Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Lasso With Missing and Grossly Corrupted Observations

This paper studies the problem of accurately recovering a <formula formulatype="inline"><tex Notation="TeX">$k$</tex> </formula>-sparse vector <formula formulatype="inline"><tex Notation="TeX">$\beta^{\star}\in\BBR^{p}$</tex> </formula> from highly corrupted linear measurements <formula formulatype="inline"><tex Notation="TeX">$y=X\beta^{\star}+e^{\star}+w$</tex> </formula>, where <formula formulatype="inline"> <tex Notation="TeX">$e^{\star}\in\BBR^{n}$</tex></formula> is a sparse error vector whose nonzero entries may be unbounded and <formula formulatype="inline"><tex Notation="TeX">$w$</tex></formula> is a stochastic noise term. We propose a so-called extended Lasso optimization which takes into consideration sparse prior information of both <formula formulatype="inline"><tex Notation="TeX">$\beta^{\star}$</tex> </formula> and <formula formulatype="inline"> <tex Notation="TeX">$e^{\star}$</tex></formula>. Our first result shows that the extended Lasso can faithfully recover both the regression as well as the corruption vector. Our analysis relies on the notion of extended restricted eigenvalue for the design matrix <formula formulatype="inline"><tex Notation="TeX">$X$</tex></formula>. Our second set of results applies to a general class of Gaussian design matrix <formula formulatype="inline"><tex Notation="TeX">$X$</tex> </formula> with i.i. d. rows <formula formulatype="inline"><tex Notation="TeX">${\cal N}(0,\Sigma)$</tex></formula>, for which we can establish a surprising result: the extended Lasso can recover exact signed supports of both <formula formulatype="inline"> <tex Notation="TeX">$\beta^{\star}$</tex></formula> and <formula formulatype="inline"><tex Notation="TeX">$e^{\star}$</tex> </formula> from only <formula formulatype="inline"> <tex Notation="TeX">$\Omega(k\log p\log n)$</tex></formula> observations, even when a linear fraction of observations is grossly corrupted. Our analysis also shows that this amount of observations required to achieve exact signed support is indeed optimal.
Merging Verb Senses of Hindi WordNet using Word Embeddings

In this paper, we present an approach for merging fine-grained verb senses of Hindi WordNet. Senses are merged based on gloss similarity score. We explore the use of word embeddings for gloss similarity computation and compare with various WordNet based gloss similarity measures. Our results indicate that word embeddings show significant improvement over WordNet based measures. Consequently, we observe an increase in accuracy on merging fine-grained senses. Gold standard data constructed for our experiments is made available.
Analyzing Activity Recognition Uncertainties in Smart Home Environments

In spite of the importance of activity recognition (AR) for intelligent human-computer interaction in emerging smart space applications, state-of-the-art AR technology is not ready or adequate for real-world deployments due to its insufficient accuracy. The accuracy limitation is directly attributed to uncertainties stemming from multiple sources in the AR system. Hence, one of the major goals of AR research is to improve system accuracy by minimizing or managing the uncertainties encountered throughout the AR process. As we cannot manage uncertainties well without measuring them, we must first quantify their impact. Nevertheless, such a quantification process is very challenging given that uncertainties come from diverse and heterogeneous sources. In this article, we propose an approach, which can account for multiple uncertainty sources and assess their impact on AR systems. We introduce several metrics to quantify the various uncertainties and their impact. We then conduct a quantitative impact analysis of uncertainties utilizing data collected from actual smart spaces that we have instrumented. The analysis is intended to serve as groundwork for developing “diagnostic” accuracy measures of AR systems capable of pinpointing the sources of accuracy loss. This is to be contrasted with the currently used accuracy measures.
Varied Pathways of Stage IA Lung Adenocarcinomas Discovered by Integrated Gene Expression Analysis

Background: Discovery of the progression-associated genes and pathways in lung adenocarcinoma (LAD) has important implications in understanding the molecular mechanism of tumor development. However, few studies had been performed to focus on the changes of pathways in lung adenocarcinoma development using microarray expression profile. Result: We performed a meta-analysis of 4 LAD microarray datasets encompassing 353 patients to reveal differentially expressed genes (DEGs) between normal lung tissues and LAD of different stages. Overall, 1 838 genes were found to be dys-regulated, and the adipogenesis, circadian rhythm, and Id pathways were significantly changed. Interestingly, most of the genes from the same gene family (such as Interleukin receptor, Matrix metallopeptidase, Histone cluster and Minichromosome maintenance complex component families) were found to be up-regulated (or down-regulated). Real-time PCR (qRT-PCR) was applied to validate the expression of randomly selected 18 DEGs in LAD cell lines. In the pathway analysis among stages, Oxidative stress, Glycolysis/Gluconeogenesis and Integrin-mediated cell adhesion pathways, which were involved in cancer cell proliferation and metastasis, were showed to be significantly regulated in stages other than IA. Conclusion: Genes involved in adipogenesis and Id pathways might play important roles in development of LADs. The similar trend of expression of the gene family members suggested coordinate regulation in tumor progression. Three pathways (Oxidative stress, Glycolysis/Gluconeogenesis and Integrin-mediated cell adhesion pathways) significantly regulated in stages other than stage IA suggested that genes and pathways conferring invasive character might be activated in the preinvasive stage IB, while the Oxidative stress and the Glycolysis/Gluconeogenesis pathways might have strong connections to cisplatin-based chemotherapy. The insignificantly regulated three pathways in stage IA might be used in early-stage detection of LAD.
Sequential Penalty Derivative-Free Methods for Nonlinear Constrained Optimization

We consider the problem of minimizing a continuously differentiable function of several variables subject to smooth nonlinear constraints. We assume that the first order derivatives of the objective function and of the constraints can be neither calculated nor explicitly approximated. Hence, every minimization procedure must use only a suitable sampling of the problem functions. These problems arise in many industrial and scientific applications, and this motivates the increasing interest in studying derivative-free methods for their solution. The aim of the paper is to extend to a derivative-free context a sequential penalty approach for nonlinear programming. This approach consists in solving the original problem by a sequence of approximate minimizations of a merit function where penalization of constraint violation is progressively increased. In particular, under some standard assumptions, we introduce a general theoretical result regarding the connections between the sampling technique and the updating of the penalization which are able to guarantee convergence to stationary points of the constrained problem. On the basis of the general theoretical result, we propose a new method and prove its convergence to stationary points of the constrained problem. The computational behavior of the method has been evaluated both on a set of test problems and on a real application. The obtained results and the comparison with other well-known derivative-free software show the viability of the proposed sequential penalty approach.
Online Learning and Exploiting Relational Models in Reinforcement Learning

In recent years, there has been a growing interest in using rich representations such as relational languages for reinforcement learning. However, while expressive languages have many advantages in terms of generalization and reasoning, extending existing approaches to such a relational setting is a non-trivial problem. In this paper, we present a first step towards the online learning and exploitation of relational models. We propose a representation for the transition and reward function that can be learned online and present a method that exploits thesemodels by augmenting Relational Reinforcement Learning algorithms with planning techniques. The benefits and robustness of our approach are evaluated experimentally.
A Regressor-free Adaptive Control for Flexible-joint Robots based on Function Approximation Technique

An adaptive controller is presented in this paper to control an n-link flexible-joint manipulator with time-varying uncertainties. The function approximation technique (FAT) is utilized to represent time-varying uncertainties in some finite combinations of orthogonal basis. The tedious computation of the regressor matrix needed in traditional adaptive control is avoided in the new design, and the controller does not require the variation bounds of time-varying uncertainties needed in traditional robust control. In addition, the joint acceleration is not needed in the controller realization. Via the Lyapunov-like stability theory, adaptive update laws are derived to give convergence of the output tracking error. Moreover, the upper bounds of tracking errors in the transient state are also derived. A 2 DOF planar manipulator with flexible joints is used in the computer simulation to verify the effectiveness of the proposed controller.
Recovering signals with variable sparsity levels from the noisy 1-bit compressive measurements

In this paper, we consider the 1-bit compressive sensing reconstruction problem in a scenario that the sparsity level of the signal is unknown and time variant, and the binary measurements are contaminated with the noise. We introduce a new reconstruction algorithm which we refer to as Noise-Adaptive Restricted Step Shrinkage (NARSS). NARSS is superior in terms of performance, complexity and speed of convergence to the algorithms already introduced in the literature for 1-bit compressive sensing reconstruction from the noisy binary measurements.
Estimation of Probe Cell Locations in High-density Synthetic-oligonucleotide DNA Microarrays

The source of a spatial contribution to the coefficient of variation in Affymetrix GeneChip r © probe cell summaries was determined to be caused by a misalignment of probe cell locations in the hybridisation image. Described, is an algorithm that corrects for misalignment resulting in improved allocation of pixel intensities to probe cells and a substantial reduction in the variance of pixel intensities attributed to individual probe cells. Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708 Departments of Surgery and Genetics, Duke University Medical Center, Durham, NC 27710 Howard Hughes Medical Institute, Durham, NC 27710
HMM-regularization for NMF-based noise robust ASR

In this work we extend a previously proposed NMF-based technique for speech enhancement of noisy speech to exploit a Hidden Markov Model (HMM). The NMF-based technique works by finding a sparse representation of specrogram segments of noisy speech in a dictionary containing both speech and noise exemplars, and uses the activated dictionary atoms to create a time-varying filter to enhance the noisy speech. In order to take into account larger temporal context and constrain the representation by the grammar of a speech recognizer, we propose to regularize the optimization problem by additionally minimizing the distance between state emission probabilities derived from the speech exemplar activations, and a posteriori state probabilities derived by applying the Forward-Backward algorithm to the emission probabilities. Experiments on Track 1 of the 2nd CHiME Challenge, which contains small vocabulary speech corrupted by both reverberation and authentic living room noise at varying SNRs ranging from 9 to -6 dB, confirm the validity of the proposed technique.
Structural Properties and Complexity of a New Network Class: Collatz Step Graphs

In this paper, we introduce a biologically inspired model to generate complex networks. In contrast to many other construction procedures for growing networks introduced so far, our method generates networks from one-dimensional symbol sequences that are related to the so called Collatz problem from number theory. The major purpose of the present paper is, first, to derive a symbol sequence from the Collatz problem, we call the step sequence, and investigate its structural properties. Second, we introduce a construction procedure for growing networks that is based on these step sequences. Third, we investigate the structural properties of this new network class including their finite scaling and asymptotic behavior of their complexity, average shortest path lengths and clustering coefficients. Interestingly, in contrast to many other network models including the small-world network from Watts & Strogatz, we find that CS graphs become ‘smaller’ with an increasing size.
The gendered effects of substance use on employment stability in transitional China

ABSTRACT Substance use is often thought to harm employment prospects, an assumption challenged by the anomaly that people who use licit substances such as alcohol and tobacco are sometimes at a lower risk of unemployment. We argue that employment stability may benefit from the socialisation afforded through using licit substances, particularly in a context where licit substance use is encouraged. Furthermore, because the norms associated with substance use often reflect the gender hierarchy in a society, the impact of substance use on employment stability may be contingent on an individual's gender. Applying Cox proportional hazard modelling to a panel dataset during the critical two decades of China's market-based transition (1991–2011), we found that the impact of substance use on unemployment hazards varies depending on the dosage of the use and the gender of the users. Compared to abstinence, moderate alcohol-drinking reduces the risk of unemployment, and the reduction benefits especially men. The standalone effect of tobacco-smoking is to elevate unemployment hazards; however, this effect is heavily moderated by gender so that female smokers were penalised while male smokers were rewarded in the labour market. Such patterns cannot be explained by community-level modernisation progress and individual-level covariates.
The effects of the ACTIVE cognitive training trial on clinically relevant declines in health-related quality of life.

OBJECTIVES The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study used three cognitive interventions (memory, reasoning, or speed of processing) in order to improve cognitive abilities. In this article, we evaluate ACTIVE's ability to avoid extensive decline in health-related quality of life (HRQoL). METHODS ACTIVE enrolled 2,802 adults aged 65 or older and randomized them into one of three cognitive interventions or a no-contact control group. Researchers obtained data on 2,147 participants at the 24-month follow-up. We measured HRQoL by using the eight Short Form-36 scales, and we defined clinically relevant decline on each as a drop of 0.5 standard deviations from baseline. We defined extensive HRQoL decline as clinically relevant drops on four or more Short Form-36 scales, and we assessed this by using multiple logistic regression methods that adjusted for sociodemographic, cognitive, and health status covariates, and incorporated propensity score derived weights in order to adjust for potential attrition bias. RESULTS We found that 25.0% of ACTIVE participants had extensive HRQoL decline. Participants in the speed-of-processing intervention arm were less likely to have extensive HRQoL decline (adjusted odds ratio = 0.643; p =.004) compared with controls, and participants in the memory and reasoning arms were equivalent to controls (adjusted odds ratios = 1.149 and 1.014, respectively; ps =.322 and.919, respectively). DISCUSSION Although all three intervention arms improved cognitive ability, only the speed-of-processing arm protected against extensive clinically relevant decline in HRQoL.
Multimethod Communication forHigh-Performance MetacomputingApplicationsIan

Metacomputing systems use high-speed networks to connect supercomputers, mass storage systems, scientiic instruments, and display devices with the objective of enabling parallel applications to access geographically distributed computing resources. However, experience shows that high performance often can be achieved only if applications can integrate diverse communication substrates, transport mechanisms, and protocols, chosen according to where communication is directed, what is communicated, or when communication is performed. In this article, we describe a software architecture that addresses this requirement. This architecture allows multiple communication methods to be supported transparently in a single application, with either automatic or user-speciied selection criteria guiding the methods used for each communication. We describe an implementation of this architecture, based on the Nexus communication library, and use this implementation to evaluate performance issues. The implementation supported a wide variety of applications in the I-WAY metacomputing experiment at Supercomputing 95; we use one of these applications to provide a quantitative demonstration of the advantages of multimethod communication in a heterogeneous networked environment.
MDMA and 5‐HT neurotoxicity: the empirical evidence for its adverse effects in humans – no need for translation

In this issue of the BJP, Green et al. suggest that animal data could not be used to predict the adverse effects of 3,4‐methylenedioxymethamphetamine (MDMA) in humans and that MDMA did not produce 5‐HT neurotoxicity in the human brain. This proposal was, however, not accompanied by a review of the empirical evidence in humans. The neuroimaging data on 5‐HT markers in abstinent recreational ecstasy/MDMA users are extensive and broadly consistent. Reduced levels of the 5‐HT transporter (SERT) have been found by research groups worldwide using a variety of assessment measures. These SERT reductions occur across the higher brain regions and remain after controlling for potential confounds. There are also extensive empirical data for impairments in memory and higher cognition, with the neurocognitive deficits correlating with the extent of SERT loss. Hence, MDMA is clearly damaging to humans, with extensive empirical data for both structural and functional deficits.
A new approach based on SVD for speech enhancement

In this paper, a new and quick method is introduced for speech enhancement. The base or basic of this method is due to filtering the singular value which is obtained from SVD. The efficiency of the proposed methods is its strong capability and the speed of in reduction the noise effect and also does not have the typical “musical tone”, which is usually present in other noise reduction methods. The signals to be experimented are combined with an additive white Gaussian noise for a variety of Signal to Noise Ratio (SNR). These methods will be evaluated in SNR optimization and mean opinion score, which led to remarkable results.
Credentialed Secure Communication “ Switchboards ” ( TR 2001-821 )

Software development in distributed computation is complicated by the extra overhead of communication between connected, dispersed hosts in dynamically changing, multiple administrative domains. Many disparate technologies exist for trust management, authentication, secure communication channels, and service discovery, but composing all of these elements into a single system can outweigh principal development efforts. The NYU Disco Switchboard consolidates these connectivity issues into a single convenient, extensible architecture, providing an abstraction for managing secure, host-pair communication with connection monitoring facilities. Switchboard extends the secure authenticated communication channel abstraction provided by standard interfaces such as SSL/TLS with mechanisms to support trust management, key sharing, service discovery, and connection liveness and monitoring. We present an extensible architecture which is particularly useful in dynamically changing, distributed coalition environments. Applications that utilize Switchboard benefit from the availability of authentication, trust management, cryptography, and discovery, while retaining the simplicity of a common interface.
Application of linearization via state feedback to heading control for Autonomous Underwater Vehicle

Most designs for heading controller adopted Taylor linearization technique and neglected its nonlinear factors. For this reason, the accuracy of heading control for autonomous underwater vehicle (AUV) was lower. In order to solve this problem and enhance the accuracy, robustness and stability for heading control system, a nonlinear model of AUV in horizontal plane was established, which include uncertainties. Based on linearization via state feedback, a linear model of AUV was obtained at the operating point, and a robust heading controller was designed with closed-loop gain shaping algorithm. The computer simulation was used to demonstrate the robust controller designed via state feedback, has a better performance than the controller, which based on a linear model using Taylor linearization technique.
Transient identification at nuclear power plants using support vector machines

Support Vector Machines (SVMs), a relatively new paradigm in learning machines, are studied for their potential to recognize transient behavior of detector signals corresponding to various accident events at Nuclear Power Plants (NPPs). Transient classification is a major task for any computer-aided system for recognition of various malfunctions. The ability to identify the state of operation or events occurring at a NPP is crucial so that personnel can select adequate response actions. Modular Accident Analysis Program (MAAP4) is a software program that can be used to model various normal and abnormal events at a NPP. In this study, it is used to simulate various accident events at boiling water reactors (BWR). The simulated sensor readings corresponding to these events have been used to train and test SVM classifiers. SVM calculations have demonstrated that they can produce classifiers with good generalization ability for our specific formulation of the learning problem. This in turn indicates that SVMs show promise as classifiers for the learning problem of identifying transients.
Global migration governance, civil society and the paradoxes of sustainability

ABSTRACT Against the presentation of an asymmetric global governance, this article analyzes the formation of global migration governance with its focus on the politics of migration and development. It traces the marginalization of a rights-based approach to migration and the streamlining of migration governance into business-friendly migration management and a geopolitical securitization agenda. It also reviews the trajectory towards factoring migration into a global development policy discourse as formulated in the UN 2030 Development Agenda. Specifically, it indicates that the inclusion of migration into the Sustainable Development Goals (SDGs) may promote migrant workers’ rights because several of these invoke universal human rights instruments, social protection and the observance of the ILO decent work agenda. However, this will only be possible if civil society critically engages powerful state and non-state actors in the process of monitoring the SDGs’ implementation, and resists their streamlining into investment and free trade neoliberal development regimes.