pwc-archive/papers-with-abstracts huggingface dataset:
CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models
Authors: Jierui Li, Hung Le, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Doyen Sahoo
Date: 2024-11-07 00:00:00
Tasks: Code Generation, Decision Making, HumanEval, mbpp

Pre-trained on massive amounts of code and text data, large language models (LLMs) have demonstrated remarkable achievements in performing code generation tasks. With additional execution-based feedback, these models can act as agents with capabilities to self-refine and improve generated code autonomously. However, on challenging coding tasks with extremely large search space, current agentic approaches still struggle with multi-stage planning, generating, and debugging. To address this problem, we propose CodeTree, a framework for LLM agents to efficiently explore the search space in different stages of the code generation process. Specifically, we adopted a unified tree structure to explicitly explore different coding strategies, generate corresponding coding solutions, and subsequently refine the solutions. In each stage, critical decision-making (ranking, termination, expanding) of the exploration process is guided by both the environmental execution-based feedback and LLM-agent-generated feedback. We comprehensively evaluated CodeTree on 7 code generation benchmarks and demonstrated the significant performance gains of CodeTree against strong baselines. Using GPT-4o as the base model, we consistently achieved top results of 95.1 on HumanEval, 98.7 on MBPP, and 43.0 on CodeContests. On the challenging SWEBench benchmark, our approach led to significant performance gains.
Balancing Transparency and Accuracy: A Comparative Analysis of Rule-Based and Deep Learning Models in Political Bias Classification
Authors: Manuel Nunez Martinez, Sonja Schmer-Galunder, Zoey Liu, Sangpil Youm, Chathuri Jayaweera, Bonnie J. Dorr
Date: 2024-11-07 00:00:00
Tasks: Articles, Deep Learning, Self-Learning

The unchecked spread of digital information, combined with increasing political polarization and the tendency of individuals to isolate themselves from opposing political viewpoints, has driven researchers to develop systems for automatically detecting political bias in media. This trend has been further fueled by discussions on social media. We explore methods for categorizing bias in US news articles, comparing rule-based and deep learning approaches. The study highlights the sensitivity of modern self-learning systems to unconstrained data ingestion, while reconsidering the strengths of traditional rule-based systems. Applying both models to left-leaning (CNN) and right-leaning (FOX) news articles, we assess their effectiveness on data beyond the original training and test sets.This analysis highlights each model's accuracy, offers a framework for exploring deep-learning explainability, and sheds light on political bias in US news media. We contrast the opaque architecture of a deep learning model with the transparency of a linguistically informed rule-based model, showing that the rule-based model performs consistently across different data conditions and offers greater transparency, whereas the deep learning model is dependent on the training set and struggles with unseen data.
A Capabilities Approach to Studying Bias and Harm in Language Technologies
Authors: Hellina Hailu Nigatu, Zeerak Talat
Date: 2024-11-06 00:00:00
Tasks: Fairness

Mainstream Natural Language Processing (NLP) research has ignored the majority of the world's languages. In moving from excluding the majority of the world's languages to blindly adopting what we make for English, we first risk importing the same harms we have at best mitigated and at least measured for English. However, in evaluating and mitigating harms arising from adopting new technologies into such contexts, we often disregard (1) the actual community needs of Language Technologies, and (2) biases and fairness issues within the context of the communities. In this extended abstract, we consider fairness, bias, and inclusion in Language Technologies through the lens of the Capabilities Approach. The Capabilities Approach centers on what people are capable of achieving, given their intersectional social, political, and economic contexts instead of what resources are (theoretically) available to them. We detail the Capabilities Approach, its relationship to multilingual and multicultural evaluation, and how the framework affords meaningful collaboration with community members in defining and measuring the harms of Language Technologies.
Diversity Helps Jailbreak Large Language Models
Authors: Weiliang Zhao, Daniel Ben-Levi, Wei Hao, Junfeng Yang, Chengzhi Mao
Date: 2024-11-06 00:00:00
Tasks: Diversity

We have uncovered a powerful jailbreak technique that leverages large language models' ability to diverge from prior context, enabling them to bypass safety constraints and generate harmful outputs. By simply instructing the LLM to deviate and obfuscate previous attacks, our method dramatically outperforms existing approaches, achieving up to a 62.83% higher success rate in compromising ten leading chatbots, including GPT-4, Gemini, and Llama, while using only 12.9% of the queries. This revelation exposes a critical flaw in current LLM safety training, suggesting that existing methods may merely mask vulnerabilities rather than eliminate them. Our findings sound an urgent alarm for the need to revolutionize testing methodologies to ensure robust and reliable LLM security.
Software Design Pattern Model and Data Structure Algorithm Abilities on Microservices Architecture Design in High-tech Enterprises
Authors: Jun Cui
Date: 2024-11-05 00:00:00
Tasks: Robust Design

This study investigates the impact of software design model capabilities and data structure algorithm abilities on microservices architecture design within enterprises. Utilizing a qualitative methodology, the research involved in-depth interviews with software architects and developers who possess extensive experience in microservices implementation. The findings reveal that organizations emphasizing robust design models and efficient algorithms achieve superior scalability, performance, and flexibility in their microservices architecture. Notably, participants highlighted that a strong foundation in these areas facilitates better service decomposition, optimizes data processing, and enhances system responsiveness. Despite these insights, gaps remain regarding the integration of emerging technologies and the evolving nature of software design practices. This paper contributes to the existing literature by underscoring the critical role of these competencies in fostering effective microservices architectures and suggests avenues for future research to address identified gaps
Unified Pathological Speech Analysis with Prompt Tuning
Authors: Fei Yang, Xuenan Xu, Mengyue Wu, Kai Yu
Date: 2024-11-05 00:00:00
Tasks: Language Modeling, Language Modelling

Pathological speech analysis has been of interest in the detection of certain diseases like depression and Alzheimer's disease and attracts much interest from researchers. However, previous pathological speech analysis models are commonly designed for a specific disease while overlooking the connection between diseases, which may constrain performance and lower training efficiency. Instead of fine-tuning deep models for different tasks, prompt tuning is a much more efficient training paradigm. We thus propose a unified pathological speech analysis system for as many as three diseases with the prompt tuning technique. This system uses prompt tuning to adjust only a small part of the parameters to detect different diseases from speeches of possible patients. Our system leverages a pre-trained spoken language model and demonstrates strong performance across multiple disorders while only fine-tuning a fraction of the parameters. This efficient training approach leads to faster convergence and improved F1 scores by allowing knowledge to be shared across tasks. Our experiments on Alzheimer's disease, Depression, and Parkinson's disease show competitive results, highlighting the effectiveness of our method in pathological speech analysis.
A Comparative Study on the Impact of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) on Enterprise Software Delivery Effectiveness
Authors: Jun Cui
Date: 2024-11-05 00:00:00
Tasks: test driven development

This paper compares the impact of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) on software delivery effectiveness within enterprise environments. Using a qualitative research design, data were collected through in-depth interviews with developers and project managers from enterprises adopting TDD or BDD. Moreover, the findings reveal distinct effects of each model on delivery speed, software quality, and team collaboration. Specifically, TDD emphasizes early testing and iterative development, leading to enhanced code quality and fewer defects, while BDD improves cross-functional communication by focusing on behavior specifications that involve stakeholders directly. However, TDD may create a higher initial time investment, and BDD might encounter challenges in requirement clarity. These differences highlight gaps in understanding how each model aligns with varying project types and stakeholder needs, which can guide enterprises in selecting the most suitable model for their unique requirements. The study contributes to the literature by providing insights into the practical application and challenges of TDD and BDD, suggesting future research on their long-term impacts in diverse settings.
Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators
Authors: Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch
Date: 2024-11-07 00:00:00
Tasks: Bayesian Optimization, Evolutionary Algorithms, reinforcement-learning, Reinforcement Learning

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithm (GA), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems using a Deep Differentiable Reinforcement Learning (DDRL) algorithm in particle accelerators. We compare DDRL algorithm with Model Free Reinforcement Learning (MFRL), GA and Bayesian Optimization (BO) for simultaneous optimization of heat load and trip rates in the Continuous Electron Beam Accelerator Facility (CEBAF). The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraint for energy requirements of the beam. A physics-based surrogate model based on real data is developed. This surrogate model is differentiable and allows back-propagation of gradients. The results are evaluated in the form of a Pareto-front for two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.
Finding Strong Lottery Ticket Networks with Genetic Algorithms
Authors: Philipp Altmann, Julian Schönberger, Maximilian Zorn, Thomas Gabor
Date: 2024-11-07 00:00:00
Tasks: Binary Classification

According to the Strong Lottery Ticket Hypothesis, every sufficiently large neural network with randomly initialized weights contains a sub-network which - still with its random weights - already performs as well for a given task as the trained super-network. We present the first approach based on a genetic algorithm to find such strong lottery ticket sub-networks without training or otherwise computing any gradient. We show that, for smaller instances of binary classification tasks, our evolutionary approach even produces smaller and better-performing lottery ticket networks than the state-of-the-art approach using gradient information.
Conjugate gradient methods for high-dimensional GLMMs
Authors: Andrea Pandolfi, Omiros Papaspiliopoulos, Giacomo Zanella
Date: 2024-11-07 00:00:00

Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does not lend itself well to default sparse linear algebra techniques. Notably, we show that, for typical GLMMs, the Cholesky factor is dense even when the original precision is sparse. We thus turn to approximate iterative techniques, in particular to the conjugate gradient (CG) method. We combine a detailed analysis of the spectrum of said precision matrices with results from random graph theory to show that CG-based methods applied to high-dimensional GLMMs typically achieve a fixed approximation error with a total cost that scales linearly with the number of parameters and observations. Numerical illustrations with both real and simulated data confirm the theoretical findings, while at the same time illustrating situations, such as nested structures, where CG-based methods struggle.
Orbit: A Framework for Designing and Evaluating Multi-objective Rankers
Authors: Chenyang Yang, Tesi Xiao, Michael Shavlovsky, Christian Kästner, Tongshuang Wu
Date: 2024-11-07 00:00:00

Machine learning in production needs to balance multiple objectives: This is particularly evident in ranking or recommendation models, where conflicting objectives such as user engagement, satisfaction, diversity, and novelty must be considered at the same time. However, designing multi-objective rankers is inherently a dynamic wicked problem -- there is no single optimal solution, and the needs evolve over time. Effective design requires collaboration between cross-functional teams and careful analysis of a wide range of information. In this work, we introduce Orbit, a conceptual framework for Objective-centric Ranker Building and Iteration. The framework places objectives at the center of the design process, to serve as boundary objects for communication and guide practitioners for design and evaluation. We implement Orbit as an interactive system, which enables stakeholders to interact with objective spaces directly and supports real-time exploration and evaluation of design trade-offs. We evaluate Orbit through a user study involving twelve industry practitioners, showing that it supports efficient design space exploration, leads to more informed decision-making, and enhances awareness of the inherent trade-offs of multiple objectives. Orbit (1) opens up new opportunities of an objective-centric design process for any multi-objective ML models, as well as (2) sheds light on future designs that push practitioners to go beyond a narrow metric-centric or example-centric mindset.
Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval
Authors: Ferdinand Schlatt, Maik Fröbe, Matthias Hagen
Date: 2024-11-07 00:00:00
Tasks: Information Retrieval, Re-Ranking, Retrieval

A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning IR, an easy-to-use PyTorch Lightning-based framework for applying transformer-based language models in retrieval scenarios. Lightning IR provides a modular and extensible architecture that supports all stages of a retrieval pipeline: from fine-tuning and indexing to searching and re-ranking. Designed to be scalable and reproducible, Lightning IR is available as open-source: https://github.com/webis-de/lightning-ir.
The Concatenator: A Bayesian Approach To Real Time Concatenative Musaicing
Authors: Christopher Tralie, Ben Cantil
Date: 2024-11-07 00:00:00

We present ``The Concatenator,'' a real time system for audio-guided concatenative synthesis. Similarly to Driedger et al.'s ``musaicing'' (or ``audio mosaicing'') technique, we concatenate a set number of windows within a corpus of audio to re-create the harmonic and percussive aspects of a target audio stream. Unlike Driedger's NMF-based technique, however, we instead use an explicitly Bayesian point of view, where corpus window indices are hidden states and the target audio stream is an observation. We use a particle filter to infer the best hidden corpus states in real-time. Our transition model includes a tunable parameter to control the time-continuity of corpus grains, and our observation model allows users to prioritize how quickly windows change to match the target. Because the computational complexity of the system is independent of the corpus size, our system scales to corpora that are hours long, which is an important feature in the age of vast audio data collections. Within The Concatenator module itself, composers can vary grain length, fit to target, and pitch shift in real time while reacting to the sounds they hear, enabling them to rapidly iterate ideas. To conclude our work, we evaluate our system with extensive quantitative tests of the effects of parameters, as well as a qualitative evaluation with artistic insights. Based on the quality of the results, we believe the real-time capability unlocks new avenues for musical expression and control, suitable for live performance and modular synthesis integration, which furthermore represents an essential breakthrough in concatenative synthesis technology.
Large-Scale Few-Shot Classification with Semi-supervised Hierarchical k-Probabilistic PCAs
Authors: Ke Han, Adrian Barbu
Date: 2024-09-09 00:00:00
Proceeding: International Joint Conference on Neural Networks (IJCNN) 2024 9
Tasks: Classification, Representation Learning, Self-Learning

This paper introduces a hierarchical semi-supervised framework for few-shot classification on large-scale datasets. The method leverages the development of self-learning-based representation learning and proposes a hierarchical semi-supervised classifier called Hierarchical k-Probabilistic Principal Component Analyzers (Hk-PPCAs), on a pretrained generic self-learned feature extractor. The classifier models the feature space using a two-level hierarchical structure. The first-level image classes and second-level super-classes are modeled as Probabilistic Principal Component Analyzers (PPCA) Gaussian distributions, which makes the framework scalable for adding new classes without retraining the whole model. The proposed PPCA-based Gaussian prototypes ensure the stability of classification under the few-shot setting and the hierarchical structure reduces the classification time from O(K) to O(K−−√) for K image classes. This makes the approach computationally efficient and practicable in large-scale classification. Experiments on ImageNet-1k and ImageNet-10k show the effectiveness of the proposed approach.
Invitro Pharmacological Evaluations Of Ethanolic Extract Of Jatropha Maheshwari
Authors: Sankar V, Anand Babu K, Deepak M, Poojitha Mallapu, Raghu S, Anandharaj G
Date: 2024-11-05 00:00:00

Objective: To assess the antioxidant, wound healing, anti-ulcer, and anti-inflammatory properties of Jatropha maheshwari. Methods: Jatropha maheshwari was collected from Kanyakumari district and authenticated. Ethanol was used for continuous hot percolation extraction of the plant. Antioxidant activity was evaluated using DPPH and ABTS assays. The wound healing potential was assessed through a wound scratch assay using 3T3-L1 murine fibroblast cell lines. Anti-ulcer activity was measured using the acid-neutralizing capacity (ANC) and H+/K+-ATPase inhibitory activity methods. Anti-inflammatory effects were determined through dose-dependent studies of the ethanol extract. Results: Antioxidant Activity: The crude extract exhibited strong antioxidant capacity with percentage inhibition values of 88% (DPPH) and 75.7% (ABTS). Wound Healing Activity: The wound closure rate in treated 3T3-L1 cell lines reached 97.88%, indicating potent wound healing properties. Anti-Ulcer Activity: The extract demonstrated an 80.1% inhibition compared to the control when tested alongside omeprazole. ANC per gram of antacid was recorded at 20 and 26 for oral doses of 10 and 500 {\mu}g/ml, respectively. Anti-Inflammatory Activity: Dose-dependent inhibition percentages of 30.1% (10 {\mu}g/ml) and 96.5% (500 {\mu}g/ml) were observed (p<0.001) relative to inflammation control. Additional percentages of 26.3% (10 {\mu}g/ml) and 42.6% (500 {\mu}g/ml) further confirmed the anti-inflammatory activity. Conclusion: Jatropha maheshwari demonstrates significant antioxidant, wound healing, anti-ulcer, and anti-inflammatory properties. Further research is warranted to elucidate the mechanisms underlying these pharmacological effects.
Dynamics of a Tuberculosis Outbreak Model in a Multi-scale Environment
Authors: Selain K. Kasereka
Date: 2024-11-06 00:00:00

Modeling and simulation approaches for infectious disease dynamics have proven to be essential tools for effective control of the spread of epidemics in the population. Among these approaches, it is obvious that compartmental mathematical models, such as SIS, SIR, SEIR, etc. are the most widely used by researchers. However, they are difficult to apply in a multi-scale environment, especially if we want to take into account the heterogeneous behaviors of individuals. The aim of this paper is to present a hybrid model in which an Equation-Based Model (EBM) of tuberculosis dynamics is coupled to an Agent-Based Model (ABM) in a two-scale environment. In this model, individuals are placed in cities considered as agents in which the dynamics of the disease is modeled by eight compartments and managed by solving a system of differential equations. Individual agents move between these cities using an ABM that controls their mobility. Considering some parametric values and assumptions, the results obtained show that human mobility has a significant impact on the spread of tuberculosis within the population. The management of population and disease dynamics at different levels (microscopic and macroscopic) testifies to the robustness of the proposed approach.
BASEPROD: The Bardenas Semi-Desert Planetary Rover Dataset
Authors: Levin Gerdes, Tim Wiese, Raúl Castilla Arquillo, Laura Bielenberg, Martin Azkarate, Hugo Leblond, Felix Wilting, Joaquín Ortega Cortés, Alberto Bernal, Santiago Palanco, Carlos Pérez del Pulgar
Date: 2024-09-27 00:00:00
Proceeding: Scientific Data 2024 9

Dataset acquisitions devised specifically for robotic planetary exploration are key for the advancement, evaluation, and validation of novel perception, localization, and navigation methods in representative environments. Originating in the Bardenas semi-desert in July 2023, the data presented in this Data Descriptor is primarily aimed at Martian exploration and contains relevant rover sensor data from approximately 1.7km of traverses, a high-resolution 3D map of the test area, laser-induced breakdown spectroscopy recordings of rock samples along the rover path, as well as local weather data. In addition to optical cameras and inertial sensors, the rover features a thermal camera and six force-torque sensors. This setup enables, for example, the study of future localization, mapping, and navigation techniques in unstructured terrains for improved Guidance, Navigation, and Control (GNC). The main features of this dataset are the combination of scientific and engineering instrument data, as well as the inclusion of the thermal camera and force-torque sensors in particular.