Miracle-dz/UniFAR-FTUs huggingface dataset:
A Water-Filling Primal-Dual Algorithm for Approximating Non-Linear Covering Problems

Obtaining strong linear relaxations of capacitated covering problems constitute a major technical challenge even for simple settings. For one of the most basic cases, the Knapsack-Cover (Min-Knapsack) problem, the relaxation based on knapsack-cover inequalities achieves an integrality gap of 2. These inequalities have been exploited in more general environments, many of which admit primal-dual approximation algorithms. Inspired by problems from power and transport systems, we introduce a new general setting in which items can be taken fractionally to cover a given demand. The cost incurred by an item is given by an arbitrary non-decreasing function of the chosen fraction. We generalize the knapsack-cover inequalities to this setting an use them to obtain a $(2+\varepsilon)$-approximate primal-dual algorithm. Our procedure has a natural interpretation as a bucket-filling algorithm, which effectively balances the difficulties given by having different slopes in the cost functions: when some superior portion of an item presents a low slope, it helps to increase the priority with which the inferior portions may be taken. We also present a rounding algorithm with an approximation guarantee of 2. We generalize our algorithm to the Unsplittable Flow-Cover problem on a line, also for the setting where items can be taken fractionally. For this problem we obtain a $(4+\varepsilon)$-approximation algorithm in polynomial time, almost matching the $4$-approximation known for the classical setting.
Total vertex product irregularity strength of graphs

Consider a simple graph $G$. We call a labeling $w:E(G)\cup V(G)\rightarrow \{1, 2, \dots, s\}$ (\textit{total vertex}) \textit{product-irregular}, if all product degrees $pd_G(v)$ induced by this labeling are distinct, where $pd_G(v)=w(v)\times\prod_{e\ni v}w(e)$. The strength of $w$ is $s$, the maximum number used to label the members of $E(G)\cup V(G)$. The minimum value of $s$ that allows some irregular labeling is called \textit{the total vertex product irregularity strength} and denoted $tvps(G)$. We provide some general bounds, as well as exact values for chosen families of graphs. Keywords: product-irregular labeling, total vertex product irregularity strength, vertex-distinguishing labeling.
Twinless articulation points and some related problems

Let $G=(V,E)$ be a twinless strongly connected graph. a vertex $v\in V$ is a twinless articulation point if the subrgraph obtained from $G$ by removing the vertex $v$ is not twinless strongly connected. An edge $e\in E$ is a twinless bridge if the subgraph obtained from $G$ by deleting $e$ is not twiless strongly connected graph. In this paper we study twinless articulation points and twinless bridges. We also study the problem of finding a minimum cardinality edge subset $E_{1} \subseteq E$ such that the subgraph $(V,E_{1})$ is twinless strongly connected. Moreover, we present an algorithm for computing the $2$-vertex-twinless connected components of $G$.
encointer - An Ecological, Egalitarian and Private Cryptocurrency and Self-Sovereign Identity System

encointer proposes a new blockchain based cryptocurrency with an ecological consensus mechanism using trusted execution environments and an egalitarian money supply policy. Money issuance is done through a universal basic income subject to a proof-of-personhood. Only individuals attending randomized pseudonym key signing events obtain such proofs. encointer also features private transactions and scalable, trustless off-chain smart contracts.
Ranking and Classification driven Feature Learning for Person Re_identification

Person re-identification has attracted many researchers' attention for its wide application, but it is still a very challenging task because only part of the image information can be used for personnel matching. Most of current methods uses CNN to learn to embeddings that can capture semantic similarity information among data points. Many of the state-of-the-arts methods use complex network structures with multiple branches that fuse multiple features while training or testing, using classification loss, Triplet loss or a combination of the two as loss function. However, the method that using Triplet loss as loss function converges slowly, and the method in which pull features of the same class as close as possible in features space leads to poor feature stability. This paper will combine the ranking motivated structured loss, proposed a new metric learning loss function that make the features of the same class are sparsely distributed into the range of small hyperspheres and the features of different classes are uniformly distributed at a clearly angle. And adopted a new single-branch network structure that only using global feature can also get great performance. The validity of our method is verified on the Market1501 and DukeMTMC-ReID person re-identification datasets. Finally acquires 90.9% rank-1 accuracy and 80.8% mAP on DukeMTMC-reID, 95.3% rank-1 accuracy and 88.7% mAP on Market1501. Codes and models are available in Github.this https URL.
Solving a Special Case of the Intensional vs Extensional Conjecture in Probabilistic Databases

We consider the problem of exact probabilistic inference for Union of Conjunctive Queries (UCQs) on tuple-independent databases. For this problem, two approaches currently coexist. In the extensional method, query evaluation is performed by exploiting the structure of the query, and relies heavily on the use of the inclusion--exclusion principle. In the intensional method, one first builds a representation of the lineage of the query in a tractable formalism of knowledge compilation. The chosen formalism should then ensure that the probability can be efficiently computed using simple disjointness and independence assumptions, without the need of performing inclusion--exclusion. The extensional approach has long been thought to be strictly more powerful than the intensional approach, the reason being that for some queries, the use of inclusion--exclusion seemed unavoidable. In this paper we introduce a new technique to construct lineage representations as deterministic decomposable circuits in polynomial time. We prove that this technique applies to a class of UCQs that had been conjectured to separate the complexity of the two approaches. In essence, we show that relying on the inclusion--exclusion formula can be avoided by using negation. This result brings back hope to prove that the intensional approach can handle all tractable UCQs.
Learning Neural Activations

An artificial neuron is modelled as a weighted summation followed by an activation function which determines its output. A wide variety of activation functions such as rectified linear units (ReLU), leaky-ReLU, Swish, MISH, etc. have been explored in the literature. In this short paper, we explore what happens when the activation function of each neuron in an artificial neural network is learned natively from data alone. This is achieved by modelling the activation function of each neuron as a small neural network whose weights are shared by all neurons in the original network. We list our primary findings in the conclusions section. The code for our analysis is available at: this https URL.
Universal rank inference via residual subsampling with application to large networks

Determining the precise rank is an important problem in many large-scale applications with matrix data exploiting low-rank plus noise models. In this paper, we suggest a universal approach to rank inference via residual subsampling (RIRS) for testing and estimating rank in a wide family of models, including many popularly used network models such as the degree corrected mixed membership model as a special case. Our procedure constructs a test statistic via subsampling entries of the residual matrix after extracting the spiked components. The test statistic converges in distribution to the standard normal under the null hypothesis, and diverges to infinity with asymptotic probability one under the alternative hypothesis. The effectiveness of RIRS procedure is justified theoretically, utilizing the asymptotic expansions of eigenvectors and eigenvalues for large random matrices recently developed in Fan et al. (2019a) and Fan et al. (2019b). The advantages of the newly suggested procedure are demonstrated through several simulation and real data examples.
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images

The 3D scene understanding is mainly considered as a crucial requirement in computer vision and robotics applications. One of the high-level tasks in 3D scene understanding is semantic segmentation of RGB-Depth images. With the availability of RGB-D cameras, it is desired to improve the accuracy of the scene understanding process by exploiting the depth features along with the appearance features. As depth images are independent of illumination, they can improve the quality of semantic labeling alongside RGB images. Consideration of both common and specific features of these two modalities improves the performance of semantic segmentation. One of the main problems in RGB-Depth semantic segmentation is how to fuse or combine these two modalities to achieve more advantages of each modality while being computationally efficient. Recently, the methods that encounter deep convolutional neural networks have reached the state-of-the-art results by early, late, and middle fusion strategies. In this paper, an efficient encoder-decoder model with the attention-based fusion block is proposed to integrate mutual influences between feature maps of these two modalities. This block explicitly extracts the interdependences among concatenated feature maps of these modalities to exploit more powerful feature maps from RGB-Depth images. The extensive experimental results on three main challenging datasets of NYU-V2, SUN RGB-D, and Stanford 2D-3D-Semantic show that the proposed network outperforms the state-of-the-art models with respect to computational cost as well as model size. Experimental results also illustrate the effectiveness of the proposed lightweight attention-based fusion model in terms of accuracy.
Learning to Answer Ambiguous Questions with Knowledge Graph

In the task of factoid question answering over knowledge base, many questions have more than one plausible interpretation. Previous works on SimpleQuestions assume only one interpretation as the ground truth for each question, so they lack the ability to answer ambiguous questions correctly. In this paper, we present a new way to utilize the dataset that takes into account the existence of ambiguous questions. Then we introduce a simple and effective model which combines local knowledge subgraph with attention mechanism. Our experimental results show that our approach achieves outstanding performance in this task.
Part I: Improving Computational Efficiency of Communication for Omniscience

Communication for omniscience (CO) refers to the problem where the users in a finite set $V$ observe a discrete multiple random source and want to exchange data over broadcast channels to reach omniscience, the state where everyone recovers the entire source. This paper studies how to improve the computational complexity for the problem of minimizing the sum-rate for attaining omniscience in $V$. While the existing algorithms rely on the submodular function minimization (SFM) techniques and complete in $O(|V|^2 \cdot \text{SFM}(|V|)$ time, we prove the strict strong map property of the nesting SFM problem. We propose a parametric (PAR) algorithm that utilizes the parametric SFM techniques and reduce the the complexity to $O(|V| \cdot \text{SFM}(|V|)$. The output of the PAR algorithm is in fact the segmented Dilworth truncation of the residual entropy for all minimum sum-rate estimates $\alpha$, which characterizes the principal sequence of partitions (PSP) and solves some related problems: It not only determines the secret capacity, a dual problem to CO, and the network strength of a graph, but also outlines the hierarchical solution to a combinatorial clustering problem. \end{abstract}
Topological terms in abelian lattice field theories

In this contribution we revisit the lattice discretization of the topological charge for abelian lattice field theories. The construction departs from an initially non-compact discretization of the gauge fields and after absorbing $2\pi$ shifts of the gauge fields leads to a generalized Villain action that also includes the topological term. The topological charge in two, as well as in four dimensions can be expressed in terms of only the integer-valued Villain variables. We test various properties of the topological charge and in particular analyze the index theorem in two dimensions and discuss the Witten effect in 4-d. As an application of our formulation we present results from a simulation of the 2-d U(1) gauge Higgs model at vacuum angle $\theta = \pi$, where we use a suitable worldline/worldsheet representation to overcome the complex action problem at non-zero $\theta$.
A further result on the aging properties of an extended additive hazard model

The passing of time is an important factor for covariates in the additive and proportional hazard models. According to this idea, the extended additive hazard model (EAHM) is introduced by considering the time-varying effects of covariates and is investigated several properties of this model related to reliability analysis. In this paper, we obtain a further result for the EAHM with respect to the aging properties.
Low-dimensional model of the large-scale circulation of turbulent Rayleigh-Bénard convection in a cubic container

We test the ability of a low-dimensional turbulence model to predict how dynamics of large-scale coherent structures such as convection rolls change in different cell geometries. We performed Rayleigh-B\'enard convection experiments in a cubic container, in which there is a single convection roll known as the large-scale circulation (LSC). The model describes the motion of the orientation $\theta_0$ of the LSC as diffusion in a potential which is predicted as a function of the shape of the cell from an approximate solutions of the Navier-Stokes equations. The model predicts advected oscillation modes, driven by a restoring force created by the non-circular cell cross-section. We observe the predicted lowest-wavenumber mode in which the LSC orientation $\theta_0$ oscillates around a corner, and a slosh angle $\alpha$ rocks back and forth, which is distinct from the higher-wavenumber advected twisting and sloshing oscillations found in cylindrical cells. The potential has quadratic minima near each corner with the same curvature in both the LSC orientation $\theta_0$ and slosh angle $\alpha$, as predicted. The new oscillation mode around corners is found above a critical Ra $=4\times10^8$, which appears in the model as a crossing of an underdamped-overdamped transition. The natural frequency of the potential, oscillation period, power spectrum, and critical Ra for oscillations are all within a factor of 3 of model predictions for the Rayleigh number range $8\times10^7 \le Ra \le 3\times 10^9$. However, these uncertainties in model parameters are too large to correctly predict whether the system is in the underdamped or overdamped state at a given Ra. The success of the model at predicting the potential and flow modes for a cubic cell suggests that such a modeling approach could be applied more generally to different cell geometries that support a single convection roll.
MoEVC: A Mixture-of-experts Voice Conversion System with Sparse Gating Mechanism for Accelerating Online Computation

With the recent advancements of deep learning technologies, the performance of voice conversion (VC) in terms of quality and similarity has been significantly improved. However, heavy computations are generally required for deep-learning-based VC systems, which can cause notable latency and thus confine their deployments in real-world applications. Therefore, increasing online computation efficiency has become an important task. In this study, we propose a novel mixture-of-experts (MoE) based VC system. The MoE model uses a gating mechanism to specify optimal weights to feature maps to increase VC performance. In addition, assigning sparse constraints on the gating mechanism can accelerate online computation by skipping the convolution process by zeroing out redundant feature maps. Experimental results show that by specifying suitable sparse constraints, we can effectively increase the online computation efficiency with a notable 70% FLOPs (floating-point operations per second) reduction while improving the VC performance in both objective evaluations and human listening tests.
Schwartz function valued solutions of the Euler and the Navier-Stokes equations

The Euler and Navier-Stokes equations describe the motion of (incompressible) fluids. The initial data are Schwartz functions $\mathcal{S}(\mathbb{R}^3)^3$ or smooth periodic functions $C^\infty(\mathbb{T}^3)^3$ with $\mathbb{T} = \mathbb{R}/\mathbb{Z}$. Because of the initial data and since it has recently been shown that the Euler equations can break down for H\"older continuous functions $C^{1,\alpha}$ [Elg19,EGM19], we are interested in the existence of $\mathcal{S}(\mathbb{R}^3)^3$ resp. $C^\infty(\mathbb{T}^3)^3$ solutions. We want to known why vorticity solutions stay in $\mathcal{S}(\mathbb{R}^3)^3$ resp. $C^\infty(\mathbb{T}^3)^3$ or how they leave these spaces. By proving the existence of solutions for a second order system of PDEs and calculating all semi-norms in $\mathcal{S}(\mathbb{R}^3)^3$ resp. $C^\infty(\mathbb{T}^3)^3$ we find the sufficient and necessary condition such that a $\mathcal{S}(\mathbb{R}^3)^3$ resp. $C^\infty(\mathbb{T}^3)^3$ vorticity solution of the Euler resp. Navier-Stokes equations exists or stops to exist.
A HISTOMORPHOLOGICAL STUDY OF PAEDIATRIC ADRENAL TUMOURS

BACKGROUND: Tumors of the adrenal gland are extremely rare in children. Those of the adrenal cortex account for only 0.3-0.4 % of all childhood neoplasms. Its rarity has hindered the identification of meaningful prognostic factors. Because very few children have been treated for adrenal tumors, even at major cancer centers, clinical experience is relatively limited and optimal therapy has not been established. Even though assessment of malignancy through histological criteria has been controversial, several investigators reported high degree of accuracy in the evaluation of malignancy by microscopic examination(1, 2). Furthermore, Bugg et al reported that histological type and tumour weight were the most reliable predictors of tumour behaviour in the paediatric age group. AIM: To analyze various adrenal tumors based on histopathological pattern and correlate with clinical findings. MATERIALS AND METHODS: The study included 12 surgical specimens of adrenal gland out of which 3 were resected and 9 were biopsied specimens, collected between JULY 2006 to JUNE 2011 at Institute of Child Health, Niloufer hospital , Hyderabad. Tissues were processed and histopathological patterns were studied on Hematoxylin and Eosin stained sections. Immuno histochemistry was done in relevant cases and the markers used were Ki-67, Synaptophysin, NSE, Chromogranin. RESULT: The study showed incidence of 75% in male and 25% in female children with predilection towards left adrenal gland.
Chaos in a melting pot

Abstract A novel flow instability emerging during a rheometric flow of a phase change material sheared in the vicinity of the melting point is reported. Right above the onset of the flow-induced crystallisation, the presence of the crystals in the flow leads to a primary bifurcation towards an oscillatory flow state. A further decrease of the temperature beyond this point leads to an increase of both the volume fraction and the size of the crystals, which ultimately triggers a fully developed chaotic flow. A full stability diagram as a function of the imposed deformation rate and the temperature is obtained experimentally. The systematic experimental observations reported herein could trigger further studies of the hydrodynamics of phase change materials and may find a number of interesting applications in polymer processing and thermal storage. The experimental findings are complemented by the analysis of a simple numerical model which provides further insights into the physical origins and mechanism of the instability.
Anomalous, anomalous Hall effect in the layered, Kagome, Dirac semimetal KV$_3$Sb$_5$

The electronic anomalous Hall effect (AHE), where charge carriers acquire a velocity component orthogonal to an applied electric field, is one of the most fundamental and widely studied phenomena in physics. There are several different AHE mechanisms known, and material examples are highly sought after, however in the highly conductive (skew scattering) regime the focus has centered around ferromagnetic metals. Here we report the observation of a giant extrinsic AHE in KV$_3$Sb$_5$, an exfoliable, Dirac semimetal with a Kagome layer of Vanadium atoms. Although there has been no reports of magnetic ordering down to 0.25 K, the anomalous Hall conductivity (AHC) reaches $\approx$ 15,507 $\Omega^{-1}$cm$^{-1}$ with an anomalous Hall ratio (AHR) of $\approx$ 1.8$ \%$; an order of magnitude larger than Fe. Defying expectations from skew scattering theory, KV$_3$Sb$_5$ shows an enhanced skew scattering effect that scales quadratically, not linearly, with the longitudinal conductivity ($\sigma_{xx}$), opening the possibility of reaching an anomalous Hall angle (AHA) of 90$^{\circ}$ in metals; an effect thought reserved for quantum anomalous Hall insulators. This observation raises fundamental questions about the AHE and opens a new frontier for AHE (and correspondingly SHE) exploration, stimulating investigation in a new direction of materials, including metallic geometrically frustrated magnets, spin-liquid candidates, and cluster magnets.
A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning

Object perception is a fundamental sub-field of Computer Vision, covering a multitude of individual areas and having contributed high-impact results. While Machine Learning has been traditionally applied to address related problems, recent works also seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects, their properties and their relations with their environment. In this paper, we attempt a systematic investigation of how knowledge-based methods contribute to diverse object perception tasks. We review the latest achievements and identify prominent research directions.
A Time Attention based Fraud Transaction Detection Framework

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security. In this work, we present a novel method for detecting fraud transactions by leveraging patterns from both users' static profiles and users' dynamic behaviors in a unified framework. To address and explore the information of users' behaviors in continuous time spaces, we propose to use \emph{time attention based recurrent layers} to embed the detailed information of the time interval, such as the durations of specific actions, time differences between different actions and sequential behavior patterns,etc., in the same latent space. We further combine the learned embeddings and users' static profiles altogether in a unified framework. Extensive experiments validate the effectiveness of our proposed methods over state-of-the-art methods on various evaluation metrics, especially on \emph{recall at top percent} which is an important metric for measuring the balance between service experiences and risk of potential losses.