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42,378 | 4 | Title: Spoofing Against Spoofing: Towards Caller ID Verification In Heterogeneous Telecommunication Systems
Abstract: Caller ID spoofing is a global industry problem and often acts as a critical enabler for telephone fraud. To address this problem, the Federal Communications Commission (FCC) has mandated telecom providers in the US to implement STIR/SHAKEN, an industry-driven solution based on digital signatures. STIR/SHAKEN relies on a public key infrastructure (PKI) to manage digital certificates, but scaling up this PKI for the global telecom industry is extremely difficult, if not impossible. Furthermore, it only works with the SIP (VoIP) system, leaving the traditional SS7 (landline and cellular) systems unprotected. So far the alternatives to the STIR/SHAKEN have not been sufficiently studied. In this paper, we propose a PKI-free solution, Caller ID Verification (CIV), to combat caller ID spoofing. CIV authenticates the caller ID based on a challenge-response process instead of digital signatures. It supports both SIP and SS7 systems. Perhaps counter-intuitively, we show that number spoofing can be leveraged, in conjunction with Dual-Tone Multi-Frequency (DTMF), to efficiently implement the challenge-response process, i.e., using spoofing to fight against spoofing. We implement CIV for VoIP, cellular, and landline phones across heterogeneous networks (SS7/SIP) by only updating the software on the user's phone. This is the first caller ID authentication solution with working prototypes for all three types of telephone systems in the current telecom architecture. Finally, we show how the implementation of CIV can be optimized by integrating it into telecom clouds as a service, which users may subscribe to. | [] | Validation |
42,379 | 24 | Title: Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates
Abstract: We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh perspective on fairness in machine learning. We link insurance fairness conceptions to their machine learning relatives, and use this bridge to problematize fairness as calibration. In this process, we bring to the forefront three themes that have been largely overlooked in the machine learning literature: responsibility, performativity and tensions between aggregate and individual. | [
29277,
12421
] | Test |
42,380 | 24 | Title: In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection
Abstract: This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conducting a novel distribution analysis on three popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the best of our knowledge, our study is the first to quantify these distributions in this area. We then perform a comprehensive set of experiments using different augmentations and parameters to evaluate the effectiveness of various SSL methods, namely SimCRL, BYOL, and SwAV, for ECG representation learning, where we observe the best performance achieved by SwAV. Furthermore, our analysis shows that SSL methods achieve highly competitive results to those achieved by supervised state-of-the-art methods. To further assess the performance of these methods on both In-Distribution (ID) and Out-of-Distribution (OOD) ECG data, we conduct cross-dataset training and testing experiments. Our comprehensive experiments show almost identical results when comparing ID and OOD schemes, indicating that SSL techniques can learn highly effective representations that generalize well across different OOD datasets. This finding can have major implications for ECG-based arrhythmia detection. Lastly, to further analyze our results, we perform detailed per-disease studies on the performance of the SSL methods on the three datasets. | [] | Test |
42,381 | 28 | Title: Full-Stack End-To-End mmWave Simulations Using 3GPP and NYUSIM Channel Model in ns-3
Abstract: Accurate channel modeling and simulation tools are vital for studying sub-THz and millimeter (mmWave) wideband communication system performance. To accurately design future high data rate, low latency wireless modems, the entire protocol stack must be appropriately modeled to understand how the physical layer impacts the end-to-end performance experienced by the end user. This paper presents a full stack end-to-end performance analysis in ns-3 using drop-based NYU channel model (NYUSIM) and 3GPP statistical channel model (SCM) in scenarios, namely urban microcell (UMi), urban macrocell (UMa), rural macrocell (RMa), and indoor hotspot (InH) at 28 GHz with 100 MHz bandwidth. Video data is transmitted at 50 Mbps using User Datagram Protocol (UDP), and we observe that the RMa channel is benign in non-line of sight (NLOS) for NYUSIM and 3GPP SCM as it exhibits no packet drops and yields maximum throughput (48.1 Mbps) and latency of $\sim$ 20 ms. In NLOS, for NYUSIM, the UMa and RMa channels are similar in terms of throughput and packet drops, and the latency in UMi and InH scenarios is 10 times and 25 times higher respectively compared to UMa. Our results indicate that mmWave bands can support data rates of 50 Mbps with negligible packet drops and latency below 150 ms in all scenarios using NYUSIM. | [] | Train |
42,382 | 24 | Title: Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
Abstract: Convolutional residual neural networks (ConvResNets), though overparameterized, can achieve remarkable prediction performance in practice, which cannot be well explained by conventional wisdom. To bridge this gap, we study the performance of ConvResNeXts, which cover ConvResNets as a special case, trained with weight decay from the perspective of nonparametric classification. Our analysis allows for infinitely many building blocks in ConvResNeXts, and shows that weight decay implicitly enforces sparsity on these blocks. Specifically, we consider a smooth target function supported on a low-dimensional manifold, then prove that ConvResNeXts can adapt to the function smoothness and low-dimensional structures and efficiently learn the function without suffering from the curse of dimensionality. Our findings partially justify the advantage of overparameterized ConvResNeXts over conventional machine learning models. | [] | Train |
42,383 | 6 | Title: Data Embroidery with Black-and-White Textures
Abstract: We investigated data embroidery with black-and-white textures, identifying challenges in the use of textures for machine embroidery based on our own experience. Data embroidery, as a method of physically representing data, offers a unique way to integrate personal data into one's everyday fabric-based objects. Owing to their monochromatic characteristics, black-and-white textures promise to be easy to employ in machine embroidery. We experimented with different textured visualizations designed by experts and, in this paper, we detail our workflow and evaluate the performance and suitability of different textures. We then conducted a survey on vegetable preferences within a family and created a canvas bag as a case study, featuring the embroidered family data to show how embroidered data can be used in practice. | [
29592,
17761,
2117
] | Validation |
42,384 | 24 | Title: SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation
Abstract: Effectively medication recommendation with complex multimorbidity conditions is a critical task in healthcare. Most existing works predicted medications based on longitudinal records, which assumed the information transmitted patterns of learning longitudinal sequence data are stable and intra-visit medical events are serialized. However, the following conditions may have been ignored: 1) A more compact encoder for intra-relationship in the intra-visit medical event is urgent; 2) Strategies for learning accurate representations of the variable longitudinal sequences of patients are different. In this paper, we proposed a novel Sample-adaptive Hierarchical medicAtion Prediction nEtwork, termed SHAPE, to tackle the above challenges in the medication recommendation task. Specifically, we design a compact intra-visit set encoder to encode the relationship in the medical event for obtaining visit-level representation and then develop an inter-visit longitudinal encoder to learn the patient-level longitudinal representation efficiently. To endow the model with the capability of modeling the variable visit length, we introduce a soft curriculum learning method to assign the difficulty of each sample automatically by the visit length. Extensive experiments on a benchmark dataset verify the superiority of our model compared with several state-of-the-art baselines. | [] | Train |
42,385 | 7 | Title: Unifying the design space of truss metamaterials by generative modeling
Abstract: The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials -- truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized properties, including exceptionally stiff, auxetic, and pentamode-like designs. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain. | [] | Train |
42,386 | 30 | Title: Reevaluating Data Partitioning for Emotion Detection in EmoWOZ
Abstract: This paper focuses on the EmoWoz dataset, an extension of MultiWOZ that provides emotion labels for the dialogues. MultiWOZ was partitioned initially for another purpose, resulting in a distributional shift when considering the new purpose of emotion recognition. The emotion tags in EmoWoz are highly imbalanced and unevenly distributed across the partitions, which causes sub-optimal performance and poor comparison of models. We propose a stratified sampling scheme based on emotion tags to address this issue, improve the dataset's distribution, and reduce dataset shift. We also introduce a special technique to handle conversation (sequential) data with many emotional tags. Using our proposed sampling method, models built upon EmoWoz can perform better, making it a more reliable resource for training conversational agents with emotional intelligence. We recommend that future researchers use this new partitioning to ensure consistent and accurate performance evaluations. | [] | Train |
42,387 | 36 | Title: Balancing Cooperativeness and Adaptiveness in the (Noisy) Iterated Prisoner's Dilemma
Abstract: Ever since Axelrod's seminal work, tournaments served as the main benchmark for evaluating strategies in the Iterated Prisoner's Dilemma (IPD). In this work, we first introduce a strategy for the IPD which outperforms previous tournament champions when evaluated against the 239 strategies in the Axelrod library, at noise levels in the IPD ranging from 0% to 10%. The basic idea behind our strategy is to start playing a version of tit-for-tat which forgives unprovoked defections if their rate is not significantly above the noise level, while building a (memory-1) model of the opponent; then switch to a strategy which is optimally adapted to the model of the opponent. We then argue that the above strategy (like other prominent strategies) lacks a couple of desirable properties which are not well tested for by tournaments, but which will be relevant in other contexts: we want our strategy to be self-cooperating, i.e., cooperate with a clone with high probability, even at high noise levels; and we want it to be cooperation-inducing, i.e., optimal play against it should entail cooperating with high probability. We show that we can guarantee these properties, at a modest cost in tournament performance, by reverting from the strategy adapted to the opponent to the forgiving tit-for-tat strategy under suitable conditions | [] | Validation |
42,388 | 24 | Title: MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Abstract: The field of machine learning (ML) has gained widespread adoption, leading to a significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The predominant approaches towards the automation of solving ML tasks (e.g., AutoML) are often time consuming and hard to understand for human developers. In contrast, though human engineers have the incredible ability to understand tasks and reason about solutions, their experience and knowledge are often sparse and difficult to utilize by quantitative approaches. In this paper, we aim to bridge the gap between machine intelligence and human knowledge by introducing a novel framework MLCopilot, which leverages the state-of-the-art LLMs to develop ML solutions for novel tasks. We showcase the possibility of extending the capability of LLMs to comprehend structured inputs and perform thorough reasoning for solving novel ML tasks. And we find that, after some dedicated design, the LLM can (i) observe from the existing experiences of ML tasks and (ii) reason effectively to deliver promising results for new tasks. The solution generated can be used directly to achieve high levels of competitiveness. | [
40192,
1441,
13700,
3804
] | Train |
42,389 | 36 | Title: Computation of Nash Equilibria of Attack and Defense Games on Networks
Abstract: nan | [] | Test |
42,390 | 30 | Title: Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias
Abstract: Pre-trained Language Models (PLMs) may be poisonous with backdoors or bias injected by the suspicious attacker during the fine-tuning process. A core challenge of purifying potentially poisonous PLMs is precisely finding poisonous dimensions. To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions. According to the relationship between parameter drifts and Hessians of different dimensions, we can detect poisonous dimensions with abnormal dynamics, purify them by resetting them to clean pre-trained weights, and then fine-tune the purified weights on a small clean dataset. To the best of our knowledge, we are the first to study the dynamics guided by the diffusion theory for safety or defense purposes. Experimental results validate the effectiveness of Fine-purifying even with a small clean dataset. | [
8308,
34076
] | Train |
42,391 | 4 | Title: DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection
Abstract: —Our increasingly connected world continues to face an ever-growing amount of network-based attacks. Intrusion detection systems (IDS) are an essential security technology for detecting these attacks. Although numerous machine learning-based IDS have been proposed for the detection of malicious network traffic, the majority have difficulty properly detecting and classifying the more uncommon attack types. In this paper, we implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) model. Our GAN model is trained with the NSL-KDD dataset for four attack categories as well as normal network flow. Ultimately, our findings demonstrate that training the DRL on specific synthetic datasets can result in better performance in correctly classifying minority classes over training on the true imbalanced dataset. | [] | Validation |
42,392 | 16 | Title: Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Abstract: Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test tasks. In this paper, we develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot classification. Different from existing prototypical few-shot methods that use the averages of support instances to calculate the class prototypes, we propose to learn class prototypes from the concatenated features of the support set in a parametric fashion and meta-learn the model by enforcing prototype-based regularization on the query set. In addition, we fine-tune the model in the target domain in a transductive manner using a weighted-moving-average self-training approach on the query instances. We conduct experiments on multiple cross-domain few-shot benchmark datasets. The empirical results demonstrate that APPL yields superior performance than many state-of-the-art cross-domain few-shot learning methods. | [] | Train |
42,393 | 30 | Title: GPT-RE: In-context Learning for Relation Extraction using Large Language Models
Abstract: In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). This is due to the two major shortcomings of LLMs in RE: (1) low relevance regarding entity and relation in retrieved demonstrations for in-context learning; and (2) the strong inclination to wrongly classify NULL examples into other pre-defined labels. In this paper, we propose GPT-RE to bridge the gap between LLMs and fully-supervised baselines. GPT-RE successfully addresses the aforementioned issues by (1) incorporating task-specific entity representations in demonstration retrieval; and (2) enriching the demonstrations with gold label-induced reasoning logic. We evaluate GPT-RE on four widely-used RE datasets, and observe that GPT-RE achieves improvements over not only existing GPT-3 baselines, but also fully-supervised baselines. Specifically, GPT-RE achieves SOTA performances on the Semeval and SciERC datasets, and competitive performances on the TACRED and ACE05 datasets. | [
35556,
44068,
1639,
21610,
43403,
26552,
18683,
23836,
43327
] | Test |
42,394 | 16 | Title: Gemtelligence: Accelerating Gemstone classification with Deep Learning
Abstract: The value of luxury goods, particularly investment-grade gemstones, is greatly influenced by their origin and authenticity, sometimes resulting in differences worth millions of dollars. Traditionally, human experts have determined the origin and detected treatments on gemstones through visual inspections and a range of analytical methods. However, the interpretation of the data can be subjective and time-consuming, resulting in inconsistencies. In this study, we propose Gemtelligence, a novel approach based on deep learning that enables accurate and consistent origin determination and treatment detection. Gemtelligence comprises convolutional and attention-based neural networks that process heterogeneous data types collected by multiple instruments. Notably, the algorithm demonstrated comparable predictive performance to expensive laser-ablation inductively-coupled-plasma mass-spectrometry (ICP-MS) analysis and visual examination by human experts, despite using input data from relatively inexpensive analytical methods. Our innovative methodology represents a major breakthrough in the field of gemstone analysis by significantly improving the automation and robustness of the entire analytical process pipeline. | [] | Validation |
42,395 | 16 | Title: MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression
Abstract: Bounding box regression (BBR) has been widely used in object detection and instance segmentation, which is an important step in object localization. However, most of the existing loss functions for bounding box regression cannot be optimized when the predicted box has the same aspect ratio as the groundtruth box, but the width and height values are exactly different. In order to tackle the issues mentioned above, we fully explore the geometric features of horizontal rectangle and propose a novel bounding box similarity comparison metric MPDIoU based on minimum point distance, which contains all of the relevant factors considered in the existing loss functions, namely overlapping or non-overlapping area, central points distance, and deviation of width and height, while simplifying the calculation process. On this basis, we propose a bounding box regression loss function based on MPDIoU, called LMPDIoU . Experimental results show that the MPDIoU loss function is applied to state-of-the-art instance segmentation (e.g., YOLACT) and object detection (e.g., YOLOv7) model trained on PASCAL VOC, MS COCO, and IIIT5k outperforms existing loss functions. | [] | Validation |
42,396 | 16 | Title: Heuristic Hyperparameter Choice for Image Anomaly Detection
Abstract: Anomaly detection (AD) in images is a fundamental computer vision problem by deep learning neural network to identify images deviating significantly from normality. The deep features extracted from pretrained models have been proved to be essential for AD based on multivariate Gaussian distribution analysis. However, since models are usually pretrained on a large dataset for classification tasks such as ImageNet, they might produce lots of redundant features for AD, which increases computational cost and degrades the performance. We aim to do the dimension reduction of Negated Principal Component Analysis (NPCA) for these features. So we proposed some heuristic to choose hyperparameter of NPCA algorithm for getting as fewer components of features as possible while ensuring a good performance. | [] | Train |
42,397 | 16 | Title: Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation
Abstract: Animating virtual avatars to make co-speech gestures facilitates various applications in human-machine interaction. The existing methods mainly rely on generative adversarial networks (GANs), which typically suffer from notorious mode collapse and unstable training, thus making it difficult to learn accurate audio-gesture joint distributions. In this work, we propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture), to effectively capture the cross-modal audio-to-gesture associations and preserve temporal coherence for high-fidelity audio-driven co-speech gesture generation. Specifically, we first establish the diffusion-conditional generation process on clips of skeleton sequences and audio to enable the whole framework. Then, a novel Diffusion Audio-Gesture Transformer is devised to better attend to the information from multiple modalities and model the long-term temporal dependency. Moreover, to eliminate temporal inconsistency, we propose an effective Diffusion Gesture Stabilizer with an annealed noise sampling strategy. Benefiting from the architectural advantages of diffusion models, we further incorporate implicit classifier-free guidance to trade off between diversity and gesture quality. Extensive experiments demonstrate that DiffGesture achieves state-of-the-art performance, which renders coherent gestures with better mode coverage and stronger audio correlations. Code is available at https://github.com/Advocate99/DiffGesture. | [
8424,
25609,
39340,
8718,
19888,
36214
] | Train |
42,398 | 16 | Title: Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
Abstract: Deep neural network models can learn clinically relevant features from millions of histopathology images. However generating high-quality annotations to train such models for each hospital, each cancer type, and each diagnostic task is prohibitively laborious. On the other hand, terabytes of training data -- while lacking reliable annotations -- are readily available in the public domain in some cases. In this work, we explore how these large datasets can be consciously utilized to pre-train deep networks to encode informative representations. We then fine-tune our pre-trained models on a fraction of annotated training data to perform specific downstream tasks. We show that our approach can reach the state-of-the-art (SOTA) for patch-level classification with only 1-10% randomly selected annotations compared to other SOTA approaches. Moreover, we propose an uncertainty-aware loss function, to quantify the model confidence during inference. Quantified uncertainty helps experts select the best instances to label for further training. Our uncertainty-aware labeling reaches the SOTA with significantly fewer annotations compared to random labeling. Last, we demonstrate how our pre-trained encoders can surpass current SOTA for whole-slide image classification with weak supervision. Our work lays the foundation for data and task-agnostic pre-trained deep networks with quantified uncertainty. | [] | Train |
42,399 | 16 | Title: BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency
Abstract: As one of the most fundamental techniques in multi-modal learning, cross-modal matching aims to project various sensory modalities into a shared feature space. To achieve this, massive and correctly aligned data pairs are required for model training. However, unlike unimodal datasets, multimodal datasets are extremely harder to collect and annotate precisely. As an alternative, the co-occurred data pairs (e.g., image-text pairs) collected from the Internet have been widely exploited in the area. Unfortunately, the cheaply collected dataset unavoidably contains many mismatched data pairs, which have been proven to be harmful to the model's performance. To address this, we propose a general framework called BiCro (Bidirectional Cross-modal similarity consistency), which can be easily integrated into existing cross-modal matching models and improve their robustness against noisy data. Specifically, BiCro aims to estimate soft labels for noisy data pairs to reflect their true correspondence degree. The basic idea of BiCro is motivated by that – taking image-text matching as an example – similar images should have similar textual descriptions and vice versa. Then the consistency of these two similarities can be recast as the estimated soft labels to train the matching model. The experiments on three popular cross-modal matching datasets demonstrate that our method significantly improves the noise-robustness of various matching models, and surpass the state-of-the-art by a clear margin. The code is available at https://github.com/xu5zhao/BiCro. | [] | Train |
42,400 | 1 | Title: Towards Balanced Active Learning for Multimodal Classification
Abstract: Training multimodal networks requires a vast amount of data due to their larger parameter space compared to unimodal networks. Active learning is a widely used technique for reducing data annotation costs by selecting only those samples that could contribute to improving model performance. However, current active learning strategies are mostly designed for unimodal tasks, and when applied to multimodal data, they often result in biased sample selection from the dominant modality. This unfairness hinders balanced multimodal learning, which is crucial for achieving optimal performance. To address this issue, we propose three guidelines for designing a more balanced multimodal active learning strategy. Following these guidelines, a novel approach is proposed to achieve more fair data selection by modulating the gradient embedding with the dominance degree among modalities. Our studies demonstrate that the proposed method achieves more balanced multimodal learning by avoiding greedy sample selection from the dominant modality. Our approach outperforms existing active learning strategies on a variety of multimodal classification tasks. Overall, our work highlights the importance of balancing sample selection in multimodal active learning and provides a practical solution for achieving more balanced active learning for multimodal classification. | [] | Train |
42,401 | 27 | Title: Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How
Abstract: We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding (MAPF) abstraction of this problem. It then utilises a nonprehensile push planner to compute actions for how the robot might realise these rearrangements and a rigid body physics simulator to check whether the actions satisfy physics constraints encoded in the problem. However, M4M greedily commits to valid pushes found during planning, and does not reason about orderings over pushes if multiple objects need to be rearranged. Furthermore, M4M does not reason about other possible MAPF solutions that lead to different rearrangements and pushes. In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene. We introduce several algorithmic optimisations to circumvent the increased computational complexity, discuss the space of problems solvable by E-M4M and show that experimentally, both on the real robot and in simulation, it significantly outperforms the original M4M algorithm, as well as other state-of-the-art alternatives when dealing with complex scenes. | [
15299
] | Validation |
42,402 | 24 | Title: In-Process Global Interpretation for Graph Learning via Distribution Matching
Abstract: Graphs neural networks (GNNs) have emerged as a powerful graph learning model due to their superior capacity in capturing critical graph patterns. To gain insights about the model mechanism for interpretable graph learning, previous efforts focus on post-hoc local interpretation by extracting the data pattern that a pre-trained GNN model uses to make an individual prediction. However, recent works show that post-hoc methods are highly sensitive to model initialization and local interpretation can only explain the model prediction specific to a particular instance. In this work, we address these limitations by answering an important question that is not yet studied: how to provide global interpretation of the model training procedure? We formulate this problem as in-process global interpretation, which targets on distilling high-level and human-intelligible patterns that dominate the training procedure of GNNs. We further propose Graph Distribution Matching (GDM) to synthesize interpretive graphs by matching the distribution of the original and interpretive graphs in the feature space of the GNN as its training proceeds. These few interpretive graphs demonstrate the most informative patterns the model captures during training. Extensive experiments on graph classification datasets demonstrate multiple advantages of the proposed method, including high explanation accuracy, time efficiency and the ability to reveal class-relevant structure. | [] | Test |
42,403 | 24 | Title: Online Arbitrary Shaped Clustering through Correlated Gaussian Functions
Abstract: There is no convincing evidence that backpropagation is a biologically plausible mechanism, and further studies of alternative learning methods are needed. A novel online clustering algorithm is presented that can produce arbitrary shaped clusters from inputs in an unsupervised manner, and requires no prior knowledge of the number of clusters in the input data. This is achieved by finding correlated outputs from functions that capture commonly occurring input patterns. The algorithm can be deemed more biologically plausible than model optimization through backpropagation, although practical applicability may require additional research. However, the method yields satisfactory results on several toy datasets on a noteworthy range of hyperparameters. | [] | Validation |
42,404 | 16 | Title: Towards accurate instance segmentation in large-scale LiDAR point clouds
Abstract: Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy. | [
18420
] | Validation |
42,405 | 6 | Title: Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents
Abstract: The recent advent of large language models (LLM) has resulted in high-performing conversational agents such as chatGPT. These agents must remember key information from an ongoing conversation to provide responses that are contextually relevant to the user. However, these agents have limited memory and can be distracted by irrelevant parts of the conversation. While many strategies exist to manage conversational memory, users currently lack affordances for viewing and controlling what the agent remembers, resulting in a poor mental model and conversational breakdowns. In this paper, we present Memory Sandbox, an interactive system and design probe that allows users to manage the conversational memory of LLM-powered agents. By treating memories as data objects that can be viewed, manipulated, recorded, summarized, and shared across conversations, Memory Sandbox provides interaction affordances for users to manage how the agent should `see' the conversation. | [
39873,
33477,
16556,
19155,
21880
] | Test |
42,406 | 30 | Title: Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement
Abstract: Prompting methods such as Chain-of-Thought (CoT) have shed new light on enhancing the reasoning capabilities of large language models, and researchers have extensively explored the generation process of rationales and answers. However, they have overlooked the potential challenges posed by the poor quality of reasoning problems, which may influence the reasoning performance significantly. In this work, we propose Self-Polish (SP), a novel method that facilitates the model's problem-solving process by prompting them to progressively refine the given problems to be more comprehensible and solvable. Specifically, the method teaches models to eliminate irrelevant information, rearrange the logic structure and organize local conditions into new ones parallelly. SP is orthogonal to all other prompting methods, making it convenient to integrate with state-of-the-art techniques for further improvement. We conduct thorough experiments on five benchmarks to illustrate the effectiveness of the proposed method. For example, with Text-davinci-003, our method boosts the performance of standard few-shot prompting by $8.0\%$ on GSM8K and $17.8\%$ on MultiArith; it also improves the performance of CoT by $6.0\%$ on GSM8K and $6.0\%$ on MathQA, respectively. Furthermore, our method also showcases impressive performance on robustness evaluation. | [
21880,
38338,
19727
] | Test |
42,407 | 11 | Title: Social Value Orientation and Integral Emotions in Multi-Agent Systems
Abstract: Human social behavior is influenced by individual differences in social preferences. Social value orientation (SVO) is a measurable personality trait which indicates the relative importance an individual places on their own and on others' welfare when making decisions. SVO and other individual difference variables are strong predictors of human behavior and social outcomes. However, there are transient changes human behavior associated with emotions that are not captured by individual differences alone. Integral emotions, the emotions which arise in direct response to a decision-making scenario, have been linked to temporary shifts in decision-making preferences. In this work, we investigated the effects of moderating social preferences with integral emotions in multi-agent societies. We developed Svoie, a method for designing agents which make decisions based on established SVO policies, as well as alternative integral emotion policies in response to task outcomes. We conducted simulation experiments in a resource-sharing task environment, and compared societies of Svoie agents with societies of agents with fixed SVO policies. We find that societies of agents which adapt their behavior through integral emotions achieved similar collective welfare to societies of agents with fixed SVO policies, but with significantly reduced inequality between the welfare of agents with different SVO traits. We observed that by allowing agents to change their policy in response to task outcomes, agents can moderate their behavior to achieve greater social equality. \end{abstract} | [] | Train |
42,408 | 25 | Title: SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify
Abstract: In this study, we approached the Hit Song Prediction problem, which aims to predict which songs will become Billboard hits. We gathered a dataset of nearly 18500 hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four machine-learning models on our dataset. We were able to predict the Billboard success of a song with approximately 86\% accuracy. The most succesful algorithms were Random Forest and Support Vector Machine. | [] | Train |
42,409 | 30 | Title: ControlRetriever: Harnessing the Power of Instructions for Controllable Retrieval
Abstract: Recent studies have shown that dense retrieval models, lacking dedicated training data, struggle to perform well across diverse retrieval tasks, as different retrieval tasks often entail distinct search intents. To address this challenge, in this work we introduce ControlRetriever, a generic and efficient approach with a parameter isolated architecture, capable of controlling dense retrieval models to directly perform varied retrieval tasks, harnessing the power of instructions that explicitly describe retrieval intents in natural language. Leveraging the foundation of ControlNet, which has proven powerful in text-to-image generation, ControlRetriever imbues different retrieval models with the new capacity of controllable retrieval, all while being guided by task-specific instructions. Furthermore, we propose a novel LLM guided Instruction Synthesizing and Iterative Training strategy, which iteratively tunes ControlRetriever based on extensive automatically-generated retrieval data with diverse instructions by capitalizing the advancement of large language models. Extensive experiments show that in the BEIR benchmark, with only natural language descriptions of specific retrieval intent for each task, ControlRetriever, as a unified multi-task retrieval system without task-specific tuning, significantly outperforms baseline methods designed with task-specific retrievers and also achieves state-of-the-art zero-shot performance. | [
40192,
34074
] | Train |
42,410 | 10 | Title: The problem with AI consciousness: A neurogenetic case against synthetic sentience
Abstract: Ever since the creation of the first artificial intelligence (AI) machinery built on machine learning (ML), public society has entertained the idea that eventually computers could become sentient and develop a consciousness of their own. As these models now get increasingly better and convincingly more anthropomorphic, even some engineers have started to believe that AI might become conscious, which would result in serious social consequences. The present paper argues against the plausibility of sentient AI primarily based on the theory of neurogenetic structuralism, which claims that the physiology of biological neurons and their structural organization into complex brains are necessary prerequisites for true consciousness to emerge. | [] | Train |
42,411 | 10 | Title: Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
Abstract: Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal sparsity and non-stationarity typically seen during online usage in autonomous agent domains. Meanwhile, interventional techniques are not always feasible due to domain restrictions. In order to better explore the issues facing observational techniques and promote further discussion of these topics we carry out a benchmark across 10 contemporary observational temporal causal discovery methods in the domain of autonomous driving. By evaluating these methods upon causal scenes drawn from real world datasets in addition to those generated synthetically we highlight where improvements need to be made in order to facilitate the application of causal discovery techniques to the aforementioned use-cases. Finally, we discuss potential directions for future work that could help better tackle the difficulties currently experienced by state of the art techniques. | [
14251,
42483,
39695
] | Train |
42,412 | 24 | Title: Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward
Abstract: We investigate an infinite-horizon average reward Markov Decision Process (MDP) with delayed, composite, and partially anonymous reward feedback. The delay and compositeness of rewards mean that rewards generated as a result of taking an action at a given state are fragmented into different components, and they are sequentially realized at delayed time instances. The partial anonymity attribute implies that a learner, for each state, only observes the aggregate of past reward components generated as a result of different actions taken at that state, but realized at the observation instance. We propose an algorithm named $\mathrm{DUCRL2}$ to obtain a near-optimal policy for this setting and show that it achieves a regret bound of $\tilde{\mathcal{O}}\left(DS\sqrt{AT} + d (SA)^3\right)$ where $S$ and $A$ are the sizes of the state and action spaces, respectively, $D$ is the diameter of the MDP, $d$ is a parameter upper bounded by the maximum reward delay, and $T$ denotes the time horizon. This demonstrates the optimality of the bound in the order of $T$, and an additive impact of the delay. | [
42926
] | Train |
42,413 | 31 | Title: Best-Case Retrieval Evaluation: Improving the Sensitivity of Reciprocal Rank with Lexicographic Precision
Abstract: Across a variety of ranking tasks, researchers use reciprocal rank to measure the effectiveness for users interested in exactly one relevant item. Despite its widespread use, evidence suggests that reciprocal rank is brittle when discriminating between systems. This brittleness, in turn, is compounded in modern evaluation settings where current, high-precision systems may be difficult to distinguish. We address the lack of sensitivity of reciprocal rank by introducing and connecting it to the concept of best-case retrieval, an evaluation method focusing on assessing the quality of a ranking for the most satisfied possible user across possible recall requirements. This perspective allows us to generalize reciprocal rank and define a new preference-based evaluation we call lexicographic precision or lexiprecision. By mathematical construction, we ensure that lexiprecision preserves differences detected by reciprocal rank, while empirically improving sensitivity and robustness across a broad set of retrieval and recommendation tasks. | [
21311
] | Train |
42,414 | 26 | Title: Using Twitter Data to Determine Hurricane Category: An Experiment
Abstract: Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data. | [
4445
] | Train |
42,415 | 27 | Title: Neural Optimal Control using Learned System Dynamics
Abstract: We study the problem of generating control laws for systems with unknown dynamics. Our approach is to represent the controller and the value function with neural networks, and to train them using loss functions adapted from the Hamilton-Jacobi-Bellman (HJB) equations. In the absence of a known dynamics model, our method first learns the state transitions from data collected by interacting with the system in an offline process. The learned transition function is then integrated to the HJB equations and used to forward simulate the control signals produced by our controller in a feedback loop. In contrast to trajectory optimization methods that optimize the controller for a single initial state, our controller can generate near-optimal control signals for initial states from a large portion of the state space. Compared to recent model-based reinforcement learning algorithms, we show that our method is more sample efficient and trains faster by an order of magnitude. We demonstrate our method in a number of tasks, including the control of a quadrotor with 12 state variables. | [] | Train |
42,416 | 24 | Title: Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Abstract: Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowadays a real scientific challenge. In this paper we introduce a precise, fast, and differentiable upper bound for the spectral norm of convolutional layers using circulant matrix theory and a new alternative to the Power iteration. Called the Gram iteration, our approach exhibits a superlinear convergence. First, we show through a comprehensive set of experiments that our approach outperforms other state-of-the-art methods in terms of precision, computational cost, and scalability. Then, it proves highly effective for the Lipschitz regularization of convolutional neural networks, with competitive results against concurrent approaches. Code is available at https://github.com/blaisedelattre/lip4conv. | [
38714
] | Train |
42,417 | 30 | Title: Certified Robustness for Large Language Models with Self-Denoising
Abstract: Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses, especially in high-stake environments. In these contexts, it is crucial to ensure that every prediction made by large language models is stable, i.e., LLM predictions should be consistent given minor differences in the input. This largely falls into the study of certified robust LLMs, i.e., all predictions of LLM are certified to be correct in a local region around the input. Randomized smoothing has demonstrated great potential in certifying the robustness and prediction stability of LLMs. However, randomized smoothing requires adding noise to the input before model prediction, and its certification performance depends largely on the model's performance on corrupted data. As a result, its direct application to LLMs remains challenging and often results in a small certification radius. To address this issue, we take advantage of the multitasking nature of LLMs and propose to denoise the corrupted inputs with LLMs in a self-denoising manner. Different from previous works like denoised smoothing, which requires training a separate model to robustify LLM, our method enjoys far better efficiency and flexibility. Our experiment results show that our method outperforms the existing certification methods under both certified robustness and empirical robustness. The codes are available at https://github.com/UCSB-NLP-Chang/SelfDenoise. | [
44481,
4610,
13700,
27815,
45455,
24308
] | Validation |
42,418 | 3 | Title: Ethical Considerations and Policy Implications for Large Language Models: Guiding Responsible Development and Deployment
Abstract: This paper examines the ethical considerations and implications of large language models (LLMs) in generating content. It highlights the potential for both positive and negative uses of generative AI programs and explores the challenges in assigning responsibility for their outputs. The discussion emphasizes the need for proactive ethical frameworks and policy measures to guide the responsible development and deployment of LLMs. | [
13224,
1899,
13278
] | Train |
42,419 | 30 | Title: B\"{u}y\"{u}k dil modellerinin T\"{u}rk\c{c}e verisetleri ile e\u{g}itilmesi ve ince ayarlanmas\i
Abstract: Large language models have advanced enormously, gained vast attraction and are having a phase of intensed research. Some of the developed models and training datasets have been made open-accessible. Hence these may be further fine-tuned with some techniques to obtain specialized models for specific tasks. When it comes to Turkish language, open-access models do not provide satisfactory coverage. This is also observed over published datasets. In this work, we propose some ideas to mitigate this issue: creating large Turkish datasets, training LLMs with these and fine-tuning pre-trained models with Turkish inputs. We report our findings on Turkish-based trainings with the problems encountered along the way. We conclude with outcomes of these experiments and propose ideas for further works. -- B\"uy\"uk dil modelleri inan{\i}lmaz \"ol\c{c}\"ude geli\c{s}mekte, b\"uy\"uk ilgi toplayarak ve \"uzerlerinde yo\u{g}un ara\c{s}tirmalarin yapildi\u{g}i bir d\"onemdedirler. Geli\c{s}tirilen modeller ve e\u{g}itimde kullanilan verisetlerinden bazilari a\c{c}ik eri\c{s}imli olarak sunulmaktadir. B\"oylece ince ayarlama teknikleri uygulayarak \"ozelle\c{s}mi\c{s} g\"orevler i\c{c}in \c{c}ali\c{s}abilir modeller elde edilmektedir. T\"urk\c{c}e s\"oz konusu oldu\u{g}unda bu modellerinin kapsayicili\u{g}i yeterli d\"uzeyde de\u{g}ildir. Bu durum, yayimlanan verisetlerinde de g\"ozlemlenebilir. Bunu a\c{s}manin yollari T\"urk\c{c}e i\c{c}erikli b\"uy\"uk verisetlerinin olu\c{s}turulmasi, b\"uy\"uk dil modellerinin bunlarla e\u{g}itilmesi ve \"onceden e\u{g}itilmi\c{s} modellerin T\"urk\c{c}e girdilerle ince ayarlanmalari olabilir. Bu \c{c}ali\c{s}mada a\c{c}ik eri\c{s}imli dil modelleri ve verisetleri \"uzerinde durulmakta ve T\"urk\c{c}e temelli bazi deneyler, kar\c{s}ila\c{s}ilan sorunlar ve sonu\c{c}lar irdelenmektedir. | [
13700
] | Test |
42,420 | 24 | Title: MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs
Abstract: New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon. | [] | Train |
42,421 | 2 | Title: Semitopology: a new topological model of heterogeneous consensus
Abstract: A distributed system is permissionless when participants can join and leave the network without permission from a central authority. Many modern distributed systems are naturally permissionless, in the sense that a central permissioning authority would defeat their design purpose: this includes blockchains, filesharing protocols, some voting systems, and more. By their permissionless nature, such systems are heterogeneous: participants may only have a partial view of the system, and they may also have different goals and beliefs. Thus, the traditional notion of consensus -- i.e. system-wide agreement -- may not be adequate, and we may need to generalise it. This is a challenge: how should we understand what heterogeneous consensus is; what mathematical framework might this require; and how can we use this to build understanding and mathematical models of robust, effective, and secure permissionless systems in practice? We analyse heterogeneous consensus using semitopology as a framework. This is like topology, but without the restriction that intersections of opens be open. Semitopologies have a rich theory which is related to topology, but with its own distinct character and mathematics. We introduce novel well-behavedness conditions, including an anti-Hausdorff property and a new notion of `topen set', and we show how these structures relate to consensus. We give a restriction of semitopologies to witness semitopologies, which are an algorithmically tractable subclass corresponding to Horn clause theories, having particularly good mathematical properties. We introduce and study several other basic notions that are specific and novel to semitopologies, and study how known quantities in topology, such as dense subsets and closures, display interesting and useful new behaviour in this new semitopological context. | [
24278
] | Train |
42,422 | 25 | Title: MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies
Abstract: Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music. | [
4481,
13186,
15628,
45104,
18777
] | Train |
42,423 | 30 | Title: Describe me an Aucklet: Generating Grounded Perceptual Category Descriptions
Abstract: Human language users can generate descriptions of perceptual concepts beyond instance-level representations and also use such descriptions to learn provisional class-level representations. However, the ability of computational models to learn and operate with class representations is under-investigated in the language-and-vision field. In this paper, we train separate neural networks to generate and interpret class-level descriptions. We then use the zero-shot classification performance of the interpretation model as a measure of communicative success and class-level conceptual grounding. We investigate the performance of prototype- and exemplar-based neural representations grounded category description. Finally, we show that communicative success reveals performance issues in the generation model that are not captured by traditional intrinsic NLG evaluation metrics, and argue that these issues can be traced to a failure to properly ground language in vision at the class level. We observe that the interpretation model performs better with descriptions that are low in diversity on the class level, possibly indicating a strong reliance on frequently occurring features. | [] | Test |
42,424 | 30 | Title: Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts
Abstract: Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its expert-layman-AI collaborative annotations facilitate controllability over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of input-output combinations, leading to two control-free and two controllable versions of the model. We add two types of controllability into text simplification, by using a multi-angle training approach: position-aware, which uses in-place annotated inputs and outputs, and position-agnostic, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated baseline. Furthermore, our position-aware control enhances the model's ability to generate better simplification than the position-agnostic version. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP. | [
41442,
35629
] | Train |
42,425 | 16 | Title: Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Abstract: Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data. | [
39788,
5205,
36918,
9945,
40669
] | Validation |
42,426 | 16 | Title: Towards Hierarchical Regional Transformer-based Multiple Instance Learning
Abstract: The classification of gigapixel histopathology images with deep multiple instance learning models has become a critical task in digital pathology and precision medicine. In this work, we propose a Transformer-based multiple instance learning approach that replaces the traditional learned attention mechanism with a regional, Vision Transformer inspired self-attention mechanism. We present a method that fuses regional patch information to derive slide-level predictions and show how this regional aggregation can be stacked to hierarchically process features on different distance levels. To increase predictive accuracy, especially for datasets with small, local morphological features, we introduce a method to focus the image processing on high attention regions during inference. Our approach is able to significantly improve performance over the baseline on two histopathology datasets and points towards promising directions for further research. | [] | Test |
42,427 | 6 | Title: “There’s so much responsibility on users right now:” Expert Advice for Staying Safer From Hate and Harassment
Abstract: Online hate and harassment poses a threat to the digital safety of people globally. In light of this risk, there is a need to equip as many people as possible with advice to stay safer online. We interviewed 24 experts to understand what threats and advice internet users should prioritize to prevent or mitigate harm. As part of this, we asked experts to evaluate 45 pieces of existing hate-and-harassment-specific digital-safety advice to understand why they felt advice was viable or not. We find that experts frequently had competing perspectives for which threats and advice they would prioritize. We synthesize sources of disagreement, while also highlighting the primary threats and advice where experts concurred. Our results inform immediate efforts to protect users from online hate and harassment, as well as more expansive socio-technical efforts to establish enduring safety. | [
30240,
30383
] | Train |
42,428 | 16 | Title: Single-round Self-supervised Distributed Learning using Vision Transformer
Abstract: Despite the recent success of deep learning in the field of medicine, the issue of data scarcity is exacerbated by concerns about privacy and data ownership. Distributed learning approaches, including federated learning, have been investigated to address these issues. However, they are hindered by the need for cumbersome communication overheads and weaknesses in privacy protection. To tackle these challenges, we propose a self-supervised masked sampling distillation method for the vision transformer. This method can be implemented without continuous communication and can enhance privacy by utilizing a vision transformer-specific encryption technique. We conducted extensive experiments on two different tasks, which demonstrated the effectiveness of our method. We achieved superior performance compared to the existing distributed learning strategy as well as the fine-tuning only baseline. Furthermore, since the self-supervised model created using our proposed method can achieve a general semantic understanding of the image, we demonstrate its potential as a task-agnostic self-supervised foundation model for various downstream tasks, thereby expanding its applicability in the medical domain. | [] | Train |
42,429 | 16 | Title: Revisiting the Encoding of Satellite Image Time Series
Abstract: Satellite Image Time Series (SITS) representation learning is complex due to high spatiotemporal resolutions, irregular acquisition times, and intricate spatiotemporal interactions. These challenges result in specialized neural network architectures tailored for SITS analysis. The field has witnessed promising results achieved by pioneering researchers, but transferring the latest advances or established paradigms from Computer Vision (CV) to SITS is still highly challenging due to the existing suboptimal representation learning framework. In this paper, we develop a novel perspective of SITS processing as a direct set prediction problem, inspired by the recent trend in adopting query-based transformer decoders to streamline the object detection or image segmentation pipeline. We further propose to decompose the representation learning process of SITS into three explicit steps: collect-update-distribute, which is computationally efficient and suits for irregularly-sampled and asynchronous temporal satellite observations. Facilitated by the unique reformulation, our proposed temporal learning backbone of SITS, initially pre-trained on the resource efficient pixel-set format and then fine-tuned on the downstream dense prediction tasks, has attained new state-of-the-art (SOTA) results on the PASTIS benchmark dataset. Specifically, the clear separation between temporal and spatial components in the semantic/panoptic segmentation pipeline of SITS makes us leverage the latest advances in CV, such as the universal image segmentation architecture, resulting in a noticeable 2.5 points increase in mIoU and 8.8 points increase in PQ, respectively, compared to the best scores reported so far. | [
33984,
11682
] | Train |
42,430 | 16 | Title: HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform
Abstract: In recent years, there has been rapid development in learned image compression techniques that prioritize rate-distortion-perceptual compression, preserving fine details even at lower bit-rates. However, current learning-based image compression methods often sacrifice human-friendly compression and require long decoding times. In this paper, we propose enhancements to the backbone network and loss function of existing image compression model, focusing on improving human perception and efficiency. Our proposed approach achieves competitive subjective results compared to state-of-the-art end-to-end learned image compression methods and classic methods, while requiring less decoding time and offering human-friendly compression. Through empirical evaluation, we demonstrate the effectiveness of our proposed method in achieving outstanding performance, with more than 25% bit-rate saving with comparable perceptual quality. | [] | Test |
42,431 | 16 | Title: Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis
Abstract: In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. In the first problem, many multi-view methods have been released for concatenating features of two or more views for the training and inference stage. Having said that, most multi-view existing methods are not explainable in the meaning of feature fusion, and treat many views equally for diagnosing. Our work aims to propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view (ipsilateral view) before learning the high-level feature that contains the cancerous features. For the second issue, we also propose a simple but novel malignant mammogram synthesis framework for upsampling minor class samples. Our easy-to-implement and no-training framework has eliminated the current limitation of the CutMix algorithm which is unreliable synthesized images with random pasted patches, hard-contour problems, and domain shift problems. Our results on VinDr-Mammo and CMMD datasets show the effectiveness of our two new frameworks for both multi-view training and synthesizing mammographic images, outperforming the previous conventional methods in our experimental settings. | [
46172
] | Train |
42,432 | 24 | Title: On the Fairness Impacts of Private Ensembles Models
Abstract: The Private Aggregation of Teacher Ensembles (PATE) is a machine learning framework that enables the creation of private models through the combination of multiple "teacher" models and a "student" model. The student model learns to predict an output based on the voting of the teachers, and the resulting model satisfies differential privacy. PATE has been shown to be effective in creating private models in semi-supervised settings or when protecting data labels is a priority.
This paper explores whether the use of PATE can result in unfairness, and demonstrates that it can lead to accuracy disparities among groups of individuals. The paper also analyzes the algorithmic and data properties that contribute to these disproportionate impacts, why these aspects are affecting different groups disproportionately, and offers recommendations for mitigating these effects. | [] | Validation |
42,433 | 10 | Title: Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text
Abstract: Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset. | [
31147
] | Train |
42,434 | 28 | Title: On the classification of completely regular codes with covering radius two and antipodal dual
Abstract: We classify all linear completely regular codes which have covering radius $\rho = 2$ and whose dual are antipodal. For this, we firstly show several properties of such dual codes, which are two-weight codes. | [] | Validation |
42,435 | 16 | Title: Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection
Abstract: Facial forgery detection is a crucial but extremely challenging topic, with the fast development of forgery techniques making the synthetic artefact highly indistinguishable. Prior works show that by mining both spatial and frequency information the forgery detection performance of deep learning models can be vastly improved. However, leveraging multiple types of information usually requires more than one branch in the neural network, which makes the model heavy and cumbersome. Knowledge distillation, as an important technique for efficient modelling, could be a possible remedy. We find that existing knowledge distillation methods have difficulties distilling a dual-branch model into a single-branch model. More specifically, knowledge distillation on both the spatial and frequency branches has degraded performance than distillation only on the spatial branch. To handle such problem, we propose a novel two-in-one knowledge distillation framework which can smoothly merge the information from a large dual-branch network into a small single-branch network, with the help of different dedicated feature projectors and the gradient homogenization technique. Experimental analysis on two datasets, FaceForensics++ and Celeb-DF, shows that our proposed framework achieves superior performance for facial forgery detection with much fewer parameters. | [] | Train |
42,436 | 4 | Title: Turning Noises to Fingerprint-Free "Credentials": Secure and Usable Authentication for Drone Delivery
Abstract: Drone delivery is an emerging service that gains growing attention. Authentication is critical to ensure a package is picked up by a legitimate drone (rather than a malicious one) and delivered to the correct receiver (rather than an attacker). As delivery drones are expensive and may carry important packages, a drone should stay away from users until the authentication succeeds. Thus, authentication approaches that require physical contact of drones cannot be applied. Bluetooth can indicate proximity without physical contact but is vulnerable to radio relay attacks. Our work leverages drone noises for authentication. While using sounds for authentication is highly usable, how to handle various attacks that manipulate sounds is an unresolved challenge. It is also unclear whether such a system is robust under various environmental sounds. We address these challenges by exploiting unique characteristics of drone noises. We thereby build an authentication system that does not rely on any sound fingerprints, keeps resilient to attacks, and is robust under environmental sounds. An extensive evaluation demonstrates its security and usability. | [] | Train |
42,437 | 24 | Title: Stochastic Approximation Approaches to Group Distributionally Robust Optimization
Abstract: This paper investigates group distributionally robust optimization (GDRO), with the purpose to learn a model that performs well over $m$ different distributions. First, we formulate GDRO as a stochastic convex-concave saddle-point problem, and demonstrate that stochastic mirror descent (SMD), using $m$ samples in each iteration, achieves an $O(m (\log m)/\epsilon^2)$ sample complexity for finding an $\epsilon$-optimal solution, which matches the $\Omega(m/\epsilon^2)$ lower bound up to a logarithmic factor. Then, we make use of techniques from online learning to reduce the number of samples required in each round from $m$ to $1$, keeping the same sample complexity. Specifically, we cast GDRO as a two-players game where one player simply performs SMD and the other executes an online algorithm for non-oblivious multi-armed bandits. Next, we consider a more practical scenario where the number of samples that can be drawn from each distribution is different, and propose a novel formulation of weighted GDRO, which allows us to derive distribution-dependent convergence rates. Denote by $n_i$ the sample budget for the $i$-th distribution, and assume $n_1 \geq n_2 \geq \cdots \geq n_m$. In the first approach, we incorporate non-uniform sampling into SMD such that the sample budget is satisfied in expectation, and prove the excess risk of the $i$-th distribution decreases at an $O(\sqrt{n_1 \log m}/n_i)$ rate. In the second approach, we use mini-batches to meet the budget exactly and also reduce the variance in stochastic gradients, and then leverage stochastic mirror-prox algorithm, which can exploit small variances, to optimize a carefully designed weighted GDRO problem. Under appropriate conditions, it attains an $O((\log m)/\sqrt{n_i})$ convergence rate, which almost matches the optimal $O(\sqrt{1/n_i})$ rate of only learning from the $i$-th distribution with $n_i$ samples. | [] | Train |
42,438 | 27 | Title: Accessible Interfaces for the Development and Deployment of Robotic Platforms
Abstract: Accessibility is one of the most important features in the design of robots and their interfaces. This thesis proposes methods that improve the accessibility of robots for three different target audiences: consumers, researchers, and learners. In order for humans and robots to work together effectively, they both must be able to communicate with each other. We tackle the problem of generating route instructions that are readily understandable by novice humans for the navigation of a priori unknown indoor environments. We then move on to the related problem of enabling robots to understand natural language utterances in the context of learning to operate articulated objects (e.g., fridges, drawers) by leveraging kinematic models. Next, we turn our focus to the development of accessible and reproducible robotic platforms for scientific research. We propose a new concept for reproducible robotics research that integrates development and benchmarking, so that reproducibility is obtained"by design"from the beginning of the research and development process. We then propose a framework called SHARC (SHared Autonomy for Remote Collaboration), to improve accessibility for underwater robotic intervention operations. SHARC allows multiple remote scientists to efficiently plan and execute high-level sampling procedures using an underwater manipulator while deferring low-level control to the robot. Lastly, we developed the first hardware-based MOOC in AI and robotics. This course allows learners to study autonomy hands-on by making real robots make their own decisions and accomplish broadly defined tasks. We design a new robotic platform from the ground up to support this new learning experience. A fully browser-based interface, based on leading tools and technologies for code development, testing, validation, and deployment serves to maximize the accessibility of these educational resources. | [] | Train |
42,439 | 9 | Title: Two Source Extractors for Asymptotically Optimal Entropy, and (Many) More
Abstract: A long line of work in the past two decades or so established close connections between several different pseudorandom objects and applications. These connections essentially show that an asymptotically optimal construction of one central object will lead to asymptotically optimal solutions to all the others. However, despite considerable effort, previous works can get close but still lack one final step to achieve truly asymptotically optimal constructions. In this paper we provide the last missing link, thus simultaneously achieving explicit, asymptotically optimal constructions and solutions for various well studied extractors and applications, that have been the subjects of long lines of research. Our results include: Asymptotically optimal seeded non-malleable extractors, which in turn give two source extractors for asymptotically optimal min-entropy of $O(\log n)$, explicit constructions of $K$-Ramsey graphs on $N$ vertices with $K=\log^{O(1)} N$, and truly optimal privacy amplification protocols with an active adversary. Two source non-malleable extractors and affine non-malleable extractors for some linear min-entropy with exponentially small error, which in turn give the first explicit construction of non-malleable codes against $2$-split state tampering and affine tampering with constant rate and \emph{exponentially} small error. Explicit extractors for affine sources, sumset sources, interleaved sources, and small space sources that achieve asymptotically optimal min-entropy of $O(\log n)$ or $2s+O(\log n)$ (for space $s$ sources). An explicit function that requires strongly linear read once branching programs of size $2^{n-O(\log n)}$, which is optimal up to the constant in $O(\cdot)$. Previously, even for standard read once branching programs, the best known size lower bound for an explicit function is $2^{n-O(\log^2 n)}$. | [
16986,
8710
] | Test |
42,440 | 8 | Title: Optimal Resource Management for Hierarchical Federated Learning over HetNets with Wireless Energy Transfer
Abstract: Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in industrial Internet of Things (IoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of Machine and Deep Learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical Federated Learning (HFL) performs training at multiple layers offloading the tasks to nearby Multi-Access Edge Computing (MEC) units. In this paper, we propose a novel energy-efficient HFL framework enabled by Wireless Energy Transfer (WET) and designed for heterogeneous networks with massive Multiple-Input Multiple-Output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a Heuristic Resource Management Algorithm, namely H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs. | [] | Train |
42,441 | 24 | Title: Experimental observation on a low-rank tensor model for eigenvalue problems
Abstract: Here we utilize a low-rank tensor model (LTM) as a function approximator, combined with the gradient descent method, to solve eigenvalue problems including the Laplacian operator and the harmonic oscillator. Experimental results show the superiority of the polynomial-based low-rank tensor model (PLTM) compared to the tensor neural network (TNN). We also test such low-rank architectures for the classification problem on the MNIST dataset. | [] | Test |
42,442 | 24 | Title: Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning
Abstract: In this paper, we examine cloud-edge-terminal IoT networks, where edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a central policy for each task can be constructed at a cloud server. The central policy can be then used by the edges conducting the task, thereby mitigating the need for them to learn their own policy from scratch. Furthermore, this central policy can be collaboratively learned at the cloud server by aggregating local experiences from the edges, thanks to the hierarchical architecture of the IoT networks. To this end, we propose a novel collaborative policy learning framework for dynamic scheduling tasks using federated reinforcement learning. For effective learning, our framework adaptively selects the tasks for collaborative learning in each round, taking into account the need for fairness among tasks. In addition, as a key enabler of the framework, we propose an edge-agnostic policy structure that enables the aggregation of local policies from different edges. We then provide the convergence analysis of the framework. Through simulations, we demonstrate that our proposed framework significantly outperforms the approaches without collaborative policy learning. Notably, it accelerates the learning speed of the policies and allows newly arrived edges to adapt to their tasks more easily. | [
15354
] | Train |
42,443 | 16 | Title: I3DOD: Towards Incremental 3D Object Detection via Prompting
Abstract: 3D object detection has achieved significant performance in many fields, e.g., robotics system, autonomous driving, and augmented reality. However, most existing methods could cause catastrophic forgetting of old classes when performing on the class-incremental scenarios. Meanwhile, the current class-incremental 3D object detection methods neglect the relationships between the object localization information and category semantic information and assume all the knowledge of old model is reliable. To address the above challenge, we present a novel Incremental 3D Object Detection framework with the guidance of prompting, i.e., I3DOD. Specifically, we propose a task-shared prompts mechanism to learn the matching relationships between the object localization information and category semantic information. After training on the current task, these prompts will be stored in our prompt pool, and perform the relationship of old classes in the next task. Moreover, we design a reliable distillation strategy to transfer knowledge from two aspects: a reliable dynamic distillation is developed to filter out the negative knowledge and transfer the reliable 3D knowledge to new detection model; the relation feature is proposed to capture the responses relation in feature space and protect plasticity of the model when learning novel 3D classes. To the end, we conduct comprehensive experiments on two benchmark datasets and our method outperforms the state-of-the-art object detection methods by 0.6% - 2.7% in terms of mAP@0.25. | [
22080
] | Train |
42,444 | 16 | Title: EXIF as Language: Learning Cross-Modal Associations between Images and Camera Metadata
Abstract: We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this meta-data by simply converting it to text and then processing it with a transformer. The features that we learn significantly outperform other self-supervised and supervised features on downstream image forensics and calibration tasks. In particular, we successfully localize spliced image regions “zero shot” by clustering the visual embeddings for all of the patches within an image. | [
13816
] | Train |
42,445 | 29 | Title: Machine Learning based Autotuning of a GPU-accelerated Computational Fluid Dynamics Code
Abstract: A machine learning-based autotuning technique is employed to optimize 14 key parameters associated with GPU kernel scheduling, including the number of thread blocks and threads within a block. This technique involves independent training for a single type of GPU as well as combined training for multiple types of GPUs. To evaluate the effectiveness of the autotuning approach, a computational fluid dynamics problem, accelerated by a single GPU, is tested for training and testing on the C2075, P100, and V100 GPUs. The training and testing results presented in this study demonstrate the potential of artificial neural networks in autotuning a wide range of parameters to achieve high performance in computational fluid dynamics applications. Remarkably, this approach requires only a small fraction of samples from a large search space. | [
31341
] | Train |
42,446 | 32 | Title: A Distributed Algebra System for Time Integration on Parallel Computers
Abstract: We present a distributed algebra system for efficient and compact implementation of numerical time integration schemes on parallel computers and graphics processing units (GPU). The software implementation combines the time integration library Odeint from Boost with the OpenFPM framework for scalable scientific computing. Implementing multi-stage, multi-step, or adaptive time integration methods in distributed-memory parallel codes or on GPUs is challenging. The present algebra system addresses this by making the time integration methods from Odeint available in a concise template-expression language for numerical simulations distributed and parallelized using OpenFPM. This allows using state-of-the-art time integration schemes, or switching between schemes, by changing one line of code, while maintaining parallel scalability. This enables scalable time integration with compact code and facilitates rapid rewriting and deployment of simulation algorithms. We benchmark the present software for exponential and sigmoidal dynamics and present an application example to the 3D Gray-Scott reaction-diffusion problem on both CPUs and GPUs in only 60 lines of code. | [] | Train |
42,447 | 6 | Title: Gazealytics: A Unified and Flexible Visual Toolkit for Exploratory and Comparative Gaze Analysis
Abstract: We present a novel, web-based visual eye-tracking analytics tool called Gazealytics. Our open-source toolkit features a unified combination of gaze analytics features that support flexible exploratory analysis, along with annotation of areas of interest (AOI) and filter options based on multiple criteria to visually analyse eye tracking data across time and space. Gazealytics features coordinated views unifying spatiotemporal exploration of fixations and scanpaths for various analytical tasks. A novel matrix representation allows analysis of relationships between such spatial or temporal features. Data can be grouped across samples, user-defined AOIs or time windows of interest (TWIs) to support aggregate or filtered analysis of gaze activity. This approach exceeds the capabilities of existing systems by supporting flexible comparison between and within subjects, hypothesis generation, data analysis and communication of insights. We demonstrate in a walkthrough that Gazealytics supports multiple types of eye tracking datasets and analytical tasks. | [
14405
] | Train |
42,448 | 24 | Title: Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators
Abstract: Causal mediation analysis (CMA) is a powerful method to dissect the total effect of a treatment into direct and mediated effects within the potential outcome framework. This is important in many scientific applications to identify the underlying mechanisms of a treatment effect. However, in many scientific applications the mediator is unobserved, but there may exist related measurements. For example, we may want to identify how changes in brain activity or structure mediate an antidepressant's effect on behavior, but we may only have access to electrophysiological or imaging brain measurements. To date, most CMA methods assume that the mediator is one-dimensional and observable, which oversimplifies such real-world scenarios. To overcome this limitation, we introduce a CMA framework that can handle complex and indirectly observed mediators based on the identifiable variational autoencoder (iVAE) architecture. We prove that the true joint distribution over observed and latent variables is identifiable with the proposed method. Additionally, our framework captures a disentangled representation of the indirectly observed mediator and yields accurate estimation of the direct and mediated effects in synthetic and semi-synthetic experiments, providing evidence of its potential utility in real-world applications. | [
36926,
14679
] | Test |
42,449 | 36 | Title: Why Majority Judgement is not yet the solution for political elections, but can help finding it
Abstract: Like many other voting systems, Majority Judgement suffers from the weaknesses of the underlying mathematical model: Elections as problem of choice or ranking. We show how the model can be enhanced to take into account the complete process starting from the whole set of persons having passive electoral rights and even the aspect of reelection. By a new view on abstentions from voting and an adaption of Majority Judgement with three grades, we are able to describe a complete process for an election that can be easily put into legislation and sets suitable incentives for politicians who want to be reelected. | [] | Test |
42,450 | 34 | Title: Constant Approximation for Network Revenue Management with Markovian-Correlated Customer Arrivals
Abstract: The Network Revenue Management (NRM) problem is a well-known challenge in dynamic decision-making under uncertainty. In this problem, fixed resources must be allocated to serve customers over a finite horizon, while customers arrive according to a stochastic process. The typical NRM model assumes that customer arrivals are independent over time. However, in this paper, we explore a more general setting where customer arrivals over different periods can be correlated. We propose a new model that assumes the existence of a system state, which determines customer arrivals for the current period. This system state evolves over time according to a time-inhomogeneous Markov chain. Our model can be used to represent correlation in various settings and synthesizes previous literature on correlation models. To solve the NRM problem under our correlated model, we derive a new linear programming (LP) approximation of the optimal policy. Our approximation provides a tighter upper bound on the total expected value collected by the optimal policy than existing upper bounds. We use our LP to develop a new bid price policy, which computes bid prices for each system state and time period in a backward induction manner. The decision is then made by comparing the reward of the customer against the associated bid prices. Our policy guarantees to collect at least $1/(1+L)$ fraction of the total reward collected by the optimal policy, where $L$ denotes the maximum number of resources required by a customer. In summary, our work presents a new model for correlated customer arrivals in the NRM problem and provides an LP approximation for solving the problem under this model. We derive a new bid price policy and provides a theoretical guarantee on the performance of the policy. | [] | Train |
42,451 | 28 | Title: Solving Systems of Algebraic Equations Over Finite Commutative Rings and Applications
Abstract: Several problems in algebraic geometry and coding theory over finite rings are modeled by systems of algebraic equations. Among these problems, we have the rank decoding problem, which is used in the construction of public-key cryptography. In 2004, Nechaev and Mikhailov proposed two methods for solving systems of polynomial equations over finite chain rings. These methods used solutions over the residual field to construct all solutions step by step. However, for some types of algebraic equations, one simply needs partial solutions. In this paper, we combine two existing approaches to show how Gr\"obner bases over finite chain rings can be used to solve systems of algebraic equations over finite commutative rings. Then, we use skew polynomials and Pl\"ucker coordinates to show that some algebraic approaches used to solve the rank decoding problem and the MinRank problem over finite fields can be extended to finite principal ideal rings. | [] | Train |
42,452 | 30 | Title: Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning
Abstract: Deep learning techniques have dominated the literature on aspect-based sentiment analysis (ABSA), yielding state-of-the-art results. However, these deep models generally suffer from spurious correlation problems between input features and output labels, which creates significant barriers to robustness and generalization capability. In this paper, we propose a novel Contrastive Variational Information Bottleneck framework (called CVIB) to reduce spurious correlations for ABSA. The proposed CVIB framework is composed of an original network and a self-pruned network, and these two networks are optimized simultaneously via contrastive learning. Concretely, we employ the Variational Information Bottleneck (VIB) principle to learn an informative and compressed network (self-pruned network) from the original network, which discards the superfluous patterns or spurious correlations between input features and prediction labels. Then, self-pruning contrastive learning is devised to pull together semantically similar positive pairs and push away dissimilar pairs, where the representations of the anchor learned by the original and self-pruned networks respectively are regarded as a positive pair while the representations of two different sentences within a mini-batch are treated as a negative pair. To verify the effectiveness of our CVIB method, we conduct extensive experiments on five benchmark ABSA datasets and the experimental results show that our approach achieves better performance than the strong competitors in terms of overall prediction performance, robustness, and generalization. | [] | Train |
42,453 | 24 | Title: Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting
Abstract: Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatio-temporal forecasting problems. While many dynamic graph construction methods have been developed, relatively few of them explore the causal relationship between neighbour nodes. Thus, the resulting models lack strong explainability for the causal relationship between the neighbour nodes of the dynamically generated graphs, which can easily lead to a risk in subsequent decisions. Moreover, few of them consider the uncertainty and noise of dynamic graphs based on the time series datasets, which are ubiquitous in real-world graph structure networks. In this paper, we propose a novel Dynamic Diffusion-Variational Graph Neural Network (DVGNN) for spatio-temporal forecasting. For dynamic graph construction, an unsupervised generative model is devised. Two layers of graph convolutional network (GCN) are applied to calculate the posterior distribution of the latent node embeddings in the encoder stage. Then, a diffusion model is used to infer the dynamic link probability and reconstruct causal graphs in the decoder stage adaptively. The new loss function is derived theoretically, and the reparameterization trick is adopted in estimating the probability distribution of the dynamic graphs by Evidence Lower Bound during the backpropagation period. After obtaining the generated graphs, dynamic GCN and temporal attention are applied to predict future states. Experiments are conducted on four real-world datasets of different graph structures in different domains. The results demonstrate that the proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result while exhibiting higher robustness. Also, by F1-score and probability distribution analysis, we demonstrate that DVGNN better reflects the causal relationship and uncertainty of dynamic graphs. | [
15111
] | Train |
42,454 | 31 | Title: Filter Bubbles in Recommender Systems: Fact or Fallacy - A Systematic Review
Abstract: A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems (RSs). This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in RSs. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in RSs. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in RSs, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and RSs. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.This article is categorized under:
Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction
Application Areas > Internet
Commercial, Legal, and Ethical Issues > Ethical Considerations
Commercial, Legal, and Ethical Issues > Security and Privacy
| [] | Test |
42,455 | 24 | Title: Formulating A Strategic Plan Based On Statistical Analyses And Applications For Financial Companies Through A Real-World Use Case
Abstract: Business statistics play a crucial role in implementing a data-driven strategic plan at the enterprise level to employ various analytics where the outcomes of such a plan enable an enterprise to enhance the decision-making process or to mitigate risks to the organization. In this work, a strategic plan informed by the statistical analysis is introduced for a financial company called LendingClub, where the plan is comprised of exploring the possibility of onboarding a big data platform along with advanced feature selection capacities. The main objectives of such a plan are to increase the company's revenue while reducing the risks of granting loans to borrowers who cannot return their loans. In this study, different hypotheses formulated to address the company's concerns are studied, where the results reveal that the amount of loans profoundly impacts the number of borrowers charging off their loans. Also, the proposed strategic plan includes onboarding advanced analytics such as machine learning technologies that allow the company to build better generalized data-driven predictive models. | [] | Train |
42,456 | 18 | Title: Machine learning using magnetic stochastic synapses
Abstract: The impressive performance of artificial neural networks has come at the cost of high energy usage and CO2 emissions. Unconventional computing architectures, with magnetic systems as a candidate, have potential as alternative energy-efficient hardware, but, still face challenges, such as stochastic behaviour, in implementation. Here, we present a methodology for exploiting the traditionally detrimental stochastic effects in magnetic domain-wall motion in nanowires. We demonstrate functional binary stochastic synapses alongside a gradient learning rule that allows their training with applicability to a range of stochastic systems. The rule, utilising the mean and variance of the neuronal output distribution, finds a trade-off between synaptic stochasticity and energy efficiency depending on the number of measurements of each synapse. For single measurements, the rule results in binary synapses with minimal stochasticity, sacrificing potential performance for robustness. For multiple measurements, synaptic distributions are broad, approximating better-performing continuous synapses. This observation allows us to choose design principles depending on the desired performance and the device’s operational speed and energy cost. We verify performance on physical hardware, showing it is comparable to a standard neural network. | [] | Test |
42,457 | 16 | Title: Patch-aware Batch Normalization for Improving Cross-domain Robustness
Abstract: Despite the significant success of deep learning in computer vision tasks, cross-domain tasks still present a challenge in which the model's performance will degrade when the training set and the test set follow different distributions. Most existing methods employ adversarial learning or instance normalization for achieving data augmentation to solve this task. In contrast, considering that the batch normalization (BN) layer may not be robust for unseen domains and there exist the differences between local patches of an image, we propose a novel method called patch-aware batch normalization (PBN). To be specific, we first split feature maps of a batch into non-overlapping patches along the spatial dimension, and then independently normalize each patch to jointly optimize the shared BN parameter at each iteration. By exploiting the differences between local patches of an image, our proposed PBN can effectively enhance the robustness of the model's parameters. Besides, considering the statistics from each patch may be inaccurate due to their smaller size compared to the global feature maps, we incorporate the globally accumulated statistics with the statistics from each batch to obtain the final statistics for normalizing each patch. Since the proposed PBN can replace the typical BN, it can be integrated into most existing state-of-the-art methods. Extensive experiments and analysis demonstrate the effectiveness of our PBN in multiple computer vision tasks, including classification, object detection, instance retrieval, and semantic segmentation. | [] | Train |
42,458 | 30 | Title: The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations
Abstract: Large Language Models (LLMs) have seen widespread deployment in various real-world applications. Understanding these biases is crucial to comprehend the potential downstream consequences when using LLMs to make decisions, particularly for historically disadvantaged groups. In this work, we propose a simple method for analyzing and comparing demographic bias in LLMs, through the lens of job recommendations. We demonstrate the effectiveness of our method by measuring intersectional biases within ChatGPT and LLaMA, two cutting-edge LLMs. Our experiments primarily focus on uncovering gender identity and nationality bias; however, our method can be extended to examine biases associated with any intersection of demographic identities. We identify distinct biases in both models toward various demographic identities, such as both models consistently suggesting low-paying jobs for Mexican workers or preferring to recommend secretarial roles to women. Our study highlights the importance of measuring the bias of LLMs in downstream applications to understand the potential for harm and inequitable outcomes. | [
14569,
13700,
38157,
43566
] | Validation |
42,459 | 16 | Title: AIR-DA: Adversarial Image Reconstruction for Unsupervised Domain Adaptive Object Detection
Abstract: Unsupervised domain adaptive object detection is a challenging vision task where object detectors are adapted from a label-rich source domain to an unlabeled target domain. Recent advances prove the efficacy of the adversarial based domain alignment where the adversarial training between the feature extractor and domain discriminator results in domain-invariance in the feature space. However, due to the domain shift, domain discrimination, especially on low-level features, is an easy task. This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor. In this work, we achieve a better domain alignment by introducing an auxiliary regularization task to improve the training balance. Specifically, we propose Adversarial Image Reconstruction (AIR) as the regularizer to facilitate the adversarial training of the feature extractor. We further design a multi-level feature alignment module to enhance the adaptation performance. Our evaluations across several datasets of challenging domain shifts demonstrate that the proposed method outperforms all previous methods, of both one- and two-stage, in most settings. | [] | Test |
42,460 | 16 | Title: JL-lemma derived Optimal Projections for Discriminative Dictionary Learning
Abstract: To overcome difficulties in classifying large dimensionality data with a large number of classes, we propose a novel approach called JLSPCADL. This paper uses the Johnson-Lindenstrauss (JL) Lemma to select the dimensionality of a transformed space in which a discriminative dictionary can be learned for signal classification. Rather than reducing dimensionality via random projections, as is often done with JL, we use a projection transformation matrix derived from Modified Supervised PC Analysis (M-SPCA) with the JL-prescribed dimension. JLSPCADL provides a heuristic to deduce suitable distortion levels and the corresponding Suitable Description Length (SDL) of dictionary atoms to derive an optimal feature space and thus the SDL of dictionary atoms for better classification. Unlike state-of-the-art dimensionality reduction-based dictionary learning methods, a projection transformation matrix derived in a single step from M-SPCA provides maximum feature-label consistency of the transformed space while preserving the cluster structure of the original data. Despite confusing pairs, the dictionary for the transformed space generates discriminative sparse coefficients, with fewer training samples. Experimentation demonstrates that JLSPCADL scales well with an increasing number of classes and dimensionality. Improved label consistency of features due to M-SPCA helps to classify better. Further, the complexity of training a discriminative dictionary is significantly reduced by using SDL. Experimentation on OCR and face recognition datasets shows relatively better classification performance than other supervised dictionary learning algorithms. | [] | Train |
42,461 | 16 | Title: Feature Chirality in Deep Learning Models
Abstract: As deep learning applications extensively increase by leaps and bounds, their interpretability has become increasingly prominent. As a universal property, chirality exists widely in nature, and applying it to the explanatory research of deep learning may be helpful to some extent. Inspired by a recent study that used CNN (convolutional neural network), which applied visual chirality, to distinguish whether an image is flipped or not. In this paper, we study feature chirality innovatively, which shows how the statistics of deep learning models’ feature data are changed by training. We rethink the feature-level chirality property, propose the feature chirality, and give the measure. Our analysis of feature chirality on AlexNet, VGG, and ResNet reveals similar but surprising results, including the prevalence of feature chirality in these models, the initialization methods of the models do not affect feature chirality. Our work shows that feature chirality implies model evaluation, interpretability of the model, and model parameters optimization. | [] | Train |
42,462 | 16 | Title: OpenMask3D: Open-Vocabulary 3D Instance Segmentation
Abstract: We introduce the task of open-vocabulary 3D instance segmentation. Traditional approaches for 3D instance segmentation largely rely on existing 3D annotated datasets, which are restricted to a closed-set of object categories. This is an important limitation for real-life applications where one might need to perform tasks guided by novel, open-vocabulary queries related to objects from a wide variety. Recently, open-vocabulary 3D scene understanding methods have emerged to address this problem by learning queryable features per each point in the scene. While such a representation can be directly employed to perform semantic segmentation, existing methods have limitations in their ability to identify object instances. In this work, we address this limitation, and propose OpenMask3D, which is a zero-shot approach for open-vocabulary 3D instance segmentation. Guided by predicted class-agnostic 3D instance masks, our model aggregates per-mask features via multi-view fusion of CLIP-based image embeddings. We conduct experiments and ablation studies on the ScanNet200 dataset to evaluate the performance of OpenMask3D, and provide insights about the open-vocabulary 3D instance segmentation task. We show that our approach outperforms other open-vocabulary counterparts, particularly on the long-tail distribution. Furthermore, OpenMask3D goes beyond the limitations of close-vocabulary approaches, and enables the segmentation of object instances based on free-form queries describing object properties such as semantics, geometry, affordances, and material properties. | [
41400,
4643,
22222
] | Train |
42,463 | 34 | Title: UltraLogLog: A Practical and More Space-Efficient Alternative to HyperLogLog for Approximate Distinct Counting
Abstract: Since its invention HyperLogLog has become the standard algorithm for approximate distinct counting. Due to its space efficiency and suitability for distributed systems, it is widely used and also implemented in numerous databases. This work presents UltraLogLog, which shares the same practical properties as HyperLogLog. It is commutative, idempotent, mergeable, and has a fast guaranteed constant-time insert operation. At the same time, it requires 28% less space to encode the same amount of distinct count information, which can be extracted using the maximum likelihood method. Alternatively, a simpler and faster estimator is proposed, which still achieves a space reduction of 24%, but at an estimation speed comparable to that of HyperLogLog. In a non-distributed setting where martingale estimation can be used, UltraLogLog is able to reduce space by 17%. Moreover, its smaller entropy and its 8-bit registers lead to better compaction when using standard compression algorithms. All this is verified by experimental results that are in perfect agreement with the theoretical analysis which also outlines potential for even more space-efficient data structures. A production-ready Java implementation of UltraLogLog has been released as part of the open-source Hash4j library. | [] | Train |
42,464 | 8 | Title: Mobile Metaverse: A Road Map from Metaverse to Metavehicles
Abstract: With the rapid development of communication technologies and extended reality (XR), the services and applications of the Metaverse are gradually entering our lives. However, the current development of the Metaverse provides users with services that are homogeneous with the user experience that the Internet has brought in the past, making them more like an extension of the Internet. In addition, as a mobile application carrier for the Metaverse, it is also worth considering how vehicles with diverse onboard components can develop in synergy with the Metaverse. In this article, we focus on the core of the Metaverse, namely user experience, and provide a road map from Metaverse to Metaverse vehicles (Metavehicles). Specifically, we first elaborate on six features of the Metaverse from the perspective of user experience and propose a hierarchical framework for the Metaverse based on the evolutionary logic of the features. Under the guidance of this framework, we discuss the empowerment of onboard components of Metavehicles on the development of the Metaverse, and analyze the service experience that Metavehicles can bring to two types of users, namely drivers and passengers. Finally, considering the differentiated development levels of Metaverse and autonomous driving, we further establish a hierarchical framework for Metavehicles from three aspects (i.e., enhance Metaverse, enhance driving experience, and enhance entertainment experience), providing an evolutionary path for the development of Metavehicles. | [
39946
] | Train |
42,465 | 16 | Title: Few-shot Classification via Ensemble Learning with Multi-Order Statistics
Abstract: Transfer learning has been widely adopted for few-shot classification. Recent studies reveal that obtaining good generalization representation of images on novel classes is the key to improving the few-shot classification accuracy. To address this need, we prove theoretically that leveraging ensemble learning on the base classes can correspondingly reduce the true error in the novel classes. Following this principle, a novel method named Ensemble Learning with Multi-Order Statistics (ELMOS) is proposed in this paper. In this method, after the backbone network, we use multiple branches to create the individual learners in the ensemble learning, with the goal to reduce the storage cost. We then introduce different order statistics pooling in each branch to increase the diversity of the individual learners. The learners are optimized with supervised losses during the pre-training phase. After pre-training, features from different branches are concatenated for classifier evaluation. Extensive experiments demonstrate that each branch can complement the others and our method can produce a state-of-the-art performance on multiple few-shot classification benchmark datasets. | [] | Train |
42,466 | 27 | Title: Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
Abstract: Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamics. This paper presents a novel reinforcement learning approach to solving high-dimensional robot navigation tasks with complex logic specifications by co-learning planning and control policies. Notably, this approach significantly reduces the sample complexity in training, allowing us to train high-quality policies with much fewer samples compared to existing reinforcement learning algorithms. In addition, our methodology streamlines complex specification extraction from map images and enables the efficient generation of long-horizon robot motion paths across different map layouts. Moreover, our approach also demonstrates capabilities for high-dimensional control and avoiding suboptimal policies via policy alignment. The efficacy of our approach is demonstrated through experiments involving simulated high-dimensional quadruped robot dynamics and a real-world differential drive robot (TurtleBot3) under different types of task specifications. | [
3782
] | Test |
42,467 | 24 | Title: The Power of Typed Affine Decision Structures: A Case Study
Abstract: nan | [
42298
] | Test |
42,468 | 25 | Title: Cost-effective Models for Detecting Depression from Speech
Abstract: Depression is the most common psychological disorder and is considered as a leading cause of disability and suicide worldwide. An automated system capable of detecting signs of depression in human speech can contribute to ensuring timely and effective mental health care for individuals suffering from the disorder. Developing such automated system requires accurate machine learning models, capable of capturing signs of depression. However, state-of-the-art models based on deep acoustic representations require abundant data, meticulous selection of features, and rigorous training; the procedure involves enormous computational resources. In this work, we explore the effectiveness of two different acoustic feature groups-conventional hand-curated and deep representation features, for predicting the severity of depression from speech. We explore the relevance of possible contributing factors to the models’ performance, including gender of the individual, severity of the disorder, content and length of speech. Our findings suggest that models trained on conventional acoustic features perform equally well or better than the ones trained on deep representation features at significantly lower computational cost, irrespective of other factors, e.g. content and length of speech, gender of the speaker and severity of the disorder. This makes such models a better fit for deployment where availability of computational resources is restricted, such as real time depression monitoring applications in smart devices. | [] | Train |
42,469 | 8 | Title: Beam Management Driven by Radio Environment Maps in O-RAN Architecture
Abstract: The Massive Multiple-Input Multiple-Output (M-MIMO) is considered as one of the key technologies in 5G, and future 6G networks. From the perspective of, e.g., channel estimation, especially for high-speed users it is easier to implement an M-MIMO network exploiting a static set of beams, i.e., Grid of Beams (GoB). While considering GoB it is important to properly assign users to the beams, i.e., to perform Beam Management (BM). BM can be enhanced by taking into account historical knowledge about the radio environment, e.g., to avoid radio link failures. The aim of this paper is to propose such a BM algorithm, that utilizes location-dependent data stored in a Radio Environment Map (REM). It utilizes received power maps, and user mobility patterns to optimize the BM process in terms of Reinforcement Learning (RL) by using the Policy Iteration method under different goal functions, e.g., maximization of received power or minimization of beam reselections while avoiding radio link failures. The proposed solution is compliant with the Open Radio Access Network (O-RAN) architecture, enabling its practical implementation. Simulation studies have shown that the proposed BM algorithm can significantly reduce the number of beam reselections or radio link failures compared to the baseline algorithm. | [] | Train |
42,470 | 39 | Title: Snapshot disjointness in temporal graphs
Abstract: In the study of temporal graphs, only paths respecting the flow of time are relevant. In this context, many concepts of walks disjointness were proposed over the years, and the validity of Menger's Theorem, as well as the complexity of related problems, has been investigated. In this paper, we introduce and investigate a type of disjointness that is only time dependent. Two walks are said to be snapshot disjoint if they are not active in a same snapshot (also called timestep). The related paths and cut problems are then defined and proved to be W[1]-hard and XP-time solvable when parameterized by the size of the solution. Additionally, in the light of the definition of Mengerian graphs given by Kempe, Kleinberg and Kumar in their seminal paper (STOC'2000), we define a Mengerian graph for time as a graph $G$ that cannot form an example where Menger's Theorem does not hold in the context of snapshot disjointness. We then give a characterization in terms of forbidden structures and provide a polynomial-time recognition algorithm. Finally, we also prove that, given a temporal graph $(G,\lambda)$ and a pair of vertices $s,z\in V(G)$, deciding whether at most $h$ multiedges can separate $s$ from $z$ is NP-complete. | [] | Test |
42,471 | 31 | Title: Integrating Item Relevance in Training Loss for Sequential Recommender Systems
Abstract: Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly impact the robustness and performance of SRSs, particularly when the entire item set to be predicted is noisy. This situation is more prevalent when only one item is used to train and evaluate the SRSs. To tackle this challenge, we propose a novel approach that addresses the issue of noise in SRSs. First, we propose a sequential multi-relevant future items training objective, leveraging a loss function aware of item relevance, thereby enhancing their robustness against noise in the training data. Additionally, to mitigate the impact of noise at evaluation time, we propose multi-relevant future items evaluation (MRFI-evaluation), aiming to improve overall performance. Our relevance-aware models obtain an improvement of 1.58% of NDCG@10 and 0.96% in terms of HR@10 in the traditional evaluation protocol, the one which utilizes one relevant future item. In the MRFI-evaluation protocol, using multiple future items, the improvement is 2.82% of NDCG@10 and 0.64% of HR@10 w.r.t the best baseline model. | [
666,
1158,
23767
] | Train |
42,472 | 16 | Title: Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction
Abstract: Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. However, existing multi-camera algorithms primarily rely on monocular image pre-training, which overlooks the spatial and temporal correlations among different camera views. To address this limitation, we propose the first multi-camera unified pre-training framework called Occ-BEV, which involves initially reconstructing the 3D scene as the foundational stage and subsequently fine-tuning the model on downstream tasks. Specifically, a 3D decoder is designed for leveraging Bird's Eye View (BEV) features from multi-view images to predict the 3D geometric occupancy to enable the model to capture a more comprehensive understanding of the 3D environment. A significant benefit of Occ-BEV is its capability of utilizing a considerable volume of unlabeled image-LiDAR pairs for pre-training purposes. The proposed multi-camera unified pre-training framework demonstrates promising results in key tasks such as multi-camera 3D object detection and surrounding semantic scene completion. When compared to monocular pre-training methods on the nuScenes dataset, Occ-BEV shows a significant improvement of about 2.0% in mAP and 2.0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion. Codes are publicly available at https://github.com/chaytonmin/Occ-BEV. | [
10240,
12576,
45474,
26151,
17612,
529,
13490,
9107,
2933,
24600,
22585,
22815
] | Train |
42,473 | 24 | Title: DITTO: Offline Imitation Learning with World Models
Abstract: We propose DITTO, an offline imitation learning algorithm which uses world models and on-policy reinforcement learning to addresses the problem of covariate shift, without access to an oracle or any additional online interactions. We discuss how world models enable offline, on-policy imitation learning, and propose a simple intrinsic reward defined in the world model latent space that induces imitation learning by reinforcement learning. Theoretically, we show that our formulation induces a divergence bound between expert and learner, in turn bounding the difference in reward. We test our method on difficult Atari environments from pixels alone, and achieve state-of-the-art performance in the offline setting. | [
14402,
4915
] | Validation |
42,474 | 24 | Title: Learning from Stochastic Labels
Abstract: Annotating multi-class instances is a crucial task in the field of machine learning. Unfortunately, identifying the correct class label from a long sequence of candidate labels is time-consuming and laborious. To alleviate this problem, we design a novel labeling mechanism called stochastic label. In this setting, stochastic label includes two cases: 1) identify a correct class label from a small number of randomly given labels; 2) annotate the instance with None label when given labels do not contain correct class label. In this paper, we propose a novel suitable approach to learn from these stochastic labels. We obtain an unbiased estimator that utilizes less supervised information in stochastic labels to train a multi-class classifier. Additionally, it is theoretically justifiable by deriving the estimation error bound of the proposed method. Finally, we conduct extensive experiments on widely-used benchmark datasets to validate the superiority of our method by comparing it with existing state-of-the-art methods. | [] | Train |
42,475 | 24 | Title: Logarithmic Bayes Regret Bounds
Abstract: We derive the first finite-time logarithmic regret bounds for Bayesian bandits. For Gaussian bandits, we obtain a $O(c_h \log^2 n)$ bound, where $c_h$ is a prior-dependent constant. This matches the asymptotic lower bound of Lai (1987). Our proofs mark a technical departure from prior works, and are simple and general. To show generality, we apply our technique to linear bandits. Our bounds shed light on the value of the prior in the Bayesian setting, both in the objective and as a side information given to the learner. They significantly improve the $\tilde{O}(\sqrt{n})$ bounds, that despite the existing lower bounds, have become standard in the literature. | [] | Test |
42,476 | 10 | Title: Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
Abstract: Rare categories abound in a number of real-world networks and play a pivotal role in a variety of high-stakes applications, including financial fraud detection, network intrusion detection, and rare disease diagnosis. Rare category analysis (RCA) refers to the task of detecting, characterizing, and comprehending the behaviors of minority classes in a highly-imbalanced data distribution. While the vast majority of existing work on RCA has focused on improving the prediction performance, a few fundamental research questions heretofore have received little attention and are less explored: How confident or uncertain is a prediction model in rare category analysis? How can we quantify the uncertainty in the learning process and enable reliable rare category analysis? To answer these questions, we start by investigating miscalibration in existing RCA methods. Empirical results reveal that state-of-the-art RCA methods are mainly over-confident in predicting minority classes and under-confident in predicting majority classes. Motivated by the observation, we propose a novel individual calibration framework, named CALIRARE, for alleviating the unique challenges of RCA, thus enabling reliable rare category analysis. In particular, to quantify the uncertainties in RCA, we develop a node-level uncertainty quantification algorithm to model the overlapping support regions with high uncertainty; to handle the rarity of minority classes in miscalibration calculation, we generalize the distribution-based calibration metric to the instance level and propose the first individual calibration measurement on graphs named Expected Individual Calibration Error (EICE). We perform extensive experimental evaluations on real-world datasets, including rare category characterization and model calibration tasks, which demonstrate the significance of our proposed framework. | [
40624
] | Train |
42,477 | 7 | Title: 4D topology optimization: Integrated optimization of the structure and self-actuation of soft bodies for dynamic motions
Abstract: nan | [] | Test |
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