Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum likelihood estimation of Toeplitz-block-Toeplitz covariances in the presence of subspace interference

The EM algorithm is a commonly cited solution in the literature for the problem of maximum likelihood estimation of covariance matrices under a Toeplitz constraint. In this paper, the solution is extended to the case of two-dimensional signals, where spatial stationarity enforces a Toeplitz-block-Toeplitz structure on the covariance matrix. A further generalisation which is presented involves the estimation of the covariance when the observations are subject to subspace interference. It is shown that this situation is amenable to a missing data interpretation, and can be incorporated into the EM iteration with moderate ease. The solution shares all the characteristics of the I-D Toeplitz estimate. The need to solve this problem arises in many invariance applications, where it is required to fit a stationary multivariate normal model to data which is subject to a certain type of interference. The case of unknown DC offset is included in this class.
QuestionBank: Creating a Corpus of Parse-Annotated Questions

This paper describes the development of QuestionBank, a corpus of 4000 parse-annotated questions for (i) use in training parsers employed in QA, and (ii) evaluation of question parsing. We present a series of experiments to investigate the effectiveness of QuestionBank as both an exclusive and supplementary training resource for a state-of-the-art parser in parsing both question and non-question test sets. We introduce a new method for recovering empty nodes and their antecedents (capturing long distance dependencies) from parser output in CFG trees using LFG f-structure reentrancies. Our main findings are (i) using QuestionBank training data improves parser performance to 89.75% labelled bracketing f-score, an increase of almost 11% over the baseline; (ii) back-testing experiments on non-question data (Penn-II WSJ Section 23) shows that the retrained parser does not suffer a performance drop on non-question material; (iii) ablation experiments show that the size of training material provided by QuestionBank is sufficient to achieve optimal results; (iv) our method for recovering empty nodes captures long distance dependencies in questions from the ATIS corpus with high precision (96.82%) and low recall (39.38%). In summary, QuestionBank provides a useful new resource in parser-based QA research.
Tracking for following and passing persons

This paper presents a multiple target tracking approach for following and passing persons in the context of human-robot interaction. The general purpose for the approach is the use in human augmented mapping. This concept is presented and it is described how navigation and person following are subsumed under it. Results from experiments under test conditions and from data collected during a user study are also provided.
Maintenance of Frequent Patterns: A Survey

AbstrAct This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern maintenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and concise-representation-based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pattern maintenance are also discussed
Figure/ground video segmentation via low-rank sparse learning

Due to its importance, figure/ground segmentation in video has gained interest recently. The key factor of the segmentation is the construction of the spatio-temporal coherence. Previous works usually use the motion approximation as a measurement of the coherence, resulting in a low accuracy. In this paper, we present a novel method to measure the coherence, and an algorithm for target segmentation and tracking is proposed. Each image is abstracted by some compact and perceptually homogeneous elements, and by representing the elements as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of representations that are learned jointly. The coefficients of the constrained representation will act as the measurement of the spatio-temporal coherence. At last, a simple energy minimization solution with an online parameter-updating scheme is adopted in segmented stage, leading to a binary object's segmentation. Meanwhile, an adaptive dictionary is proposed to enhance the system's robust against occlusion. Our approach outperforms the state-of-the-art methods in object segmentation accuracy.
Decentralized Formation Selection Mechanisms Inspired by Foraging Bottlenose Dolphins

Formation control is an important sub-problem in multi-agent robotics and a number of decentralized control strategies have been developed to solve the formation control problem, e.g., [1], [2], [3]. However, there is still no underlying theory that governs the selection of a particular formation. In other words, if an agent has a choice between two formations, which one should it choose? In this paper, we would like to answer this question by drawing inspiration from foraging bottlenose dolphins, Tursiops truncatus. In fact, the networked control community has drawn significant inspiration from interaction-rules in social animals and insects [4], [5], [6]. In particular, the widely used nearest-neighbor-based interaction rules, used for example for formation control [7], [8], [9], consensus [3], [10], and coverage control [11], [12], has a direct biological counterpart, as shown in [4]. Bottlenose dolphins employ an unusual foraging technique known as the horizontal carousal; here the dolphins, after locating a sizeable amount of prey, form a large circle to trap the prey inside that circle. The prey is usually a school of fish and the dolphins slowly tighten the encirclement to restrict the movement of the fish. Eventually, the circle becomes small enough and the dolphins then take turns to charge through the fish while maintaining the integrity of the circle. In this paper, we model the first phase of this method of capturing prey, and offer a choice of two formations for each agent — Large and Small Circle and by using
Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation

For extended periods of time, sequence generation models rely on beam search algorithm to generate output sequence. However, the correctness of beam search degrades when the a model is over-confident about a suboptimal prediction. In this paper, we propose to perform minimum Bayes-risk (MBR) decoding for some extra steps at a later stage. In order to speed up MBR decoding, we compute the Bayes risks on GPU in batch mode. In our experiments, we found that MBR reranking works with a large beam size. Later-stage MBR decoding is shown to outperform simple MBR reranking in machine translation tasks.
Proposal of a Decision-Making Guide for Classroom Control: A Case Study of a Private Secondary School in Subang, Selangor

This case study was carried to investigate teachers’ classroom control and decision-making in a bureaucratic organization. As highlighted by the Head of Discipline (HOD), teachers were prone to transfer discipline cases without much consideration to personal involvement. Through purposive sampling, fourteen teachers were selected in respondents. The Pupil Control Ideology (PCI) and Teachers’ Sense of Efficacy Scale (TSES) were used as an individual profiling test to explain teachers’ cognitive and behavioral perceptions in the school. In addition, a Decision-making questionnaire was designed to emphasize on qualitative analyses pertaining to the problem stated by the HOD. Results revealed that respondents portrayed different rates of transfer that were dependent on the seriousness of the discipline problems. Comparisons among respondents showed common characteristics in terms of ability to conduct personal investigation, obstacles to personal decision-making and likelihood of transfer for serious discipline cases. In terms of differences, respondents differed in rates of transfer, perception towards the scale of discipline problems, personal tactics and classroom leadership styles. As the outcome of the study, the researcher proposes a decision-making guide to address discipline-related issues within the level of personal involvement.
Iterative Regularization for Learning with Convex Loss Functions

We consider the problem of supervised learning with convex loss functions and propose a new form of iterative regularization based on the subgradient method. Unlike other regularization approaches, in iterative regularization no constraint or penalization is considered, and generalization is achieved by (early) stopping an empirical iteration. We consider a nonparametric setting, in the framework of reproducing kernel Hilbert spaces, and prove finite sample bounds on the excess risk under general regularity conditions. Our study provides a new class of efficient regularized learning algorithms and gives insights on the interplay between statistics and optimization in machine learning.
Joint optimization of segmentation and appearance models

Many interactive image segmentation approaches use an objective function which includes appearance models as an unknown variable. Since the resulting optimization problem is NP-hard the segmentation and appearance are typically optimized separately, in an EM-style fashion. One contribution of this paper is to express the objective function purely in terms of the unknown segmentation, using higher-order cliques. This formulation reveals an interesting bias of the model towards balanced segmentations. Furthermore, it enables us to develop a new dual decomposition optimization procedure, which provides additionally a lower bound. Hence, we are able to improve on existing optimizers, and verify that for a considerable number of real world examples we even achieve global optimality. This is important since we are able, for the first time, to analyze the deficiencies of the model. Another contribution is to establish a property of a particular dual decomposition approach which involves convex functions depending on foreground area. As a consequence, we show that the optimal decomposition for our problem can be computed efficiently via a parametric maxflow algorithm.
Religious Care Required for Japanese Terminally Ill Patients With Cancer From the Perspective of Bereaved Family Members

The aim of this study was to explore the most suitable religious care for Japanese terminally ill patients with cancer based on the opinions of bereaved family members. A multicenter questionnaire survey on palliative care service was sent to 592 bereaved family members of patients with cancer who were admitted to palliative care units in Japan, and 430 responded by mail. In the section of the questionnaire about religious care, 382 responses were used for quantitative analysis, and 71 responses about religious care for qualitative analysis. In the current study, the 71 responses were grouped into families with and without a religion and were analyzed qualitatively. Families with a religion (N = 28) chose answers such as ‘‘Instrumental care’’ such as music or a religious event, ‘‘Freedom of choice of kinds for religious care,’’ ‘‘Staff involvement of religious care,’’ ‘‘Meeting with a pastoral care workers,’’ and ‘‘Burden of offering a different kind of personal religion. ’’ In contrast, families without a religion (N = 44) chose answers such as ‘‘Instrumental care,’’ ‘‘Freedom of choice whether patients receive religious care or not,’’ ‘‘Spiritual care,’’ ‘‘Not being able to accept religious care,’’ and ‘‘Burden of thinking about a religion and nuisance. ’’ These findings suggest that Japanese bereaved families with a religion generally regard religious care positively and prefer care through their own religion, whereas some families without a religion require religious care but some do not prefer it.
Optimized and Scalable Co-Processor for McEliece with Binary Goppa Codes

Asymmetric cryptographic primitives are essential to enable secure communications in public networks or public mediums. Such primitives can be deployed as software libraries or hardware co-processors, the latter being more commonly employed in systems on chip (SoC) scenarios, embedded devices, or application-specific servers. Unfortunately, the most commonly available solutions, based on RSA or elliptic curve cryptography (ECC), are highly processing intensive due to the underlying extended-precision modular arithmetic. Consequently, they are not available on highly constrained platforms. Aiming to tackle this issue, we here investigate an alternative asymmetric encryption scheme that relies on lightweight arithmetic: McEliece. This scheme is especially appealing because, being based on error correction codes, it displays a simpler arithmetic and leads to better performance when compared to RSA or ECC. To evaluate the implementation of this scheme in hardware, we propose and analyze a flexible architecture whose security level and time versus area usage characteristics can be reconfigured as desired. The proposed architecture is suitable to all usual security levels, ranging from 80 to 256 bits. It is also very efficient, being able to perform data decryption with binary Goppa codes in 56µs with 3,402 slices on a Xilinx Spartan-3AN FPGA, whereas the best-known result in the literature for the same FPGA is 115µs with 7,331 slices. Alternatively, the architecture can operate with quasi-dyadic Goppa (QD-Goppa) codes, which involves smaller keys than traditional binary Goppa codes. In the latter case, for an 80-bit security level, the decryption operation can take from 1.1ms with 1,129 slices to 68µs with 8,268 sices. By choosing a more hardware-friendly decoding algorithm, focusing hardware resources on most bottleneck operations and sharing hardware resource for two different algorithms, better results than the those in the literature were obtained.
Low-dose TNF augments fracture healing in normal and osteoporotic bone by up-regulating the innate immune response

The mechanism by which trauma initiates healing remains unclear. Precise understanding of these events may define interventions for accelerating healing that could be translated to the clinical arena. We previously reported that addition of low‐dose recombinant human TNF (rhTNF) at the fracture site augmented fracture repair in a murine tibial fracture model. Here, we show that local rhTNF treatment is only effective when administered within 24 h of injury, when neutrophils are the major inflammatory cell infiltrate. Systemic administration of anti‐TNF impaired fracture healing. Addition of rhTNF enhanced neutrophil recruitment and promoted recruitment of monocytes through CCL2 production. Conversely, depletion of neutrophils or inhibition of the chemokine receptor CCR2 resulted in significantly impaired fracture healing. Fragility, or osteoporotic, fractures represent a major medical problem as they are associated with permanent disability and premature death. Using a murine model of fragility fractures, we found that local rhTNF treatment improved fracture healing during the early phase of repair. If translated clinically, this promotion of fracture healing would reduce the morbidity and mortality associated with delayed patient mobilization.
Weakly-supervised relation classification for information extraction

This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed <i>BootProject</i> algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.
External motivations scale for sport spectators (EMSSS) and internal motivations scale for sport spectators (IMSSS) validity and reliability study Spor seyircileri için dışsal güdü ölçeği (SSİDGÖ) ve spor seyircileri için içsel güdü ölçeği (SSİİGÖ) geçerlik ve güvenilirlik çalışması

The purpose of this study was to develop an instrument to assess the sport spectators’ motives to watch sporting events. The sample group was composed of 415 students who were sampled through convenient sampling method. Initially, a literature review was done in the scale development process. Then, a semi-constructed qualitative interview was conducted with 12 groups (n=75). Based upon the data an item pool was formed and expert views were obtained accordingly. A pool with 113 items, in light of the feedbacks, was eventually reached ready. The items were divided into two groups. External factors were composed of 48 items and internal factors group included 65 items. According to the exploratory factor analysis, which was conducted for the 48 items composing the External Motivations for Sport Spectators Scale, 34 items were grouped under 9 factors which had eigenvalues over 1. The Cronbach’s Alpha value for the total scale was . 92. In the analysis of the Internal Motivations for Sport Spectators Scale, which was composed of 65 items, 40 items were grouped under 7 factors which had eigenvalues over 1. The Cronbach’s Alpha value of the total scale was . 96. In conclusion, “External Motivations Scale for Sport Spectators (EMSSS)” and “Internal Motivations Scale for Sport Spectators (IMSSS)” were found to be a valid and reliable instrument. Ozet Bu calismanin amaci spor seyircilerinin spor musabakalarini seyretme gudulerini belirlemek icin kullanilacak bir olcum araci gelistirmektir. Arastirmaninin orneklem grubu ulasilabilir orneklem yolu ile secilen 415 ogrenciden olusmustur. Olceklerin gelistirilme surecinde oncelikle literatur taramasi gerceklestirilmistir. Sonrasinda 12 grupla (n=75) yari yapilandirilmis nitel gorusme yapilmistir. Edinilen bilgilere gore madde havuzu olusturulmustur ve uzman gorusleri alinmistir. Gelen donutlerle madde havuzu 113 maddeli olarak hazirlanmistir. Maddeler icsel ve dissal faktorler olarak gruplara ayrilmistir. Dissal faktorler 48, icsel faktorler ise 65 maddeden olusmustur. Aciklayici Faktor Analizi’nin kullanildigi Seyirciler Icin Dissal Faktorler Olcegi icin test edilen 48 maddeden 34’unun oz degerinin 1’den buyuk dokuz faktor altinda toplandigi gorulmustur. Toplam olcegin Cronbach Alpha ic tutarlilik degeri α=0,92 olarak bulunmustur. Seyirciler Icin Icsel faktorler Olcegi icin ise analize alinan 65 maddeden 40’inin oz degerinin 1’den buyuk yedi faktor altinda toplandigi belirlenmistir. Toplam olcegin Cronbach Alpha ic tutarlilik degerleri ise α=0,96’dir. Sonuc olarak “Spor Seyircileri Icin Dissal Gudu Olcegi (SSIDGO)” ve “Spor Seyircileri Icin Icsel Gudu Olcegi (SSIIGO)”nin yapilan analizler dogrultusunda gecerli ve guvenilir oldugu kabul edilmistir.
Default , Commitment , and Domestic Bank Holdings of Sovereign Debt †

How do the incentives of domestic banks and sovereign governments interact? This paper presents a model of government default and banks that invest in the debt of their own sovereign. In the model, banks demand safe assets to use as collateral, and default affects bank equity. These losses inhibit banks’ ability to attract deposits, leading to lower private credit provision, and lower output. This disincentivizes the sovereign from defaulting. The extent of output losses depends on characteristics of the banking system, including sovereign exposures, equity, and deposits. In turn, bank exposures are affected by default risk. The model is also used to show that policies such as financial repression can improve welfare, but worsen output losses in the event of default. †I would like to thank Patrick Bolton, Mark Gertler, Juan Navarro-Staicos, Jaromir Nosal, Ricardo Reis, Stephanie Schmitt-Grohe, Valentin Somma, Jon Steinsson, and Martin Uribe for their advice and support over the course of this project, and seminar participants at Columbia University and the RIDGE Workshop on Sovereign Debt at University of Buenos Aires for helpful discussions. I also thank the Macro Financial Modeling Group funded by the Alfred P. Sloan Foundation for financial support. ‡Department of Economics, Columbia University, 1022 International Affairs Building, 420 W 118th Street, New York, NY 10027. Email: cmb2239@columbia.edu.
The meaning of job performance in collectivistic and high power distance cultures

Purpose – Three broad behavioral categories have been related to organizational goals: task (in‐role), citizenship (extra‐role), and counterproductive behaviors. Because most studies modeling these behaviors have been conducted in culturally similar contexts (individualistic and relatively low power distance settings), the purpose of this paper is to test the invariance of such a triad categorization of performance in collectivistic and high power distance contexts.Design/methodology/approach – Data of employees' proficiencies (n = 1,022) in 34 work activities representing the three behavioral performance categories were factor analyzed. Data were collected by adapting existing behavioral‐based instruments exhibiting strong psychometric properties.Findings – Although results corroborate the existence of a triad categorization of employee behaviors, culture‐specific variations attesting to the partitioning of in‐role behaviors according to the distribution of power in organizations were found. Results also.
SHAM‐sensitive alternative respiration in the xylose‐metabolizing yeast Pichia stipitis

SHAM‐sensitive (STO) alternative respiration is present in the xylose‐metabolizing, Crabtree‐negative yeast, Pichia stipitis, but its pathway components and physiological roles during xylose metabolism are poorly understood. We cloned PsSTO1, which encodes the SHAM‐sensitive terminal oxidase (PsSto1p), by genome walking from wild‐type CBS 6054 and subsequently deleted PsSTO1 by targeted gene disruption. The resulting sto1‐Δ deletion mutant, FPL‐Shi31, did not contain other isoforms of Sto protein that were detectable by Western blot analysis using an alternative oxidase monoclonal antibody raised against the Sto protein from Sauromatum guttatum. Levels of cytochromes b, c, c1 and a·a3 did not change in the sto1‐Δ mutant, which indicated that deleting PsSto1p did not alter the cytochrome pool. Interestingly, the sto1‐Δ deletion mutant stopped growing earlier than the parent and produced 20% more ethanol from xylose. Heterologous expression of PsSTO1 in Saccharomyces cerevisiae increased its total oxygen consumption rate and imparted cyanide‐resistant oxygen uptake but did not enable growth on ethanol, indicating that PsSto1p is not coupled to ATP synthesis. We present evidence that the mitochondrial NADH dehydrogenase complex (Complex I) was present in wild‐type CBS 6054 but was bypassed in the cells during xylose metabolism. Unexpectedly, deleting PsSto1p led to the use of Complex I in the mutant cells when xylose was the carbon source. We propose that the non‐proton‐translocating NAD(P)H dehydrogenases are linked to PsSto1p in xylose‐metabolizing cells and that this non‐ATP‐generating route serves a regulatory function in the complex redox network of P. stipitis. Published in 2002 by John Wiley & Sons, Ltd.