Miracle-dz/UniFAR-FTUs huggingface dataset:
Linearly-Involved Moreau-Enhanced-Over-Subspace Model: Debiased Sparse Modeling and Stable Outlier-Robust Regression

We present an efficient mathematical framework to derive promising methods that enjoy “enhanced” desirable properties. The popular minimax concave penalty for sparse modeling subtracts, from the $\ell _{1}$ norm, its Moreau envelope, inducing nearly unbiased estimates and thus yielding considerable performance enhancements. To extend it to underdetermined linear systems, we propose the projective minimax concave penalty, which leads to “enhanced” sparseness over the input subspace. We also present a promising regression method which has an “enhanced” robustness and substantial stability by distinguishing outlier and noise explicitly. The proposed framework, named the linearly-involved Moreau-enhanced-over-subspace (LiMES) model, encompasses those two specific examples as well as two others: stable principal component pursuit and robust classification. The LiMES function involved in the model is an “additively nonseparable” weakly convex function, while the ‘inner’ objective function to define the Moreau envelope is “separable”. This mixed nature of separability and nonseparability allows an application of the LiMES model to the underdetermined case with an efficient algorithmic implementation. Two linear/affine operators play key roles in the model: one corresponds to the projection mentioned above and the other takes care of robust regression/classification. A necessary and sufficient condition for convexity of the smooth part of the objective function is studied. Numerical examples show the efficacy of LiMES in applications to sparse modeling and robust regression.
FROM A NON-TARGETED METABOLOMICS APPROACH TO A TARGETED BIOMARKERS STRATEGY TO HIGHLIGHT TESTOSTERONE ABUSE IN EQUINE. ILLUSTRATION OF A METHODOLOGICAL TRANSFER BETWEEN PLATFORMS AND LABORATORIES.

In order to overcome the challenge associated with the screening of Anabolic-Androgenic Steroids (AAS) abuses in animal competitions, a non-targeted LC-HRMS based metabolomics approach was implemented on equine urine samples to highlight potential biomarkers associated with the administration of such compounds, using testosterone esters as model steroids. A statistical model relying on 4 potential biomarkers intensity could be defined to predict the status of the samples. With a routine application perspective, the monitoring of the highlighted potential biomarkers was first transferred into high-throughput LC-SRM. The model's performances and robustness of the approach were preserved and providing a first demonstration of metabolomics-based biomarkers integration within a targeted workflow using common benchtop MS instrumentation. In addition, with a view to the widespread implementation of such biomarker-based tools, we have transferred the method to a second laboratory with similar instrumentation. This proof of concept allows the development and application of biomarker-based strategies to meet current doping control needs.
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing

Although mission-critical applications require the use of deep neural networks (DNNs), their continuous execution at mobile devices results in a significant increase in energy consumption. While edge offloading can decrease energy consumption, erratic patterns in channel quality, network and edge server load can lead to severe disruption of the system’s key operations. An alternative approach, called split computing, generates compressed representations within the model (called "bottlenecks"), to reduce bandwidth usage and energy consumption. Prior work has proposed approaches that introduce additional layers, to the detriment of energy consumption and latency. For this reason, we propose a new framework called BottleFit, which, in addition to targeted DNN architecture modifications, includes a novel training strategy to achieve high accuracy even with strong compression rates. We apply BottleFit on cutting-edge DNN models in image classification, and show that BottleFit achieves 77.1% data compression with up to 0.6% accuracy loss on ImageNet dataset, while state of the art such as SPINN loses up to 6% in accuracy. We experimentally measure the power consumption and latency of an image classification application running on an NVIDIA Jetson Nano board (GPU-based) and a Raspberry PI board (GPU-less). We show that BottleFit decreases power consumption and latency respectively by up to 49% and 89% with respect to (w.r.t.) local computing and by 37% and 55% w.r. t. edge offloading. We also compare BottleFit with state-of-the-art autoencoders-based approaches, and show that (i) BottleFit reduces power consumption and execution time respectively by up to 54% and 44% on the Jetson and 40% and 62% on Raspberry PI; (ii) the size of the head model executed on the mobile device is 83 times smaller. We publish the code repository for reproducibility of the results in this study.
Does inspiratory muscle training provide additional benefits during pulmonary rehabilitation in people with interstitial lung disease? A randomized control trial

ABSTRACT Background Interstitial lung disease (ILD) encompasses a diverse group of chronic lung conditions which is often characterized by inspiratory muscle weakness (IMW). Despite the potential importance of inspiratory muscle dysfunction in ILD, the effect of inspiratory muscle training (IMT) added to pulmonary rehabilitation (PR) in ILD largely remains unknown. Objective The primary objective of the present study was to evaluate the benefits of IMT added to PR on inspiratory muscle strength and secondary objectives were to assess its effects on functional capacity, health-related quality of life (HRQoL), pulmonary function test (PFT) and dyspnea in ILD along with IMW. Methods Fifty-one participants were randomly allocated into two groups; PR + IMT (n = 26) or PR alone (n = 25). The primary outcome [maximal inspiratory pressure (PImax)] and secondary outcomes [6-min walk distance (6MWD), St. George’s Respiratory Questionnaire (SGRQ), PFT and modified Medical Research Council dyspnea scale (mMRC)] were evaluated before and after the 8-weeks intervention. Independent t-test or Mann Whitney-U test was applied for between-group comparisons while for within-group comparison Wilcoxon’s Sign Rank test or paired t test was performed. Results At the end of 8 weeks exercise intervention inspiratory muscle strength (PImax + 11.10 cm H2O, p< .001), functional capacity (6MWD, + 47.90 m, p= .001), HRQoL (SGRQ-total – 4 points, p= .038) and dyspnea (mMRC dyspnea scale, −1.27, p< .001) improved significantly in PR+IMT group alone. Conclusion Inclusion of IMT to PR may have superior benefits as compared to PR alone in ILD accompanied with IMW.
Magneto-optical study of metamagnetic transitions in the antiferromagnetic phase of $\alpha$-RuCl$_3$

Julian Wagner, Anuja Sahasrabudhe, Rolf Versteeg, Lena Wysocki, Zhe Wang, V. Tsurkan, 5 A. Loidl, D. I. Khomskii, Hamoon Hedayat, ∗ and Paul H. M. van Loosdrecht † Universität zu Köln, II. Physikalisches Institut, Zülpicher Straße 77, Köln D-50937, Germany Laboratoire de Spectroscopie Ultrarapide and Lausanne Centre for Ultrafast Science (LACUS), ISIC-FSB, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland Department of Physics, TU Dortmund University, Otto-Hahn-Str. 4, 44227 Dortmund, Germany Experimental Physics V, Center for Electronic Correlations and Magnetism, University of Augsburg, 86135 Augsburg, Germany Institute of Applied Physics, MD 2028, Chisinau, Republic of Moldova
A New Amharic Speech Emotion Dataset and Classification Benchmark

In this paper we present the Amharic Speech Emotion Dataset (ASED), which covers four dialects (Gojjam, Wollo, Shewa and Gonder) and five different emotions (neutral, fearful, happy, sad and angry). We believe it is the first Speech Emotion Recognition (SER) dataset for the Amharic language. 65 volunteer participants, all native speakers of Amharic, recorded 2,474 sound samples, two to four seconds in length. Eight judges (two for each dialect) assigned emotions to the samples with high agreement level (Fleiss kappa = 0.8). The resulting dataset is freely available for download. Next, we developed a four-layer variant of the well-known VGG model which we call VGGb. Three experiments were then carried out using VGGb for SER, using ASED. First, we investigated which features work best for Amharic, FilterBank, Mel Spectrogram, or Mel-frequency Cepstral Coefficient (MFCC). This was done by training three VGGb SER models on ASED, using FilterBank, Mel Spectrogram and MFCC features respectively. Four forms of training were tried, standard cross-validation, and three variants based on sentences, dialects and speaker groups. Thus, a sentence used for training would not be used for testing, and the same for a dialect and speaker group. MFCC features were superior under all four training schemes. MFCC was therefore adopted for Experiment 2, where VGGb and three well-known existing models were compared on ASED: RESNet50, AlexNet and LSTM. VGGb was found to have very good accuracy (90.73%) as well as the fastest training time. In Experiment 3, the performance of VGGb was compared when trained on two existing SER datasets – RAVDESS (English) and EMO-DB (German) – as well as on ASED (Amharic). Results are comparable across these languages, with ASED being the highest. This suggests that VGGb can be successfully applied to other languages. We hope that ASED will encourage researchers to explore the Amharic language and to experiment with other models for Amharic SER.
Role of solvent in enhancement of thermal conductivity of epoxy/graphene nanocomposites

In this work, we demonstrate that dimethylformamide (DMF) leads to better dispersion of graphene nanoplatelets relative to acetone, leading to higher thermal conductivity epoxygraphene nanocomposites. Uniform dispersion of graphene nanoparticles into epoxy is critical for achieving high thermal conductivity epoxy-graphene nanocomposites. Uniform dispersion of graphene nanoplatelets can improve thermal contact with polymer leading to higher thermal conductivity of the composite. Organic solvents typically lead to efficient dispersion of graphene into the epoxy matrix. In this study, we compare the effect of two organic solvents, dimethylformamide (DMF) and acetone, in terms of their efficiency in dispersing graphene into the epoxy matrix and their effect on enhancing thermal conductivity of the composite. While the effect of solvents on mechanical properties of polymer-graphene nanocomposites has been studied, their effect on thermal conductivity is not well understood. In this study we find that polymergraphene composites made with DMF show 44% higher thermal conductivity than those made using acetone. Laser scanning confocal microscopy (LSCM) imaging reveals improved dispersion of graphene-nanoplatelets in samples prepared using DMF compared to acetone. These results provide new avenues to achieve higher thermal conductivity graphene-epoxy composites, of key importance for a wide range of thermal management technologies.
Compartmental epidemic model to assess undocumented infections: applications to SARS-CoV-2 epidemics in Brazil (preprint)/ en

Nowcasting and forecasting of epidemic spreading, fundamental support for policy makers' decisions, rely on incidence series of reported cases to derive the fundamental epidemiological parameters. Two relevant drawbacks for predictions are the unknown fraction of undocumented cases and levels of nonpharmacological interventions that span highly heterogeneously across different places. We describe a simple approach using a compartmental model including asymptomatic and pre-asymptomatic contagions that allows to estimate both the level of undocumented infections and the value of $R_t$ from reported case series in terms of epidemiological parameters. The method was applied to epidemic series for of COVID-19 across different municipalities in Brazil allowing to quantify the heterogeneity level of under-reporting across different places. The reproductive number derived within the current framework is little sensitive to both diagnosis and infection rates during the asymptomatic states, while being very sensitive to variations in case count series. The methods described here are general and we expect that they can be extended to other epidemiological approaches and surveillance data.
Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observability

In this paper, we present a novel Bayesian online prediction algorithm for the problem setting of ad hoc teamwork under partial observability (ATPO), which enables on-the-fly collaboration with unknown teammates performing an unknown task without needing a pre-coordination protocol. Unlike previous works that assume a fully observable state of the environment, ATPO accommodates partial observability, using the agent's observations to identify which task is being performed by the teammates. Our approach assumes neither that the teammate's actions are visible nor an environment reward signal. We evaluate ATPO in three domains -- two modified versions of the Pursuit domain with partial observability and the overcooked domain. Our results show that ATPO is effective and robust in identifying the teammate's task from a large library of possible tasks, efficient at solving it in near-optimal time, and scalable in adapting to increasingly larger problem sizes.
Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model

Statistical shape modeling is an essential tool for the quantitative analysis of anatomical populations. Point distribution models (PDMs) represent the anatomical surface via a dense set of correspondences, an intuitive and easy-to-use shape representation for subsequent applications. These correspondences are exhibited in two coordinate spaces: the local coordinates describing the geometrical features of each individual anatomical surface and the world coordinates representing the population-level statistical shape information after removing global alignment differences across samples in the given cohort. We propose a deep-learning-based framework that simultaneously learns these two coordinate spaces directly from the volumetric images. The proposed joint model serves a dual purpose; the world correspondences can directly be used for shape analysis applications, circumventing the heavy pre-processing and segmentation involved in traditional PDM models. Additionally, the local correspondences can be used for anatomy segmentation. We demonstrate the efficacy of this joint model for both shape modeling applications on two datasets and its utility in inferring the anatomical surface.
Machine Learning-Based Disease Diagnosis: A Bibliometric Analysis

Machine Learning (ML) has garnered considerable attention from researchers and practitioners as a new and adaptable tool for disease diagnosis. With the advancement of ML and the proliferation of papers and research in this field, a complete examination of Machine Learning-Based Disease Diagnosis (MLBDD) is required. From a bibliometrics standpoint, this article comprehensively studies MLBDD papers from 2012 to 2021. Consequently, with particular keywords, 1710 papers with associate information have been extracted from the Scopus and Web of Science (WOS) database and integrated into the excel datasheet for further analysis. First, we examine the publication structures based on yearly publications and the most productive countries/regions, institutions, and authors. Second, the co-citation networks of countries/regions, institutions, authors, and articles are visualized using R-studio software. They are further examined in terms of citation structure and the most influential ones. This article gives an overview of MLBDD for researchers interested in the subject and conducts a thorough and complete study of MLBDD for those interested in conducting more research in this field.
Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer

In this paper, we follow Eftekhari's work to give a non-local convergence analysis of deep linear networks. Specifically, we consider optimizing deep linear networks which have a layer with one neuron under quadratic loss. We describe the convergent point of trajectories with arbitrary starting point under gradient flow, including the paths which converge to one of the saddle points or the original point. We also show specific convergence rates of trajectories that converge to the global minimizer by stages. To achieve these results, this paper mainly extends the machinery in Eftekhari's work to provably identify the rank-stable set and the global minimizer convergent set. We also give specific examples to show the necessity of our definitions. Crucially, as far as we know, our results appear to be the first to give a non-local global analysis of linear neural networks from arbitrary initialized points, rather than the lazy training regime which has dominated the literature of neural networks, and restricted benign initialization in Eftekhari's work. We also note that extending our results to general linear networks without one hidden neuron assumption remains a challenging open problem.
Steady asymptotic equilibria in conformal relativistic fluids

When one considers a shock wave in the frame where the shock is at rest, on either side one has a steady flow which converges to equilibrium away from the shock. However, hydrodynamics is unable to describe this flow if the asymptotic velocity is higher than the characteristic speed of the theory. We obtain an exact solution for the decay rate to equilibrium for a conformal fluid in kinetic theory under the relaxation time approximation, and compare it to two hydrodynamic schemes, one accounting for the second moments of the distribution function and thus equivalent, in the small deviations from equilibrium limit, to an Israel-Stewart framework, and another accounting for both second and third moments. While still having a finite characteristic speed, the second model is a significant improvement on the first.
Exact Lagrangian tori in symplectic Milnor fibers constructed with fillings

We use exact Lagrangian fillings and Weinstein handlebody diagrams to construct infinitely many distinct exact Lagrangian tori in $4$-dimensional Milnor fibers of isolated hypersurface singularities with positive modality. We also provide a generalization of a criterion for when the symplectic homology of a Weinstein $4$-manifold is non-vanishing given an explicit Weinstein handlebody diagrams.
Discrete‐time risk sensitive portfolio optimization with proportional transaction costs

In this paper we consider a discrete‐time risk sensitive portfolio optimization over a long time horizon with proportional transaction costs. We show that within the log‐return i.i. d. framework the solution to a suitable Bellman equation exists under minimal assumptions and can be used to characterize the optimal strategies for both risk‐averse and risk‐seeking cases. Moreover, using numerical examples, we show how a Bellman equation analysis can be used to construct or refine optimal trading strategies in the presence of transaction costs.
Style, Content, and the Success of Ideas

Why do some things succeed in the marketplace of ideas? While some argue that content drives success, others suggest that style, or the way ideas are presented, also plays an important role. To provide a stringent test of style's importance, we examine it in a context where content should be paramount: academic research. While scientists often see writing as a disinterested way to communicate unobstructed truth, a multi-method investigation indicates that writing style shapes impact. Separating style from content can be difficult as papers that tend to use certain language may also write about certain topics. Consequently, we focus on a unique class of words linked to style (i.e., function words such as"and,""the,"and"on") that are completely devoid of content. Natural language processing of almost 30,000 articles from a range of disciplines finds that function words explain 13-27% of language's impact on citations. Ancillary analyses explore specific categories of function words to suggest how style matters, highlighting the role of writing simplicity, personal voice, and temporal perspective. Experiments further underscore the causal impact of style. The results suggest how to boost communication's impact and highlight the value of natural language processing for understanding the success of ideas.
Building Personal Development Planning (PDP) into the curriculum

Abstract Personal Development Planning (PDP) is a process whereby an individual reflects on their own learning or achievement and the plans accordingly for their personal / educational / career development. It has been introduced in a variety of forms across the sector. At the University of Central Lancashire (UCLAN) the approach can be considered at four levels: Institution; Faculty; Department; and Course. Efforts have been made for Departments to take ‘ownership’ of their own approaches. Crucial to this has been engaging staff in designing-in PDP down to course level. The result is a model in which PDP is embedded within and relates directly to the uniqueness of individual courses.
Bandlimited Signal Reconstruction From Leaky Integrate-and-Fire Encoding Using POCS

Leaky integrate-and-fire (LIF) encoding is a model of neuron transfer function in biology that has recently attracted the attention of the signal processing and neuromorphic computing communities as a technique of event-based sampling for data acquisition. While LIF enables the implementation of analog-circuit signal samplers of lower complexity and higher accuracy simultaneously, the core difficulty of this technique is the retrieval of an input from its LIF-encoded output. In this article, we study this problem in the context of bandlimited inputs, by extracting the most abstract features of an LIF encoder as a generalized nonuniform sampler. In this view, the LIF output is seen as the transformation of the input by a known linear operator. We show that the signal reconstruction method of projection onto convex sets (POCS) converges to a weighted pseudo-inverse of this operator. This allows perfect recovery under uniqueness of reconstruction, minimum-norm reconstruction under incomplete sampling, as well as a noise shaping of time quantization that outperforms standard pseudo-inversion. On the practical side, a single iteration of the POCS method can be used to improve any estimate whose LIF samples are not consistent with those of the input, and a rigorous discrete-time implementation of this iteration is proposed that does not require a Nyquist-rate representation of the signals.
An experiment of the complexity of sliding block puzzles by 2D heat flow in paramodulation

In this paper, we present a curious experiment with the hot list strategy in solving sliding block puzzles by paramodulation. The hot list strategy is one of the look-ahead strategies using paramodulation in automated reasoning. We define two heat flows in the reasoning process - vertical with the hot list of permutations along the Y-axis and horizontal along the X-axis. In the experiment, we have generated 500 * 8 puzzles under the test of the solvability checking by counting inversions. We have obtained curious 2D and 3D plots of the complexity by defining heat flow with hot lists. We can distinguish a few groups in 500 boards (puzzles) based on the concept of heat-resisting.