Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstructing Classes of Non-Bandlimited Signals From Time Encoded Information

We investigate time encoding as an alternative method to classical sampling, and address the problem of reconstructing classes of non-bandlimited signals from time-based samples. We consider a sampling mechanism based on first filtering the input, before obtaining the timing information using a time encoding machine. Within this framework, we show that sampling by timing is equivalent to a non-uniform sampling problem, where the reconstruction of the input depends on the characteristics of the filter and on its non-uniform shifts. The classes of filters we focus on are exponential and polynomial splines, and we show that their fundamental properties are locally preserved in the context of non-uniform sampling. Leveraging these properties, we then derive sufficient conditions and propose novel algorithms for perfect reconstruction of classes of non-bandlimited signals such as: streams of Diracs, sequences of pulses and piecewise constant signals. Next, we extend these methods to operate with arbitrary filters, and also present simulation results on synthetic noisy data.
Conspicuous Veils Formed by Vibrioid Bacteria on Sulfidic Marine Sediment

ABSTRACT We describe the morphology and behavior of a hitherto unknown bacterial species that forms conspicuous veils (typical dimensions, 30 by 30 mm) on sulfidic marine sediment. The new bacteria were enriched on complex sulfidic medium within a benthic gradient chamber in oxygen-sulfide countergradients, but the bacteria have so far not been isolated in pure culture, and a detailed characterization of their metabolism is still lacking. The bacteria are colorless, gram-negative, and vibrioid-shaped (1.3- to 2.5- by 4- to 10-μm) cells that multiply by binary division and contain several spherical inclusions of poly-β-hydroxybutyric acid. The cells have bipolar polytrichous flagella and exhibit a unique swimming pattern, rotating and translating along their short axis. Free-swimming cells showed aerotaxis and aggregated at ca. 2 μM oxygen within opposing oxygen-sulfide gradients, where they were able to attach via a mucous stalk, forming a cohesive whitish veil at the oxic-anoxic interface. Bacteria attached to the veil kept rotating and adapted their stalk lengths dynamically to changing oxygen concentrations. The joint action of rotating bacteria on the veil induced a homogeneous water flow from the oxic water region toward the veil, whereby the oxygen uptake rate could be enhanced up to six times, as shown by model calculations. The veils showed a pronounced succession pattern. New veils were generated de novo within 24 h and had a homogeneous whitish translucent appearance. Bacterial competitors or eukaryotic predators were apparently kept away by the low oxygen concentration prevailing at the veil surface. Frequently, within 2 days the veil developed a honeycomb pattern of regularly spaced holes. After 4 days, most veils were colonized by grazing ciliates, leading to the fast disappearance of the new bacteria. Several-week-old veils finally developed into microbial mats consisting of green, purple, and colorless sulfur bacteria.
Generalization ability of region proposal networks for multispectral person detection

Multispectral person detection aims at automatically localizing humans in images that consist of multiple spectral bands. Usually, the visual-optical (VIS) and the thermal infrared (IR) spectra are combined to achieve higher robustness for person detection especially in insufficiently illuminated scenes. This paper focuses on analyzing existing detection approaches for their generalization ability. Generalization is a key feature for machine learning based detection algorithms that are supposed to perform well across different datasets. Inspired by recent literature regarding person detection in the VIS spectrum, we perform a cross-validation study to empirically determine the most promising dataset to train a well-generalizing detector. Therefore, we pick one reference Deep Convolutional Neural Network (DCNN) architecture and three different multispectral datasets. The Region Proposal Network (RPN) originally introduced for object detection within the popular Faster R-CNN is chosen as a reference DCNN. The reason is that a stand-alone RPN is able to serve as a competitive detector for two-class problems such as person detection. Furthermore, current state-of-the-art approaches initially apply an RPN followed by individual classifiers. The three considered datasets are the KAIST Multispectral Pedestrian Benchmark including recently published improved annotations for training and testing, the Tokyo Multi-spectral Semantic Segmentation dataset, and the OSU Color-Thermal dataset including recently released annotations. The experimental results show that the KAIST Multispectral Pedestrian Benchmark with its improved annotations provides the best basis to train a DCNN with good generalization ability compared to the other two multispectral datasets. On average, this detection model achieves a log-average Miss Rate (MR) of 29.74 % evaluated on the reasonable test subsets of the three datasets.
Threonine appears to be essential for proliferation of human as well as mouse embryonic stem cells

Mouse embryonic stem (mES) cell proliferation depends exclusively on the nutritionally essential amino acid, L-threonine, in the medium. Other essential and non-essential amino acids need not be added to the medium for mES cell proliferation. Furthermore, the threonine analog, 3-hydroxynorvaline (3-HNV), selectively inhibits mES cell proliferation (Wang et al., 2009). HeLa, MEF and 3T3 cell growth all are not affected by 3-HNV. Selective inhibition of ES cell proliferation by 3-HNV is expected to have teratogenic and embryotoxic effects on development, and these effects have been observed in chicken and mouse embryos (Louw et al., 2005). It has been proposed that rapid catabolism via threonine dehydrogenase (TDH) accounts for how threonine supplementation is needed to support mES cell proliferation and that TDH is the site of 3-HNV inhibition of mES cell proliferation (Wang et al., 2009; Han et al., 2013; Chen and Wang, 2014). In support of this possibility, TDH induction enhances reprogramming of mouse somatic cells into induced pluripotent stem (iPS) cells, and 3-HNV inhibits this induction (Han et al., 2013). The concentrations of threonine or 3-HNV needed for mES cell proliferation or its inhibition are, however, over an order of magnitude lower than the apparent Km or Ki values for interaction of threonine or 3-HNV with TDH (Wang et al., 2009). Hence, another mechanism may account for the influences of threonine and 3-HNV on mES cell proliferation. To test this hypothesis, we determined whether 3-HNV inhibits human embryonic stem (hES) cell proliferation. Humans produce truncated and apparently inactive TDH proteins that cannot make appropriate contact with 3-HNV, threonine or NAD+, the cofactor needed for TDH activity (Edgar, 2002).
Evaluation of host-targeted acaricide for reducing risk of Lyme disease in southern New York state.

A 2-yr evaluation of a commercial product designed to reduce the risk of Lyme disease by delivering permethrin-treated cotton to white-footed mice (Peromyscus leucopus) was conducted at three sites in Westchester County, N.Y. , an area where Lyme disease is endemic. We examined the numbers of host-seeking nymphal Ixodes dammini Spielman, Clifford, Piesman & Corwin, the numbers of larval I. dammini infesting mice, and the proportion of nymphs infected with Borrelia burgdorferi, the causative agent of Lyme disease. The density of nymphs collected by drag sampling did not differ between treatment and control areas in the first year for either a woodland or recreational site. In the second year, no significant decrease in the density of nymphs was observed in the treatment areas compared with the control areas. Numbers of larvae on 67 mice captured on treatment and control grids did not differ for woodland and recreational sites, but significantly fewer larvae were found on mice captured at the residential treatment site. The percentage of host-seeking ticks infected with spirochetes did not differ between treatment and control sites in any habitat for either year. The use of permethrin-treated cotton did not measurably reduce the numbers of I. dammini or the proportion of ticks infected with spirochetes at our study sites.
Factor VIIa‐antithrombin complexes in patients with non‐neoplastic portal vein thrombosis with and without cirrhosis

Portal vein thrombosis (PVT) is caused by local and systemic prothrombotic risk factors. In this case‐control study, we evaluated the use of the Factor VIIa‐antithrombin complex (FVIIa‐AT) complex assay as a hypercoagulability marker in patients with PVT.
Reaching an agreement in a distributed environment in absence of omission faults

Many studies have considered solving Byzantine Agreement problem for different network topologies. On the other hand, the proposed protocols need huge amounts of message interchange that makes them inapplicable when networks size increases. There are many cases in which faulty nodes show only a specific symptom of faults or a faulty behavior is detectable in the transmission layer. However, reaching the agreement in previous works does not benefit from this asset to ease the way of agreement. In this paper, a new protocol is provided to solve the agreement problem in the case of detectable omission fault. The proposed protocol shows a dramatic save on message transmissions and message exchange rounds as the network increases in size.
Understanding attention in graph neural networks

We aim to better understand attention over nodes in graph neural networks and identify factors influencing its effectiveness. Motivated by insights from the work on Graph Isomorphism Networks (Xu et al., 2019), we design simple graph reasoning tasks that allow us to study attention in a controlled environment. We find that under typical conditions the effect of attention is negligible or even harmful, but under certain conditions it provides an exceptional gain in performance of more than 40% in some of our classification tasks. However, we have yet to satisfy these conditions in practice.
An Entropy Power Inequality for Discrete Random Variables

Let $\mathsf{N}_{\rm d}\left[X\right]=\frac{1}{2\pi {\rm e}}{\rm e}^{2\mathsf{H}\left[X\right]}$ denote the entropy power of the discrete random variable $X$ where $\mathsf{H}\left[X\right]$ denotes the discrete entropy of $X$. In this paper, we show that for two independent discrete random variables $X$ and $Y$, the entropy power inequality $\mathsf{N}_{\rm d}\left[X\right]+\mathsf{N}_{\rm d}\left[Y\right]\leq 2 \mathsf{N}_{\rm d}\left[X+Y\right]$ holds and it can be tight. The basic idea behind the proof is to perturb the discrete random variables using suitably designed continuous random variables. Then, the continuous entropy power inequality is applied to the sum of the perturbed random variables and the resulting lower bound is optimized.
Convergence of Krasulina Estimator

Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications. Consider the points X1,X2, . ,Xn are vectors drawn i.i. d. from a distribution with mean zero and covariance Σ, where Σ is unknown. Let An = XnX n , then E[An] = Σ. This paper considers the problem of finding the smallest eigenvalue and eigenvector of matrix Σ. A classical estimator of this type is due to Krasulina[11]. We are going to state the convergence proof of Krasulina for the smallest eigenvalue and corresponding eigenvector, and then find their convergence rate.
SAWL: A Self-adaptive Wear-leveling NVM Scheme for High Performance Storage Systems

In order to meet the needs of high performance computing (HPC) in terms of large memory, high throughput and energy savings, the non-volatile memory (NVM) has been widely studied due to its salient features of high density, near-zero standby power, byte-addressable and non-volatile properties. In HPC systems, the multi-level cell (MLC) technique is used to significantly increase device density and decrease the cost, which however leads to much weaker endurance than the single-level cell (SLC) counterpart. Although wear-leveling techniques can mitigate this weakness in MLC, the improvements upon MLC-based NVM become very limited due to not achieving uniform write distribution before some cells are really worn out. To address this problem, our paper proposes a self-adaptive wear-leveling (SAWL) scheme for MLC-based NVM. The idea behind SAWL is to dynamically tune the wear-leveling granularities and balance the writes across the cells of entire memory, thus achieving suitable tradeoff between the lifetime and cache hit rate. Moreover, to reduce the size of the address-mapping table, SAWL maintains a few recently-accessed mappings in a small on-chip cache. Experimental results demonstrate that SAWL significantly improves the NVM lifetime and the performance for HPC systems, compared with state-of-the-art schemes.
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations

Syntax has been demonstrated highly effective in neural machine translation (NMT). Previous NMT models integrate syntax by representing 1-best tree outputs from a well-trained parsing system, e.g., the representative Tree-RNN and Tree-Linearization methods, which may suffer from error propagation. In this work, we propose a novel method to integrate source-side syntax implicitly for NMT. The basic idea is to use the intermediate hidden representations of a well-trained end-to-end dependency parser, which are referred to as syntax-aware word representations (SAWRs). Then, we simply concatenate such SAWRs with ordinary word embeddings to enhance basic NMT models. The method can be straightforwardly integrated into the widely-used sequence-to-sequence (Seq2Seq) NMT models. We start with a representative RNN-based Seq2Seq baseline system, and test the effectiveness of our proposed method on two benchmark datasets of the Chinese-English and English-Vietnamese translation tasks, respectively. Experimental results show that the proposed approach is able to bring significant BLEU score improvements on the two datasets compared with the baseline, 1.74 points for Chinese-English translation and 0.80 point for English-Vietnamese translation, respectively. In addition, the approach also outperforms the explicit Tree-RNN and Tree-Linearization methods.
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

Emotion is intrinsic to humans and consequently, emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration), and more. In Addition, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user’s emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a difficult problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.
Molecular Characterization of Pseudomonas spp. Isolated from Root Nodules of Various Leguminous Plants of Shekhawati Region, Rajasthan, India

Plant growth promontory Pseudomonas strains were isolated from root nodules of five plant species, viz., Trifolium pretense, Cicer arietinum, Amaranthus polygamus, Vigna mungo, and Trigonella foenum; that plants were denizen of Shekhawati region of Rajasthan. A total of 8 bacterial isolates were evaluated for growth promotion using PGP properties. Partial 16S rDNA sequencing data showed that these 8 bacterial isolates belonged to genus Pseudomonas. MEGA 4.0. 2, software was used to construct a neighbor joining tree by employing boot strap method. Result exhibited significant diversity among recovered Pseudomonas strains.
Thermal spin photonics in the near-field of nonreciprocal media

The interplay of spin angular momentum and thermal radiation is a frontier area of interest to nanophotonics as well as topological physics. Here, we show that a thick planar slab of a nonreciprocal material, despite being at thermal equilibrium with its environment, can exhibit nonzero photon spin angular momentum and nonzero radiative heat flux in its vicinity. We identify them as the persistent thermal photon spin and the persistent planar heat current respectively. With a practical example system, we reveal that the fundamental origin of these phenomena is connected to the spin-momentum locking of thermally excited evanescent waves. We also discover spin magnetic moment of surface polaritons that further clarifies these features. We then propose an imaging experiment based on Brownian motion that allows one to witness these surprising features by directly looking at them using a lab microscope. We further demonstrate the universal behavior of these near-field thermal radiation phenomena through a comprehensive analysis of gyroelectric, gyromagnetic and magneto-electric nonreciprocal materials. Together, these results expose a surprisingly little explored research area of thermal spin photonics with prospects for new avenues related to non-Hermitian topological photonics and radiative heat transport.
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem

We consider the combinatorial multi-armed bandit (CMAB) problem, where the reward function is nonlinear. In this setting, the agent chooses a batch of arms on each round and receives feedback from each arm of the batch. The reward that the agent aims to maximize is a function of the selected arms and their expectations. In many applications, the reward function is highly nonlinear, and the performance of existing algorithms relies on a global Lipschitz constant to encapsulate the function's nonlinearity. This may lead to loose regret bounds, since by itself, a large gradient does not necessarily cause a large regret, but only in regions where the uncertainty in the reward's parameters is high. To overcome this problem, we introduce a new smoothness criterion, which we term \emph{Gini-weighted smoothness}, that takes into account both the nonlinearity of the reward and concentration properties of the arms. We show that a linear dependence of the regret in the batch size in existing algorithms can be replaced by this smoothness parameter. This, in turn, leads to much tighter regret bounds when the smoothness parameter is batch-size independent. For example, in the probabilistic maximum coverage (PMC) problem, that has many applications, including influence maximization, diverse recommendations and more, we achieve dramatic improvements in the upper bounds. We also prove matching lower bounds for the PMC problem and show that our algorithm is tight, up to a logarithmic factor in the problem's parameters.
Applications of Social Media in Hydroinformatics: A Survey

Floods of research and practical applications employ social media data for a wide range of public applications, including environmental monitoring, water resource managing, disaster and emergency response.Hydroinformatics can benefit from the social media technologies with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined.This paper first proposes a 4W (What, Why, When, hoW) model and a methodological structure to better understand and represent the application of social media to hydroinformatics, then provides an overview of academic research of applying social media to hydroinformatics such as water environment, water resources, flood, drought and water Scarcity management. At last,some advanced topics and suggestions of water related social media applications from data collection, data quality management, fake news detection, privacy issues, algorithms and platforms was present to hydroinformatics managers and researchers based on previous discussion.
The geometry of groups containing almost normal subgroups

A subgroup $H\leq G$ is said to be almost normal if every conjugate of $H$ is commensurable to $H$. If $H$ is almost normal, there is a well-defined quotient space $G/H$. We show that if a group $G$ has type $F_{n+1}$ and contains an almost normal coarse $PD_n$ subgroup $H$ with $e(G/H)=\infty$, then whenever $G'$ is quasi-isometric to $G$, it contains an almost normal subgroup $H'$ that is quasi-isometric to $H$. Moreover, the quotient spaces $G/H$ and $G'/H'$ are quasi-isometric. This generalises a theorem of Mosher-Sageev-Whyte, who prove the case in which $G/H$ is quasi-isometric to a finite valence bushy tree. Using work of Mosher, we generalise a result of Farb-Mosher to show that for many surface group extensions $\Gamma_L$, any group quasi-isometric to $\Gamma_L$ is virtually isomorphic to $\Gamma_L$. We also prove quasi-isometric rigidity for the class of finitely presented $\mathbb{Z}$-by-($\infty$ ended) groups.
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets

Clinical diagnostic decision making and population-based studies often rely on multi-modal data which is noisy and incomplete. Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal features. Many of these approaches are limited by assuming modality- and feature-completeness, and by transductive inference, which requires re-training of the entire model for each new test sample. In this work, we propose a novel inductive graph-based approach that can generalize to out-of-sample patients, despite missing features from entire modalities per patient. We propose multi-modal graph fusion which is trained end-to-end towards node-level classification. We demonstrate the fundamental working principle of this method on a simplified MNIST toy dataset. In experiments on medical data, our method outperforms single static graph approach in multi-modal disease classification.
Model-Based Reconstruction for Quantitative MRI using the Bloch Equations

In this work a generic model-based reconstruction for the quantification of relaxation parameters is developed. In contrast to previous approaches that rely on simplified models derived from the Bloch equations, this work includes the Bloch equations directly into the reconstruction. Therefore, non-linear calibrationless parallel imaging is combined with a generic Runge-Kutta 5(4) based forward operator to simulate spin dynamics described by arbitrary sequences. Gradients are determined by using a sensitivity analysis and solving the resulting ordinary differential equations parallel to the signal simulation. Based on this formulation an IRGNM-FISTA algorithm is used to estimate quantitative maps for $T_1$, $T_2$, $M_0$, and the coil profiles $c_N$ from fully-sampled multi-inversion and golden-angle single-shot inversion-recovery radial bSSFP measurement with a custom-built $T_1$-$T_2$ phantom.