Miracle-dz/UniFAR-FTUs huggingface dataset:
LiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes

In the rapidly evolving field of text generation, the demand for more precise control mechanisms has become increasingly apparent. To address this need, we present a novel methodology, LIFI, which offers a lightweight approach with fine-grained control for controlled text generation. Unlike previous studies that train pre-trained language models to follow discrete, categorical, and exclusive control codes, LIFI learns controlled text generation under the guidance of continuous, relative, and nonexclusive control codes. These fine-grained codes are automatically derived from an attribute classifier, initially trained with a small amount of labeled data and subsequently employed to label abundant unlabeled data, thus garnering more extensive supervision signals. Moreover, to achieve efficient control, we incorporate the fine-grained control codes with adapters, a parameter- and compute-efficient way to steer a pre-trained language model. We evaluate LIFI on two conventional tasks -- sentiment control and topic control -- and one newly proposed task -- stylistic novel writing. Comprehensive experimental results validate the effectiveness of our proposed methods, demonstrating substantial performance improvements over existing baselines.
Investigating Consistency in Query-Based Meeting Summarization: A Comparative Study of Different Embedding Methods

With more and more advanced data analysis techniques emerging, people will expect these techniques to be applied in more complex tasks and solve problems in our daily lives. Text Summarization is one of famous applications in Natural Language Processing (NLP) field. It aims to automatically generate summary with important information based on a given context, which is important when you have to deal with piles of documents. Summarization techniques can help capture key points in a short time and bring convenience in works. One of applicable situation is meeting summarization, especially for important meeting that tend to be long, complicated, multi-topic and multi-person. Therefore, when people want to review specific content from a meeting, it will be hard and time-consuming to find the related spans in the meeting transcript. However, most of previous works focus on doing summarization for newsletters, scientific articles.etc, which have a clear document structure and an official format. For the documents with complex structure like transcripts, we think those works are not quite suitable for meeting summarization. Besides, the consistency of summary is another issue common to be discussed in NLP field. To conquer challenges of meeting summarization, we are inspired by"QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization"proposed by Microsoft and we also propose our Locater model designed to extract relevant spans based on given transcript and query, which are then summarized by Summarizer model. Furthermore, we perform a comparative study by applying different word embedding techniques to improve summary consistency.
Reading Rational Univariate Representations on lexicographic Groebner bases

In this contribution, we consider a zero-dimensional polynomial system in $n$ variables defined over a field $\mathbb{K}$. In the context of computing a Rational Univariate Representation (RUR) of its solutions, we address the problem of certifying a separating linear form and, once certified, calculating the RUR that comes from it, without any condition on the ideal else than being zero-dimensional. Our key result is that the RUR can be read (closed formula) from lexicographic Groebner bases of bivariate elimination ideals, even in the case where the original ideal that is not in shape position, so that one can use the same core as the well known FGLM method to propose a simple algorithm. Our first experiments, either with a very short code (300 lines) written in Maple or with a Julia code using straightforward implementations performing only classical Gaussian reductions in addition to Groebner bases for the degree reverse lexicographic ordering, show that this new method is already competitive with sophisticated state of the art implementations which do not certify the parameterizations.
Error Estimation for Physics-informed Neural Networks Approximating Semilinear Wave Equations

This paper provides rigorous error bounds for physics-informed neural networks approximating the semilinear wave equation. We provide bounds for the generalization and training error in terms of the width of the network's layers and the number of training points for a tanh neural network with two hidden layers. Our main result is a bound of the total error in the $H^1([0,T];L^2(\Omega))$-norm in terms of the training error and the number of training points, which can be made arbitrarily small under some assumptions. We illustrate our theoretical bounds with numerical experiments.
Adaptive Finite Element Methods

This is a survey on the theory of adaptive finite element methods (AFEMs), which are fundamental in modern computational science and engineering but whose mathematical assessment is a formidable challenge. We present a self-contained and up-to-date discussion of AFEMs for linear second order elliptic PDEs and dimension d>1, with emphasis on foundational issues. After a brief review of functional analysis and basic finite element theory, including piecewise polynomial approximation in graded meshes, we present the core material for coercive problems. We start with a novel a posteriori error analysis applicable to rough data, which delivers estimators fully equivalent to the solution error. They are used in the design and study of three AFEMs depending on the structure of data. We prove linear convergence of these algorithms and rate-optimality provided the solution and data belong to suitable approximation classes. We also address the relation between approximation and regularity classes. We finally extend this theory to discontinuous Galerkin methods as prototypes of non-conforming AFEMs and beyond coercive problems to inf-sup stable AFEMs.
Resampling methods for Private Statistical Inference

We consider the task of constructing confidence intervals with differential privacy. We propose two private variants of the non-parametric bootstrap, which privately compute the median of the results of multiple ``little'' bootstraps run on partitions of the data and give asymptotic bounds on the coverage error of the resulting confidence intervals. For a fixed differential privacy parameter $\epsilon$, our methods enjoy the same error rates as that of the non-private bootstrap to within logarithmic factors in the sample size $n$. We empirically validate the performance of our methods for mean estimation, median estimation, and logistic regression with both real and synthetic data. Our methods achieve similar coverage accuracy to existing methods (and non-private baselines) while providing notably shorter ($\gtrsim 10$ times) confidence intervals than previous approaches.
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion Recognition

Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeling the intricate temporal and spectral informative structures of speech. Recently, SSL on large speech datasets, as well as new audio-specific SSL proxy tasks, such as, temporal and frequency masking, have emerged, yielding superior performance compared to classic approaches drawn from the image augmentation domain. Our proposed contribution builds upon this successful paradigm by introducing CochCeps-Augment, a novel bio-inspired masking augmentation task for self-supervised contrastive learning of speech representations. Specifically, we utilize the newly introduced bio-inspired cochlear cepstrogram (CCGRAM) to derive noise robust representations of input speech, that are then further refined through a self-supervised learning scheme. The latter employs SimCLR to generate contrastive views of a CCGRAM through masking of its angle and quefrency dimensions. Our experimental approach and validations on the emotion recognition K-EmoCon benchmark dataset, for the first time via a speaker-independent approach, features unsupervised pre-training, linear probing and fine-tuning. Our results potentiate CochCeps-Augment to serve as a standard tool in speech emotion recognition analysis, showing the added value of incorporating bio-inspired masking as an informative augmentation task for self-supervision. Our code for implementing CochCeps-Augment will be made available at: https://github.com/GiannisZgs/CochCepsAugment.
Travelling wave amplification in stationary gratings

We show that a grating amplitude stationary in space but oscillating in time can be accurately modelled as a set of independent gratings travelling in opposite directions, interacting almost exclusively with waves travelling in the same direction. This structure reproduces the key features of travelling gratings: amplification of a wave at points where the local wave speed equals the grating velocity. The same field compression and photon production is evident when even a single Fourier component of the grating profile has a velocity that matches the local wave speed. We speculate that these stationary but oscillating gratings may prove easier to realise experimentally than travelling gratings.
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations

The Fokker-Planck (FP) equation is a foundational PDE in stochastic processes. However, curse of dimensionality (CoD) poses challenge when dealing with high-dimensional FP PDEs. Although Monte Carlo and vanilla Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The point-wise PDF values tend to decrease exponentially as dimension increases, surpassing the precision of numerical simulations and resulting in substantial errors. Moreover, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a difficult HJB equation, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in SDEs. The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling. Three fitting methods, Score Matching (SM), Sliced SM (SSM), and Score-PINN, are introduced. The proposed score-based SDE solver operates in two stages: first, employing SM, SSM, or Score-PINN to acquire the score; and second, solving the LL via an ODE using the obtained score. Comparative evaluations across these methods showcase varying trade-offs. The proposed method is evaluated across diverse SDEs, including anisotropic OU processes, geometric Brownian, and Brownian with varying eigenspace. We also test various distributions, including Gaussian, Log-normal, Laplace, and Cauchy. The numerical results demonstrate the score-based SDE solver's stability, speed, and performance across different settings, solidifying its potential as a solution to CoD for high-dimensional FP equations.
NLP for Knowledge Discovery and Information Extraction from Energetics Corpora

We present a demonstration of the utility of NLP for aiding research into energetic materials and associated systems. The NLP method enables machine understanding of textual data, offering an automated route to knowledge discovery and information extraction from energetics text. We apply three established unsupervised NLP models: Latent Dirichlet Allocation, Word2Vec, and the Transformer to a large curated dataset of energetics-related scientific articles. We demonstrate that each NLP algorithm is capable of identifying energetic topics and concepts, generating a language model which aligns with Subject Matter Expert knowledge. Furthermore, we present a document classification pipeline for energetics text. Our classification pipeline achieves 59-76\% accuracy depending on the NLP model used, with the highest performing Transformer model rivaling inter-annotator agreement metrics. The NLP approaches studied in this work can identify concepts germane to energetics and therefore hold promise as a tool for accelerating energetics research efforts and energetics material development.
On graphs with well-distributed edge density

In this paper, we introduce a class of graphs which we call average hereditary graphs. Most graphs that occur in the usual graph theory applications belong to this class of graphs. Many popular types of graphs fall under this class, such as regular graphs, trees and other popular classes of graphs. We prove a new upper bound for the chromatic number of a graph in terms of its maximum average degree and show that this bound is an improvement on previous bounds. From this, we show a relationship between the average degree and the chromatic number of an average hereditary graph. This class of graphs is explored further by proving some interesting properties regarding the class of average hereditary graphs. An equivalent condition is provided for a graph to be average hereditary, through which we show that we can decide if a given graph is average hereditary in polynomial time. We then provide a construction for average hereditary graphs, using which an average hereditary graph can be recursively constructed. We also show that this class of graphs is closed under a binary operation, from this another construction is obtained for average hereditary graphs, and we see some interesting algebraic properties this class of graphs has. We then explore the effect on the complexity of graph 3-coloring problem when the input is restricted to average hereditary graphs.
Millennials’ national and global identities as drivers of materialism and consumer ethnocentrism

ABSTRACT A major effect of globalization is one that occurs on the self-concept. This is especially the case for young consumers, and particularly for millennials. Despite this cohort’s idiosyncrasies, little attention has been paid to the study of their consumer identities, an important aspect of self-concept. The current research addresses this gap by examining the way millennial consumers’ global and national identities help explain two attitudinal outcomes associated with globalization: materialism and consumer ethnocentrism. Data were collected from millennials in two distinct socio-cultural contexts. A key finding suggests that distinct contexts (i.e., collectivist and ethnically homogeneous vs. individualistic and ethnically diverse) exhibit differences in the formation of materialism and consumer ethnocentrism among millennials. Additionally, results indicate that for similar consumer segments, each context’s configuration of millennials shows differences in global and national identities. Implications for future researchers and practitioners are discussed.
Does amplitude compression help or hinder attentional neural speech tracking?

Amplitude compression is an indispensable feature of contemporary audio production and especially relevant in modern hearing aids. The cortical fate of amplitude-compressed speech signals is not well-studied, however, and may yield undesired side effects: We hypothesize that compressing the amplitude envelope of continuous speech reduces neural tracking. Yet, leveraging such ‘compression side effect’ on unwanted, distracting sounds could potentially support attentive listening if effectively reducing their neural tracking. Here we studied 24 normal-hearing (NH) and 9 hearing-impaired (HI) individuals who were asked to focus on one of two competing talkers while ignoring the other. Envelope compression (1:8 ratio, loudness-matched) was applied to one or both streams containing short speech repeats. Electroencephalography (EEG) allowed us to quantify the cortical response function and degree of speech tracking. With compression applied to the attended target stream, NH and HI participants showed reduced behavioural accuracy, and compressed speech yielded generally lowered metrics of neural tracking. Despite the expected weaker tracking of a compressed distractor stream, however, HI individuals exhibited stronger tracking of the concurrently presented target stream. Our results imply that intelligent compression algorithms, with variable compression ratios applied to separated sources, could help individuals with hearing loss suppress distraction in complex multi-talker environments. Significant statement Amplitude compression, integral in contemporary audio production and hearing aids, poses an underexplored cortical challenge. Compressing the amplitude envelope of continuous speech is hypothesized to diminish neural tracking. Yet, capitalizing on this ’compression side effect’ for distracting sounds might enhance attentive listening. Studying 24 normal-hearing (NH) and 9 hearing-impaired (HI) individuals in dual-talker scenarios, we applied envelope compression to speech streams. Both NH and HI participants showed diminished neural tracking with compression on the speech streams. Despite weaker tracking of a compressed distractor, HI individuals exhibited stronger tracking of the concurrent target. This suggests that adaptive compression algorithms, employing variable ratios for distinct sources, could aid individuals with hearing loss in suppressing distractions in complex multi-talker environments.
Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction

The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly. Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. Deep learning methods, while successful in protein structure prediction, have shown some promise for RNA structure prediction as well but face unique challenges. This study systematically benchmarks state-of-the-art deep learning methods for RNA structure prediction across diverse datasets. Our aim is to identify factors influencing performance variation, such as RNA family diversity, sequence length, RNA type, multiple sequence alignment (MSA) quality, and deep learning model architecture. We show that generally ML-based methods perform much better than non-ML methods on most RNA targets, although the performance difference isn’t substantial when working with unseen novel or synthetic RNAs. The quality of the MSA and secondary structure prediction both play an important role and most methods aren’t able to predict non-Watson-Crick pairs in the RNAs. Overall, DeepFoldRNA has the best prediction followed by DRFold as the second best method. Finally, we also suggest possible mitigations to improve the quality of the prediction for future method development.
The Memorability of Voices is Predictable and Consistent across Listeners

Memorability, the likelihood that a stimulus is remembered, is an intrinsic stimulus property that is highly consistent across people—participants tend to remember and forget the same faces, objects, and more. However, these consistencies in memory have thus far only been observed for visual stimuli. We provide the first study of auditory memorability, collecting recognition memory scores from over 3000 participants listening to a sequence of different speakers saying the same sentence. We found significant consistency across participants in their memory for voice clips and for speakers across different utterances. Next, we tested regression models incorporating both low-level (e.g., fundamental frequency) and high-level (e.g., dialect) voice properties to predict their memorability. These models were significantly predictive, and cross-validated out-of-sample, supporting an inherent memorability of speakers’ voices. These results provide the first evidence that listeners are similar in the voices they remember, which can be reliably predicted by quantifiable voice features.
STREAMLINED PROTEOME-WIDE IDENTIFICATION OF DRUG TARGETS INDICATES ORGAN-SPECIFIC ENGAGEMENT

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify novel drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions.
FedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning

Federated learning (FL) is widely employed for collaborative training on decentralized data but faces challenges like data , system , and model heterogeneity . This prompted the emergency of model-heterogeneouspersonalizedfederatedlearning(MHPFL) . However, concerns persist regarding data and model privacy, model performance, communication, and computational costs in current MHPFL methods. To tackle these concerns, we propose a novel model-heterogeneous personalized Fed erated learning algorithm ( FedMoE ) with the Mixture of Experts ( MoE ), renowned for enhancing large language models (LLMs). It assigns a shared homogeneous small feature extractor and a local gating network for each client’s local heterogeneous large model. (1) During local training, the local heterogeneous model’s feature extractor acts as a local expert for personalized feature (representation) extraction, while the shared homogeneous small feature extractor serves as a global expert for generalized feature extraction. The local gating network produces personalized weights for extracted representations from both experts on each data sample. The three models form a local heterogeneous MoE . The weighted mixed representation fuses global generalized and local personalized features and is processed by the local heterogeneous large model’s header with personalized prediction information for output. The MoE and prediction header are updated synchronously. (2) The trained local homogeneous small feature extractors are sent to the server for cross-client information fusion via aggregation. Briefly, FedMoE first enhances local model personalization at a fine-grained data level while supporting model heterogeneity . We theoretically prove FedMoE ’s convergence over time. Extensive experiments demonstrate its superior model performance, with up to 2 . 8% and 22 . 16% accuracy improvement over the state-of-the-art and the same-category best baselines, and lower computation and acceptable communication costs.