Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature learning in neural networks and kernel machines that recursively learn features

Neural networks have achieved impressive results on many technological and scientiﬁc tasks. Yet, their empirical successes have outpaced our fundamental understanding of their structure and function. Identifying mechanisms driving the successes of neural networks can provide principled approaches for improving neural network performance and developing simple and eﬀective alternatives. In this work, we isolate a key mechanism driving feature learning in fully connected neural networks by connecting neural feature learning to a statistical estimator known as average gradient outer product. We subsequently leverage this mechanism to design Recursive Feature Machines (RFMs), which are kernel machines that learn features. We show that RFMs (1) accurately capture features learned by deep fully connected neural networks, and (2) outperform a broad spectrum of models including neural networks on tabular data. Furthermore, we show how RFMs shed light on recently observed deep learning phenomena including grokking, lottery tickets, simplicity biases, and spurious features. We provide a Python implementation to make our method easily accessible. 4
CCFL: Computationally Customized Federated Learning

Federated learning (FL) is a method to train model with distributed data from numerous participants such as IoT devices. It inherently assumes a uniform capacity among participants. However, participants have diverse computational resources in practice due to different conditions such as different energy budgets or executing parallel unrelated tasks. It is necessary to reduce the computation overhead for participants with inefﬁcient computational resources, otherwise they would be unable to ﬁnish the full training process. To address the computation heterogeneity, in this paper we propose a strategy for estimating local models without computationally intensive iterations. Based on it, we propose Computationally Customized Federated Learning (CCFL), which allows each participant to determine whether to perform conventional local training or model estimation in each round based on its current computational resources. Both theoretical analysis and exhaustive experiments indicate that CCFL has the same convergence rate as FedAvg without resource constraints. Furthermore, CCFL can be view of a computation-efﬁcient extension of FedAvg that retains model performance while considerably reducing computation overhead.
Web-Based Volunteer Distributed Computing for Handling Time-Critical Urgent Workloads

Urgent computing workloads are time critical, unpredictable, and highly dynamic. Whilst efforts are on-going to run these on traditional HPC machines, another option is to leverage the computing power donated by volunteers. Volunteer computing, where members of the public donate some of their CPU time to large scale projects has been popular for many years because it is a powerful way of delivering compute for specific problems, with the public often eager to contribute to a good cause with societal benefits. However, traditional volunteer computing has required user installation of specialist software which is a barrier to entry, and the development of the software itself by the projects, even on-top of existing frameworks, is nontrivial. As such, the number of users donating CPU time to these volunteer computing projects has decreased in recent years, and this comes at a time when the frequency of disasters, often driven by climate change, are rising fast. We believe that an alternative approach, where visitors to websites donate some of their CPU time whilst they are browsing, has the potential to address these issues. However, web-based distributed computing is an immature field and there are numerous questions that must be answered to fully understand the viability of leveraging the large scale parallelism that website visitors represent. In this paper we describe our web-based distributed computing framework, Panther, and perform in-depth performance experiments for two benchmarks using real world hardware and real world browsing habits for the first time. By exploring the performance characteristics of our approach we demonstrate that this is viable for urgent workloads, but there are numerous caveats, not least the most appropriate visitor patterns to a website, that must be considered.
DiffFace: Diffusion-based Face Swapping with Facial Guidance

In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending. In specific, in the training process, the ID conditional DDPM is trained to generate face images with the desired identity. In the sampling process, we use the off-the-shelf facial expert models to make the model transfer source identity while preserving target attributes faithfully. During this process, to preserve the background of the target image and obtain the desired face swapping result, we additionally propose a target-preserving blending strategy. It helps our model to keep the attributes of the target face from noise while transferring the source facial identity. In addition, without any re-training, our model can flexibly apply additional facial guidance and adaptively control the ID-attributes trade-off to achieve the desired results. To the best of our knowledge, this is the first approach that applies the diffusion model in face swapping task. Compared with previous GAN-based approaches, by taking advantage of the diffusion model for the face swapping task, DiffFace achieves better benefits such as training stability, high fidelity, diversity of the samples, and controllability. Extensive experiments show that our DiffFace is comparable or superior to the state-of-the-art methods on several standard face swapping benchmarks.
Automatic Text Simplification of News Articles in the Context of Public Broadcasting

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Université de Montréal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS). In order to make its written content more widely accessible, and to support its second-language teaching activities, CBC/RC has recently been exploring the potential of automatic methods to simplify texts. They have developed a modular lexical simplification system (LSS), which identifies complex words in French and English texts, and replaces them with simpler, more common equivalents. Recently however, the ATS research community has proposed a number of approaches that rely on deep learning methods to perform more elaborate transformations, not limited to just lexical substitutions, but covering syntactic restructuring and conceptual simplifications as well. The main goal of CBC/RC’s participation in the workshop was to examine these new methods and to compare their performance to that of their own LSS.
Semilinear Elliptic Equations on Thin Network-Shaped Domains with Variable Thickness

Abstract We study semilinear elliptic equations in a thin domain which is shaped like a network and degenerate into a geometric graph when a certain parameter tends to zero. It is shown by using a comparison technique that a solution of the equation in the network-shaped domain approaches a solution of an associated limit equation on the graph. Conversely, when the limit equation on the graph has a solution, we prove the existence of a solution of the PDE in the network-shaped domain which converges to the solution of the limit equation.
Atention Mechanism Architecture for Arabic Sentiment Analysis

This paper tackles the problem of sentiment analysis in the Arabic language where a new deep learning model has been put forward. The proposed model uses a hybrid bidirectional gated recurrent unit (BiGRU) and bidirectional long short-term memory (BiLSTM) additive-attention model where the Bidirectional GRU/LSTM reads the individual sentence input from left to right and vice versa enabling the capture of the contextual information. On the other hand, the model is trained on two types of embeddings: FastText and local learnable embeddings. The BiLSTM and BiGRU architectures are put into competition to identify the best hyperparameter set for the model. The developed model has been tested on three large-scale commonly employed Arabic sentiment dataset: large-scale Arabic Book Reviews Dataset (ABRD), Hotel Arabic-Reviews Dataset (HARD), and Books Reviews in the Arabic Dataset (BRAD). The testing results demonstrate that our model outperforms both the baseline models and the state-of-the-art models reported in the original references of these datasets, achieving an accuracy score of 98.6%, 96.19%,95.65% for LARB, HARD and BRAD, respectively. Furthermore, to demonstrate the generalization capabilities of our model, the performances of the model have been evaluated on three other natural language processing tasks: news categorization, ofensive speech detection, and Russian sentiment analysis. The results demonstrated the developed model is language and task-independent, which ofers new perspectives for the application of the developed models in several other natural language processing challenges.
Upper-bounding ℓ1-optimization sectional thresholds

In this paper we look at a particular problem related to under-determined linear systems of equations with sparse solutions. $\ell_1$-minimization is a fairly successful polynomial technique that can in certain statistical scenarios find sparse enough solutions of such systems. Barriers of $\ell_1$ performance are typically referred to as its thresholds. Depending if one is interested in a typical or worst case behavior one then distinguishes between the \emph{weak} thresholds that relate to a typical behavior on one side and the \emph{sectional} and \emph{strong} thresholds that relate to the worst case behavior on the other side. Starting with seminal works \cite{CRT,DonohoPol,DOnoho06CS} a substantial progress has been achieved in theoretical characterization of $\ell_1$-minimization statistical thresholds. More precisely, \cite{CRT,DOnoho06CS} presented for the first time linear lower bounds on all of these thresholds. Donoho's work \cite{DonohoPol} (and our own \cite{StojnicCSetam09,StojnicUpper10}) went a bit further and essentially settled the $\ell_1$'s \emph{weak} thresholds. At the same time they also provided fairly good lower bounds on the values on the \emph{sectional} and \emph{strong} thresholds. In this paper, we revisit the \emph{sectional} thresholds and present a simple mechanism that can be used to create solid upper bounds as well. The method we present relies on a seemingly simple but substantial progress we made in studying Hopfield models in \cite{StojnicHopBnds10}.
Assessing Species Boundaries in the Freshwater Snail Family Physidae Using Coalescent-Based Delimitation Methods

ABSTRACT A common issue for several freshwater snail families is the fact that, for some genera, the criteria for species delimitation are scarce, subjected to intraspecific and ontogenetic variation, or insufficiently discrete to allow a clear distinction between species. For these genera, the use of molecular approaches is decisive in recognizing valid species. Herein, we applied coalescent methods to delimit evolutionary significant units (ESUs) in Physidae. We used 496 sequences of the mitochondrial cytochrome oxidase subunit I (COI) gene available in GenBank, ascribed to six genera and 25 nominal species, and the algorithms GMYC (General Mixed Yule-Coalescent) with two criteria (single-threshold and multiple-threshold), PTP (Poisson Tree Process), and mPTP (multi-rate Tree Poisson Process). The method that presented the best performance was the mPTP. This method showed a higher frequency of taxonomic species recovery and has delimited a lower number of ESUs (25 ESUs from a dataset attributed to 25 nominal species). Physidae is a family of freshwater snails that presents several issues related to the recognition of species boundaries. Herein, the use of coalescent delimitation methods allowed us to address important taxonomic questions, opening new avenues for future taxonomic research. Our findings have shown that the morphological operational criteria used to delimit several valid species fall within the limits of intraspecific variability, demonstrating taxonomic inflation within this family. Our results also demonstrated the presence of cryptic diversity under the nominal species Physella acuta (Draparnaud, 1805), Beringophysa jennessi (Dall, 1919), Physella pomilia (Conrad, 1834), and Physella gyrina (Say, 1821).
Macroinvertebrates in the Diet of the Apple Snail Pomacea canaliculata in Its Native Range

ABSTRACT The study of diet and how a species obtains food is relevant to understand its role within natural environments. The apple snail Pomacea canaliculata (Lamarck, 1822) is a freshwater dweller that primarily consumes aquatic macrophytes and detritus but also incorporates resources of animal origin in its diet. Our aim was to investigate the ingestion of macroinvertebrates by P. canaliculata in four watercourses from its native range by analyzing its digestive contents. The frequency of occurrence of animal remains in the digestive contents of P. canaliculata snails was 68.50% across different seasons, with values of more than 50% in each of the watercourses studied. The spring-summer contents showed high frequencies of animal remains, whereas in autumn they were recorded only in 10% of the contents from one of the watercourses. The macroinvertebrates ingested belonged to six different insect orders, crustaceans, mites, bivalves, and gastropods. The frequencies of occurrence were higher for arthropods than for mollusks in two watercourses and similar in the other two. In two watercourses, the relative abundances of mollusks were higher than those of arthropods and the opposite was true in the other two. The number of individuals ingested by an average P. canaliculata was highest for the snail Heleobia parchappii, followed by arthropods, H. parchappii eggs, and the snails Physella acuta and Chilina parchappii. Most ingested macroinvertebrates were small, slow or with little or no mobility, such as snails and case-bearing insects. Arthropods able to swim or to swiftly escape, such as Amphipoda and nymphs of Odonata and Ephemeroptera, were not detected at all in the digestive contents. Ingestion of macroinvertebrates appears to be opportunistic and even accidental and probably depends more on their microhabitats or behavior than on preferences of P. canaliculata. Notwithstanding, the high frequency of ingestion observed on some species of snails could have a significant negative effect on the abundance of their populations.
Phylogeny of the asexual lineage Murrayidae (Macrobiotoidea, Eutardigrada) with the description of Paramurrayon gen. nov. and Paramurrayon meieri sp. nov.

ABSTRACT The peculiar family Murrayidae, comprising the genera Murrayon, Dactylobiotus and Macroversum, contains relatively rare species living in hydrophilic and freshwater habitats on all continents, and contains two of the six exclusively freshwater tardigrade genera. This family probably represents an example of the evolution and persistence of an asexual lineage that differentiated into several taxa without sexual reproduction. Analyses of nuclear and mitochondrial genes (18S, 28S, ITS2 and cox1), and the increase of five taxa to the phylogenetic analyses of Murrayidae led us to infer that Murrayon is polyphyletic, being composed of two ‘species groups’ that also find morphological supports: the ‘dianeae group’ characterised by peculiar egg processes (rod-shaped and covered with a cuticular layer), animals with large, evident epicuticular pillars and small claws; and the ‘pullari group’ characterised by conical egg processes, animals with very small epicuticular pillars, and proportionally larger and longer claws. This latter group is a sister group to Dactylobiotus. Murrayon hastatus is the only species within the genus that has an uncertain position with eggs of the ‘dianeae group’ and animals of the ‘pullari group’. We propose the erection of Paramurrayon gen. nov. (for the ‘dianeae group’ of species), the emendation of Murrayon, and new taxonomic keys for both genera. Possible scenarios of the evolution of taxa within Murrayidae are hypothesised based on synapomorphic characters. Paramurrayon meieri sp. nov. from Norway is described with an integrative approach. Photographs of type material of Murrayon stellatus, Murrayon nocentiniae, Murrayon ovoglabellus and Macroversum mirum are shown for the first time, together with descriptions of new characters. Murrayon hibernicus is considered as nomen dubium and Murrayon hyperoncus is transferred to Macrobiotus pending further analyses.
Efficacy and safety of polymyxin E sulfate in the treatment of critically ill patients with carbapenem-resistant organism infections

Objective Polymyxins are currently the last line of defense in the treatment of carbapenem-resistant organisms (CRO). As a kind of polymyxin available for clinical use in China, we aim to explore the efficacy and safety of colistin sulfate (Polymyxin E sulfate, PES) in this study. Methods This real-world retrospective study included 119 patients diagnosed with CRO infection and treated with PES for more than 72 h, from May 2020 to July 2022 at West China Hospital. The primary outcome was clinical efficacy at the end of treatment, and secondary outcomes included microbial response, in-hospital mortality and incidence of nephrotoxicity. Results The effective clinical and microbiological responses were 53.8% and 49.1%, respectively. And the in-hospital mortality was 27.7%. Only 9.2% of patients occurred with PES-related nephrotoxicity. Multivariate analysis revealed that duration of PES was an independent predictor of effective therapy, while age-adjusted Charlson comorbidity index (aCCI) and post-treatment PCT(p-PCT) were independent risk factors for poor outcome. Conclusions PES can be a salvage treatment for CRO-induced infections with favorable efficacy and low nephrotoxicity. The treatment duration of PES, aCCI and p-PCT were factors related to the clinical effectiveness of PES.
Rating scales institutionalise a network of logical errors and conceptual problems in research practices: A rigorous analysis showing ways to tackle psychology’s crises

This article explores in-depth the metatheoretical and methodological foundations on which rating scales—by their very conception, design and application—are built and traces their historical origins. It brings together independent lines of critique from different scholars and disciplines to map out the problem landscape, which centres on the failed distinction between psychology’s study phenomena (e.g., experiences, everyday constructs) and the means of their exploration (e.g., terms, data, scientific constructs)—psychologists’ cardinal error. Rigorous analyses reveal a dense network of 12 complexes of problematic concepts, misconceived assumptions and fallacies that support each other, making it difficult to be identified and recognised by those (unwittingly) relying on them (e.g., various forms of reductionism, logical errors of operationalism, constructification, naïve use of language, quantificationism, statisticism, result-based data generation, misconceived nomotheticism). Through the popularity of rating scales for efficient quantitative data generation, uncritically interpreted as psychological measurement, these problems have become institutionalised in a wide range of research practices and perpetuate psychology’s crises (e.g., replication, confidence, validation, generalizability). The article provides an in-depth understanding that is needed to get to the root of these problems, which preclude not just measurement but also the scientific exploration of psychology’s study phenomena and thus its development as a science. From each of the 12 problem complexes; specific theoretical concepts, methodologies and methods are derived as well as key directions of development. The analyses—based on three central axioms for transdisciplinary research on individuals, (1) complexity, (2) complementarity and (3) anthropogenicity—highlight that psychologists must (further) develop an explicit metatheory and unambiguous terminology as well as concepts and theories that conceive individuals as living beings, open self-organising systems with complementary phenomena and dynamic interrelations across their multi-layered systemic contexts—thus, theories not simply of elemental properties and structures but of processes, relations, dynamicity, subjectivity, emergence, catalysis and transformation. Philosophical and theoretical foundations of approaches suited for exploring these phenomena must be developed together with methods of data generation and methods of data analysis that are appropriately adapted to the peculiarities of psychologists’ study phenomena (e.g., intra-individual variation, momentariness, contextuality). Psychology can profit greatly from its unique position at the intersection of many other disciplines and can learn from their advancements to develop research practices that are suited to tackle its crises holistically.
On-farm performance of genetically-improved rohu (Labeo rohita) in Bangladesh

Introduction Rohu (Labeo rohita Hamilton) is a globally significant aquaculture species for which genetically improved strains are increasingly available. In 2020, a multiplier population, comprised of highly–ranked generation-three (G3) families from the WorldFish Rohu Genetic Improvement Program, was released to hatcheries in Bangladesh for development into broodstock. Methods To estimate realised genetic gain for harvest weight in the G3-multiplier population, one pond from each of 19 Bangladeshi semi-commercial farms (ten in Jashore and nine in Natore–Rajshahi districts) were stocked with equal numbers of tagged fish from each of three strains. Strains included in the study were the ‘G3-multiplier’ released to hatcheries, a ‘control’ (putatively genetically equivalent to the unimproved base population which was spawned from fish of river origin) and fish sourced from a ‘commercial’ hatchery. Once stocked, fish were managed according to each farmer’s normal practices. Results The G3-multiplier was found to be the most rapidly growing strain. Back-transformed means for harvest weight across farms for the commercial, control and G3-multiplier strains were 594 g, 659 g and 914 g, respectively, in Jashore, and 545 g, 626 g and 845 g in Natore–Rajshahi. Discussion These results equate to realised genetic gains of 38.6% (Jashore) and 34.9% (Natore–Rajshahi) for the G3-multiplier over the control strain and provide evidence that a family-based (i.e. pedigree-based) approach to genetic improvement is able to generate substantial levels of genetic gain in rohu. Furthermore, the clear growth advantages exhibited by the G3-multiplier strain over the control and commercial strains in this study, should encourage more Bangladeshi hatcheries, nurseries and farmers to adopt genetically improved rohu.