Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2202.06434	Perception-Aware Perching on Powerlines with Multirotors	Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection without human intervention, the robots must be able to perch on the powerlines to recharge their batteries. Highly versatile perching capabilities are necessary to adapt to the variety of configur...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	280,217
1309.7843	Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation	Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,429
2008.09943	Quantum Language Model with Entanglement Embedding for Question Answering	Quantum Language Models (QLMs) in which words are modelled as quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability. Nevertheless, in the current literature word sequences are basically modelled as a classical mixture of word states, which cannot fully ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	192,863
2201.12322	Bioinspired Cortex-based Fast Codebook Generation	A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	277,599
1902.03964	Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification	Network node embedding is an active research subfield of complex network analysis. This paper contributes a novel approach to learning network node embeddings and direct node classification using a node ranking scheme coupled with an autoencoder-based neural network architecture. The main advantages of the proposed Dee...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,232
2104.07149	On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise	Intent Classification (IC) and Slot Labeling (SL) models, which form the basis of dialogue systems, often encounter noisy data in real-word environments. In this work, we investigate how robust IC/SL models are to noisy data. We collect and publicly release a test-suite for seven common noise types found in production ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	230,305
2407.18782	Understanding XAI Through the Philosopher's Lens: A Historical Perspective	Despite explainable AI (XAI) has recently become a hot topic and several different approaches have been developed, there is still a widespread belief that it lacks a convincing unifying foundation. On the other hand, over the past centuries, the very concept of explanation has been the subject of extensive philosophica...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	476,514
2402.09579	Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies	The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language m...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	429,581
1805.01967	Estimation of Power System Inertia Using Nonlinear Koopman Modes	We report a new approach to estimating power system inertia directly from time-series data on power system dynamics. The approach is based on the so-called Koopman Mode Decomposition (KMD) of such dynamic data, which is a nonlinear generalization of linear modal decomposition through spectral analysis of the Koopman op...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	96,742
1401.3443	Computational Logic Foundations of KGP Agents	This paper presents the computational logic foundations of a model of agency called the KGP (Knowledge, Goals and Plan model. This model allows the specification of heterogeneous agents that can interact with each other, and can exhibit both proactive and reactive behaviour allowing them to function in dynamic environm...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	29,854
1807.09434	Distinctive-attribute Extraction for Image Captioning	Image captioning, an open research issue, has been evolved with the progress of deep neural networks. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to compute image features and generate natural language descriptions in the research. In previous works, a caption involving semant...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	103,722
2401.14999	The dynamics of the Reddit collective action leading to the GameStop short squeeze	In early 2021, the stock prices of GameStop, AMC, Nokia and BlackBerry experienced dramatic increases, triggered by short-squeeze operations that have been largely attributed to Reddit's retail investors. Here, we shed light on the extent and timing of Reddit users' influence on the GameStop short squeeze. Using statis...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	424,286
1808.10086	Artifacts Detection and Error Block Analysis from Broadcasted Videos	With the advancement of IPTV and HDTV technology, previous subtle errors in videos are now becoming more prominent because of the structure oriented and compression based artifacts. In this paper, we focus towards the development of a real-time video quality check system. Light weighted edge gradient magnitude informat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	106,330
2311.18158	HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation	Diffusion models have revolutionized text-to-image generation, but their real-world applications are hampered by the extensive time needed for hundreds of diffusion steps. Although progressive distillation has been proposed to speed up diffusion sampling to 2-8 steps, it still falls short in one-step generation, and ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,577
2204.09636	Residual Mixture of Experts	Mixture of Experts (MoE) is able to scale up vision transformers effectively. However, it requires prohibiting computation resources to train a large MoE transformer. In this paper, we propose Residual Mixture of Experts (RMoE), an efficient training pipeline for MoE vision transformers on downstream tasks, such as seg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,508
2208.12144	Automatic Mapping of Unstructured Cyber Threat Intelligence: An Experimental Study	Proactive approaches to security, such as adversary emulation, leverage information about threat actors and their techniques (Cyber Threat Intelligence, CTI). However, most CTI still comes in unstructured forms (i.e., natural language), such as incident reports and leaked documents. To support proactive security effort...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	314,636
2405.00557	Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment	As the capabilities of large language models (LLMs) continue to expand, aligning these models with human values remains a significant challenge. Recent studies show that reasoning abilities contribute significantly to model safety, while integrating Mixture-of-Experts (MoE) architectures can further enhance alignment. ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	450,967
2103.02071	Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making	Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked to use ML predictions to make high-stakes decisions. Multiple ML usability challenges can appear as result, such as lack of user trust in the...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,832
2212.10078	Constructing Organism Networks from Collaborative Self-Replicators	We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights. As organism networks feature va...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	337,330
2107.04244	WinoCNN: Kernel Sharing Winograd Systolic Array for Efficient Convolutional Neural Network Acceleration on FPGAs	The combination of Winograd's algorithm and systolic array architecture has demonstrated the capability of improving DSP efficiency in accelerating convolutional neural networks (CNNs) on FPGA platforms. However, handling arbitrary convolution kernel sizes in FPGA-based Winograd processing elements and supporting effic...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	245,398
1910.06583	TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction	Human motion prediction is an increasingly interesting topic in computer vision and robotics. In this paper, we propose a new 2D CNN based network, TrajectoryNet, to predict future poses in the trajectory space. Compared with most existing methods, our model focuses on modeling the motion dynamics with coupled spatio-t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	149,385
2308.05346	Towards General and Fast Video Derain via Knowledge Distillation	As a common natural weather condition, rain can obscure video frames and thus affect the performance of the visual system, so video derain receives a lot of attention. In natural environments, rain has a wide variety of streak types, which increases the difficulty of the rain removal task. In this paper, we propose a R...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,758
2211.05922	Internal feedback in the cortical perception-action loop enables fast and accurate behavior	Animals move smoothly and reliably in unpredictable environments. Models of sensorimotor control have assumed that sensory information from the environment leads to actions, which then act back on the environment, creating a single, unidirectional perception-action loop. This loop contains internal delays in sensory an...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	329,708
2106.04892	A 2D front-tracking Lagrangian model for the modeling of anisotropic grain growth	Grain growth is a well-known and complex phenomenon occurring during annealing of all polycrystalline materials. Its numerical modeling is a complex task when anisotropy sources such as grain orientation and grain boundary inclination have to be taken into account. This article presents the application of the front-tra...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	239,891
2311.04503	Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data	State-of-the-art deep learning models for tabular data have recently achieved acceptable performance to be deployed in industrial settings. However, the robustness of these models remains scarcely explored. Contrary to computer vision, there is to date no realistic protocol to properly evaluate the adversarial robustne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	406,247
2201.01843	Novel Information-theoretic Game-theoretical Insights to Broadcasting in Internet-of-UAVs	For the Internet-of-unmanned aerial vehicles (UAVs) some challenges in broadcasting and from new points of view are explored. In this paper, first, we investigate a single broadcast transceiver. From a control of noisy-channel viewpoint, we consider: (\textit{i}) Alice sends $\mathcal{X}$ to Bob as more \textcolor{blac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	274,371
2501.06400	Mathematics of Digital Twins and Transfer Learning for PDE Models	We define a digital twin (DT) of a physical system governed by partial differential equations (PDEs) as a model for real-time simulations and control of the system behavior under changing conditions. We construct DTs using the Karhunen-Lo\`{e}ve Neural Network (KL-NN) surrogate model and transfer learning (TL). The sur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	523,966
2412.06465	Agent Journey Beyond RGB: Unveiling Hybrid Semantic-Spatial Environmental Representations for Vision-and-Language Navigation	Navigating unseen environments based on natural language instructions remains difficult for egocentric agents in Vision-and-Language Navigation (VLN). While recent advancements have yielded promising outcomes, they primarily rely on RGB images for environmental representation, often overlooking the underlying semantic ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	515,255
2305.08234	Introducing Tales of Tribute AI Competition	This paper presents a new AI challenge, the Tales of Tribute AI Competition (TOTAIC), based on a two-player deck-building card game released with the High Isle chapter of The Elder Scrolls Online. Currently, there is no other AI competition covering Collectible Card Games (CCG) genre, and there has never been one that ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	364,211
2004.14325	Don't Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation	State-of-the-art methods for Word Sense Disambiguation (WSD) combine two different features: the power of pre-trained language models and a propagation method to extend the coverage of such models. This propagation is needed as current sense-annotated corpora lack coverage of many instances in the underlying sense inve...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	174,847
2403.07611	Efficient Knowledge Deletion from Trained Models through Layer-wise Partial Machine Unlearning	Machine unlearning has garnered significant attention due to its ability to selectively erase knowledge obtained from specific training data samples in an already trained machine learning model. This capability enables data holders to adhere strictly to data protection regulations. However, existing unlearning techniqu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	436,962
2307.03109	A Survey on Evaluation of Large Language Models	Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at th...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	377,925
2406.13869	Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning	Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations or rules that can better explain the high-level properties of the mod...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	466,032
2311.10754	A Recent Survey of the Advancements in Deep Learning Techniques for Monkeypox Disease Detection	Monkeypox (MPox) is a zoonotic infectious disease induced by the MPox Virus, part of the poxviridae orthopoxvirus group initially discovered in Africa and gained global attention in mid-2022 with cases reported outside endemic areas. Symptoms include headaches, chills, fever, smallpox, measles, and chickenpox-like skin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	408,632
2003.01262	Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs	The properties of individual neurons are often analyzed in order to understand the biological and artificial neural networks in which they're embedded. Class selectivity-typically defined as how different a neuron's responses are across different classes of stimuli or data samples-is commonly used for this purpose. How...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	166,587
2410.06847	A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering	This paper proposes a safety modulator actor-critic (SMAC) method to address safety constraint and overestimation mitigation in model-free safe reinforcement learning (RL). A safety modulator is developed to satisfy safety constraints by modulating actions, allowing the policy to ignore safety constraint and focus on m...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	496,372
2410.09289	Multimodal Audio-based Disease Prediction with Transformer-based Hierarchical Fusion Network	Audio-based disease prediction is emerging as a promising supplement to traditional medical diagnosis methods, facilitating early, convenient, and non-invasive disease detection and prevention. Multimodal fusion, which integrates features from various domains within or across bio-acoustic modalities, has proven effecti...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	497,516
2006.14718	Asynchronous Multi Agent Active Search	Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions, and has many applications including detecting gas leaks, radiation sources or human survivors of disasters using aerial and/or ground robots (agents). Existing active search method...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	184,307
2502.07608	Beyond Prompting: Time2Lang -- Bridging Time-Series Foundation Models and Large Language Models for Health Sensing	Large language models (LLMs) show promise for health applications when combined with behavioral sensing data. Traditional approaches convert sensor data into text prompts, but this process is prone to errors, computationally expensive, and requires domain expertise. These challenges are particularly acute when processi...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	532,684
2408.16308	AdaMotif: Graph Simplification via Adaptive Motif Design	With the increase of graph size, it becomes difficult or even impossible to visualize graph structures clearly within the limited screen space. Consequently, it is crucial to design effective visual representations for large graphs. In this paper, we propose AdaMotif, a novel approach that can capture the essential str...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	484,284
2207.14112	Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation	Diversification in a set of solutions has become a hot research topic in the evolutionary computation community. It has been proven beneficial for optimisation problems in several ways, such as computing a diverse set of high-quality solutions and obtaining robustness against imperfect modeling. For the first time in t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	310,483
1508.06973	The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks	We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events. These semantic similarities constitute acausal connections according to the Synchronicity principle and provide new relationships to quantum like probabilistic graphical models. As a conseq...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	46,371
2212.08589	Data-Driven Model Reduction by Two-Sided Moment Matching	In this brief paper, we propose a time-domain data-driven method for model order reduction by two-sided moment matching for linear systems. An algorithm that asymptotically approximates the matrix product $\Upsilon \Pi$ from time-domain samples of the so-called two-sided interconnection is provided. Exploiting this est...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	336,802
1510.04347	Processing Regular Path Queries on Arbitrarily Distributed Data	Regular Path Queries (RPQs) are a type of graph query where answers are pairs of nodes connected by a sequence of edges matching a regular expression. We study the techniques to process such queries on a distributed graph of data. While many techniques assume the location of each data element (node or edge) is known, w...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	47,907
2104.05674	GPflux: A Library for Deep Gaussian Processes	We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the various mathematical subtleties that arise when dealing with multivariate Gaussian distributions and the complex bookkeeping of indices. To d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	229,810
2311.01007	Effective Human-AI Teams via Learned Natural Language Rules and Onboarding	People are relying on AI agents to assist them with various tasks. The human must know when to rely on the agent, collaborate with the agent, or ignore its suggestions. In this work, we propose to learn rules, grounded in data regions and described in natural language, that illustrate how the human should collaborate w...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	404,871
1704.06869	Argument Mining with Structured SVMs and RNNs	We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	72,242
2111.08492	Real-time 3D human action recognition based on Hyperpoint sequence	Real-time 3D human action recognition has broad industrial applications, such as surveillance, human-computer interaction, and healthcare monitoring. By relying on complex spatio-temporal local encoding, most existing point cloud sequence networks capture spatio-temporal local structures to recognize 3D human actions. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	266,714
2310.20098	Robust Learning for Smoothed Online Convex Optimization with Feedback Delay	We study a challenging form of Smoothed Online Convex Optimization, a.k.a. SOCO, including multi-step nonlinear switching costs and feedback delay. We propose a novel machine learning (ML) augmented online algorithm, Robustness-Constrained Learning (RCL), which combines untrusted ML predictions with a trusted expert on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	404,255
2304.10046	Optimal Kernel for Kernel-Based Modal Statistical Methods	Kernel-based modal statistical methods include mode estimation, regression, and clustering. Estimation accuracy of these methods depends on the kernel used as well as the bandwidth. We study effect of the selection of the kernel function to the estimation accuracy of these methods. In particular, we theoretically show ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	359,271
2310.11520	Automatic News Summerization	Natural Language Processing is booming with its applications in the real world, one of which is Text Summarization for large texts including news articles. This research paper provides an extensive comparative evaluation of extractive and abstractive approaches for news text summarization, with an emphasis on the ROUGE...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	400,669
2311.05115	A Survey on Convex Optimization for Guidance and Control of Vehicular Systems	Guidance and control (G&C) technologies play a central role in the development and operation of vehicular systems. The emergence of computational guidance and control (CG&C) and highly efficient numerical algorithms has opened up the great potential for solving complex constrained G&C problems onboard, enabling higher ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	406,478
1612.05110	Efficient Detection of Complex Event Patterns Using Lazy Chain Automata	Complex Event Processing (CEP) is an emerging field with important applications in many areas. CEP systems collect events arriving from input data streams and use them to infer more complex events according to predefined patterns. The Non-deterministic Finite Automaton (NFA) is one of the most popular mechanisms on whi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	65,638
2006.05587	Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy	Classifying sequential data as early and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high. One algorithm that achieves this goal is the sequential probability ratio test (SPRT), which is known as Bayes-optimal: it can keep the expected number of data samples as sm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,122
1609.01178	A New Approach to Constructing Quadratic Pseudo-Planar Functions over $\gf_{2^n}$	Planar functions over finite fields give rise to finite projective planes. They were also used in the constructions of DES-like iterated ciphers, error-correcting codes, and codebooks. They were originally defined only in finite fields with odd characteristic, but recently Zhou introduced pesudo-planar functions in eve...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,563
1701.07245	Optimal Binary $(5,3)$ Projective Space Codes from Maximal Partial Spreads	Recently a construction of optimal non-constant dimension subspace codes, also termed projective space codes, has been reported in a paper of Honold-Kiermaier-Kurz. Restricted to binary codes in a 5-dimensional ambient space with minimum subspace distance 3, these optimal codes were interpreted in terms of maximal part...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,258
2410.21256	Multi-modal AI for comprehensive breast cancer prognostication	Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We deve...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	503,148
2307.05629	Characterization of AGM Belief Contraction in Terms of Conditionals	We provide a semantic characterization of AGM belief contraction based on frames consisting of a Kripke belief relation and a Stalnaker-Lewis selection function. The central idea is as follows. Let K be the initial belief set and K-A be the contraction of K by the formula A; then B belongs to the set K-A if and only if...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	378,808
2203.11836	A Real-time Junk Food Recognition System based on Machine Learning	$ $As a result of bad eating habits, humanity may be destroyed. People are constantly on the lookout for tasty foods, with junk foods being the most common source. As a consequence, our eating patterns are shifting, and we're gravitating toward junk food more than ever, which is bad for our health and increases our ris...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	287,058
2210.12531	Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts	Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people's emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with m...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,786
2205.10407	Prototyping three key properties of specific curiosity in computational reinforcement learning	Curiosity for machine agents has been a focus of intense research. The study of human and animal curiosity, particularly specific curiosity, has unearthed several properties that would offer important benefits for machine learners, but that have not yet been well-explored in machine intelligence. In this work, we intro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,691
2410.02079	Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems	A significant challenge in many fields of science and engineering is making sense of time-dependent measurement data by recovering governing equations in the form of differential equations. We focus on finding parsimonious ordinary differential equation (ODE) models for nonlinear, noisy, and non-autonomous dynamical sy...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	494,090
2210.10765	When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning	A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge to such autonomy is the presence of irreversible states which require external assistance to recover from, such as when a robot arm has pushed an object off of a table. While standa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,053
2404.16985	Humans prefer interacting with slow, less realistic butterfly simulations	How should zoomorphic, or bio-inspired, robots indicate to humans that interactions will be safe and fun? Here, a survey is used to measure how human willingness to interact with a simulated butterfly robot is affected by different flight patterns. Flapping frequency, flap to glide ratio, and flapping pattern were inde...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	449,694
2502.13646	D.Va: Validate Your Demonstration First Before You Use It	In-context learning (ICL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs) during inference. It's well-established that ICL heavily relies on selecting effective demonstrations to generate outputs that better align with the expected results. As for demonstration selec...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	535,453
2401.08298	Online Elasticity Estimation and Material Sorting Using Standard Robot Grippers	We experimentally evaluated the accuracy with which material properties can be estimated through object compression by two standard parallel jaw grippers and a force/torque sensor mounted at the robot wrist, with a professional biaxial compression device used as reference. Gripper effort versus position curves were obt...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	421,841
2106.00014	Diffusion Self-Organizing Map on the Hypersphere	We discuss a diffusion based implementation of the self-organizing map on the unit hypersphere. We show that this approach can be efficiently implemented using just linear algebra methods, we give a python numpy implementation, and we illustrate the approach using the well known MNIST dataset.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	237,957
1910.02923	A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis	Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges p...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	148,369
2011.06704	Diffusion models for Handwriting Generation	In this paper, we propose a diffusion probabilistic model for handwriting generation. Diffusion models are a class of generative models where samples start from Gaussian noise and are gradually denoised to produce output. Our method of handwriting generation does not require using any text-recognition based, writer-sty...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	206,308
1809.09095	On Reinforcement Learning for Full-length Game of StarCraft	StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II. The hierarchy involves two levels of abstraction. One is the macro-acti...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	108,644
2502.10215	Do Large Language Models Reason Causally Like Us? Even Better?	Causal reasoning is a core component of intelligence. Large language models (LLMs) have shown impressive capabilities in generating human-like text, raising questions about whether their responses reflect true understanding or statistical patterns. We compared causal reasoning in humans and four LLMs using tasks based ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	533,782
1105.1279	Wireless MIMO Switching with Network Coding	In a generic switching problem, a switching pattern consists of a one-to-one mapping from a set of inputs to a set of outputs (i.e., a permutation). We propose and investigate a wireless switching framework in which a multi-antenna relay is responsible for switching traffic among a set of $N$ stations. We refer to such...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	10,272
2001.06209	Registration made easy -- standalone orthopedic navigation with HoloLens	In surgical navigation, finding correspondence between preoperative plan and intraoperative anatomy, the so-called registration task, is imperative. One promising approach is to intraoperatively digitize anatomy and register it with the preoperative plan. State-of-the-art commercial navigation systems implement such ap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	160,749
2208.06828	Multinomial Logistic Regression Algorithms via Quadratic Gradient	Multinomial logistic regression, also known by other names such as multiclass logistic regression and softmax regression, is a fundamental classification method that generalizes binary logistic regression to multiclass problems. A recently work proposed a faster gradient called $\texttt{quadratic gradient}$ that can ac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	312,841
1509.00174	A Telescopic Binary Learning Machine for Training Neural Networks	This paper proposes a new algorithm based on multi-scale stochastic local search with binary representation for training neural networks. In particular, we study the effects of neighborhood evaluation strategies, the effect of the number of bits per weight and that of the maximum weight range used for mapping binary ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	46,475
2105.10535	Ising Machines' Dynamics and Regularization for Near-Optimal Large and Massive MIMO Detection	Optimal MIMO detection has been one of the most challenging and computationally inefficient tasks in wireless systems. We show that the new analog computing techniques like Coherent Ising Machines (CIM) are promising candidates for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	236,424
1901.02873	Waiting before Serving: A Companion to Packet Management in Status Update Systems	In this paper, we explore the potential of server waiting before packet transmission in improving the Age of Information (AoI) in status update systems. We consider a non-preemptive queue with Poisson arrivals and independent general service distribution and we incorporate waiting before serving in two packet managemen...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	118,292
2402.03204	Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems	We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep mode...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	426,904
2007.12336	T-BFA: Targeted Bit-Flip Adversarial Weight Attack	Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack. Recently, another dimension of adversarial attack, namely, attack on DNN weight parameters, has been shown to be very powerful. As a representative one, the Bit-Flip-based adversarial weight Attack (BFA)...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	188,791
1610.07379	Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation	We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set of potential maximizers (BO) or unclassified points (LSE), which...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	62,778
2005.12991	Kernel Self-Attention in Deep Multiple Instance Learning	Not all supervised learning problems are described by a pair of a fixed-size input tensor and a label. In some cases, especially in medical image analysis, a label corresponds to a bag of instances (e.g. image patches), and to classify such bag, aggregation of information from all of the instances is needed. There have...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	178,874
2209.14000	Personalization of Web Search During the 2020 US Elections	Search engines play a central role in routing political information to citizens. The algorithmic personalization of search results by large search engines like Google implies that different users may be offered systematically different information. However, measuring the causal effect of user characteristics and behavi...	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	true	320,100
1303.5730	Representation Requirements for Supporting Decision Model Formulation	This paper outlines a methodology for analyzing the representational support for knowledge-based decision-modeling in a broad domain. A relevant set of inference patterns and knowledge types are identified. By comparing the analysis results to existing representations, some insights are gained into a design approach fo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,178
2204.12861	Spectral dynamics of guided edge removals and identifying transient amplifiers for death-Birth updating	The paper deals with two interrelated topics, identifying transient amplifiers in an iterative process and analyzing the process by its spectral dynamics, which is the change in the graph spectra by edge manipulations. Transient amplifiers are networks representing population structures which shift the balance between ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	293,633
2405.04726	Learning Phonotactics from Linguistic Informants	We propose an interactive approach to language learning that utilizes linguistic acceptability judgments from an informant (a competent language user) to learn a grammar. Given a grammar formalism and a framework for synthesizing data, our model iteratively selects or synthesizes a data-point according to one of a rang...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	452,655
2305.18563	SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual Learning	Deep neural networks (DNNs) struggle to learn in dynamic environments since they rely on fixed datasets or stationary environments. Continual learning (CL) aims to address this limitation and enable DNNs to accumulate knowledge incrementally, similar to human learning. Inspired by how our brain consolidates memories, a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	369,137
2501.02189	Benchmark Evaluations, Applications, and Challenges of Large Vision Language Models: A Survey	Multimodal Vision Language Models (VLMs) have emerged as a transformative technology at the intersection of computer vision and natural language processing, enabling machines to perceive and reason about the world through both visual and textual modalities. For example, models such as CLIP, Claude, and GPT-4V demonstra...	false	false	false	false	true	false	true	true	true	false	false	true	false	false	false	false	false	false	522,386
2502.01349	Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product Recommendations	The advent of Large Language Models (LLMs) has revolutionized product recommendation systems, yet their susceptibility to adversarial manipulation poses critical challenges, particularly in real-world commercial applications. Our approach is the first one to tap into human psychological principles, seamlessly modifying...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	529,818
1712.09702	Optimal Control Computation via Evolution Partial Differential Equation with Arbitrary Definite Conditions	The compact Variation Evolving Method (VEM) that originates from the continuous-time dynamics stability theory seeks the optimal solutions with variation evolution principle. It is further developed to be more flexible in solving the Optimal Control Problems (OCPs), by relaxing the definite conditions from a feasible s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	87,393
1609.06395	Analyzing Dependent Placements of Small Cells in a Two-Layer Heterogeneous Network with a Rate Coverage Constraint	We consider the downlink of a two-layer heterogeneous network, comprising macro cells (MCs) and small cells (SCs). The existing literature generally assumes independent placements of the access points (APs) in different layers; in contrast, we analyze a dependent placement where SC APs are placed at locations with poor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	61,282
1911.04553	Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras	Event cameras are a promising candidate to enable high speed vision-based control due to their low sensor latency and high temporal resolution. However, purely event-based feedback has yet to be used in the control of drones. In this work, a first step towards implementing low-latency high-bandwidth control of quadroto...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	153,016
1805.11189	Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models	Encoder-decoder models typically only employ words that are frequently used in the training corpus to reduce the computational costs and exclude noise. However, this vocabulary set may still include words that interfere with learning in encoder-decoder models. This paper proposes a method for selecting more suitable wo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	98,856
2005.01697	Setting up experimental Bell test with reinforcement learning	Finding optical setups producing measurement results with a targeted probability distribution is hard as a priori the number of possible experimental implementations grows exponentially with the number of modes and the number of devices. To tackle this complexity, we introduce a method combining reinforcement learning ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	175,657
2406.04808	VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation	Two-dimensional embeddings obtained from dimensionality reduction techniques, such as MDS, t-SNE, and UMAP, are widely used across various disciplines to visualize high-dimensional data. These visualizations provide a valuable tool for exploratory data analysis, allowing researchers to visually identify clusters, outli...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,849
2404.17184	Low-Rank Knowledge Decomposition for Medical Foundation Models	The popularity of large-scale pre-training has promoted the development of medical foundation models. However, some studies have shown that although foundation models exhibit strong general feature extraction capabilities, their performance on specific tasks is still inferior to task-specific methods. In this paper, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,769
2312.16267	Maximizing the Success Probability of Policy Allocations in Online Systems	The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse objectives of advertisers. In this paper we conside...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	418,336
2407.10768	ISMRNN: An Implicitly Segmented RNN Method with Mamba for Long-Term Time Series Forecasting	Long time series forecasting aims to utilize historical information to forecast future states over extended horizons. Traditional RNN-based series forecasting methods struggle to effectively address long-term dependencies and gradient issues in long time series problems. Recently, SegRNN has emerged as a leading RNN-ba...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	473,119
2204.09874	Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics	Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated neural models to obtain coherent and interpretable topics? In this paper, w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	292,592
2101.12090	Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network	Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	217,483
2008.12463	Accelerated WGAN update strategy with loss change rate balancing	Optimizing the discriminator in Generative Adversarial Networks (GANs) to completion in the inner training loop is computationally prohibitive, and on finite datasets would result in overfitting. To address this, a common update strategy is to alternate between k optimization steps for the discriminator D and one optim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	193,585

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