Miracle-dz/UniFAR-FTUs huggingface dataset:
A numerical investigation of the invertibility of room transfer functions

It has been stated that the inverse filtering of room transfer functions is possible using multiple inputs under certain conditions. We address these conditions from a numerical perspective. A method is developed to determine the numerical precision of multiple input inverse filtering techniques. This method is applied to two simulated rooms, and the numerical precision is estimated as a function of the number of inputs and outputs into the system.
Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network

Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.
Genome-wide association study of classical Hodgkin lymphoma and Epstein-Barr virus status-defined subgroups.

BACKGROUND Accumulating evidence suggests that risk factors for classical Hodgkin lymphoma (cHL) differ by tumor Epstein-Barr virus (EBV) status. This potential etiological heterogeneity is not recognized in current disease classification. METHODS We conducted a genome-wide association study of 1200 cHL patients and 6417 control subjects, with validation in an independent replication series, to identify common genetic variants associated with total cHL and subtypes defined by tumor EBV status. Multiple logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) assuming a log-additive genetic model for the variants. All statistical tests were two-sided. RESULTS Two novel loci associated with total cHL irrespective of EBV status were identified in the major histocompatibility complex region; one resides adjacent to MICB (rs2248462: OR = 0.61, 95% CI = 0.53 to 0.69, P = 1.3 × 10(-13)) and the other at HLA-DRA (rs2395185: OR = 0.56, 95% CI = 0.50 to 0.62, P = 8.3 × 10(-25)) with both results confirmed in an independent replication series. Consistent with previous reports, associations were found between EBV-positive cHL and genetic variants within the class I region (rs2734986, HLA-A: OR = 2.45, 95% CI = 2.00 to 3.00, P = 1.2 × 10(-15); rs6904029, HCG9: OR = 0.46, 95% CI = 0.36 to 0.59, P = 5.5 × 10(-10)) and between EBV-negative cHL and rs6903608 within the class II region (rs6903608, HLA-DRA: OR = 2.08, 95% CI = 1.84 to 2.35, P = 6.1 × 10(-31)). The association between rs6903608 and EBV-negative cHL was confined to the nodular sclerosis histological subtype. Evidence for an association between EBV-negative cHL and rs20541 (5q31, IL13: OR = 1.53, 95% CI = 1.32 to 1.76, P = 5.4 x 10(-9)), a variant previously linked to psoriasis and asthma, was observed; however, the evidence for replication was less clear. Notably, one additional psoriasis-associated variant, rs27524 (5q15, ERAP1), showed evidence of an association with cHL in the genome-wide association study (OR = 1.21, 95% CI = 1.10 to 1.33, P = 1.5 × 10(-4)) and replication series (P = .03). CONCLUSION Overall, these results provide strong evidence that EBV status is an etiologically important classification of cHL and also suggest that some components of the pathological process are common to both EBV-positive and EBV-negative patients.
Social capital, mortality, cardiovascular events and cancer: a systematic review of prospective studies.

BACKGROUND Social capital is considered to be an important determinant of life expectancy and cardiovascular health. Evidence on the association between social capital and all-cause mortality, cardiovascular disease (CVD) and cancer was systematically reviewed. METHODS Prospective studies examining the association of social capital with these outcomes were systematically sought in Medline, Embase and PsycInfo, all from inception to 8 October 2012. We categorized the findings from studies according to seven dimensions of social capital, including social participation, social network, civic participation,social support, trust, norm of reciprocity and sense of community, and pooled the estimates across studies to obtain summary relative risks of the health outcomes for each social capital dimension. We excluded studies focusing on children, refugees or immigrants and studies conducted in the former Soviet Union. RESULTS Fourteen prospective studies were identified. The pooled estimates showed no association between most social capital dimensions and all-cause mortality, CVD or cancer. Limited evidence was found for association of increased mortality with social participation and civic participation when comparing the most extreme risk comparisons. CONCLUSIONS Evidence to support an association between social capital and health outcomes is limited. Lack of consensus on measurements for social capital hinders the comparability of studies and weakens the evidence base.
On-Chip Droop-Induced Circuit Delay Prediction Based on Support-Vector Machines

Voltage droop is a major reliability concern in nano-scale very large-scale integration designs. Undesirable voltage droop is often a result of excessive IR drop. On the other hand, Ldi/dt-induced droop occurs when logic gates in the circuit draw high-switching current from the on-chip power supply network, and this problem is exacerbated at high-clock frequencies and smaller technology nodes. A consequence of voltage droop is usually an increase in path delays and the occurrence of intermittent faults during circuit operation. The addition of conservative timing margins, also known as guardbands, is a common practice to tackle the problem of voltage droop. However, such static and pessimistic guardbands, which are calculated at design time based on worst-case conditions, lead to significant performance loss. Dynamic frequency scaling is an alternative approach that enables the dynamic adjustment of clock frequency based on the actual voltage droop seen during runtime. For dynamic voltage-frequency to be effective, accurate and real-time prediction of voltage droop is essential. We propose a support-vector machine (SVM)-based regression method to predict voltage droop due to pattern-dependent IR drop based on inputs to the chip at runtime. Moreover, we reduce the amount of data needed for accurate prediction by using correlation-based feature selection. Several benchmarks from ITC'99 and International Work on Logic and Synthesis'05 highlight the effectiveness of the proposed method in terms of delay-prediction accuracy. Since real-time droop prediction requires hardware implementation of the predictor, we present the hardware design and synthesis results to demonstrate that the hardware overhead for the SVM predictor is negligible for large circuits.
The design and applications of a recursive molecular modeling framework

In this paper, we present a recursive molecular model that is suitable for multi-resolution analysis in a variety of application areas. Our approach allows ease of use by computer scientists and biologists by proposing a software interface for molecular analysis that hides programming details. We demonstrate that our design is flexible enough for use in desktop analytical applications, in large-scale parallel simulations, and as a server for remote molecular analysis across wide-area networks.
Evolutionary Genetic Algorithm for Efficient Clustering of Wireless Sensor Networks

Sensor nodes forming a sensor network usually have limited energy capacity so it is important to minimize sensor nodes' energy consumption because of difficulty in supplying additional energy for the sensor nodes. Much attention has been given to the clustering technique as an efficient way of reducing the energy consumption of a sensor node. Energy saving results can vary greatly depending on the number and size of clusters and the distance among the sensor nodes. In this paper, we aim to find an optimal cluster formation by applying a genetic algorithm in which the chromosome contains the information about the relative position of the nodes. The Location-aware two-dimensional GA (LA2D-GA) proposed in this paper can performs more efficient gene evolution than one-dimensional GA (1D-GA) by giving unique location information to each node. The effectiveness of our algorithm is shown by simulation.
Algorithmic transformation techniques for efficient exploration of alternative application instances

Following the Y-chart paradigm for designing a system, an application and an architecture are modeled separately and mapped onto each other in an explicit design step. Next, a performance analysis for alternative application instances, architecture instances and mappings has to be done, thereby exploring the design space of the target system. Deriving alternative application instances is not trivially done. Nevertheless, many instances of a single application exist that are worth being derived for exploration. We present algorithmic transformation techniques for systematic and fast generation of alternative application instances that express task-level concurrency hidden in an application in some degree of explicitness. These techniques help a system designer to speedup significantly the design space exploration process.
Planar Pose Graph Optimization: Duality, Optimal Solutions, and Verification

Pose graph optimization (PGO) is the problem of estimating a set of poses from pairwise relative measurements. PGO is a nonconvex problem and, currently, no known technique can guarantee the computation of a global optimal solution. In this paper, we show that Lagrangian duality allows computing a globally optimal solution under conditions that are satisfied in most robotics applications and enables to certify optimality of a given estimate. Our first contribution is to frame planar PGO in the complex domain. This makes analysis easier and allows drawing connections with existing literature on unit gain graphs. The second contribution is to formulate and analyze the properties of the Lagrangian dual problem in the complex domain. Our analysis shows that the duality gap is connected to the number of zero eigenvalues of the penalized pose graph matrix. We prove that if this matrix has a single zero eigenvalue, then 1) the duality gap is zero, 2) the primal PGO problem has a unique solution (up to an arbitrary roto-translation), and 3) the primal solution can be computed by scaling an eigenvector of the penalized pose graph matrix. The third contribution is algorithmic: We leverage duality to devise and algorithm that computes the optimal solution when the penalized matrix has a single zero eigenvalue. We also propose a suboptimal variant when the zero eigenvalues are multiple. Finally, we show that duality provides computational tools to verify if a given estimate (e.g., computed using iterative solvers) is globally optimal. We conclude the paper with an extensive numerical analysis. Empirical evidence shows that, in the vast majority of cases (100% of the tests under noise regimes of practical robotics applications), the penalized pose graph matrix has a single zero eigenvalue; hence, our approach allows computing (or verifying) the optimal solution.
Energy efficiency of Gaussian channel with random data arrival

A point-to-point communication system with an AWGN channel is considered. It is assumed that data randomly arrive at the system and the transmitter has a finite buffer. Supposing that, to save energy, the transmitter is able to adapt the rate depending on the number of bits existing in the buffer, the stationary probability distribution of the number of bits in the buffer is obtained and an approximate relation for the average of saving in energy is found.
NF-Y loss triggers p53 stabilization and apoptosis in HPV18-positive cells by affecting E6 transcription

The expression of the high risk HPV18 E6 and E7 oncogenic proteins induces the transformation of epithelial cells, through the disruption of p53 and Rb function. The binding of cellular transcription factors to cis-regulatory elements in the viral Upstream Regulatory Region (URR) stimulates E6/E7 transcription. Here, we demonstrate that the CCAAT-transcription factor NF-Y binds to a non-canonical motif within the URR and activates viral gene expression. In addition, NF-Y indirectly up-regulates HPV18 transcription through the transactivation of multiple cellular transcription factors. NF-YA depletion inhibits the expression of E6 and E7 genes and re-establishes functional p53. The activation of p53 target genes in turn leads to apoptotic cell death. Finally, we show that NF-YA loss sensitizes HPV18-positive cells toward the DNA damaging agent Doxorubicin, via p53-mediated transcriptional response.
Platelet to lymphocyte ratio and neutrophil to lymphocyte ratio in patients with rheumatoid arthritis

Abstract Objectives: It has been well documented that the platelet to lymphocyte ratio (PLR) and the neutrophil to lymphocyte ratio (NLR) are associated with outcomes for patients with gastric cancer, non-small cell lung cancer and acute heart failure. Inflammation may be the hidden factor that explains the correlation between NLP, PLR, and these diseases. However, to date, the data concerning NLR, PLR, and its association with inflammation are lacking in patients with rheumatoid arthritis (RA), thus, our aim to discuss whether NLR and PLR are associated with RA. Methods: Patients with RA and healthy individuals were included according to the determined criteria, and laboratory indicators were measured. Results: PLR and NLR were significantly higher in RA patients compared with healthy controls (3.20±2.06 vs. 1.56±0.47, P<0.01; 192.85±101.78 vs. 103.49±28.68, P<0.01). When leukocytes, neutrophil percentage, neutrophil, lymphocyte, platelet, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and rheumatoid factor (RF) were considered as confounders (crude model), our results indicated that ESR and RF were correlated to RA. Of note, ESR, RF, and PLR were associated with RA after further adjustment based on crude model for PLR and NLR. Receiver operating characteristic (ROC) curves analysis showed that PLR values higher than >115.7 evaluated RA with a sensitivity of 82.5%, a specificity of 74.8% and area under the curve ( AUC ) of 0.847. Conclusions:Our results suggest that PLR is associated with RA, and PLR may be an underlying indicator indicating the chronic subclinical inflammation in patients with RA.
Evaluation of phrasal query suggestions

This paper evaluates the uptake and efficacy of a unified approach to phrasal query suggestions in the context of a high-precision search engine. The search engine performs ranked extended-Boolean searches with the proximity operator <scp>NEAR</scp> being the default operation. Suggestions are offered to the searcher when the length of the result list falls outside predefined bounds. If the list is too long, the engine suggests narrowing the query through the use of super phrases; if the list is too short, the engine suggests broadening the query through the use of proximal subphrases. We evaluated uptake of phrasal query suggestions by analyzing search log data from before and after the suggestion feature was added to a commercial version of the search engine. We looked at approximately 1.5 million queries and found that, after they were added, suggestions represented nearly 30% of the total queries. We evaluated efficacy through a controlled study of 24 participants performing nine searches using three different search engines. We found that the engine with phrase suggestions had better high-precision recall than both the same search engine without suggestions and a search engine with a similar interface but using an Okapi BM25 ranking algorithm.
Experimental realization of quantum algorithm for solving linear systems of equations

National Key Basic Research Program of China [2013CB921800, 2014CB848700]; National Natural Science Foundation of China [11227901, 91021005, 11375167, 11004181, 11161160553]; Strategic Priority Research Program (B) of the CAS [XDB01030400]; NSF Center for Quantum Information and Computation for Chemistry [CHE-1037992]
Krüppel-Like Factor 4, a Tumor Suppressor in Hepatocellular Carcinoma Cells Reverts Epithelial Mesenchymal Transition by Suppressing Slug Expression

Krüppel-like factor 4 (KLF4) is a zinc-finger transcription factor that plays an important role in differentiation and pathogenesis. KLF4 has been suggested to act as an oncogene or tumor suppressor in different tumor types. However, the role of KLF4 in hepatocellular carcinoma (HCC) remains unclear. Here, we demonstrate that forced expression of Klf4 in murine HCC cell lines reduced anchorage-independent growth in soft agar as well as cell migration and invasion activities in vitro. Ectopic Klf4 expression impaired subcutaneous tumor growth and lung colonization in vivo. By contrast, Klf4 knockdown enhanced HCC cell migration. Interestingly, ectopic expression of Klf4 changed the morphology of murine HCC cells to a more epithelial phenotype. Associated with this, we found that expression of Slug, a critical epithelial mesenchymal transition (EMT)-related transcription factor, was significantly down-regulated in Klf4-expressing cells. Chromatin immunoprecipitation (ChIP) and luciferase reporter assays showed that Klf4 is able to bind and repress the activity of the Slug promoter. Furthermore, ectopic Slug expression partially reverts the Klf4-mediated phenotypes. Consistent with a role as a tumor suppressor in HCC, analysis of the public microarray databases from Oncomine revealed reduced KLF4 expression in human HCC tissues in comparison with normal liver tissues in 3 out of 4 data sets. By quantitative reverse transcription-polymerase chain reaction (qRT-PCR), we found reduced KLF4 mRNA in 50% of HCC tissues. Importantly, an inverse correlation between the expression of KLF4 and SLUG was found in HCC tissues. Our data suggest that KLF4 acts as a tumor suppressor in HCC cells, in part by suppressing SLUG transcription.
Consistency of Recursive Definitions via Shallow Confluence of Non-Orthogonal Non-Terminating Conditional Term Rewriting Systems with any kind of Extra Variables

Recursive definitions can be adequately and conveniently modeled with left-linear conditional term rewriting systems, provided that non-termination, non-trivial critical pairs, and extra variables are admitted. Confluence of such systems guarantees the object-level consistency of the underlying data types. We present a novel sufficient criterion for shallow confluence, a property which is logically stronger than level confluence and confluence, and which is not only needed as a generalization for the hard inductive proof of the sufficiency of the criterion, but has other applications also, e.g. in functional logic programming. By restricting the introduction of extra variables to binding equations that mirror local variable-declarations and constructor-matching variable-introduction constructs in programming languages (such as and in ML), and by requiring the critical pairs to have somehow complementary literals in their conditions, we arrive at the first decidable confluence criterion with which we can write recursive function definitions adequately and conveniently: It admits non-termination; non-trivial critical pairs; extra variables in right-hand-sides introduced by binding equations and arbitrary extra variables in conditions; and non-proper-orientation, non-right-stability, and non-normality of conditional equations.
Shared Faculty–Student Lifestyle Habits and Their Implications for College Student Retention

Previous research confirms that first-semester grade-point average (GPA) is related to college student persistence, retention, and graduation. Thus, it is important to identify factors related to enhancing first-semester GPA. In this study, researchers asked faculty and students in the disciplines of journalism, strategic communication or public relations, mass media production and design, motion picture or film, and mass media in general, to rate themselves on success-related lifestyle habits. Students were grouped according to five GPA categories, and each GPA group's survey responses were compared with faculty survey responses and with every other GPA group's responses. Statistical analyses identified five highly differentiating lifestyle habits that separated both faculty and the highest GPA group from all other GPA groups. Because these five habits related so closely to higher-level GPAs, one major implication of the findings is that institutions could improve retention by encouraging freshmen to develop these five critical lifestyle habits.
Enhancing Topic Modeling on Short Texts with Crowdsourcing

Topic modeling is nowadays widely used in text archive analytics, to find significant topics in news articles and important aspects of product comments available on the Internet. While statistical approaches, e.g. Latent Dirichlet Allocation (LDA) and its variants, are effective on building topic models on long texts, it remains difficult to identify meaningful topics over short texts, e.g. news titles and social-media messages. With the emergence and prosperity of crowdsourcing platforms, it becomes possible and easier for analytical systems to incorporate human intelligence into text analytics. Different from traditional active learning techniques, the combination of crowdsourcing and machine learning poses new challenges on the design of simple tasks for non-experts to finish in seconds. In this paper, we design a new topic modeling technique, fully exploiting the basic intuitions of humans on short text reading. By requesting human labors to subjectively measure the similarity between short text pairs, the accuracy of the topic modeling algorithms could be greatly enhanced, regardless of the prior used in the graphical model. We present well-designed short text pair selection strategies for crowdsourcing and provide analysis on the convergence property of the inference algorithm. Empirical studies show that our proposed approaches improve the result topics on English tweets and Chinese microblogs, by requesting only a small number of labels from crowd.