Tootfinder

Amazon rolls out a generative AI feature that creates a product page, including a description and images, using information from a seller's external website (Emilia David/The Verge)
https://www.theverge.com/2024/3/13/24099685/amazon-generative…

Amazon will let sellers paste a link so AI can make a product page
Amazon will begin rolling out the new AI tool that lets sellers paste a link to generate product pages to US users soon.

Well, well, well... it looks like my original Facebook page -- verified at that! -- is still available after all! Hmph!
Come on over and give us a LIKE or a FOLLOW to celebrate the rising from the ashes!
https://facebook.com/boles

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TaxAI: A Dynamic Economic Simulator and Benchmark for Multi-Agent Reinforcement Learning
Taxation and government spending are crucial tools for governments to promote economic growth and maintain social equity. However, the difficulty in accurately predicting the dynamic strategies of diverse self-interested households presents a challenge for governments to implement effective tax policies. Given its proficiency in modeling other agents in partially observable environments and adaptively learning to find optimal policies, Multi-Agent Reinforcement Learning (MARL) is highly suitabl…

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Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multiple scenarios (domains) within and even outside the application. Multi-domain learning is a typical solution in this regard. While considerable efforts have been made in this regard, there are still two long-standing challenges: (1) Accurately depicting the differences among domains using domain features is crucial fo…

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White Dwarf Merger Remnants: The DAQ Subclass
Mukremin Kilic, Pierre Bergeron, Simon Blouin, Gracyn Jewett, Warren R. Brown, Adam Moss
https://arxiv.org/abs/2403.08878

White Dwarf Merger Remnants: The DAQ Subclass
Four years after the discovery of a unique DAQ white dwarf with a hydrogen-dominated and carbon-rich atmosphere, we report the discovery of four new DAQ white dwarfs, including two that were not recognized properly in the literature. We find all five DAQs in a relatively narrow mass and temperature range of $M=1.14-1.19~M_{\odot}$ and $T_{\rm eff}=13,000-17,000$ K. In addition, at least two show photometric variations due to rapid rotation with $\approx10$ min periods. All five are also kinemat…

Page Transitions API just got a shout out at I/O. Props to @… for all his hard work on this spec

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A Fair and Memory/Time-efficient Hashmap
Hashmap is a fundamental data structure in computer science. There has been extensive research on constructing hashmaps that minimize the number of collisions leading to efficient lookup query time. Recently, the data-dependant approaches, construct hashmaps tailored for a target data distribution that guarantee to uniformly distribute data across different buckets and hence minimize the collisions. Still, to the best of our knowledge, none of the existing technique guarantees group fairness am…

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Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators
Advanced packaging offers a new design paradigm in the post-Moore era, where many small chiplets can be assembled into a large system. Based on heterogeneous integration, a chiplet-based accelerator can be highly specialized for a specific workload, demonstrating extreme efficiency and cost reduction. To fully leverage this potential, it is critical to explore both the architectural design space for individual chiplets and different integration options to assemble these chiplets, which have yet…

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Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
This study tackles the challenges of adversarial corruption in model-based reinforcement learning (RL), where the transition dynamics can be corrupted by an adversary. Existing studies on corruption-robust RL mostly focus on the setting of model-free RL, where robust least-square regression is often employed for value function estimation. However, these techniques cannot be directly applied to model-based RL. In this paper, we focus on model-based RL and take the maximum likelihood estimation (…

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A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis
Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotatio…

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Remarks on "Successive Convexification: A Superlinearly Convergent Algorithm for Non-convex Optimal Control Problems"
The purpose of this note is to highlight and address inaccuracies in the convergence guarantees of SCvx, a nonconvex trajectory optimization algorithm proposed by Mao et al. (arXiv:1804.06539), and make connections to relevant prior work. Specifically, we identify errors in the convergence proof within Mao et al. (arXiv:1804.06539) and reestablish the proof of convergence by employing a new method under stricter assumptions.

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IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
Multimodal foundation models are transformative in sequential recommender systems, leveraging powerful representation learning capabilities. While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt foundation models for recommendation tasks, most research prioritizes parameter efficiency, often overlooking critical factors like GPU memory efficiency and training speed. Addressing this gap, our paper introduces IISAN (Intra- and Inter-modal Side Adapted Network for Multimodal Repre…

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Minimum sum vertex cover: kernelization and parameterized algorithms
Given an ordering of the vertices of a graph, the cost of covering an edge is the smaller number of its two ends. The minimum sum vertex cover problem asks for an ordering that minimizes the total cost of covering all edges. We consider parameterized complexity of this problem, using the largest cost~$k$ of covering a single edge as the parameter. Note that the first $k$ vertices form a (not necessarily minimal) vertex cover of the graph, and the ordering of vertices after $k$ is irrelevant. We…

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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 $\mathcal{O}(d^{1/4}ε^{-1/2})$…

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Brain decoding: toward real-time reconstruction of visual perception
In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution ($\approx$0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroim…

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The Price of Adaptivity in Stochastic Convex Optimization
We prove impossibility results for adaptivity in non-smooth stochastic convex optimization. Given a set of problem parameters we wish to adapt to, we define a "price of adaptivity" (PoA) that, roughly speaking, measures the multiplicative increase in suboptimality due to uncertainty in these parameters. When the initial distance to the optimum is unknown but a gradient norm bound is known, we show that the PoA is at least logarithmic for expected suboptimality, and double-logarithmic for median…

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Deep Neural Network based Optimal Control of Greenhouses
Automatic control of greenhouse crop production is of great interest owing to the increasing energy and labor costs. In this work, we use two-level control, where the upper level generates suitable reference trajectories for states and control inputs based on day-ahead predictions. These references are tracked in the lower level using Nonlinear Model Predictive Control (NMPC). In order to apply NMPC, a model of the greenhouse dynamics is essential. However, the complex nature of the underlying …

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Hidden-Role Games: Equilibrium Concepts and Computation
In this paper, we study the class of games known as hidden-role games in which players are assigned privately to teams and are faced with the challenge of recognizing and cooperating with teammates. This model includes both popular recreational games such as the Mafia/Werewolf family and The Resistance (Avalon) and many real-world settings, such as distributed systems where nodes need to work together to accomplish a goal in the face of possible corruptions. There has been little to no formal m…

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M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions in scenarios with limited data. Current mainstream efforts mainly center around innovative model network architectures, with the aim of enabling the network to implicitly acquire knowledge from diverse scenarios. However, the uncertainty of implicit learning in networks arises from the absence of explicit modeling, l…

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On Sparse Covers of Minor Free Graphs, Low Dimensional Metric Embeddings, and other applications
Given a metric space $(X,d_X)$, a $(β,s,Δ)$-sparse cover is a collection of clusters $\mathcal{C}\subseteq P(X)$ with diameter at most $Δ$, such that for every point $x\in X$, the ball $B_X(x,\fracΔβ)$ is fully contained in some cluster $C\in \mathcal{C}$, and $x$ belongs to at most $s$ clusters in $\mathcal{C}$. Our main contribution is to show that the shortest path metric of every $K_r$-minor free graphs admits $(O(r),O(r^2),Δ)$-sparse cover, and for every $ε>0$, $(4+ε,O(\frac1ε)^r,…

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Out-Of-Domain Unlabeled Data Improves Generalization
We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly (in total variation sense) from the in-domain distribution. The core idea behind our framework is to combine Distributionally Robust Optimization (DRO) with self-supervised training. As a result, we …

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IBVC: Interpolation-driven B-frame Video Compression
Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-dr…

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Optimal Control of Stationary Doubly Diffusive Flows on Two and Three Dimensional Bounded Lipschitz Domains: A Theoretical Study
In this work, a theoretical framework is developed to study the control constrained distributed optimal control of a stationary double diffusion model presented in [Burger, Mendez, Ruiz-Baier, SINUM (2019), 57:1318-1343]. For the control problem, as the source term belongs to a weaker space, a new solvability analysis of the governing equation is presented using Faedo- Galerkin approximation techniques. Some new minimal regularity results for the governing equation are established on two and th…

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Load Shifting Versus Manual Frequency Reserve: Which One is More Appealing to Flexible Loads?
This paper investigates how a thermostatically controlled load can deliver flexibility either in form of manual frequency restoration reserves (mFRR) or load shifting, and which one is financially more appealing to such a load. A supermarket freezer is considered as a representative flexible load, and a grey-box model describing its temperature dynamics is developed using real data from a supermarket in Denmark. Taking into account price and activation uncertainties, a two-stage stochastic mixe…

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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…

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Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment
Personalized fairness in recommendations has been attracting increasing attention from researchers. The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairne…

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Concentrated Differential Privacy for Bandits
Bandits serve as the theoretical foundation of sequential learning and an algorithmic foundation of modern recommender systems. However, recommender systems often rely on user-sensitive data, making privacy a critical concern. This paper contributes to the understanding of Differential Privacy (DP) in bandits with a trusted centralised decision-maker, and especially the implications of ensuring zero Concentrated Differential Privacy (zCDP). First, we formalise and compare different adaptations …

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EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation
We introduce EGIC, an enhanced generative image compression method that allows traversing the distortion-perception curve efficiently from a single model. EGIC is based on two novel building blocks: i) OASIS-C, a conditional pre-trained semantic segmentation-guided discriminator, which provides both spatially and semantically-aware gradient feedback to the generator, conditioned on the latent image distribution, and ii) Output Residual Prediction (ORP), a retrofit solution for multi-realism ima…

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An Inexact Conditional Gradient Method for Constrained Bilevel Optimization
Bilevel optimization is an important class of optimization problems where one optimization problem is nested within another. While various methods have emerged to address unconstrained general bilevel optimization problems, there has been a noticeable gap in research when it comes to methods tailored for the constrained scenario. The few methods that do accommodate constrained problems, often exhibit slow convergence rates or demand a high computational cost per iteration. To tackle this issue,…

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Simultaneous NICER and NuSTAR observations of the Ultraluminous source NGC 4190 ULX-1
We present an X-ray analysis of three different XMM-Newton observations together with simultaneous NICER and NuSTAR observations of the ultraluminous X-ray source NGC 4190 ULX-1. Our goal is to constrain the structure of the accretion disk and the geometrical properties of the source. We performed a temporal and spectral analyses in the 0.4--30 keV energy range where the source is significantly detected in dedicated XMM-Newton, NICER and NuSTAR observations. The temporal analysis shows no flari…

Confined subgroups in groups with contracting elements
Inhyeok Choi, Ilya Gekhtman, Wenyuan Yang, Tianyi Zheng
https://arxiv.org/abs/2405.09070 https://

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Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand the accuracy of knowledge. The issue becomes even more noticeable when addressing logic queries that require multiple logic reasoning steps. On the other hand, knowledge graph (KG) based question answering methods are capable of accurately identifying the correct answers with the help of knowledge graph, yet its accurac…

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Plug-and-Play Regularization on Magnitude with Deep Priors for 3D Near-Field MIMO Imaging
Near-field radar imaging systems are used in a wide range of applications such as concealed weapon detection and medical diagnosis. In this paper, we consider the problem of reconstructing the three-dimensional (3D) complex-valued reflectivity distribution of the near-field scene by enforcing regularization on its magnitude. We solve this inverse problem by using the alternating direction method of multipliers (ADMM) framework. For this, we provide a general expression for the proximal mapping …

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A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is presented, analyzed, and demonstrated through experimental results. The algorithm is unique from other interior-point methods for solving smooth \edit{nonconvex} optimization problems since the search directions are computed using stochastic gradient estimates. It is also unique in its use of inner neighborhoods of the feasi…

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On the orbits of a finite solvable primitive linear group
Yong Yang, Mengxi You
https://arxiv.org/abs/2405.09000 https://arxiv.org/pd…

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AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this…

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On two-generator subgroups of mapping torus groups
Naomi Andrew, Edgar A. Bering IV, Ilya Kapovich, Peter Shalen, Stefano Vidussi
https://arxiv.org/abs/2405.08985

Gravitational-wave model for neutron star merger remnants with supervised learning
Theodoros Soultanis, Kiril Maltsev, Andreas Bauswein, Katerina Chatziioannou, Friedrich K. Roepke, Nikolaos Stergioulas
https://arxiv.org/abs/2405.09513

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Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches regard EA as a graph representation learning task, concentrating on enhancing graph encoders. However, the decoding process in EA - essential for effective operation and alignment accuracy - has received limited attention and remains tailored to specific datasets and model architectures, necessitating both entity and…

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[2024-05-16 Thu (UTC), 3 new articles found for math.GR Group Theory]

Probing particle acceleration in Abell 2256: from to 16 MHz to gamma rays
E. Osinga, R. J. van Weeren, G. Brunetti, R. Adam, K. Rajpurohit, A. Botteon, J. R. Callingham, V. Cuciti, F. de Gasperin, G. K. Miley, H. J. A. R\"ottgering, T. W. Shimwell
https://arxiv.org/abs/2405.09384

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Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests. However, previous sequential modeling methods still have limitations in capturing contextual information. The primary reason is the lack of understanding of domain-specific knowledge and item-related textual content. Fortunately, the emergence of powerful language models has unlocked …

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Time-dependent models of AGN disks with radiation from embedded stellar-mass black holes
Marguerite Epstein-Martin, Hiromichi Tagawa, Zoltan Haiman, Rosalba Perna
https://arxiv.org/abs/2405.09380

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Generative Retrieval via Term Set Generation
Recently, generative retrieval emerges as a promising alternative to traditional retrieval paradigms. It assigns each document a unique identifier, known as DocID, and employs a generative model to directly generate the relevant DocID for the input query. A common choice for DocID is one or several natural language sequences, e.g. the title or n-grams, so that the pre-trained knowledge of the generative model can be utilized. However, a sequence is generated token by token, where only the most …

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This https://arxiv.org/abs/2401.07838 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

This https://arxiv.org/abs/2312.12502 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…