Miracle-dz/UniFAR-FTUs huggingface dataset:
An enhanced adaptive Bi-clustering algorithm through building a shielding complex sub-matrix

Bi-clustering refers to the task of finding sub-matrices (indexed by a group of columns and a group of rows) within a matrix of data such that the elements of each sub-matrix (data and features) are related in a particular way, for instance, that they are similar with respect to some metric. In this paper, after analyzing the well-known Cheng and Church bi-clustering algorithm which has been proved to be an effective tool for mining co-expressed genes. However, Cheng and Church bi-clustering algorithm and summarizing its limitations (such as interference of random numbers in the greedy strategy; ignoring overlapping bi-clusters), we propose a novel enhancement of the adaptive bi-clustering algorithm, where a shielding complex sub-matrix is constructed to shield the bi-clusters that have been obtained and to discover the overlapping bi-clusters. In the shielding complex sub-matrix, the imaginary and the real parts are used to shield and extend the new bi-clusters, respectively, and to form a series of optimal bi-clusters. To assure that the obtained bi-clusters have no effect on the bi-clusters already produced, a unit impulse signal is introduced to adaptively detect and shield the constructed bi-clusters. Meanwhile, to effectively shield the null data (zero-size data), another unit impulse signal is set for adaptive detecting and shielding. In addition, we add a shielding factor to adjust the mean squared residue score of the rows (or columns), which contains the shielded data of the sub-matrix, to decide whether to retain them or not. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the theoretical analysis. The results obtained on a publicly available real microarray dataset show the enhancement of the bi-clusters performance thanks to the proposed method.
Space-time reduced-order modeling for uncertainty quantification

This work focuses on the space-time reduced-order modeling (ROM) method for solving large-scale uncertainty quantification (UQ) problems with multiple random coefficients. In contrast with the traditional space ROM approach, which performs dimension reduction in the spatial dimension, the space-time ROM approach performs dimension reduction on both the spatial and temporal domains, and thus enables accurate approximate solutions at a low cost. We incorporate the space-time ROM strategy with various classical stochastic UQ propagation methods such as stochastic Galerkin and Monte Carlo. Numerical results demonstrate that our methodology has significant computational advantages compared to state-of-the-art ROM approaches. By testing the approximation errors, we show that there is no obvious loss of simulation accuracy for space-time ROM given its high computational efficiency.
Teaching Ancient Technology using “Hands-On” Learning and Experimental Archaeology

Universities increasingly demand high quality courses that are engaging, develop high level analytical skills and foster deep understanding of key disciplinary subject matter. We describe an approach to teaching ancient technology to undergraduate students that is successful in engaging students through hands-on learning, and develops familiarity with general scientific approaches to building knowledge, while also enabling deeper understanding of core archaeological skills and subject matter, such as artifact analysis and the evolution of technology. There is value in sharing approaches to teaching ancient technology and experimental archaeology and we hope others will find this discussion helpful in developing their own courses or comparing our approach with those which are already on offer.
High-Dimensional Functional Mixed-effect Model for Bilevel Repeated Measurements

The bilevel functional data under consideration has two sources of repeated measurements. One is to densely and repeatedly measure a variable from each subject at a series of regular time/spatial points, which is named as functional data. The other is to repeatedly collect one functional data at each of the multiple visits. Compared to the well-established single-level functional data analysis approaches, those that are related to high-dimensional bilevel functional data are limited. In this article, we propose a high-dimensional functional mixed-effect model (HDFMM) to analyze the ∗Corresponding Author: gfu@binghamton.edu 1 ar X iv :2 11 1. 06 79 6v 1 [ st at . M E ] 1 2 N ov 2 02 1 association between the bilevel functional response and a large scale of scalar predictors. We utilize B-splines to smooth and estimate the infinite-dimensional functional coefficient, a sandwich smoother to estimate the covariance function, and integrate the estimation of covariance related parameters together with all regression parameters into one framework through a fast updating MCMC procedure. We demonstrate that the performance of the HDFMM method is promising under various simulation studies and a real data analysis. As an extension of the well-established linear mixed model, the HDFMM model extends the response from repeatedly measured scalars to repeatedly measured functional data/curves, while maintaining the ability to account for relatedness among samples and control for confounding factors.
The complete weight enumerator of a subclass of optimal three-weight cyclic codes

A class of optimal three-weight cyclic codes of dimension 3 over any finite field was presented by Vega [Finite Fields Appl. , 42 (2016) 23-38]. Shortly thereafter, Heng and Yue [IEEE Trans. Inf. Theory, 62(8) (2016) 4501-4513] generalized this result by presenting several classes of cyclic codes with either optimal three weights or a few weights. On the other hand, a class of optimal five-weight cyclic codes of dimension 4 over a prime field was recently presented by Li, et al. [Adv. Math. Commun. , 13(1) (2019) 137-156]. One of the purposes of this work is to present a more general description for these optimal five-weight cyclic codes, which gives place to an enlarged class of optimal five-weight cyclic codes of dimension 4 over any finite field. As an application of this enlarged class, we present the complete weight enumerator of a subclass of the optimal three-weight cyclic codes over any finite field that were studied by Vega [Finite Fields Appl. , 42 (2016) 23-38]. In addition, we study the dual codes in this enlarged class of optimal five-weight cyclic codes, and show that they are cyclic codes of length $q^2-1$, dimension $q^2-5$, and minimum Hamming distance 4. In fact, through several examples, we see that those parameters are the best known parameters for linear codes.
Fermionic quantum field theories as probabilistic cellular automata

A class of fermionic quantum field theories with interactions is shown to be equivalent to probabilistic cellular automata, namely cellular automata with a probability distribution for the initial states. Probabilistic cellular automata on a one-dimensional lattice are equivalent to two dimensional quantum field theories for fermions. They can be viewed as generalized Ising models on a square lattice and therefore as classical statistical systems. As quantum field theories they are quantum systems. Thus quantum mechanics emerges from classical statistics. As an explicit example for an interacting fermionic quantum field theory we describe a type of discretized Thirring model as a cellular automaton. The updating rule of the automaton is encoded in the step evolution operator that can be expressed in terms of fermionic annihilation and creation operators. The complex structure of quantum mechanics is associated to particle – hole transformations. The naive continuum limit exhibits Lorentz symmetry. We exploit the equivalence to quantum field theory in order to show how quantum concepts as wave functions, density matrix, non-commuting operators for observables and similarity transformations are convenient and useful concepts for the description of probabilistic cellular automata.
APPLICATION OF SOBEK HYDRAULIC SURFACE WATER MODELS IN THE NETHERLANDS HYDROLOGICAL MODELLING INSTRUMENT

The Netherlands is a low‐lying country in the delta of the Rhine and Meuse rivers. Water management is a challenging task at times of high river discharges, but also during droughts, with salt intrusion being an important issue. When river discharges are low, several inlets in the western parts of the country cannot be used, because salinities exceed the standards. Climate change will increase the frequency of these problems. The Freshwater Policy study within the Dutch Delta Programme investigates how to deal with future freshwater supply and demand in the Netherlands.
Using Bayesian Network Analysis to Reveal Complex Natures of Relationships

Relationships are vital for mankind in many aspects. According to Maslow hierarchy of needs, it is suggested that while a healthy relationship is an essential part of a human life that fundamentally determines our goals and purposes, an unsuccessful relationship can lead to suicide and other major psychological problems. However, a complete understanding of this topic still remains a challenge and the divorce rate is rising more than ever before to almost 50 percents. The objective of this research is to explore the association between each group of behaviors by performing Bayesian network analysis on a large publically available Experiences in Close Relationships Scale, a test of attachment style survey (ECR) data from openpsychometrics database. The resulting directed acyclic graph has 2 root nodes (Q02 from avoidant and Q05 from anxious attachment) and 5 end nodes (Q16, Q34, and Q36 from anxious attachment). The network can be divided into 5 clusters, 2 avoidance and 3 anxiety clusters. Furthermore, our list of items in the clusters are consistent with the findings of previous factor analysis studies and our estimated coefficients are significantly correlated with those of one partial correlation network study. Keyword : Bayesian Network; Experiences in Close Relationships Scale (ECR), a test of attachment style; open psychometrics
Generalizing quasi-categories via model structures on simplicial sets

We use Cisinski's machinery to construct and study model structures on the category of simplicial sets whose classes of fibrant objects generalize quasi-categories. We identify a lifting condition which captures the homotopical behavior of quasi-categories without the algebraic aspects and show that there is a model structure whose fibrant objects are precisely those which satisfy this condition. We also identify a localization of this model structure whose fibrant objects satisfy a"special horn lifting"condition similar to the one satisfied by quasi-categories. This special horn model structure leads to a conjecture characterization of the bijective-on-0-simplices trivial cofibrations of the Joyal model structure. We also discuss how these model structures all relate to one another and to the minimal model structure.
Speeding Up Entmax

Softmax is the de facto standard for normaliz- 001 ing logits in modern neural networks for lan- 002 guage processing. However, by producing a 003 dense probability distribution each token in the 004 vocabulary has a nonzero chance of being se- 005 lected at each generation step, leading to a vari- 006 ety of reported problems in text generation. α - 007 entmax of Peters et al. (2019) solves this prob- 008 lem, but is unfortunately slower than softmax. 009 In this paper, we propose an alternative to α - 010 entmax, which keeps its virtuous characteris- 011 tics, but is as fast as optimized softmax and 012 achieves on par or better performance in ma- 013 chine translation task.
Locally analysing the risk factors for fatal single vehicle crashes hot spots in Western Australia

Fatal single vehicle crashes (FSVC) have been identified as one of the most serious road-related risks; therefore, it is necessary to investigate their potential risk factors. A number of studies have been conducted to suggest the major factors and the relevant intervention strategies of FSVC. However, most of these studies ignored the spatial heterogeneity in estimating relationships between FSVC and potential factors. The aim of the study is to identify spatial patterns of FSVC in an attempt to explain its relationship with the underlying possible factors in Western Australia. Two types of factors in relation to FSVC are investigated in this research: (1) road condition (slope, sealed, wet, curve, cross and speed limits) and (2) environmental factors (light condition and rain weather). Geographic weighted regression model is applied to identify relationships between the factors and FSVC hot spots. The results show that the impact from significant risk factors is different on rural and metropolitan areas; therefore, the ways of roads accident prevention, especially for the FSVC, should be different.
Achieving high survivability in distributed systems through automated response

We propose a new model for automated response in distributed systems. We formalize the process of providing automated responses and the criterion for asserting global optimality of the selection of responses. We show that reaching the globally optimal solution is an NP-hard problem. Therefore we design a genetic algorithm framework for searching for good selections of responses in the runtime. Our system constantly adapts itself to the changing environment based on short-term history and also tracks the patterns of attacks in a long-term history. Unknown security attacks, or zero-day attacks, exploit unknown or undisclosed vulnerabilities and can cause devastating damage. The escalation pattern, commonly represented as an attack graph, is not known a priori for a zero-day attack. Hence, a typical response system provides ineffective or drastic responses. Our system “conceptualizes” nodes in an attack graph, whereby they are generalized based on the object-oriented hierarchy for components and alerts. This is done based on our insight that high level manifestations of unknown attacks may bear similarity with those of previously seen attacks. This allows the use of history such as effectiveness of each response from past attacks to assist responses to the unknown attack. This thesis lays down three distinct claims and validates them empirically. The claims are: (i) For automated response, consider a baseline mechanism that has a static mapping from the local detector symptom to a local response. This corresponds to the state-of-the-art in deployed response systems. Now consider our proposed model which takes into account global optimality from choosing a set of responses and also does a dynamic computation of the response combination from the set of detectors and other system parameters (inferences about the accuracy of the attack diagnosis, response effectiveness, etc.). The survivability of the application system with our proposed model is an upper bound of the survivability achievable through the baseline model. (ii) In some practical situations, the proposed model gives higher survivability than the baseline model. (iii) The survivability with our proposed model is improved when the system takes into account history from prior similar attacks. This kind of history is particularly important when the system deals with zero-day attacks.
Multiband light curves from eccentric accreting supermassive black hole binaries

We use long-run, high-resolution hydrodynamics simulations to compute the multi-wavelength light curves (LCs) from thermal disk emission around accreting equal-mass supermassive black hole (BH) binaries, with a focus on revealing binary eccentricity. LCs are obtained by modeling the disk thermodynamics with an adiabatic equation of state, a local blackbody cooling prescription, and corrections to approximate the effects of radiation pressure. We find that modulation of multi-band LCs on the orbital time scale are generally in-phase (to within $\sim\,$2% of a binary orbital period), but they contain pulse substructure in the time domain that is not necessarily reflected in BH accretion rates $\dot M$. We thus predict that binary-hosting AGN will exhibit highly correlated, in-phase, periodic brightness modulations in their low-energy disk emission. However, detectability of these modulations in multi-wavelength observing campaigns could be seriously compromised because observed stochastic variability in AGNs typically has a higher amplitude than our proposed signal. It is possible that observations over temporal baselines of many binary periods may make the signal more prominent, but this would need to be analyzed carefully. If jet emission is predicted by $\dot{M}$, then we predict a weaker correlation with low-energy disk emission due to the differing sub-peak structure. For the binary parameters we explore, we show that LC variability due to hydrodynamics likely dominates Doppler brightening for all equal-mass binaries with disk Mach numbers $\lesssim 20$. A promising signature of eccentricity is weak or absent"lump"periodicity. We find hints that a significant lag exists between $\dot{M}$ and low-energy disk emission for circular binaries, but they are in-phase for eccentric binaries, which might explain some"orphan"blazar flares with no $\gamma$-ray counterpart.
Mobility prediction Based on Machine Learning Algorithms

Nowadays mobile communication is growing fast in the 5G communication industry. With the increasing capacity requirements and requirements for quality of experience, mobility prediction has been widely applied to mobile communication and has becoming one of the key enablers that utilizes historical traffic information to predict future locations of traffic users, Since accurate mobility prediction can help enable efficient radio resource management, assist route planning, guide vehicle dispatching, or mitigate traffic congestion. However, mobility prediction is a challenging problem due to the complicated traffic network. In the past few years, plenty of researches have been done in this area, including Non-Machine-Learning (Non-ML)- based and Machine-Learning (ML)-based mobility prediction. In this paper, firstly we introduce the state of the art technologies for mobility prediction. Then, we selected Support Vector Machine (SVM) algorithm, the ML algorithm for practical traffic date training. Lastly, we analyse the simulation results for mobility prediction and introduce a future work plan where mobility prediction will be applied for improving mobile communication.
A Reverse Jensen Inequality Result with Application to Mutual Information Estimation

The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard inequalities such as the inequality of arithmetic and geometric means or the Hölder inequality. In a probabilistic setting, the Jensen inequality describes the relationship between a convex function and the expected value. In this work, we want to look at the probabilistic setting from the reverse direction of the inequality. We show that under minimal constraints and with a proper scaling, the Jensen inequality can be reversed. We believe that the resulting tool can be helpful for many applications and provide a variational estimation of mutual information, where the reverse inequality leads to a new estimator with superior training behavior compared to current estimators.
Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR

We present a method for cross-lingual training an ASR system using absolutely no transcribed training data from the target language, and with no phonetic knowledge of the language in question. Our approach uses a novel application of a decipherment algorithm, which operates given only unpaired speech and text data from the target language. We apply this decipherment to phone sequences generated by a universal phone recogniser trained on out-of-language speech corpora, which we follow with flat-start semi-supervised training to obtain an acoustic model for the new language. To the best of our knowledge, this is the first practical approach to zero-resource cross-lingual ASR which does not rely on any hand-crafted phonetic information. We carry out experiments on read speech from the GlobalPhone corpus, and show that it is possible to learn a decipherment model on just 20 minutes of data from the target language. When used to generate pseudo-labels for semi-supervised training, we obtain WERs that range from 32.5% to just 1.9% absolute worse than the equivalent fully supervised models trained on the same data.
Bi-discriminator GAN for tabular data synthesis

This paper introduces a bi-discriminator GAN for synthesizing tabular datasets containing continuous, binary, and discrete columns. Our proposed approach employs an adapted preprocessing scheme and a novel conditional term for the generator network to more effectively capture the input sample distributions. Additionally, we implement straightforward yet effective architectures for discriminator networks aiming at providing more discriminative gradient information to the generator. Our experimental results on four benchmarking public datasets corroborates the superior performance of our GAN both in terms of likelihood fitness metric and machine learning efficacy.
Time-linear scaling nonequilibrium Green's function method for real-time simulations of interacting electrons and bosons. II. Dynamics of polarons and doublons

Nonequilibrium dynamics of the open chain Holstein-Hubbard model is studied using the linear time-scaling GKBA+ODE scheme developed in the preceeding paper. We focus on the set of parameters relevant for photovoltaic materials, i. \,e. , a pair of electrons interacting with phonons at the cross-over between the adiabatic and anti-adiabatic regimes and at moderately large electron-electron interaction. By comparing with exact solutions for two corner cases, we demonstrate the accuracy of the $T$-matrix (in the $pp$ channel) and the second-order Fan ($GD$) approximations for the treatment of electronic ($e$-$e$) and electron-phonon ($e$-ph) correlations, respectively. The feedback of electron on phonons is consistently included and is shown to be mandatory for the total energy conservation. When two interactions are simultaneously present, our simulations offer a glimpse into the dynamics of doublons and polarons unveiling the formation, propagation and decay of these quasiparticles, energy redistribution between them and self-trapping of electrons.
Chemical warfare protective clothing: identification of performance limitations and their possible solution

Abstract : The major factors which contribute to the increased thermal burden imposed by chemical warfare (CW) protective clothing are the insulation characteristics (clo) and the evaporative impedance (im) of the material; and, increased levels of energy expenditure for performing physical exercise while wearing these clothing systems. An approach to alleviating heat stress is through the use of auxiliary cooling. A number of prototype microclimate cooling systems which employ either air-cooled or liquid-cooled vests have been shown to be effective in reducing soldier heat strain during exercise while wearing CW protective clothing in hot environments. Our Institute has also developed the ability to predict the thermal strain, water requirements, tolerance time and optimal work-rest ratios for soldiers exercising in CW protective clothing in a wide variety of environmental conditions.