Miracle-dz/UniFAR-FTUs huggingface dataset:
Model selection in compositional spaces

We often build complex probabilistic models by composing simpler models—using one model to generate parameters or latent variables for another model. This allows us to express complex distributions over the observed data and to share statistical structure between different parts of a model. In this thesis, we present a space of matrix decomposition models defined by the composition of a small number of motifs of probabilistic modeling, including clustering, low rank factorizations, and binary latent factor models. This compositional structure can be represented by a contextfree grammar whose production rules correspond to these motifs. By exploiting the structure of this grammar, we can generically and efficiently infer latent components and estimate predictive likelihood for nearly 2500 model structures using a small toolbox of reusable algorithms. Using a greedy search over this grammar, we automatically choose the decomposition structure from raw data by evaluating only a small fraction of all models. The proposed method typically finds the correct structure for synthetic data and backs off gracefully to simpler models under heavy noise. It learns sensible structures for datasets as diverse as image patches, motion capture, 20 Questions, and U.S. Senate votes, all using exactly the same code. We then consider several improvements to compositional structure search. We present compositional importance sampling (CIS), a novel procedure for marginal likelihood estimation which requires only posterior inference and marginal likelihood estimation algorithms corresponding to the production rules of the grammar. We analyze the performance of CIS in the case of identifying additional structure within a low-rank decomposition. This analysis yields insights into how one should design a space of models to be recursively searchable. We next consider the problem of marginal likelihood estimation for the production rules. We present a novel method for obtaining ground truth marginal likelihood values on synthetic data, which enables the rigorous quantitative comparison of marginal likelihood estimators. Using
Atlantic pycnocline theory scrutinized using a coupled climate model

Simulations with changed Southern Ocean wind‐stress, oceanic vertical mixing, surface freshwater forcing and global warming confirm the basic equations of Gnanadesikan's (1999) theory for the Atlantic: one vertical scale, the pycnocline depth D, contributes inversely proportional to low‐latitudinal upwelling and linearly to Southern Ocean eddy transport. The maximum Atlantic overturning is confirmed to be quadratic in D but is also shown to be linear in a meridional density difference Δρ. Our simulations strongly suggest that the theory needs to be complemented by a dynamical equation for Δρ since changes in both D and Δρ are significant and mutually independent. While, under global warming D varies strongly and Δρ is practically constant, the situation is reversed for altered surface freshwater fluxes. Similarly, variations in the meridional length scale of Southern Ocean outcropping require a dynamical equation in order to capture the fundamental behavior of the Atlantic meridional overturning circulation.
GRID DATA STREAMING

Flourish development of grid computing have been seen in recent years which has enabled researchers to collaborate more efficiently by sharing computing, storage, and instrumental resources. Data grids focusing on large-scale data sharing and processing are most popular and data management is one of most essential functionalities in a grid software infrastructure. Applications such as astronomical observations, large-scale numerical simulation, and sensor networks generate more and more data, which constitutes great challenges to storage and processing capabilities. Most of these data intensive applications can be considered as data stream processing with fixed processing patterns and periodical looping. Grid data streaming management is gaining more and more attention in the grid community. In this work, a detailed survey of current grid data streaming research efforts is provided and features of corresponding implementations are summarized. While traditional grid data management systems provide functions like data transfers, placements and locating, data streaming in a grid environment requires additional supports, e.g. data cleanup and transfer scheduling. For example, at storage-constraint grid nodes, data can be streamed, made available to corresponding applications in an on-demand manner, and finally cleaned up after processing is completed. Grid data streaming management is particularly essential to enable grid applications on CPU-rich but storage-limit grid nodes. In this work, a grid data streaming environment is proposed with detailed system analysis and design. Several additional modules, e.g. performance sensors, predictors and schedulers, are implemented. Initial experimental results show that data streaming leads to a better utilization of data storage and improves system performance significantly. Key Words—Grid computing, data streams, and data streaming applications. * E-mail: jcao@tsinghua.edu. cn. This work is funded by the Ministry of Education of China under the quality engineering program for higher education and the Ministry of Science and Technology of China under the national 863 high-tech R&D program (grant No. 2006AA10Z237). Wen Zhang, Junwei Cao, Lianchen Liu, and Cheng Wu 2
Modelling Psychological Responses to the Great East Japan Earthquake and Nuclear Incident

The Great East Japan (Tōhoku/Kanto) earthquake of March 2011was followed by a major tsunami and nuclear incident. Several previous studies have suggested a number of psychological responses to such disasters. However, few previous studies have modelled individual differences in the risk perceptions of major events, or the implications of these perceptions for relevant behaviours. We conducted a survey specifically examining responses to the Great Japan earthquake and nuclear incident, with data collected 11–13 weeks following these events. 844 young respondents completed a questionnaire in three regions of Japan; Miyagi (close to the earthquake and leaking nuclear plants), Tokyo/Chiba (approximately 220 km from the nuclear plants), and Western Japan (Yamaguchi and Nagasaki, some 1000 km from the plants). Results indicated significant regional differences in risk perception, with greater concern over earthquake risks in Tokyo than in Miyagi or Western Japan. Structural equation analyses showed that shared normative concerns about earthquake and nuclear risks, conservation values, lack of trust in governmental advice about the nuclear hazard, and poor personal control over the nuclear incident were positively correlated with perceived earthquake and nuclear risks. These risk perceptions further predicted specific outcomes (e.g. modifying homes, avoiding going outside, contemplating leaving Japan). The strength and significance of these pathways varied by region. Mental health and practical implications of these findings are discussed in the light of the continuing uncertainties in Japan following the March 2011 events.
An NEH-based heuristic algorithm for distributed permutation flowshop scheduling problems

Distributed Permutation Flowshop Scheduling Problem (DPFSP) is a newly proposed scheduling problem with a strong engineering background. Whereas there is a body of work on scheduling problems in the past decades, the literature on DPFSPs is scant and in its infancy. Motivated by the good performances of some heuristics in a very recent work, we propose a constructive heuristic algorithm enhanced through a novel dispatching rule to deal with the DPFSP. Given multiple factories in a DPFSP, our dispatching rule will insert a group of jobs to the factories at one time instead of inserting one job at one time like the original rules. The time complexity of the proposed heuristic algorithm is the same as that of the NEH with the original rule. To validate the proposed heuristic, intensive benchmark experiments are carried out on the large problem instances, and the results show that the proposed algorithm outperforms the existing heuristics in terms of tradeoff between solution quality and running time.
Mining Associations Using Directed Hypergraphs

We introduce the notion of association rules for multi-valued attributes, which is an adaptation of the definition of quantitative association rules known in the literature. The association rules for multi-valued attributes are integrated in building a novel directed hypergraph based model for databases that allows to capture attribute-level associations and their strength. Basing on this model, we provide association-based similarity notions between any two attributes and present a method for finding clusters of similar attributes. We then propose an algorithm to identify a subset of attributes known as a leading indicator that influences the values of almost all other attributes. Finally, we present an association-based classifier that can be used to predict values of attributes. We demonstrate the effectiveness of our proposed model through experiments on a financial timeseries data set (S&P 500).
Phase behavior of mixtures of human lens proteins Gamma D and Beta B1

We have experimentally determined the coexistence surface characterizing the phase behavior of γD-βB1-water ternary solutions. The coexistence surface fully describes the solution conditions, i.e., temperature, protein concentration, and protein composition, at which liquid-liquid phase separation occurs in a ternary solution. We have observed a significant demixing of γD and βB1 i.e., large difference of composition in the two coexisting phases. This demixing suggests that the energy of the γD-βB1 attractive interaction is significantly smaller than the energy of the γD-γD attractive interaction. We also observed the lowering of the phase separation temperature upon increasing of the fraction of βB1 in solution. We provide a theoretical analysis of our experimental data, which enables a quantitative description of our principal experimental findings. In this way, we have evaluated the magnitude and temperature dependence of the relevant interprotein interaction energies. Our findings provide insight into the factors essential for maintaining lens proteins in a single homogeneous phase, thereby enabling lens transparency.
Correctional and Treatment Staff Attitudes Toward Reentry

Staff support of agency mission is an accepted axiom in organizational research. This analysis examined staff attitudes toward reentry preparation in a large prison system. Analysis examined whether local conditions at different prisons had an impact upon average views about the importance of reentry efforts. The study also investigated whether local conditions across prisons mediated differences between custody and treatment staff evaluations of reentry importance. Multilevel models demonstrated that average levels of reentry importance did vary across prisons, but the gap between custody and treatment staff evaluations was consistent across prisons.
Automatic lip identification applied under soft facial emotion conditions

This work shows an identification approach based on lip characteristics. For this proposal, the subject has to be without movement, with frontal face, without moving the lips by a neutral expression, angry or indifferent, therefore, not changing significantly the shape of the lips. After a preprocessing stage, global information from lips are extracted, reaching up to 9979%, using 5 training samples for the public RaFD database.
Coffee Consumption and Risk of Gastric Cancer: A Large Updated Meta-Analysis of Prospective Studies

The potential role of coffee consumption in the development of various types of cancer has been extensively investigated in epidemiologic studies. How coffee consumption may modulate risk of gastric cancer, however, remains a subject open for investigation. To better quantify this relation, we quantitatively summarized evidence from prospective studies. Eligible studies were identified on PubMed databases. The summary risk estimates were obtained using the random-effects model. Subgroup, sensitivity and dose-response analyses were conducted. The present meta-analysis included 12 prospective cohort studies. A pooled analysis of these studies suggested that coffee consumption (highest vs. lowest consumption) was not associated with risk of gastric cancer (RR = 1.12, 95% CI = 0.93–1.36). In the subgroup analysis, significant increased risk was detected in the U.S. studies (RR = 1.36, 95% CI = 1.06–1.74) and in the studies with <10 years of follow-up (RR = 1.24, 95% CI = 1.00–1.54), and the greatest increase in risk was observed in those studies without adjustment for smoking (RR = 1.48, 95% CI = 1.13–1.93). There was some evidence of publication bias (P for Egger’s test = 0.03). Cumulative evidence from prospective studies suggests that coffee consumption is not associated with risk of gastric cancer. The observed positive results may be confounded by smoking and need further investigation.
On the preservation of limit cycles in Boolean networks under different updating schemes

Boolean networks under different deterministic updating schemes are analyzed. It is direct to show that fixed points are invariant against changes in the updating scheme, nevertheless, it is still an open problem to fully understand what happens to the limit cycles. In this paper, a theorem is presented which gives a sufficient condition for a Boolean network not to share the same limit cycle under different updating modes. We show that the hypotheses of the theorem are sharp, in the sense that if any of these hypotheses do not hold, then shared limit cycles may appear. We find that the connectivity of the network is an important factor as well as the Boolean functions in each node, in particular the XOR functions.
Secure Health Information Exchanging in Cluster Computing

--Cloud computing has emerged as a technology that exhibits new model of computing. It has become a solution for individuals and organizations for outsourcing data. The cloud services are rendered in pay per use fashion that gets rid of the need for capital investment. Enterprises that need huge storage facility such as a data centre are moving their data to cloud storage service. With respect to health care domain huge amount of data is being produced every day. Out of the Personal Health Records (PHRs) is the model that contains patients’ health related information. This kind of data is very huge and needs to be outsourced. This data is very sensitive when compared with data of other domains. It is essential that the data is controlled by patients or the owners. Thus patient centric storage and retrieval has become a requirement. In order to achieve this with good security measure PHR of a patient is encrypted before that is outsourced to cloud. However, there are many concerns in people about cloud computing and its security especially. For this reason secure and scalable solution is required. Recently Li et al. proposed a solution to this problem. There scheme is named “Multi-Authority Attribute Based Encryption” which will encrypt the data based on attributes before being outsourced. This scheme is scalable and provides sharing of data simultaneously for multiple users. In this paper we implement the scheme proposed by Li et al. and our experimental results reveal that the scheme is very effective and can be used in the real world.
Exploring learning potentials of late talking children through a structured dynamic assessment

Late talking children have variable language trajectories. Some spontaneously “catch up” with their peers before early school age and some late talking children are later diagnosed with specific language impairments. Currently, there is no way to conclusively predict later language development. Since all of these children score low on static measures of expressive language, it is likely that a dynamic assessment can expose these children’s learning potential, modifiability and readiness to learn. The goal of this research is to construct a preliminary dynamic assessment to identify cues that aid in word learning for late talking children and younger vocabulary-matched typically developing children. Ten 12-18-month-old typically developing children and three 20-30-month-old late talking children who were receiving speech-language pathology services were included in this dynamic assessment. Children were taught four words that varied in phonotactic probability (i.e., the likelihood of occurrence of a sound sequence), neighborhood density (i.e., the number of similar sounding words), and receptive knowledge. Four levels of support were presented to the children (e.g., no support, auditory semantic and phonological cues, visual semantic and phonological cues, and naming/imitation). The children were tested and scored at the end of all the exposures to see if they could name the target object. It was found that both groups produced the same patterns of word learning when given the scaffold of supports. Furthermore, both groups needed more support for words they had no receptive knowledge of than words of which they had previous knowledge, especially for words with low phonotactic probability/neighborhood density. Lastly, auditory phonological and visual semantic cues were more effective than auditory semantic and visual phonological cues for both groups. Taken together, this
ZNF804A genetic variation (rs1344706) affects brain grey but not white matter in schizophrenia and healthy subjects

Background Genetic variation in the gene encoding ZNF804A, a risk gene for schizophrenia, has been shown to affect brain functional endophenotypes of the disorder, while studies of white matter structure have been inconclusive. Method We analysed effects of ZNF804A single nucleotide polymorphism rs1344706 on grey and white matter using voxel-based morphometry (VBM) in high-resolution T1-weighted magnetic resonance imaging scans of 62 schizophrenia patients and 54 matched healthy controls. Results We found a significant (p < 0.05, family-wise error corrected for multiple comparisons) interaction effect of diagnostic group x genotype for local grey matter in the left orbitofrontal and right and left lateral temporal cortices, where patients and controls showed diverging effects of genotype. Analysing the groups separately (at p < 0.001, uncorrected), variation in rs1344706 showed effects on brain structure within the schizophrenia patients in several areas including the left and right inferior temporal, right supramarginal/superior temporal, right and left inferior frontal, left frontopolar, right and left dorsolateral/ventrolateral prefrontal cortices, and the right thalamus, as well as effects within the healthy controls in left lateral temporal, right anterior insula and left orbitofrontal cortical areas. We did not find effects of genotype of regional white matter in either of the two cohorts. Conclusions Our findings demonstrate effects of ZNF804A genetic variation on brain structure, with diverging regional effects in schizophrenia patients and healthy controls in frontal and temporal brain areas. These effects, however, might be dependent on the impact of other (genetic or non-genetic) disease factors.
NADE, a p75NTR-associated Cell Death Executor, Is Involved in Signal Transduction Mediated by the Common Neurotrophin Receptor p75NTR*

The low affinity neurotrophin receptor p75NTR can mediate cell survival as well as cell death of neural cells by NGF and other neurotrophins. To elucidate p75NTR-mediated signal transduction, we screened p75NTR-associated proteins by a yeast two-hybrid system. We identified one positive clone and named NADE (p75NTR-associated cell deathexecutor). Mouse NADE has marked homology to the human HGR74 protein. NADE specifically binds to the cell-death domain of p75NTR. Co-expression of NADE and p75NTR induced caspase-2 and caspase-3 activities and the fragmentation of nuclear DNA in 293T cells. However, in the absence of p75NTR, NADE failed to induce apoptosis, suggesting that NADE expression is necessary but insufficient for p75NTR-mediated apoptosis. Furthermore, p75NTR/NADE-induced cell death was dependent on NGF but not BDNF, NT-3, or NT-4/5, and the recruitment of NADE to p75NTR (intracellular domain) was dose-dependent. We obtained similar results from PC12 cells, nnr5 cells, and oligodendrocytes. Taken together, NADE is the first signaling adaptor molecule identified in the involvement of p75NTR-mediated apoptosis induced by NGF, and it may play an important role in the pathogenesis of neurogenetic diseases.
A new quality estimation methodology for mixed-signal and analogue ICs

IC product quality is commonly described as the faulty device level at shipment and is becoming an increasingly important metric in the Microelectronics Industry. This paper presents and demonstrates a quality estimation approach based on Inductive Fault Analysis for mixed-signal and analogue ICs, that quantitatively models the quality related parameters prior to production. It is shown how the approach can be used to optimise the manufacturing test program.
Maximizing spectral radius of unoriented Laplacian matrix over bicyclic graphs of a given order

For every integer n≥4, it is proved that there is a unique graph of order n which maximizes the spectral radius of the unoriented Laplacian matrix over all bicyclic graphs of order n, namely, the graph obtained from the cycle C 4 by first adding a chord and then attaching n − 4 pendant edges to one end of the chord.
Parallel continuous collision detection for high-performance GPU cluster

Continuous collision detection (CCD) is a process to interpolate the trajectory of polygons and detect collisions between successive time steps. However, primitive-level CCD is a very time-consuming process especially for a large number of moving polygons. Over the years, a number of approaches have been proposed to improve the computational efficiency of CCD by culling out the non-colliding primitives before exact overlap tests. These approaches have two fundamental disadvantages. First, they are mainly designed for self-and pairwise CCD and thus the performance gain would be limited when they are applied to large-scale scenes that contain thousands of moving polygons. Second, they are designed as sequential processes appropriate for execution on a single processor. Therefore, deploying them on high-performance parallel computing systems would not increase their computational efficiency. In this paper, we present a parallel CCD algorithm, which aims to accelerate N-body CCD culling by distributing the load across a high-performance GPU cluster. Our implementation integrates frameworks such as Message Passing Interface and CUDA, which is particularly suitable for large-scale distributed simulations. Experimental results, based on simulations conducted on a supercomputer, demonstrate that our approach is more computationally efficient than existing sequential CCD approaches.
Automatic Classification of Spinal Deformities Using Support Vector Machines

The goal of this study was to investigate a learning approach to deduce King curve types from postero-anterior x-rays in Adolescent Idiopathic Scoliosis. Anatomical landmarks of vertebrae from T1 to L5 were manually identified on the postero-anterior x-ray of 285 patients. A subset of the database (n=252) was processed and submitted to Support Vector Machines for the learning phase. The remaining curves (n=33) were used for validation. The system was successful in classifying 28/33 of the testing set. Thus, a classification tool with knowledge on previously assessed curves can contribute to significantly improve reliability in ambiguous curves patterns.