pwc-archive/papers-with-abstracts huggingface dataset:
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game
Authors: Guangzheng Hu, Yuanheng Zhu, Haoran Li, Dongbin Zhao
Date: 2024-02-01 00:00:00
Tasks: Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning

Many real-world applications involve some agents that fall into two teams, with payoffs that are equal within the same team but of opposite sign across the opponent team. The so-called two-team zero-sum Markov games (2t0sMGs) can be resolved with reinforcement learning in recent years. However, existing methods are thus inefficient in light of insufficient consideration of intra-team credit assignment, data utilization and computational intractability. In this paper, we propose the individual-global-minimax (IGMM) principle to ensure the coherence between two-team minimax behaviors and the individual greedy behaviors through Q functions in 2t0sMGs. Based on it, we present a novel multi-agent reinforcement learning framework, Factorized Multi-Agent MiniMax Q-Learning (FM3Q), which can factorize the joint minimax Q function into individual ones and iteratively solve for the IGMM-satisfied minimax Q functions for 2t0sMGs. Moreover, an online learning algorithm with neural networks is proposed to implement FM3Q and obtain the deterministic and decentralized minimax policies for two-team players. A theoretical analysis is provided to prove the convergence of FM3Q. Empirically, we use three environments to evaluate the learning efficiency and final performance of FM3Q and show its superiority on 2t0sMGs.
Neural Style Transfer with Twin-Delayed DDPG for Shared Control of Robotic Manipulators
Authors: Raul Fernandez-Fernandez, Marco Aggravi, Paolo Robuffo Giordano, Juan G. Victores, Claudio Pacchierotti
Date: 2024-02-01 00:00:00
Tasks: Style Transfer

Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defined as a combination of Content and Style: the Content can be conceptually defined as the what and the Style as the how of said element. In this context, we propose a custom NST framework for transferring a set of styles to the motion of a robotic manipulator, e.g., the same robotic task can be carried out in an angry, happy, calm, or sad way. An autoencoder architecture extracts and defines the Content and the Style of the target robot motions. A Twin Delayed Deep Deterministic Policy Gradient (TD3) network generates the robot control policy using the loss defined by the autoencoder. The proposed Neural Policy Style Transfer TD3 (NPST3) alters the robot motion by introducing the trained style. Such an approach can be implemented either offline, for carrying out autonomous robot motions in dynamic environments, or online, for adapting at runtime the style of a teleoperated robot. The considered styles can be learned online from human demonstrations. We carried out an evaluation with human subjects enrolling 73 volunteers, asking them to recognize the style behind some representative robotic motions. Results show a good recognition rate, proving that it is possible to convey different styles to a robot using this approach.
Intent Assurance using LLMs guided by Intent Drift
Authors: Kristina Dzeparoska, Ali Tizghadam, Alberto Leon-Garcia
Date: 2024-02-01 00:00:00
Tasks: Management

Intent-Based Networking (IBN) presents a paradigm shift for network management, by promising to align intents and business objectives with network operations--in an automated manner. However, its practical realization is challenging: 1) processing intents, i.e., translate, decompose and identify the logic to fulfill the intent, and 2) intent conformance, that is, considering dynamic networks, the logic should be adequately adapted to assure intents. To address the latter, intent assurance is tasked with continuous verification and validation, including taking the necessary actions to align the operational and target states. In this paper, we define an assurance framework that allows us to detect and act when intent drift occurs. To do so, we leverage AI-driven policies, generated by Large Language Models (LLMs) which can quickly learn the necessary in-context requirements, and assist with the fulfillment and assurance of intents.
Non-Exchangeable Conformal Language Generation with Nearest Neighbors
Authors: Dennis Ulmer, Chrysoula Zerva, André F. T. Martins
Date: 2024-02-01 00:00:00
Tasks: Conformal Prediction, Language Modeling, Language Modelling, Machine Translation, Prediction, Text Generation

Quantifying uncertainty in automatically generated text is important for letting humans check potential hallucinations and making systems more reliable. Conformal prediction is an attractive framework to provide predictions imbued with statistical guarantees, however, its application to text generation is challenging since any i.i.d. assumptions are not realistic. In this paper, we bridge this gap by leveraging recent results on non-exchangeable conformal prediction, which still ensures bounds on coverage. The result, non-exchangeable conformal nucleus sampling, is a novel extension of the conformal prediction framework to generation based on nearest neighbors. Our method can be used post-hoc for an arbitrary model without extra training and supplies token-level, calibrated prediction sets equipped with statistical guarantees. Experiments in machine translation and language modeling show encouraging results in generation quality. By also producing tighter prediction sets with good coverage, we thus give a more theoretically principled way to perform sampling with conformal guarantees.
PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
Authors: Wenxin Jiang, Jerin Yasmin, Jason Jones, Nicholas Synovic, Jiashen Kuo, Nathaniel Bielanski, Yuan Tian, George K. Thiruvathukal, James C. Davis
Date: 2024-02-01 00:00:00
Tasks: Language Modelling, Large Language Model

The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse. This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with 28,575 open-source software repositories from GitHub that utilize these models. Additionally, the dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. To enhance the dataset's comprehensiveness, we developed prompts for a large language model to automatically extract model metadata, including the model's training datasets, parameters, and evaluation metrics. Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation. Our example application reveals inconsistencies in software licenses across PTMs and their dependent projects. PeaTMOSS lays the foundation for future research, offering rich opportunities to investigate the PTM supply chain. We outline mining opportunities on PTMs, their downstream usage, and cross-cutting questions.
Ocassionally Secure: A Comparative Analysis of Code Generation Assistants
Authors: Ran Elgedawy, John Sadik, Senjuti Dutta, Anuj Gautam, Konstantinos Georgiou, Farzin Gholamrezae, Fujiao Ji, Kyungchan Lim, Qian Liu, Scott Ruoti
Date: 2024-02-01 00:00:00
Tasks: Code Generation

$ $Large Language Models (LLMs) are being increasingly utilized in various applications, with code generations being a notable example. While previous research has shown that LLMs have the capability to generate both secure and insecure code, the literature does not take into account what factors help generate secure and effective code. Therefore in this paper we focus on identifying and understanding the conditions and contexts in which LLMs can be effectively and safely deployed in real-world scenarios to generate quality code. We conducted a comparative analysis of four advanced LLMs--GPT-3.5 and GPT-4 using ChatGPT and Bard and Gemini from Google--using 9 separate tasks to assess each model's code generation capabilities. We contextualized our study to represent the typical use cases of a real-life developer employing LLMs for everyday tasks as work. Additionally, we place an emphasis on security awareness which is represented through the use of two distinct versions of our developer persona. In total, we collected 61 code outputs and analyzed them across several aspects: functionality, security, performance, complexity, and reliability. These insights are crucial for understanding the models' capabilities and limitations, guiding future development and practical applications in the field of automated code generation.
Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications
Authors: Raul Fernandez-Fernandez, Juan G. Victores, David Estevez, Carlos Balaguer
Date: 2024-02-01 00:00:00
Tasks: Evolutionary Algorithms

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through demonstrations. In classical frameworks, actions are modeled using joint or Cartesian space trajectories. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these methods, as it encodes actions as changes of any feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. Current strategies involve performing evaluations in a simulation, transferring the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within CGDA: naive PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO). The second approach studied the introduction of geometrical and velocity constraints within CGDA. The effects of both approaches were analyzed and compared in the wax and paint actions, two CGDA commonly studied use cases. Results from this paper depict an important reduction in the number of evaluations.
Neural Policy Style Transfer
Authors: Raul Fernandez-Fernandez, Juan G. Victores, Jennifer J. Gago, David Estevez, Carlos Balaguer
Date: 2024-02-01 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Style Transfer

Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep Reinforcement Learning infrastructure. Each network is trained to maximize the expected reward, which typically encodes the goal of an action, and can be described as the content. The expressive power of deep neural networks enables encoding a secondary task, which can be described as the style. The Neural Policy Style Transfer (NPST) algorithm is proposed to transfer the style of one policy to another, while maintaining the content of the latter. Different policies are defined via Deep Q-Network architectures. These models are trained using demonstrations through Inverse Reinforcement Learning. Two different sets of user demonstrations are performed, one for content and other for style. Different styles are encoded as defined by user demonstrations. The generated policy is the result of feeding a content policy and a style policy to the NPST algorithm. Experiments are performed in a catch-ball game inspired by the Deep Reinforcement Learning classical Atari games; and a real-world painting scenario with a full-sized humanoid robot, based on previous works of the authors. The implementation of three different Q-Network architectures (Shallow, Deep and Deep Recurrent Q-Network) to encode the policies within the NPST framework is proposed and the results obtained in the experiments with each of these architectures compared.
Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching
Authors: Raul Fernandez-Fernandez, Juan G. Victores, Carlos Balaguer
Date: 2024-02-01 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning

The current success of Reinforcement Learning algorithms for its performance in complex environments has inspired many recent theoretical approaches to cognitive science. Artistic environments are studied within the cognitive science community as rich, natural, multi-sensory, multi-cultural environments. In this work, we propose the introduction of Reinforcement Learning for improving the control of artistic robot applications. Deep Q-learning Neural Networks (DQN) is one of the most successful algorithms for the implementation of Reinforcement Learning in robotics. DQN methods generate complex control policies for the execution of complex robot applications in a wide set of environments. Current art painting robot applications use simple control laws that limits the adaptability of the frameworks to a set of simple environments. In this work, the introduction of DQN within an art painting robot application is proposed. The goal is to study how the introduction of a complex control policy impacts the performance of a basic art painting robot application. The main expected contribution of this work is to serve as a first baseline for future works introducing DQN methods for complex art painting robot frameworks. Experiments consist of real world executions of human drawn sketches using the DQN generated policy and TEO, the humanoid robot. Results are compared in terms of similarity and obtained reward with respect to the reference inputs
Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID
Authors: Lingfeng He, De Cheng, Nannan Wang, Xinbo Gao
Date: 2024-02-01 00:00:00
Tasks: Person Re-Identification, Pseudo Label, Representation Learning

Unsupervised visible-infrared person re-identification (USL-VI-ReID) endeavors to retrieve pedestrian images of the same identity from different modalities without annotations. While prior work focuses on establishing cross-modality pseudo-label associations to bridge the modality-gap, they ignore maintaining the instance-level homogeneous and heterogeneous consistency between the feature space and the pseudo-label space, resulting in coarse associations. In response, we introduce a Modality-Unified Label Transfer (MULT) module that simultaneously accounts for both homogeneous and heterogeneous fine-grained instance-level structures, yielding high-quality cross-modality label associations. It models both homogeneous and heterogeneous affinities, leveraging them to quantify the inconsistency between the pseudo-label space and the feature space, subsequently minimizing it. The proposed MULT ensures that the generated pseudo-labels maintain alignment across modalities while upholding structural consistency within intra-modality. Additionally, a straightforward plug-and-play Online Cross-memory Label Refinement (OCLR) module is proposed to further mitigate the side effects of noisy pseudo-labels while simultaneously aligning different modalities, coupled with an Alternative Modality-Invariant Representation Learning (AMIRL) framework. Experiments demonstrate that our proposed method outperforms existing state-of-the-art USL-VI-ReID methods, highlighting the superiority of our MULT in comparison to other cross-modality association methods. Code is available at https://github.com/FranklinLingfeng/code_for_MULT.
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing
Authors: Fangkai Jiao, Chengwei Qin, Zhengyuan Liu, Nancy F. Chen, Shafiq Joty
Date: 2024-02-01 00:00:00
Tasks: Hallucination, Logical Reasoning

Large Language Models (LLMs) have demonstrated significant potential in handling complex reasoning tasks through step-by-step rationale generation. However, recent studies have raised concerns regarding the hallucination and flaws in their reasoning process. Substantial efforts are being made to improve the reliability and faithfulness of the generated rationales. Some approaches model reasoning as planning, while others focus on annotating for process supervision. Nevertheless, the planning-based search process often results in high latency due to the frequent assessment of intermediate reasoning states and the extensive exploration space. Additionally, supervising the reasoning process with human annotation is costly and challenging to scale for LLM training. To address these issues, in this paper, we propose a framework to learn planning-based reasoning through Direct Preference Optimization (DPO) on collected trajectories, which are ranked according to synthesized process rewards. Our results on challenging logical reasoning benchmarks demonstrate the effectiveness of our learning framework, showing that our 7B model can surpass the strong counterparts like GPT-3.5-Turbo.
CapHuman: Capture Your Moments in Parallel Universes
Authors: Chao Liang, Fan Ma, Linchao Zhu, Yingying Deng, Yi Yang
Date: 2024-02-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Generation

We concentrate on a novel human-centric image synthesis task, that is, given only one reference facial photograph, it is expected to generate specific individual images with diverse head positions, poses, facial expressions, and illuminations in different contexts. To accomplish this goal, we argue that our generative model should be capable of the following favorable characteristics: (1) a strong visual and semantic understanding of our world and human society for basic object and human image generation. (2) generalizable identity preservation ability. (3) flexible and fine-grained head control. Recently, large pre-trained text-to-image diffusion models have shown remarkable results, serving as a powerful generative foundation. As a basis, we aim to unleash the above two capabilities of the pre-trained model. In this work, we present a new framework named CapHuman. We embrace the "encode then learn to align" paradigm, which enables generalizable identity preservation for new individuals without cumbersome tuning at inference. CapHuman encodes identity features and then learns to align them into the latent space. Moreover, we introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner. Extensive qualitative and quantitative analyses demonstrate our CapHuman can produce well-identity-preserved, photo-realistic, and high-fidelity portraits with content-rich representations and various head renditions, superior to established baselines. Code and checkpoint will be released at https://github.com/VamosC/CapHuman.
Are Synthetic Time-series Data Really not as Good as Real Data?
Authors: Fanzhe Fu, Junru Chen, Jing Zhang, Carl Yang, Lvbin Ma, Yang Yang
Date: 2024-02-01 00:00:00
Tasks: Representation Learning, Time Series

Time-series data presents limitations stemming from data quality issues, bias and vulnerabilities, and generalization problem. Integrating universal data synthesis methods holds promise in improving generalization. However, current methods cannot guarantee that the generator's output covers all unseen real data. In this paper, we introduce InfoBoost -- a highly versatile cross-domain data synthesizing framework with time series representation learning capability. We have developed a method based on synthetic data that enables model training without the need for real data, surpassing the performance of models trained with real data. Additionally, we have trained a universal feature extractor based on our synthetic data that is applicable to all time-series data. Our approach overcomes interference from multiple sources rhythmic signal, noise interference, and long-period features that exceed sampling window capabilities. Through experiments, our non-deep-learning synthetic data enables models to achieve superior reconstruction performance and universal explicit representation extraction without the need for real data.
Sandra -- A Neuro-Symbolic Reasoner Based On Descriptions And Situations
Authors: Nicolas Lazzari, Stefano De Giorgis, Aldo Gangemi, Valentina Presutti
Date: 2024-02-01 00:00:00

This paper presents sandra, a neuro-symbolic reasoner combining vectorial representations with deductive reasoning. Sandra builds a vector space constrained by an ontology and performs reasoning over it. The geometric nature of the reasoner allows its combination with neural networks, bridging the gap with symbolic knowledge representations. Sandra is based on the Description and Situation (DnS) ontology design pattern, a formalization of frame semantics. Given a set of facts (a situation) it allows to infer all possible perspectives (descriptions) that can provide a plausible interpretation for it, even in presence of incomplete information. We prove that our method is correct with respect to the DnS model. We experiment with two different tasks and their standard benchmarks, demonstrating that, without increasing complexity, sandra (i) outperforms all the baselines (ii) provides interpretability in the classification process, and (iii) allows control over the vector space, which is designed a priori.
BrainSLAM: SLAM on Neural Population Activity Data
Authors: Kipp Freud, Nathan Lepora, Matt W. Jones, Cian O'Donnell
Date: 2024-02-01 00:00:00
Tasks: Decision Making, Hippocampus, Navigate

Simultaneous localisation and mapping (SLAM) algorithms are commonly used in robotic systems for learning maps of novel environments. Brains also appear to learn maps, but the mechanisms are not known and it is unclear how to infer these maps from neural activity data. We present BrainSLAM; a method for performing SLAM using only population activity (local field potential, LFP) data simultaneously recorded from three brain regions in rats: hippocampus, prefrontal cortex, and parietal cortex. This system uses a convolutional neural network (CNN) to decode velocity and familiarity information from wavelet scalograms of neural local field potential data recorded from rats as they navigate a 2D maze. The CNN's output drives a RatSLAM-inspired architecture, powering an attractor network which performs path integration plus a separate system which performs `loop closure' (detecting previously visited locations and correcting map aliasing errors). Together, these three components can construct faithful representations of the environment while simultaneously tracking the animal's location. This is the first demonstration of inference of a spatial map from brain recordings. Our findings expand SLAM to a new modality, enabling a new method of mapping environments and facilitating a better understanding of the role of cognitive maps in navigation and decision making.