Miracle-dz/UniFAR-FTUs huggingface dataset:
ALUMINUM INDUCED ENZYMATIC DISORDER AS AN IMPORTANT ECO BIOMARKER IN SEEDLINGS OF LENS CULINARIS MEDIC.

This article discusses the Al (Aluminum) induced disorder on the activities of nitrate and nitrite reductase (NR), protease (PA) and proline contents of seedlings of Lens culinaris as some important eco-biomarkers. The seedlings were cultured hydroponically in the nutrient solution with or without AlCl3. 6H2O (pH = 4.0) for 15 days. The relative toxicity of Al 3+ was found to be directly related with Al concentration in the nutrient medium. The reduction in the seedlings growth may be attributed with the poor root growth which in turns related with an inhibition in the cell division. Al treatments for 15 days increased the nitrate reductase activities in the seedlings while protease activity was decreased. Increase in the proline contents may cause a substantial shield to the enzymes against the detrimental effects of the tense components Al. The essential electrolyte like sodium (Na) and potassium (K) contents were found to be decreased, accredited to the rupturing of cell membrane. These results suggest that inhibition of the root growth by Al, closely related to the metabolic changes including an increase in nitrate reductase activity and decline in proteases activity in of the seedlings.
EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-Time Facial Expression Recognition

While recent advances in deep learning have led to significant improvements in facial expression classification (FEC), a major challenge that remains a bottleneck for the widespread deployment of such systems is their high architectural and computational complexities. This is especially challenging given the operational requirements of various FEC applications, such as safety, marketing, learning, and assistive living, where real-time requirements on low-cost embedded devices is desired. Motivated by this need for a compact, low latency, yet accurate system capable of performing FEC in real-time on low-cost embedded devices, this study proposes EmotionNet Nano, an efficient deep convolutional neural network created through a human-machine collaborative design strategy, where human experience is combined with machine meticulousness and speed in order to craft a deep neural network design catered toward real-time embedded usage. To the best of the author’s knowledge, this is the very first deep neural network architecture for facial expression recognition leveraging machine-driven design exploration in its design process, and exhibits unique architectural characteristics such as high architectural heterogeneity and selective long-range connectivity not seen in previous FEC network architectures. Two different variants of EmotionNet Nano are presented, each with a different trade-off between architectural and computational complexity and accuracy. Experimental results using the CK + facial expression benchmark dataset demonstrate that the proposed EmotionNet Nano networks achieved accuracy comparable to state-of-the-art FEC networks, while requiring significantly fewer parameters. Furthermore, we demonstrate that the proposed EmotionNet Nano networks achieved real-time inference speeds (e.g., >25 FPS and >70 FPS at 15 and 30 W, respectively) and high energy efficiency (e.g., >1.7 images/sec/watt at 15 W) on an ARM embedded processor, thus further illustrating the efficacy of EmotionNet Nano for deployment on embedded devices.
Dynamic Sampling Networks for Efficient Action Recognition in Videos

The existing action recognition methods are mainly based on clip-level classifiers such as two-stream CNNs or 3D CNNs, which are trained from the randomly selected clips and applied to densely sampled clips during testing. However, this standard setting might be suboptimal for training classifiers and also requires huge computational overhead when deployed in practice. To address these issues, we propose a new framework for action recognition in videos, called Dynamic Sampling Networks (DSN), by designing a dynamic sampling module to improve the discriminative power of learned clip-level classifiers and as well increase the inference efficiency during testing. Specifically, DSN is composed of a sampling module and a classification module, whose objective is to learn a sampling policy to on-the-fly select which clips to keep and train a clip-level classifier to perform action recognition based on these selected clips, respectively. In particular, given an input video, we train an observation network in an associative reinforcement learning setting to maximize the rewards of the selected clips with a correct prediction. We perform extensive experiments to study different aspects of the DSN framework on four action recognition datasets: UCF101, HMDB51, THUMOS14, and ActivityNet v1.3. The experimental results demonstrate that DSN is able to greatly improve the inference efficiency by only using less than half of the clips, which can still obtain a slightly better or comparable recognition accuracy to the state-of-the-art approaches.
Deep Orthogonal Decompositions for Convective Nowcasting

Near-term prediction of the structured spatio-temporal processes driving our climate is of profound importance to the safety and well-being of millions, but the prounced nonlinear convection of these processes make a complete mechanistic description even of the short-term dynamics challenging. However, convective transport provides not only a principled physical description of the problem, but is also indicative of the transport in time of informative features which has lead to the recent successful development of ``physics free'' approaches to the now-casting problem. In this work we demonstrate that their remains an important role to be played by physically informed models, which can successfully leverage deep learning (DL) to project the process onto a lower dimensional space on which a minimal dynamical description holds. Our approach synthesises the feature extraction capabilities of DL with physically motivated dynamics to outperform existing model free approaches, as well as state of the art hybrid approaches, on complex real world datasets including sea surface temperature and precipitation.
Population Synthesis of Black Hole X-Ray Binaries

We present a systematic study of the X-ray binaries (XRBs) containing a black hole (BH) and a nondegenerate companion, in which mass transfer takes place via either capturing the companion’s wind or Roche lobe overflow (RLO). As shown in our previous work, which focused on the formation and evolution of detached BH binaries, our assumed models relevant to BHs’ progenitors predicted significantly different binary properties. In this paper, we further follow the evolutionary paths of BH systems that appear as XRBs. By use of both binary population synthesis and detailed binary evolution calculations, we can obtain the potential population of BH XRBs. Distributions at the current epoch of various binary parameters have been computed. The observed sample of wind-fed XRBs can be well reproduced under the assumptions of all of our models. Wind-fed XRBs are expected to be so rare (≲100) that only a couple such systems have been detected. Comparison of known RLO XRBs with the calculated distributions of various binary parameters indicates that only the models assuming relatively small masses for BH progenitors can roughly match the observations. Accordingly we estimate that there are hundreds of RLO XRBs in the Milky Way, of which the majority are low-mass XRBs. RLO systems may become ultraluminous X-ray sources (ULXs) if the BH accretes at a very high rate, and we expect that about a dozen ULXs with a BH accretor may exist in a Milky Way–like galaxy.
Likelihood Maximization and Moment Matching in Low SNR Gaussian Mixture Models

We derive an asymptotic expansion for the log‐likelihood of Gaussian mixture models (GMMs) with equal covariance matrices in the low signal‐to‐noise regime. The expansion reveals an intimate connection between two types of algorithms for parameter estimation: the method of moments and likelihood optimizing algorithms such as Expectation‐Maximization (EM). We show that likelihood optimization in the low SNR regime reduces to a sequence of least squares optimization problems that match the moments of the estimate to the ground truth moments one by one. This connection is a stepping stone towards the analysis of EM and maximum likelihood estimation in a wide range of models. A motivating application for the study of low SNR mixture models is cryo‐electron microscopy data, which can be modeled as a GMM with algebraic constraints imposed on the mixture centers. We discuss the application of our expansion to algebraically constrained GMMs, among other example models of interest. © 2022 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.
Vertical mixing in a shallow, eutrophic lake: Possible consequences for the light climate of phytoplankton

Profiles of temperature-gradient microstructure are used to define the size and location of mixing regions, the intensity of turbulence, and the potential exposure of phytoplankton to fluctuating irradiance in a shallow, turbid, productive lake. The part of the water column which was mixing tended to be subdivided into two regions with different dynamics, one in which the turbulence was active and one in which it was constrained by buoyancy. Generally the upper layer, which ranged from 0.3 to 1.5 m deep, was actively mixing. Energy dissipation rates were on the order of lo- 7 m2 s-~, vertical eddy diffusivities ranged from 1O-3 to 1 Op5 m2 s- I, and overturns mixed on a time scale of minutes. Phytoplankton could become well mixed before turbulent transport within overturns ceased and, while the wind persisted, were likely to experience continuous fluctuations in irradiance. In one of the largest overturns, phytoplankton could circulate between the 90% light level and the 5% light level in 3-4 min. Where buoyancy affected turbulence, energy dissipation rates ranged from 1O-9 to 1O-7 m2 ss3 and vertical eddy diffusivities from 1O-7 to 1 O-4 m2 s- ’ . Mixing times based on these diffusivities exceeded r/N, the time scale for turbulent transport, indicating overturns would mix only partially. Phytoplankton could still experience large fluctuations in irradiance, but the fluctuations probably were not continuous.
DNF-Net: A Deep Normal Filtering Network for Mesh Denoising

This article presents a deep normal filtering network, called DNF-Net, for mesh denoising. To better capture local geometry, our network processes the mesh in terms of local patches extracted from the mesh. Overall, DNF-Net is an end-to-end network that takes patches of facet normals as inputs and directly outputs the corresponding denoised facet normals of the patches. In this way, we can reconstruct the geometry from the denoised normals with feature preservation. Besides the overall network architecture, our contributions include a novel multi-scale feature embedding unit, a residual learning strategy to remove noise, and a deeply-supervised joint loss function. Compared with the recent data-driven works on mesh denoising, DNF-Net does not require manual input to extract features and better utilizes the training data to enhance its denoising performance. Finally, we present comprehensive experiments to evaluate our method and demonstrate its superiority over the state of the art on both synthetic and real-scanned meshes.
The Ratio-Cut Polytope and K-Means Clustering

We introduce the ratio-cut polytope defined as the convex hull of ratio-cut vectors corresponding to all partitions of $n$ points in $\mathbb R^m$ into at most $K$ clusters. This polytope is closely related to the convex hull of the feasible region of a number of clustering problems such as K-means clustering and spectral clustering. We study the facial structure of the ratio-cut polytope and derive several types of facet-defining inequalities. We then consider the problem of K-means clustering and introduce a novel linear programming (LP) relaxation for it. Subsequently, we focus on the case of two clusters and derive sufficient conditions under which the proposed LP relaxation recovers the underlying clusters exactly. Namely, we consider the stochastic ball model, a popular generative model for K-means clustering, and we show that if the separation distance between cluster centers satisfies $\Delta > 1+\sqrt 3$, then the LP relaxation recovers the planted clusters with high probability. This is a major improvement over the only existing recovery guarantee for an LP relaxation of K-means clustering stating that recovery is possible with high probability if and only if $\Delta > 4$. Our numerical experiments indicate that the proposed LP relaxation significantly outperforms a popular semidefinite programming relaxation in recovering the planted clusters.
Towards Causality Extraction from Requirements

behavior is often based on causal relations between certain events (e.g. If event1, then event2). Consequently, those causal relations are also textually embedded in requirements. We want to extract this causal knowledge and utilize it to derive test cases automatically and to reason about dependencies between requirements. Existing NLP approaches fail to extract causality from natural language (NL) with reasonable performance. In this paper, we describe first steps towards building a new approach for causality extraction and contribute: (1) an NLP architecture based on Tree Recursive Neural Networks (TRNN) that we will train to identify causal relations in NL requirements and (2) an annotation scheme and a dataset that is suitable for training TRNNs. Our dataset contains 212,186 sentences from 463 publicly available requirement documents and is a first step towards a gold standard corpus for causality extraction. We encourage fellow researchers to contribute to our dataset and help us in finalizing the causality annotation process. Additionally, the dataset can also be annotated further to serve as a benchmark for other RE-relevant NLP tasks such as requirements classification.
Reconstructing Biological and Digital Phylogenetic Trees in Parallel

In this paper, we study the parallel query complexity of reconstructing biological and digital phylogenetic trees from simple queries involving their nodes. This is motivated from computational biology, data protection, and computer security settings, which can be abstracted in terms of two parties, a responder, Alice, who must correctly answer queries of a given type regarding a degree-d tree, T, and a querier, Bob, who issues batches of queries, with each query in a batch being independent of the others, so as to eventually infer the structure of T. We show that a querier can efficiently reconstruct an n-node degree-d tree, T, with a logarithmic number of rounds and quasilinear number of queries, with high probability, for various types of queries, including relative-distance queries and path queries. Our results are all asymptotically optimal and improve the asymptotic (sequential) query complexity for one of the problems we study. Moreover, through an experimental analysis using both real-world and synthetic data, we provide empirical evidence that our algorithms provide significant parallel speedups while also improving the total query complexities for the problems we study.
Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes

An online problem called dynamic resource allocation with capacity constraints (DRACC) is introduced and studied in the realm of posted price mechanisms. This problem subsumes several applications of stateful pricing, including but not limited to posted prices for online job scheduling and matching over a dynamic bipartite graph. Because existing online learning techniques do not yield vanishing regret for this problem, we develop a novel online learning framework over deterministic Markov decision processes with dynamic state transition and reward functions. Following that, we prove, based on a reduction to the well-studied problem of online learning with switching costs, that if the Markov decision process admits a chasing oracle (i.e., an oracle that simulates any given policy from any initial state with bounded loss), then the online learning problem can be solved with vanishing regret. Our results for the DRACC problem and its applications are then obtained by devising (randomized and deterministic) chasing oracles that exploit the particular structure of these problems. Funding: The work of Y. Emek was supported in part by the Israel Science Foundation [Grant 1016/17]. The work of R. Lavi was partially supported by the Israel Science Foundation [Grant 2560/17] and the National Natural Science Foundation of China [Grant 2560/17]. The work of R. Niazadeh was supported by the University of Chicago Booth School of Business. The work of Y. Shi was partially supported at the Technion by the Council for Higher Education [fellowship], and at Shandong University by the Science Fund Program of Shandong Province for Distinguished Oversea Young Scholars [Grant 2023HWYQ-006].
Spatiotemporal Calibration of Camera and 3D Laser Scanner

The multi-sensory setups consisting of the laser scanners and cameras are popular as the measurements complement each other and provide necessary robustness for applications. Under dynamic conditions or when in motion, a direct transformation (spatial calibration) and time offset between sensors (temporal calibration) is needed to determine the correspondence between measurements. We propose an open-source spatiotemporal calibration framework for a camera and a 3D laser scanner. Our solution is based on commonly available chessboard markers requiring one-minute calibration before the operation that offers accurate and repeatable results. The framework is based on batch optimization of point-to-plane constraints with a time offset calibration possible by a novel minimal, continuous representation of the plane equations in the Lie algebra and the use of B-splines. The framework's properties are evaluated in simulation while performance is verified with two distinct sensory setups with Velodyne VLP-16 and SICK MRS6124 3D laser scanners.
Entrainment of particles during the withdrawal of a fibre from a dilute suspension

Abstract A fibre withdrawn from a bath of a dilute particulate suspension exhibits different coating regimes depending on the physical properties of the fluid, the withdrawal speed, the particle sizes and the radius of the fibre. Our experiments indicate that only the liquid without particles is entrained for thin coating films. Beyond a threshold capillary number, the fibre is coated by a liquid film with entrained particles. We systematically characterize the role of the capillary number, the particle size and the fibre radius on the threshold speed for particle entrainment. We discuss the boundary between these two regimes and show that the thickness of the liquid film at the stagnation point controls the entrainment process. The radius of the fibre provides a new degree of control in capillary filtering, allowing greater control over the size of the particles entrained in the film.
Classification and dynamic simulation of the vertical density structure of lakes1

Field data from two lakes of widely differing geometry and size are analyzed in terms of four nondimensional numbers which allow the principal mixing processes in each lake to be identified. The numbers are based on basin geometry, density stratification, wind stress, and rates of inflow and outflow. The procedure highlights the differences in the dynamics of the two lakes and allows assessment of the validity of the assumption of one-dimensionality. The result is that both lakes were dominated by one-dimensional, but different processes. The dynamics of the epilimnion of the smaller lake were dominated by stirring from surface wind and cooling, whereas shear at the pycnocline was also significant in the larger lake. In neither case did the effects of the earth’s rotation, inflow, or outflow generate significant horizontal gradients. A one-dimensional numerical model (DYRESM) was used to simulate the vertical temperature and salinity structures of both lakes over lengthy periods, with good results. The model is based on the parameterization of the important physical processes in a framework of horizontal layers of variable thickness and was applied in both lakes without alteration. The interpretative power of the model is demonstrated by examination of the formation and erosion of a thermal inversion in the larger lake.
Catalase A from Botrytis cinerea is not expressed during infection on tomato leaves

Abstract Catalase mediates the enzymatic breakdown of hydrogen peroxide to water and molecular oxygen. During the infection of broad bean ( Vicia faba ) by Botrytis cinerea the release of hydrogen peroxide by fungal oxidase activity was proposed to facilitate penetration by the pathogen. Catalase activity might play a role in protecting the fungus against the damaging effects of hydrogen peroxide. A cDNA clone encoding catalase was isolated from a library of Botrytis cinerea . Southern blot analysis of genomic DNA indicated the presence of a single copy gene, denoted as catA . The cDNA clone encoded a protein, CAT-A, of 480 amino acids showing 56–65% similarity to fungal catalases. Detailed analysis of sequence homologies between other fungal catalases enabled grouping of catalases according to their cellular location. CAT-A of B. cinerea resembled most closely the peroxisomal catalases. Northern blot analysis showed induction of the gene by H 2 O 2 in vitro. In planta , no catA expression could be detected by northern blot analysis, whereas a constitutively expressed β-tubulin mRNA of B. cinerea was detectable and symptom development on the inoculated leaves was very clear. However, a catalase gene cat1 of tomato appeared to be induced from the time of fungal penetration onwards, suggesting that H 2 O 2 is produced during the interaction.
Listen Carefully and Tell: An Audio Captioning System Based on Residual Learning and Gammatone Audio Representation

Automated audio captioning is machine listening task whose goal is to describe an audio using free text. An automated audio captioning system has to be implemented as it accepts an audio as input and outputs as textual description, that is, the caption of the signal. This task can be useful in many applications such as automatic content description or machine-to-machine interaction. In this work, an automatic audio captioning based on residual learning on the encoder phase is proposed. The encoder phase is implemented via different Residual Networks configurations. The decoder phase (create the caption) is run using recurrent layers plus attention mechanism. The audio representation chosen has been Gammatone. Results show that the framework proposed in this work surpass the baseline system in challenge results.
What $Ell$ sees that $K$ doesn't (when $p >3$)

We use Andrew Baker's analysis of the cofiber of the endomorphism of the $p$-adic elliptic spectrum ($p>3$) defined by multiplication by the `Hasse invariant' $E_{p-1}$ to present its completion away from the locus of ordinary elliptic curves as a sum of roughly $p/12$ copies (indexed by supesingular elliptic curves) of $p$-adic lifts of the height two mod $p$ cohomology theory $K(2)$. See a recent paper of Zhu Yifei for a much deeper exploration of the topics considered in this note.