and Limit Surfaces
Contact surfaces in planar motion exhibit a coupling between tangential and rotational friction forces. This paper proposes planar friction models grounded in the LuGre model and limit surface theory. First, distributed planar extended state models are proposed and the Elasto-Plastic model is extended for multi-dimensional friction. Subsequently, we derive a reduced planar friction model, coupled with a pre-calculated limit surface, that offers reduced computational cost. The limit surface appr…

This https://arxiv.org/abs/2404.13908 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Normalized grounded states for a coupled nonlinear schrödinger system on $\mathbb{R}^3$
We investigate the existence of normalized ground states to the system of coupled Schrödinger equations: \begin{equation}\label{eq:0.1} \begin{cases} -Δu_1 + λ_1 u_1 = μ_1 |u_1|^{p_1-2}u_1 + βr_1|u_1|^{r_1-2}u_1|u_2|^{r_2} & \text{ in } \mathbb{R}^{3}, -Δu_2 + λ_2 u_2 = μ_2|u_2|^{p_2-2}u_2 + βr_2|u_1|^{r_1}|u_2|^{r_2-2}u_2 & \text{ in } \mathbb{R}^3, \end{cases} \end{equation} subject to the constraints $\mathcal{S}_…

Multiparameter regularization and aggregation in the context of polynomial functional regression
Elke R. Gizewski, Markus Holzleitner, Lukas Mayer-Suess, Sergiy Pereverzyev Jr., Sergei V. Pereverzyev
https://arxiv.org/abs/2405.04147

Multiparameter regularization and aggregation in the context of polynomial functional regression
Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes. In contrast, in this study we go beyond that framework by introducing an algorithm for multiple parameter regularization and presenting a theoretically grounded method for dealing with the associated parameters. This method facilitates the aggregation of models with varying regularization parameters. The efficacy of the proposed approach is asse…

Machine Learning in Viscoelastic Fluids via Energy-Based Kernel Embedding
Samuel E. Otto, Cassio M. Oishi, Fabio Amaral, Steven L. Brunton, J. Nathan Kutz
https://arxiv.org/abs/2404.14347

Machine Learning in Viscoelastic Fluids via Energy-Based Kernel Embedding
The ability to measure differences in collected data is of fundamental importance for quantitative science and machine learning, motivating the establishment of metrics grounded in physical principles. In this study, we focus on the development of such metrics for viscoelastic fluid flows governed by a large class of linear and nonlinear stress models. To do this, we introduce a kernel function corresponding to a given viscoelastic stress model that implicitly embeds flowfield snapshots into a …

Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin
Hugo Schnoering, Pierre Porthaux, Michalis Vazirgiannis
https://arxiv.org/abs/2403.00523

Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin
Exploring transactions within the Bitcoin blockchain entails examining the transfer of bitcoins among several hundred million entities. However, it is often impractical and resource-consuming to study such a vast number of entities. Consequently, entity clustering serves as an initial step in most analytical studies. This process often employs heuristics grounded in the practices and behaviors of these entities. In this research, we delve into the examination of two widely used heuristics, alon…

Foundations of Value of Information: A Semantic Metric for Networked Control Systems Tasks
Touraj Soleymani, John S. Baras, Sandra Hirche, Karl H. Johansson
https://arxiv.org/abs/2403.11927

Foundations of Value of Information: A Semantic Metric for Networked Control Systems Tasks
In this chapter, we present our recent invention, i.e., the notion of the value of information$\unicode{x2014}$a semantic metric that is fundamental for networked control systems tasks. We begin our analysis by formulating a causal tradeoff between the packet rate and the regulation cost, with an encoder and a decoder as two distributed decision makers, and show that the valuation of information is conceivable and quantifiable grounded on this tradeoff. More precisely, we characterize an equili…

This https://arxiv.org/abs/2306.11371 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Visually grounded few-shot word learning in low-resource settings
We propose a visually grounded speech model that learns new words and their visual depictions from just a few word-image example pairs. Given a set of test images and a spoken query, we ask the model which image depicts the query word. Previous work has simplified this few-shot learning problem by either using an artificial setting with digit word-image pairs or by using a large number of examples per class. Moreover, all previous studies were performed using English speech-image data. We propo…

This https://arxiv.org/abs/2311.05605 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

A Spin-Optical Quantum Computing Architecture
We introduce an adaptable and modular hybrid architecture designed for fault-tolerant quantum computing. It combines quantum emitters and linear-optical entangling gates to leverage the strength of both matter-based and photonic-based approaches. A key feature of the architecture is its practicality, grounded in the utilisation of experimentally proven optical components. Our framework enables the execution of any quantum error correcting code, but in particular maintains scalability for low-de…

"How do people decide?": A Model for Software Library Selection
Minaoar Hossain Tanzil, Gias Uddin, Ann Barcomb
https://arxiv.org/abs/2403.16245 …

"How do people decide?": A Model for Software Library Selection
Modern-day software development is often facilitated by the reuse of third-party software libraries. Despite the significant effort to understand the factors contributing to library selection, it is relatively unknown how the libraries are selected and what tools are still needed to support the selection process. Using Straussian grounded theory, we conducted and analyzed the interviews of 24 professionals across the world and derived a model of library selection process which is governed by si…

This https://arxiv.org/abs/2403.04460 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such…

ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models
Vishnunandan L. N. Venkatesh, Byung-Cheol Min
https://arxiv.org/abs/2404.02318

ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models
Incorporating language comprehension into robotic operations unlocks significant advancements in robotics, but also presents distinct challenges, particularly in executing spatially oriented tasks like pattern formation. This paper introduces ZeroCAP, a novel system that integrates large language models with multi-robot systems for zero-shot context aware pattern formation. Grounded in the principles of language-conditioned robotics, ZeroCAP leverages the interpretative power of language models…

#NutrientScience
1/9
Lifting the Veil on L-Carnitine: Unveiling Its Multifaceted Health Benefits 🌟
In the ever-evolving landscape of nutritional science, L-carnitine emerges as a beacon of hope, promising an array of health benefits that cater to both the body and mind. As our understanding deepens, it becomes imperative to sift through the wealth of information, presenting findings grounded in scientific research.

VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
Seoyeon Kim, Kwangwook Seo, Hyungjoo Chae, Jinyoung Yeo, Dongha Lee
https://arxiv.org/abs/2402.18374

VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
Recent approaches in domain-specific named entity recognition (NER), such as biomedical NER, have shown remarkable advances. However, they still lack of faithfulness, producing erroneous predictions. We assume that knowledge of entities can be useful in verifying the correctness of the predictions. Despite the usefulness of knowledge, resolving such errors with knowledge is nontrivial, since the knowledge itself does not directly indicate the ground-truth label. To this end, we propose VerifiNE…

ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models
Siyuan Huang, Iaroslav Ponomarenko, Zhengkai Jiang, Xiaoqi Li, Xiaobin Hu, Peng Gao, Hongsheng Li, Hao Dong
https://arxiv.org/abs/2403.11289

ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models
The integration of Multimodal Large Language Models (MLLMs) with robotic systems has significantly enhanced the ability of robots to interpret and act upon natural language instructions. Despite these advancements, conventional MLLMs are typically trained on generic image-text pairs, lacking essential robotics knowledge such as affordances and physical knowledge, which hampers their efficacy in manipulation tasks. To bridge this gap, we introduce ManipVQA, a novel framework designed to endow ML…

Spontaneous Theory of Mind for Artificial Intelligence
Nikolos Gurney, David V. Pynadath, Volkan Ustun
https://arxiv.org/abs/2402.13272 https://

Spontaneous Theory of Mind for Artificial Intelligence
Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM -- reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled…

This https://arxiv.org/abs/2311.04694 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Evaluating Generative Ad Hoc Information Retrieval
Recent advances in large language models have enabled the development of viable generative retrieval systems. Instead of a traditional document ranking, many generative retrieval systems directly return a grounded generated text as an answer to an information need expressed as a query or question. Quantifying the utility of the textual responses is essential for appropriately evaluating such generative ad hoc retrieval. Yet, the established evaluation methodology for ranking-based retrieval is …

A Sociotechnical Framework For Addressing Stigma and Designing Personalized Digital Health Products
Danielly de Paula, Daniel Juehling, Falk Uebernickel
https://arxiv.org/abs/2403.17843

A Sociotechnical Framework For Addressing Stigma and Designing Personalized Digital Health Products
Stigma, a recognized global barrier to effective disease management, impacts social interactions, resource access, and psychological well-being. In this study, we developed a patient-centered framework for deriving design requirements and interventions for health conditions subject to social stigma. This study introduces a patient-centered framework, grounded in sociotechnical systems theory, to create tailored interventions and design requirements for health conditions influenced by social sti…

This https://arxiv.org/abs/2403.05452 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
Radio-interferometric (RI) imaging entails solving high-resolution high-dynamic range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denois…

ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models
Siyuan Huang, Iaroslav Ponomarenko, Zhengkai Jiang, Xiaoqi Li, Xiaobin Hu, Peng Gao, Hongsheng Li, Hao Dong
https://arxiv.org/abs/2403.11289

ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models
The integration of Multimodal Large Language Models (MLLMs) with robotic systems has significantly enhanced the ability of robots to interpret and act upon natural language instructions. Despite these advancements, conventional MLLMs are typically trained on generic image-text pairs, lacking essential robotics knowledge such as affordances and physical knowledge, which hampers their efficacy in manipulation tasks. To bridge this gap, we introduce ManipVQA, a novel framework designed to endow ML…

A Conceptual Model for the Analysis of Investigation Elements in Games
Pedro Marques, Marcus Parreiras, Joshua Kritz, Geraldo Xexeo
https://arxiv.org/abs/2403.10272

A Conceptual Model for the Analysis of Investigation Elements in Games
This paper presents the 4E conceptual model, developed to formally analyze investigation games from a game design perspective. The model encompasses four components: Exploration, Elicitation, Experimentation, and Evaluation. Grounded Theory was employed as the methodology for constructing the model, allowing for an in-depth understanding of the underlying concepts. The resulting model was then compared to existing literature, and its contributions were thoroughly discussed. Overall, the 4E mode…

SwissNYF: Tool Grounded LLM Agents for Black Box Setting
Somnath Sendhil Kumar, Dhruv Jain, Eshaan Agarwal, Raunak Pandey
https://arxiv.org/abs/2402.10051 …

This https://arxiv.org/abs/2404.19232 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

GRAMMAR: Grounded and Modular Evaluation of Domain-Specific Retrieval-Augmented Language Models
Retrieval-augmented Generation (RAG) systems have been actively studied and deployed across various industries to query on domain-specific knowledge base. However, evaluating these systems presents unique challenges due to the scarcity of domain-specific queries and corresponding ground truths, as well as a lack of systematic approaches to diagnosing the cause of failure cases -- whether they stem from knowledge deficits or issues related to system robustness. To address these challenges, we in…

This https://arxiv.org/abs/2309.02561 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Physically Grounded Vision-Language Models for Robotic Manipulation
Recent advances in vision-language models (VLMs) have led to improved performance on tasks such as visual question answering and image captioning. Consequently, these models are now well-positioned to reason about the physical world, particularly within domains such as robotic manipulation. However, current VLMs are limited in their understanding of the physical concepts (e.g., material, fragility) of common objects, which restricts their usefulness for robotic manipulation tasks that involve i…

BCS surrogate models for floating superconductor-semiconductor hybrids
Virgil V. Baran, Jens Paaske
https://arxiv.org/abs/2402.18357 https://

BCS surrogate models for floating superconductor-semiconductor hybrids
Superconductor-semiconductor hybrid devices, involving quantum dots interfaced with floating and/or grounded superconductors, have reached a level of complexity which calls for the development of versatile and numerically efficient modelling tools. Here, we propose an extension of the surrogate model solver for sub-gap states [Phys. Rev. B 108, L220506 (2023)], which is able to handle floating superconducting islands with finite charging energy. Upon eliminating all finite-size effects of the c…

#NutrientScience
1/6
Delving into the elemental treasures of wellness, copper emerges as an unsung hero with a tale as old as time, yet its implications for modern health are nothing short of groundbreaking. This essential trace mineral, deeply woven into the fabric of our body's physiological tapestry, reveals a story rich in benefits and grounded in scientific rigor.

This https://arxiv.org/abs/2404.03787 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes
Existing guidelines for categorical color selection are heuristic, often grounded in intuition rather than empirical studies of readers' abilities. While design conventions recommend palettes maximize hue differences, more recent exploratory findings indicate other factors, such as lightness, may play a role in effective categorical palette design. We conducted a crowdsourced experiment on mean value judgments in multi-class scatterplots using five color palette families--single-hue sequential,…

This https://arxiv.org/abs/2402.19471 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling
Questions combine our mastery of language with our remarkable facility for reasoning about uncertainty. How do people navigate vast hypothesis spaces to pose informative questions given limited cognitive resources? We study these tradeoffs in a classic grounded question-asking task based on the board game Battleship. Our language-informed program sampling (LIPS) model uses large language models (LLMs) to generate natural language questions, translate them into symbolic programs, and evaluate th…

Parasitic Circus:On the Feasibility of Golden Free PCB Verification
Maryam Saadat Safa, Patrick Schaumont, Shahin Tajik
https://arxiv.org/abs/2403.12252 ht…

Parasitic Circus:On the Feasibility of Golden Free PCB Verification
Printed circuit boards (PCBs) are an integral part of electronic systems. Hence, verifying their physical integrity in the presence of supply chain attacks (e.g., tampering and counterfeiting) is of utmost importance. Recently, tamper detection techniques grounded in impedance characterization of PCB's Power Delivery Network (PDN) have gained prominence due to their global detection coverage, non-invasive, and low-cost nature. Similar to other physical verification methods, these techniques rel…

Modeling Empathetic Alignment in Conversation
Jiamin Yang, David Jurgens
https://arxiv.org/abs/2405.00948 https://arxiv.org/pdf/2405.…

Modeling Empathetic Alignment in Conversation
Empathy requires perspective-taking: empathetic responses require a person to reason about what another has experienced and communicate that understanding in language. However, most NLP approaches to empathy do not explicitly model this alignment process. Here, we introduce a new approach to recognizing alignment in empathetic speech, grounded in Appraisal Theory. We introduce a new dataset of over 9.2K span-level annotations of different types of appraisals of a person's experience and over 3K…

This https://arxiv.org/abs/2310.10221 has been replaced.
link: https://scholar.google.com/scholar?q=a

RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models
Robotic vision applications often necessitate a wide range of visual perception tasks, such as object detection, segmentation, and identification. While there have been substantial advances in these individual tasks, integrating specialized models into a unified vision pipeline presents significant engineering challenges and costs. Recently, Multimodal Large Language Models (MLLMs) have emerged as novel backbones for various downstream tasks. We argue that leveraging the pre-training capabiliti…

#NutrientScience
1/5
In the dynamic realm of health and nutrition, amidst the parade of superfoods and trendsetting diets, magnesium stands out as a pivotal yet underappreciated mineral. It's instrumental in over 300 biochemical processes, influencing everything from energy synthesis to nerve function. Today, I'm casting a spotlight on the myriad benefits of magnesium, grounded in peer-reviewed research, to underscore its vital role in our wellness regimens.

This https://arxiv.org/abs/2404.10830 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Fewer Truncations Improve Language Modeling
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens. Despite its efficiency, the concatenation approach compromises data integrity -- it inevitably breaks many documents into incomplete pieces, leading to excessive truncations that hinder the model from learning to compose logically coherent and factually consistent content that is grounded on the complete context. To address the issue, we pr…

Leveraging Translation For Optimal Recall: Tailoring LLM Personalization With User Profiles
Karthik Ravichandran, Sarmistha Sarna Gomasta
https://arxiv.org/abs/2402.13500

Leveraging Translation For Optimal Recall: Tailoring LLM Personalization With User Profiles
This paper explores a novel technique for improving recall in cross-language information retrieval (CLIR) systems using iterative query refinement grounded in the user's lexical-semantic space. The proposed methodology combines multi-level translation, semantic embedding-based expansion, and user profile-centered augmentation to address the challenge of matching variance between user queries and relevant documents. Through an initial BM25 retrieval, translation into intermediate languages, embe…

This https://arxiv.org/abs/2404.18759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective
In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes. Amidst the steady flow of research solutions stemming from the NLP domain, the study of use cases has fallen behind, leading to a number of innovative technical methods without a place in practice. In this work, we aim to build a structured overview of Legal Tech use cases, grounded in NLP literature, but also supple…

This https://arxiv.org/abs/2306.08651 has been replaced.
link: https://scholar.google.com/scholar?q=a

Toward Grounded Commonsense Reasoning
Consider a robot tasked with tidying a desk with a meticulously constructed Lego sports car. A human may recognize that it is not appropriate to disassemble the sports car and put it away as part of the "tidying." How can a robot reach that conclusion? Although large language models (LLMs) have recently been used to enable commonsense reasoning, grounding this reasoning in the real world has been challenging. To reason in the real world, robots must go beyond passively querying LLMs and activel…

Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling
Gabriel Grand, Valerio Pepe, Jacob Andreas, Joshua B. Tenenbaum
https://arxiv.org/abs/2402.19471

Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling
Questions combine our mastery of language with our remarkable facility for reasoning about uncertainty. How do people navigate vast hypothesis spaces to pose informative questions given limited cognitive resources? We study these tradeoffs in a classic grounded question-asking task based on the board game Battleship. Our language-informed program sampling (LIPS) model uses large language models (LLMs) to generate natural language questions, translate them into symbolic programs, and evaluate th…

This https://arxiv.org/abs/2401.15371 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning
Most existing Legal Judgment Prediction (LJP) models focus on discovering the legal triggers in the criminal fact description. However, in real-world scenarios, a professional judge not only needs to assimilate the law case experience that thrives on past sentenced legal judgments but also depends on the professional legal grounded reasoning that learned from professional legal knowledge. In this paper, we propose a LegalDuet model, which pretrains language models to learn a tailored embedding …

Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer
Xinshuo Hu, Baotian Hu, Dongfang Li, Xiaoguang Li, Lifeng Shang
https://arxiv.org/abs/2402.14488

Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer
The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context. Previous research has predominantly focused on examining hallucinations stemming from static input, such as in the domains of summarization or machine translation. However, our investigation delves into the faithfulness of generative question answering in the presence of dynamic knowledge. O…

Daisy-TTS: Simulating Wider Spectrum of Emotions via Prosody Embedding Decomposition
Rendi Chevi, Alham Fikri Aji
https://arxiv.org/abs/2402.14523 https://…

Daisy-TTS: Simulating Wider Spectrum of Emotions via Prosody Embedding Decomposition
We often verbally express emotions in a multifaceted manner, they may vary in their intensities and may be expressed not just as a single but as a mixture of emotions. This wide spectrum of emotions is well-studied in the structural model of emotions, which represents variety of emotions as derivative products of primary emotions with varying degrees of intensity. In this paper, we propose an emotional text-to-speech design to simulate a wider spectrum of emotions grounded on the structural mod…

Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
Chengxu Zhuang, Evelina Fedorenko, Jacob Andreas
https://arxiv.org/abs/2403.14551 htt…

Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
Today's most accurate language models are trained on orders of magnitude more language data than human language learners receive - but with no supervision from other sensory modalities that play a crucial role in human learning. Can we make LMs' representations and predictions more accurate (and more human-like) with more ecologically plausible supervision? This paper describes LexiContrastive Grounding (LCG), a grounded language learning procedure that leverages visual supervision to improve t…