pwc-archive/papers-with-abstracts huggingface dataset:
Inferring Stellar Parameters from Iodine-Imprinted Keck/HIRES Spectra with Machine Learning
Authors: Jude Gussman, Malena Rice
Date: 2024-01-12 00:00:00

The properties of exoplanet host stars are traditionally characterized through a detailed forward-modeling analysis of high-resolution spectra. However, many exoplanet radial velocity surveys employ iodine-cell-calibrated spectrographs, such that the vast majority of spectra obtained include an imprinted forest of iodine absorption lines. For surveys that use iodine cells, iodine-free "template" spectra must be separately obtained for precise stellar characterization. These template spectra often require extensive additional observing time to obtain, and they are not always feasible to obtain for faint stars. In this paper, we demonstrate that machine learning methods can be applied to infer stellar parameters and chemical abundances from iodine-imprinted spectra with high accuracy and precision. The methods presented in this work are broadly applicable to any iodine-cell-calibrated spectrograph. We make publicly available our spectroscopic pipeline, the Cannon HIRES Iodine Pipeline (CHIP), which derives stellar parameters and 15 chemical abundances from iodine-imprinted spectra of FGK stars and which has been set up for ease of use with Keck/HIRES spectra. Our proof-of-concept offers an efficient new avenue to rapidly estimate a large number of stellar parameters even in the absence of an iodine-free template spectrum.
Optimization of Discrete Parameters Using the Adaptive Gradient Method and Directed Evolution
Authors: Andrei Beinarovich, Sergey Stepanov, Alexander Zaslavsky
Date: 2024-01-12 00:00:00

The problem is considered of optimizing discrete parameters in the presence of constraints. We use the stochastic sigmoid with temperature and put forward the new adaptive gradient method CONGA. The search for an optimal solution is carried out by a population of individuals. Each of them varies according to gradients of the 'environment' and is characterized by two temperature parameters with different annealing schedules. Unadapted individuals die, and optimal ones interbreed, the result is directed evolutionary dynamics. The proposed method is illustrated using the well-known combinatorial problem for optimal packing of a backpack (0-1 KP).
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese
Authors: Panji Arisaputra, Alif Tri Handoyo, Amalia Zahra
Date: 2024-01-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Diversity, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

This research paper focuses on the development and evaluation of Automatic Speech Recognition (ASR) technology using the XLS-R 300m model. The study aims to improve ASR performance in converting spoken language into written text, specifically for Indonesian, Javanese, and Sundanese languages. The paper discusses the testing procedures, datasets used, and methodology employed in training and evaluating the ASR systems. The results show that the XLS-R 300m model achieves competitive Word Error Rate (WER) measurements, with a slight compromise in performance for Javanese and Sundanese languages. The integration of a 5-gram KenLM language model significantly reduces WER and enhances ASR accuracy. The research contributes to the advancement of ASR technology by addressing linguistic diversity and improving performance across various languages. The findings provide insights into optimizing ASR accuracy and applicability for diverse linguistic contexts.
Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network
Authors: Kieran Holland, Andreas Ipp, David I. Müller, Urs Wenger
Date: 2024-01-12 00:00:00

Fixed point lattice actions are designed to have continuum classical properties unaffected by discretization effects and reduced lattice artifacts at the quantum level. They provide a possible way to extract continuum physics with coarser lattices, thereby allowing one to circumvent problems with critical slowing down and topological freezing toward the continuum limit. A crucial ingredient for practical applications is to find an accurate and compact parametrization of a fixed point action, since many of its properties are only implicitly defined. Here we use machine learning methods to revisit the question of how to parametrize fixed point actions. In particular, we obtain a fixed point action for four-dimensional SU(3) gauge theory using convolutional neural networks with exact gauge invariance. The large operator space allows us to find superior parametrizations compared to previous studies, a necessary first step for future Monte Carlo simulations and scaling studies.
Interpretable deep learning in single-cell omics
Authors: Manoj M Wagle, Siqu Long, Carissa Chen, Chunlei Liu, Pengyi Yang
Date: 2024-01-11 00:00:00
Tasks: Deep Learning

Recent developments in single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them `black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions. We anticipate this review to bring together the single-cell and machine learning research communities to foster future development and application of interpretable deep learning in single-cell omics research.
Surrogate Neural Networks Local Stability for Aircraft Predictive Maintenance
Authors: Mélanie Ducoffe, Guillaume Povéda, Audrey Galametz, Ryma Boumazouza, Marion-Cécile Martin, Julien Baris, Derk Daverschot, Eugene O'Higgins
Date: 2024-01-11 00:00:00

Surrogate Neural Networks are nowadays routinely used in industry as substitutes for computationally demanding engineering simulations (e.g., in structural analysis). They allow to generate faster predictions and thus analyses in industrial applications e.g., during a product design, testing or monitoring phases. Due to their performance and time-efficiency, these surrogate models are now being developed for use in safety-critical applications. Neural network verification and in particular the assessment of their robustness (e.g., to perturbations) is the next critical step to allow their inclusion in real-life applications and certification. We assess the applicability and scalability of empirical and formal methods in the context of aircraft predictive maintenance for surrogate neural networks designed to predict the stress sustained by an aircraft part from external loads. The case study covers a high-dimensional input and output space and the verification process thus accommodates multi-objective constraints. We explore the complementarity of verification methods in assessing the local stability property of such surrogate models to input noise. We showcase the effectiveness of sequentially combining methods in one verification 'pipeline' and demonstrate the subsequent gain in runtime required to assess the targeted property.
QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
Authors: Sihan Zeng, Youngdae Kim, Yuxuan Ren, Kibaek Kim
Date: 2024-01-11 00:00:00

At the heart of power system operations, alternating current optimal power flow (ACOPF) studies the generation of electric power in the most economical way under network-wide load requirement, and can be formulated as a highly structured non-convex quadratically constrained quadratic program (QCQP). Optimization-based solutions to ACOPF (such as ADMM or interior-point method), as the classic approach, require large amount of computation and cannot meet the need to repeatedly solve the problem as load requirement frequently changes. On the other hand, learning-based methods that directly predict the ACOPF solution given the load input incur little computational cost but often generates infeasible solutions (i.e. violate the constraints of ACOPF). In this work, we combine the best of both worlds -- we propose an innovated framework for learning ACOPF, where the input load is mapped to the ACOPF solution through a neural network in a computationally efficient and reliable manner. Key to our innovation is a specific-purpose "activation function" defined implicitly by a QCQP and a novel loss, which enforce constraint satisfaction. We show through numerical simulations that our proposed method achieves superior feasibility rate and generation cost in situations where the existing learning-based approaches fail.
Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques
Authors: Tanwi Mallick, John Murphy, Joshua David Bergerson, Duane R. Verner, John K Hutchison, Leslie-Anne Levy
Date: 2024-01-11 00:00:00
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Understanding the multifaceted effects of climate change across diverse geographic locations is crucial for timely adaptation and the development of effective mitigation strategies. As the volume of scientific literature on this topic continues to grow exponentially, manually reviewing these documents has become an immensely challenging task. Utilizing Natural Language Processing (NLP) techniques to analyze this wealth of information presents an efficient and scalable solution. By gathering extensive amounts of peer-reviewed articles and studies, we can extract and process critical information about the effects of climate change in specific regions. We employ BERT (Bidirectional Encoder Representations from Transformers) for Named Entity Recognition (NER), which enables us to efficiently identify specific geographies within the climate literature. This, in turn, facilitates location-specific analyses. We conduct region-specific climate trend analyses to pinpoint the predominant themes or concerns related to climate change within a particular area, trace the temporal progression of these identified issues, and evaluate their frequency, severity, and potential development over time. These in-depth examinations of location-specific climate data enable the creation of more customized policy-making, adaptation, and mitigation strategies, addressing each region's unique challenges and providing more effective solutions rooted in data-driven insights. This approach, founded on a thorough exploration of scientific texts, offers actionable insights to a wide range of stakeholders, from policymakers to engineers to environmentalists. By proactively understanding these impacts, societies are better positioned to prepare, allocate resources wisely, and design tailored strategies to cope with future climate conditions, ensuring a more resilient future for all.
Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation
Authors: Miquel Esplà-Gomis, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez
Date: 2024-01-16 00:00:00
Tasks: Sentence, Sentence Embeddings, Translation

Computer-aided translation (CAT) tools based on translation memories (MT) play a prominent role in the translation workflow of professional translators. However, the reduced availability of in-domain TMs, as compared to in-domain monolingual corpora, limits its adoption for a number of translation tasks. In this paper, we introduce a novel neural approach aimed at overcoming this limitation by exploiting not only TMs, but also in-domain target-language (TL) monolingual corpora, and still enabling a similar functionality to that offered by conventional TM-based CAT tools. Our approach relies on cross-lingual sentence embeddings to retrieve translation proposals from TL monolingual corpora, and on a neural model to estimate their post-editing effort. The paper presents an automatic evaluation of these techniques on four language pairs that shows that our approach can successfully exploit monolingual texts in a TM-based CAT environment, increasing the amount of useful translation proposals, and that our neural model for estimating the post-editing effort enables the combination of translation proposals obtained from monolingual corpora and from TMs in the usual way. A human evaluation performed on a single language pair confirms the results of the automatic evaluation and seems to indicate that the translation proposals retrieved with our approach are more useful than what the automatic evaluation shows.
Morphology and Syntax of the Tamil Language
Authors: Kengatharaiyer Sarveswaran
Date: 2024-01-16 00:00:00

This paper provides an overview of the morphology and syntax of the Tamil language, focusing on its contemporary usage. The paper also highlights the complexity and richness of Tamil in terms of its morphological and syntactic features, which will be useful for linguists analysing the language and conducting comparative studies. In addition, the paper will be useful for those developing computational resources for the Tamil language. It is proven as a rule-based morphological analyser cum generator and a computational grammar for Tamil have already been developed based on this paper. To enhance accessibility for a broader audience, the analysis is conducted without relying on any specific grammatical formalism.
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
Authors: Jianhui Pang, Fanghua Ye, Longyue Wang, Dian Yu, Derek F. Wong, Shuming Shi, Zhaopeng Tu
Date: 2024-01-16 00:00:00
Tasks: Machine Translation, NMT, Translation, Word Alignment

The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges (Koehn and Knowles, 2017), which have acted as benchmarks for progress in this field. This study revisits these challenges, offering insights into their ongoing relevance in the context of advanced Large Language Models (LLMs): domain mismatch, amount of parallel data, rare word prediction, translation of long sentences, attention model as word alignment, and sub-optimal beam search. Our empirical findings indicate that LLMs effectively lessen the reliance on parallel data for major languages in the pretraining phase. Additionally, the LLM-based translation system significantly enhances the translation of long sentences that contain approximately 80 words and shows the capability to translate documents of up to 512 words. However, despite these significant improvements, the challenges of domain mismatch and prediction of rare words persist. While the challenges of word alignment and beam search, specifically associated with NMT, may not apply to LLMs, we identify three new challenges for LLMs in translation tasks: inference efficiency, translation of low-resource languages in the pretraining phase, and human-aligned evaluation. The datasets and models are released at https://github.com/pangjh3/LLM4MT.
Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening
Authors: Chengguang Gan, Qinghao Zhang, Tatsunori Mori
Date: 2024-01-16 00:00:00
Tasks: Decision Making, Sentence, Sentence Classification

The automation of resume screening is a crucial aspect of the recruitment process in organizations. Automated resume screening systems often encompass a range of natural language processing (NLP) tasks. This paper introduces a novel Large Language Models (LLMs) based agent framework for resume screening, aimed at enhancing efficiency and time management in recruitment processes. Our framework is distinct in its ability to efficiently summarize and grade each resume from a large dataset. Moreover, it utilizes LLM agents for decision-making. To evaluate our framework, we constructed a dataset from actual resumes and simulated a resume screening process. Subsequently, the outcomes of the simulation experiment were compared and subjected to detailed analysis. The results demonstrate that our automated resume screening framework is 11 times faster than traditional manual methods. Furthermore, by fine-tuning the LLMs, we observed a significant improvement in the F1 score, reaching 87.73\%, during the resume sentence classification phase. In the resume summarization and grading phase, our fine-tuned model surpassed the baseline performance of the GPT-3.5 model. Analysis of the decision-making efficacy of the LLM agents in the final offer stage further underscores the potential of LLM agents in transforming resume screening processes.
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Authors: Weixiang Zhao, Shilong Wang, Yulin Hu, Yanyan Zhao, Bing Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
Date: 2024-01-16 00:00:00
Tasks: Continual Learning, Transfer Learning

The continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL. However, these methods tend to address only one of the challenges, ignoring the potential of aligning the two modules to effectively address catastrophic forgetting and knowledge transfer simultaneously. To this end, we propose a novel Shared Attention Framework (SAPT), to align the PET learning and selection via the Shared Attentive Learning \& Selection module. Extensive Experiments on two CL benchmarks demonstrate the superiority of SAPT. Moreover, SAPT consistently demonstrates its superiority when we scale it to different model sizes (from 770M to 13B), different model architectures (T5 and LLaMA-2) and unseen tasks.
Inferflow: an Efficient and Highly Configurable Inference Engine for Large Language Models
Authors: Shuming Shi, Enbo Zhao, Deng Cai, Leyang Cui, Xinting Huang, Huayang Li
Date: 2024-01-16 00:00:00
Tasks: GPU, Quantization

We present Inferflow, an efficient and highly configurable inference engine for large language models (LLMs). With Inferflow, users can serve most of the common transformer models by simply modifying some lines in corresponding configuration files, without writing a single line of source code. Compared with most existing inference engines, Inferflow has some key features. First, by implementing a modular framework of atomic build-blocks and technologies, Inferflow is compositionally generalizable to new models. Second, 3.5-bit quantization is introduced in Inferflow as a tradeoff between 3-bit and 4-bit quantization. Third, hybrid model partitioning for multi-GPU inference is introduced in Inferflow to better balance inference speed and throughput than the existing partition-by-layer and partition-by-tensor strategies.
Generative Multi-Modal Knowledge Retrieval with Large Language Models
Authors: Xinwei Long, Jiali Zeng, Fandong Meng, Zhiyuan Ma, Kaiyan Zhang, BoWen Zhou, Jie zhou
Date: 2024-01-16 00:00:00
Tasks: Retrieval

Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of their effectiveness and training efficiency, especially when it comes to training and integrating multiple retrievers to handle multi-modal queries. In this paper, we propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases, even when trained with limited data. We retrieve knowledge via a two-step process: 1) generating knowledge clues related to the queries, and 2) obtaining the relevant document by searching databases using the knowledge clue. In particular, we first introduce an object-aware prefix-tuning technique to guide multi-grained visual learning. Then, we align multi-grained visual features into the textual feature space of the LLM, employing the LLM to capture cross-modal interactions. Subsequently, we construct instruction data with a unified format for model training. Finally, we propose the knowledge-guided generation strategy to impose prior constraints in the decoding steps, thereby promoting the generation of distinctive knowledge clues. Through experiments conducted on three benchmarks, we demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline
Authors: Minpeng Liao, Wei Luo, Chengxi Li, Jing Wu, Kai Fan
Date: 2024-01-16 00:00:00
Tasks: GSM8K, Math, Mathematical Reasoning, Natural Language Understanding

Large language models (LLMs) have seen considerable advancements in natural language understanding tasks, yet there remains a gap to bridge before attaining true artificial general intelligence, especially concerning shortcomings in mathematical reasoning capabilities. We postulate that the inherent nature of LLM training, which focuses on predicting probabilities of next token, presents challenges in effectively modeling mathematical reasoning that demands exact calculations, both from data-driven and theoretical standpoints. In this paper, we address this challenge by enriching the data landscape and introducing a novel math dataset, enhanced with a capability to utilize a Python code interpreter. This dataset is derived from GSM8K and MATH and has been further refined through a combination of GPT-4 annotations, human review, and self-training processes, where the errors in the original GSM8K training set have been fixed. Additionally, we propose a tentative, easily replicable protocol for the fine-tuning of math-specific LLMs, which has led to a significant improvement in the performance of a 7B-parameter LLM on the GSM8K and MATH datasets. We are committed to advancing the field of mathematical reasoning in LLMs and, to that end, we have made source code for data generation / training / inference, and the model checkpoints publicly available at \url{https://github.com/MARIO-Math-Reasoning/MARIO}. We hope this will facilitate further research and development within the community.