Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of galaxy scaling relations in clusters at 0.5 < z < 1.5

Aims. We present new gas kinematic observations with the OSIRIS instrument at the GTC for galaxies in the Cl1604 cluster system at z ∼ 0.9. These observations together with a collection of other cluster samples at different epochs analyzed by our group are used to study the evolution of the Tully-Fisher, velocity-size, and specific angular momentum-stellar mass relations in dense environments over cosmic time. Methods. We used 2D and 3D spectroscopy to analyze the kinematics of our cluster galaxies and extract their maximum rotation velocities (Vmax), which were used as the common parameter in all scaling relations under scrutiny. We determined the structural parameters of our objects by fitting surface brightness profiles to the images of our objects, while stellar-mass values were computed by fitting the spectral energy distribution by making use of extensive archival optical to near-IR photometry. Our methods were consistently applied to all our cluster samples. This makes them ideal for an evolutionary comparison. Results. Up to redshift one, our cluster samples show evolutionary trends compatible with previous observational results in the field and in accordance with semianalytical models and hydrodynamical simulations concerning the Tully-Fisher and velocity-size relations. However, we find a drop of a factor ∼3 in disk sizes and an average B-band luminosity enhancement ⟨ΔMB⟩∼2 mag by z ∼ 1.5. We discuss the role that different cluster-specific interactions may play in producing this observational result. In addition, we find that our intermediate-to-high redshift cluster galaxies follow parallel sequences with respect to the local specific angular momentum to stellar mass relation, although they display lower specific angular momentum values than field samples at similar redshifts. This may be explained by the stronger interacting nature of dense environments in comparison with the field.
Quantitative spectral gaps and uniform lower bounds in the small noise limit for Markov semigroups generated by hypoelliptic stochastic differential equations

We study the convergence rate to equilibrium for a family of Markov semigroups $\{\mathcal{P}_t^{\epsilon}\}_{\epsilon > 0}$ generated by a class of hypoelliptic stochastic differential equations on $\mathbb{R}^d$, including Galerkin truncations of the incompressible Navier-Stokes equations, Lorenz-96, and the shell model SABRA. In the regime of vanishing, balanced noise and dissipation, we obtain a sharp (in terms of scaling) quantitative estimate on the exponential convergence in terms of the small parameter $\epsilon$. By scaling, this regime implies corresponding optimal results both for fixed dissipation and large noise limits or fixed noise and vanishing dissipation limits. As part of the proof, and of independent interest, we obtain uniform-in-$\epsilon$ upper and lower bounds on the density of the stationary measure. Upper bounds are obtained by a hypoelliptic Moser iteration, the lower bounds by a de Giorgi-type iteration (both uniform in $\epsilon$). The spectral gap estimate on the semigroup is obtained by a weak Poincare inequality argument combined with quantitative hypoelliptic regularization of the time-dependent problem.
On the Use of Audio Fingerprinting Features for Speech Enhancement with Generative Adversarial Network

The advent of learning-based methods in speech enhancement has revived the need for robust and reliable training features that can compactly represent speech signals while preserving their vital information. Time-frequency domain features, such as the Short-Term Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCC), are preferred in many approaches. While the MFCC provide for a compact representation, they ignore the dynamics and distribution of energy in each mel-scale subband. In this work, a speech enhancement system based on Generative Adversarial Network (GAN) is implemented and tested with a combination of Audio FingerPrinting (AFP) features obtained from the MFCC and the Normalized Spectral Subband Centroids (NSSC). The NSSC capture the locations of speech formants and complement the MFCC in a crucial way. In experiments with diverse speakers and noise types, GAN-based speech enhancement with the proposed AFP feature combination achieves the best objective performance while reducing memory requirements and training time.
The Unsupervised Method of Vessel Movement Trajectory Prediction

In real-world application scenarios, it is crucial for marine navigators and security analysts to predict vessel movement trajectories at sea based on the Automated Identification System (AIS) data in a given time span. This article presents an unsupervised method of ship movement trajectory prediction which represents the data in a three-dimensional space which consists of time difference between points, the scaled error distance between the tested and its predicted forward and backward locations, and the space-time angle. The representation feature space reduces the search scope for the next point to a collection of candidates which fit the local path prediction well, and therefore improve the accuracy. Unlike most statistical learning or deep learning methods, the proposed clustering-based trajectory reconstruction method does not require computationally expensive model training. This makes real-time reliable and accurate prediction feasible without using a training set. Our results show that the most prediction trajectories accurately consist of the true vessel paths.
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic

Low-resolution neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity. Nonetheless, these products are accumulated using high-resolution (typically 32-bit) additions, an operation that dominates the arithmetic complexity of inference when using extreme quantization (e.g., binary weights). To further optimize inference, we propose a method that adapts neural networks to use low-resolution (8-bit) additions in the accumulators, achieving classification accuracy comparable to their 32-bit counterparts. We achieve resilience to low-resolution accumulation by inserting a cyclic activation layer, as well as an overflow penalty regularizer. We demonstrate the efficacy of our approach on both software and hardware platforms.
Learning Variational Data Assimilation Models and Solvers

Data assimilation is a key component of operational systems and scientific studies for the understanding, modeling, forecasting and reconstruction of earth systems informed by observation data. Here, we investigate how physics‐informed deep learning may provide new means to revisit data assimilation problems. We develop a so‐called end‐to‐end learning approach, which explicitly relies on a variational data assimilation formulation. Using automatic differentiation embedded in deep learning framework, the key novelty of the proposed physics‐informed approach is to allow the joint training of the representation of the dynamical process of interest as well as of the solver of the data assimilation problem. We may perform this joint training using both supervised and unsupervised strategies. Our numerical experiments on Lorenz‐63 and Lorenz‐96 systems report significant gain w.r. t. a classic gradient‐based minimization of the variational cost both in terms of reconstruction performance and optimization complexity. Intriguingly, we also show that the variational models issued from the true Lorenz‐63 and Lorenz‐96 ODE representations may not lead to the best reconstruction performance. We believe these results may open new research avenues for the specification of assimilation models for earth systems, both to speed‐up the inversion problem with trainable solvers but possibly more importantly in the way data assimilation systems are designed, for instance regarding the representation of geophysical dynamics.
Construction of Escherichia coli strains deleted for the attachment site by transposon-linked generalized transduction

A strain of Escherichia coli K-12 has been constructed which can be used in combination with P1 or T4GT7-mediated transduction to generate strains deleted for the λ attachment site. This allows the quick and simple construction of well defined deletion strains, with a minimum of transfer of other genes.
Proactive Tasks Management based on a Deep Learning Model

Pervasive computing applications deal with intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close distance with end users.One example infrastructure that can host intelligent pervasive services is the Edge Computing (EC) infrastructure. EC nodes can execute a number of tasks for data collected by devices present in the Internet of Things (IoT) infrastructure. In this paper, we propose an intelligent, proactive tasks management model based on the demand. Demand depicts the number of users or applications interested in using the available tasks in EC nodes, thus, characterizing their popularity. We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network to learn the distribution of demand indicators for each task and estimate the future interest. This information is combined with historical observations and support a decision making scheme to conclude which tasks will be offloaded due to limited interest on them. We have to notice that in our decision making, we also take into consideration the load that every task may add to the processing node where it will be allocated. The description of our model is accompanied by a large set of experimental simulations for evaluating the proposed mechanism. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.
Optimal Communication Complexity of Byzantine Consensus under Honest Majority

Communication complexity is one of the most important efficiency metrics for distributed algorithms, but considerable gaps in the communication complexity still exist for Byzantine consensus, one of the most fundamental problems in distributed computing. This paper provides three results that help close some of these gaps. (1) We present a Byzantine consistent broadcast (BCB) protocol with linear communication complexity when $f \leq (1/2 - \varepsilon)n$ where $\varepsilon$ is any positive constant. The new protocol relies on an expander graph and a threshold signature scheme. (2) The linear BCB protocol is used to obtain quadratic Byzantine broadcast (BB) and Byzantine agreement (BA) under the same $f \leq (1/2 - \varepsilon)n$ resilience (which is close to optimal for BA). (3) We also show a quadratic communication lower bound of BCB with $f \ge (1/2 + \varepsilon)n$.
Scalable Derivative-Free Optimization for Nonlinear Least-Squares Problems

Derivative-free - or zeroth-order - optimization (DFO) has gained recent attention for its ability to solve problems in a variety of application areas, including machine learning, particularly involving objectives which are stochastic and/or expensive to compute. In this work, we develop a novel model-based DFO method for solving nonlinear least-squares problems. We improve on state-of-the-art DFO by performing dimensionality reduction in the observational space using sketching methods, avoiding the construction of a full local model. Our approach has a per-iteration computational cost which is linear in problem dimension in a big data regime, and numerical evidence demonstrates that, compared to existing software, it has dramatically improved runtime performance on overdetermined least-squares problems.
Black soybean peptide mixture purified from Rhynchosia volubilis exerts antioxidant activity against H2O2-induced cytotoxicity and improves thrombosis

Oxidative stress has been reported to be associated with cellular injury. Here, we investigated the antioxidant ability of a novel peptide mixture purified from black soybean (Rhynchosia volubilis) that exhibited significant antioxidant ability (IC50 = 119.8 ± 1.2 µg/ml) under in vitro ABTS assay conditions. To confirm this result, we next examined the antioxidant ability of the peptide mixture using cell culture systems. Black soybean peptide (BSP) significantly reduced the H2O2-induced cell death in C2C12 myocyte. Moreover, extracellular signal-regulated kinase (ERK) signaling was activated by BSP treatment. Under the same conditions, inhibition of the ERK signaling pathway with PD98059 significantly reduced the antioxidant ability of BSP in C2C12 cells. Finally, we also assessed the antithrombotic ability of BSP using a rat model; we found that the BSP also exerted antithrombotic effects in rat blood. Collectively, these results indicate that BSP purified from black soybean exerts an inhibitory effect on both oxidative stress and thrombosis. Key words: Black soybean peptide (BSP), anti-oxidant, thrombosis, extra cellular signal regulated protein kinase, oxidative stress.
Sublinear-Time Algorithms for Computing & Embedding Gap Edit Distance

In this paper, we design new sublinear-time algorithms for solving the gap edit distance problem and for embedding edit distance to Hamming distance. For the gap edit distance problem, we give a greedy algorithm that distinguishes in time <tex>$\tilde{\mathcal{O}}(\frac{n}{k}+k^{2})$</tex> between length-n input strings with edit distance at most <tex>$k$</tex> and those with edit distance more than <tex>$4k^{2}$</tex>. This is an improvement and a simplification upon the main result of [Goldenberg, Krauthgamer, Saha, FOCS 2019], where the <tex>$k$</tex> vs <tex>$\Theta(k^{2})$</tex> gap edit distance problem is solved in <tex>$\tilde{\mathcal{O}}(\frac{n}{k}+k^{3})$</tex> time. We further generalize our result to solve the <tex>$k$</tex> vs <tex>$\alpha k$</tex> gap edit distance problem in time <tex>$\tilde{\mathcal{O}}(\frac{n}{\alpha}+k^{2}+\frac{k}{\alpha}\sqrt{nk})$</tex>, strictly improving upon the previously known bound <tex>$\tilde{\mathcal{O}}(\frac{n}{\alpha}+k^{3})$</tex>. Finally, we show that if the input strings do not have long highly periodic substrings, then the gap edit distance problem can be solved in sublinear time within any factor <tex>$\alpha > 1$</tex>. Specifically, if the strings contain no substring of length <tex>$\ell$</tex> with the shortest period of length at most <tex>$2k$</tex>, then the <tex>$k$</tex> vs <tex>$(1+\varepsilon)k$</tex> gap edit distance problem can be solved in time <tex>$\tilde{\mathcal{O}}(\frac{n}{\varepsilon^{2}k}+k^{2}\ell)$</tex>. We further give the first sublinear-time algorithm for the probabilistic embedding of edit distance to Hamming distance. Our <tex>$\tilde{\mathcal{O}}(\frac{n}{p})$</tex>-time procedure yields an embedding with distortion <tex>$k^{2}p$</tex>, where <tex>$k$</tex> is the edit distance of the original strings. Specifically, the Hamming distance of the resultant strings is between <tex>$\frac{k-p+1}{p}$</tex> and <tex>$k^{2}$</tex> with good probability. This generalizes the linear-time embedding of [Chakraborty, Goldenberg, Koucký, STOC 2016], where the resultant Hamming distance is between <tex>$k$</tex> and <tex>$k^{2}$</tex>. Our algorithm is based on a random walk over samples, which we believe will find other applications in sublinear-time algorithms.
NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification

This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly.
DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification

This paper presents a Depthwise Disout Convolutional Neural Network (DD-CNN) for the detection and classification of urban acoustic scenes. Specifically, we use log-mel as feature representations of acoustic signals for the inputs of our network. In the proposed DD-CNN, depthwise separable convolution is used to reduce the network complexity. Besides, SpecAugment and Disout are used for further performance boosting. Experimental results demonstrate that our DD-CNN can learn discriminative acoustic characteristics from audio fragments and effectively reduce the network complexity. Our DD-CNN was used for the low-complexity acoustic scene classification task of the DCASE2020 Challenge, which achieves 92.04% accuracy on the validation set.
The Milky Way's bar and bulge revealed by APOGEE DR16 and Gaia DR2

We investigate the inner regions of the Milky Way with a sample of unprecedented size and coverage thanks to APOGEE DR16 and {\it Gaia} DR2 data. Our inner Galactic sample has more than 24,000 stars within $|X_{\rm Gal}| <5$ kpc, $|Y_{\rm Gal}| <3.5$ kpc, $|Z_{\rm Gal}| <1$ kpc, and we also make the analysis for a foreground-cleaned sub-sample of 7,000 stars more representative of the bulge-bar populations. The inner Galaxy shows a clear chemical discontinuity in key abundance ratios [$\alpha$/Fe], [C/N], and [Mn/O], probing different enrichment timescales, which suggests a star formation gap (quenching) between the high- and low-$\alpha$ populations. For the first time we are able to fully characterize the different populations co-existing in the innermost regions of the Galaxy via a joint analysis of the distributions of rotational velocities, metallicities, orbital parameters and chemical abundances. The chemo-kinematic analysis reveal presence of the bar; of an inner thin disk; of a thick disk, and of a broad metallicity population, with a large velocity dispersion, indicative of a pressure supported component. We find and characterize chemically and kinematically a group of counter-rotating stars, which could be the result of gas-rich merger event or just the result of clumpy star formation during the earliest phases of the early disk, which migrated into the bulge. Finally, based on the 6D information we assign stars a probability value of being on a bar orbit and find that most of the stars with large bar orbit probabilities come from the innermost 3 kpc. Even stars with high probability of belonging to the bar show the chemical bimodality in the [$\alpha$/Fe] vs. [Fe/H] diagram. This suggests bar trapping to be an efficient mechanism, explaining why stars on bar orbits do not show a significant distinct chemical abundance ratio signature.
Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this article, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the explainable DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.
Nutritive value of Adenodolichos rhomboideus leaves compared with Leucaena leucocephala and Stylosanthes guianensis forages in indigenous goats in Lubumbashi (DR Congo)

Valeur nutritive des feuilles de Adenodolichos rhomboideus en comparaison de fourrages de Leucaena leucocephala et de Stylosanthes guianensis chez la chevre locale a Lubumbashi (R.D. Congo). Les fourrages de trois especes vegetales (Adenodolichos rhomboideus, Leucaena leucocephala, Stylosanthes guianensis) ont ete evalues des points de vue de leur composition chimique, consommation volontaire et digestibilite apparente de la matiere seche (MS), de la matiere organique (MO), de la proteine brute (PB), des fibres insolubles dans le detergent neutre (NDF) et des fibres insolubles dans le detergent acide (ADF) utilisant a cette fin six chevres mâles (17,1 kg ± 0,7) dans un dispositif en double carre latin 3 × 3. Le fourrage de S. guianensis a presente une faible teneur en PB (p < 0,001) par rapport aux feuilles de A. rhomboideus et au fourrage de L. leucocephala. Les teneurs en fibres (ADF et NDF) ont ete plus faibles (p < 0,001) dans le fourrage de L. leucocephala que dans les feuilles de A. rhomboideus et le fourrage de S. guianensis. Le fourrage de L. leucocephala a montre les teneurs les plus elevees en PB, matieres minerales et extraits etheres. La digestibilite apparente et la consommation volontaire de PB ont ete les plus elevees pour L. leucocephala et les plus faibles pour les feuilles de A. rhomboideus (p < 0,05). La teneur en proteines digestibles a ete similaire pour les trois fourrages. Leucaena leucocephala semble etre le mieux adapte pour la production carpine. Les faibles digestibilites et consommations de feuilles de A. rhomboideus peuvent etre dues aux effets negatifs de certains facteurs anti-nutritionnels comme les tanins.
The strong CP problem, general covariance, and horizons

We discuss the strong CP problem in the context of quantum field theory in the presence of horizons. We argue that general covariance places constraints on the topological structure of the theory. In particular, it means that coherence between different topological sectors must have no observable consequence, because the degrees of freedom beyond a causal horizon must be traced over for general covariance to apply. Since the only way for this to occur in QCD is for θ = 0, this might lead to a solution of the strong CP problem without extra observable dynamics.
Representation Learning with Video Deep InfoMax

Self-supervised learning has made unsupervised pretraining relevant again for difficult computer vision tasks. The most effective self-supervised methods involve prediction tasks based on features extracted from diverse views of the data. DeepInfoMax (DIM) is a self-supervised method which leverages the internal structure of deep networks to construct such views, forming prediction tasks between local features which depend on small patches in an image and global features which depend on the whole image. In this paper, we extend DIM to the video domain by leveraging similar structure in spatio-temporal networks, producing a method we call Video Deep InfoMax(VDIM). We find that drawing views from both natural-rate sequences and temporally-downsampled sequences yields results on Kinetics-pretrained action recognition tasks which match or outperform prior state-of-the-art methods that use more costly large-time-scale transformer models. We also examine the effects of data augmentation and fine-tuning methods, accomplishingSoTA by a large margin when training only on the UCF-101 dataset.