Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined Belief Propagation-Mean Field Message Passing Algorithm for Dirichlet Process Mixtures

This letter deals with variational inference for Dirichlet process mixtures (DPM) models. We propose a combined message-passing algorithm introducing belief propagation (BP) into the original mean field (MF) rules, which leads to a more precise approximate posterior in DPM. To compute the BP message, we change an exponential distribution to a non-exponential utilizing a flexible expression of Dirac delta function. Therefore, BP rules can be used to handle such functions, resulting to a local exact expectation instead of approximate expectation from the original MF method. Simulation results show that the proposed combined BP–MF algorithm results in a significant performance improvement compared to the state-of-the-art inference methods.
The Relationship between Ischemia Modified Albumin and Lipids in Type 2 Egyptian Diabetic Patients

3 Abstract: The most important reason for risk increase in diabetic patients is endothelial dysfunction and subclinical low grade systemic inflammation. Ischemia modified albumin (IMA) is produced as a result of serum albumin flowing through ischemic tissues and is a marker of oxidative stress and ischemia, as serum level of modified albumin rise in many diseases accompanied by ischemia. In this study we evaluated the relationship between serum IMA and lipid profile in type 2 Egyptian diabetic patients. Forty subjects participated in this study; 20 of them were type 2 diabetic patients with normal lipid profile; 20 patients were type 2 diabetic patients with dyslipidemia; and 10 subjects as a control group. The study revealed a significant positive correlation of serum IMA to glycosylated hemoglobin of type 2 diabetic patients without dyslipidemia and a non significant correlation of serum IMA to all lipids of the patients of the same group. The study also revealed a significant positive correlation of serum IMA to serum triglycerides, total cholesterol, low density lipoprotein cholesterol, very low density cholesterol, glycosylated hemoglobin and high density lipoprotein of type 2 diabetic patients with dyslipidemia. The present study indicates that measuring serum IMA in diabetic patients with dyslipidemia would provide an index of ischemia due to structural modification of circulating albumin in serum. This will aid better prognosis and management of diabetes mellitus.
Multi-User Full Duplex Transceiver Design for mmWave Systems Using Learning-Aided Channel Prediction

Millimeter Wave (mmWave) technology coupled with full duplex (FD) communication has the potential of increasing the spectral efficiency. However, the self-interference (SI) encountered in the FD mode and the ubiquitous multi-user interference (MI) contaminates the signal. Furthermore, the system performance may also be limited by channel aging that arises because of the time-varying nature of the channel. Therefore, in this paper, we conceive FD hybrid beamforming (HBF) for <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-user multiple-input multiple-output (MIMO)-aided orthogonal frequency division multiplexing (OFDM) using learning-aided channel prediction. We first derive a joint precoder and combiner design for full duplex <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-user MIMO-OFDM interference channels, where we aim for minimizing both the residual SI and the MI, followed by an iterative hybrid decomposition technique developed for OFDM systems. Then, we propose a learning-aided channel prediction technique for systems suffering from channel aging relying on a radial basis neural network, where we show by simulation that upon using sufficient training, learning-assisted channel prediction can faithfully estimate the current channel. Furthermore, we demonstrate by simulations that our proposed joint hybrid precoder and combiner design outperforms the popular Eigen beamforming (EBF) technique by about 5 dB for a <inline-formula> <tex-math notation="LaTeX">$128 \times 32$ </tex-math></inline-formula>-element MIMO aided OFDM system having 32 sub-carriers.
A Sinusoidal-Hyperbolic Family of Transforms With Potential Applications in Compressive Sensing

Efficient source coding is desired for any data storage and transmission. It could be enabled by adopting a transform inspired by natural phenomena. Based on the mechanical vibration models, a family of bases applicable to data compression is constructed. The eigenvectors of vibrating thin square plates, which are composed of sinusoidal and hyperbolic functions, are used to construct these real-orthonormal bases. Our analyses show that their attributes and performance are comparable to those of the widely used discrete cosine transform. Since compressive sensing is a way to build the data compression directly into the acquisition, we propose to apply the set of low-frequency atoms of these bases that carry the main portion of the signal energy as sensing patterns. Thus, the measurement ensemble contains the high-energy sinusoidal-hyperbolic transform’s (SHT’s) coefficients of the scene underview. This sampling method leads to high fidelity reconstruction along with good efficiency in encoding and decoding. The dictionary matrix that is made by the SHTs and a non-trigonometric sparsifying basis like Hadamard is well-conditioned for the pursuit algorithm. Both the subjective and objective evaluation of the reconstruction results validates the effectiveness of our method. Compared with the random Gaussian sensing patterns, for the same compression ratio (CR), the proposed sampling method results in images with significantly higher fidelity. The sensing patterns are also shown to be robust against Gaussian and Poisson noise. Application of our scheme to image compression is also discussed.
Coverage-Driven Test Generation for Thread-Safe Classes via Parallel and Conflict Dependencies

Thread-safe classes are common in concurrent object-oriented programs. Testing such classes is important to ensure the reliability of the concurrent programs that rely on them. Recently, researchers have proposed the automated generation of concurrent (multi-threaded) tests to expose concurrency faults in thread-safe classes (thread-safety violations). However, generating fault-revealing concurrent tests within an affordable time-budget is difficult due to the huge search space of possible concurrent tests. In this paper, we present DepCon, an approach to effectively reduce the search space of concurrent tests by means of both parallel and conflict dependency analyses. DepCon is based on the intuition that only methods that can both interleave (parallel dependent) and access the same shared memory locations (conflict dependent) can lead to thread-safety violations when concurrently executed. DepCon implements an efficient static analysis to compute the parallel and conflict dependencies among the methods of a class and uses the computed dependencies to steer the generation of tests towards concurrent tests that exhibit the computed dependencies. We evaluated DepCon by experimenting with a prototype implementation for Java programs on a set of thread-safe classes with known concurrency faults. The experimental results show that DepCon is more effective in exposing concurrency faults than state-of-the-art techniques.
A self-biased PLL with current-mode filter for clock generation

A self-biased PLL with current mode filter is designed to achieve a bandwidth that scales with reference clock and is independent of PVT and multiplication factor. It provides wide tuning range with a simple robust structure. The 0.2mm/sup 2/ chip is fabricated in 0.35 /spl mu/m CMOS. Running at 800MHz, the phase jitter is 72.7ps/sub pp/ and the power consumption is 20mW.
Mixed treatment comparisons using aggregate and individual participant level data

Mixed treatment comparisons (MTC) extend the traditional pair‐wise meta‐analytic framework to synthesize information on more than two interventions. Although most MTCs use aggregate data (AD), a proportion of the evidence base might be available at the individual level (IPD). We develop a series of novel Bayesian statistical MTC models to allow for the simultaneous synthesis of IPD and AD, potentially incorporating study and individual level covariates. The effectiveness of different interventions to increase the provision of functioning smoke alarms in households with children was used as a motivating dataset. This included 20 studies (11 AD and 9 IPD), including 11 500 participants. Incorporating the IPD into the network allowed the inclusion of information on subject level covariates, which produced markedly more accurate treatment–covariate interaction estimates than an analysis solely on the AD from all studies. Including evidence at the IPD level in the MTC is desirable when exploring participant level covariates; even when IPD is available only for a fraction of the studies. Such modelling may not only reduce inconsistencies within networks of trials but also assist the estimation of intervention subgroup effects to guide more individualised treatment decisions. Copyright © 2012 John Wiley & Sons, Ltd.
Hybrid WSN Node Deployment optimization Strategy Based on CS Algorithm

The performance of wireless sensor network (WSN) system is significantly affected by the coverage quality. For the problem of low coverage of static sensor network nodes and high deployment cost of mobile sensor network nodes, a hybrid sensor network based on cuckoo search (CS) is studied. Node deployment optimization strategy. Firstly, the candidate target position of the mobile node is initially determined by the CS algorithm, and then the position optimization scheme is used to reduce the number of mobile nodes and the average moving distance. The experimental results show that the proposed algorithm can effectively optimize the deployment of mobile nodes in hybrid WSN, improve the coverage of target areas, and reduce the energy consumption of nodes.
SAC: A System for Big Data Lineage Tracking

In the era of big data, a data processing flow contains various types of tasks. It is nontrivial to discover the data flow/movement from its source to destination, such that monitoring different transformations and hops on its way in an enterprise environment. Therefore, data lineage or provenance is useful to learn how the data gets transformed along the way, how the representation and parameters change, and how the data splits or converges after each hop. However, existing systems offer limited support for such use cases in a distributed computing setup. To address this issue, we build Spark-Atlas-Connector (short as SAC), a new system to track data lineage in a distributed computation platform, e.g., Spark. SAC tracks different processes involved in the data flow and their dependencies, supporting different data storage (e.g., HBase, HDFS, and Hive) and data processing paradigms (e.g., SQL, ETL, machine learning, and streaming). SAC provides a visual representation of data lineage to track data from its origin to downstreams, and is deployed in a distributed production environment for demonstrating its efficiency and scalability.
Classifying False Positive Static Checker Alarms in Continuous Integration Using Convolutional Neural Networks

Static code analysis in Continuous Integration (CI) environment can significantly improve the quality of a software system because it enables early detection of defects without any test executions or user interactions. However, being a conservative over-approximation of system behaviours, static analysis also produces a large number of false positive alarms, identification of which takes up valuable developer time. We present an automated classifier based on Convolutional Neural Networks (CNNs). We hypothesise that many false positive alarms can be classified by identifying specific lexical patterns in the parts of the code that raised the alarm: human engineers adopt a similar tactic. We train a CNN based classifier to learn and detect these lexical patterns, using a total of about 10K historical static analysis alarms generated by six static analysis checkers for over 27 million LOC, and their labels assigned by actual developers. The results of our empirical evaluation suggest that our classifier can be highly effective for identifying false positive alarms, with the average precision across all six checkers of 79.72%.
Quasi-Local Energy-Momentum and Angular Momentum in GR: A Review Article

The present status of the quasi-local mass-energy-momentum and angular momentum constructions in general relativity is reviewed. First the general ideas, concepts, and strategies, as well as the necessary tools to construct and analyze the quasi-local quantities are recalled. Then the various specific constructions and their properties (both successes and defects) are discussed. Finally, some of the (actual and potential) applications of the quasi-local concepts and specific constructions are briefly mentioned.This review is based on the talks given at the Erwin Schrödinger Institute, Vienna, in July 1997, at the Universität Tübingen, in May 1998, and at the National Center for Theoretical Sciences in Hsinchu and at the National Central University, Chungli, Taiwan, in July 2000.
Testicular Phenotype in Luteinizing Hormone Receptor Knockout Animals and the Effect of Testosterone Replacement Therapy1

Abstract The LH receptor knockout model, developed in our laboratory, was used in determining what FSH alone can do in the absence of LH signaling and whether any of the testicular LH actions are not mediated by androgens. The results revealed that null animals contained smaller seminiferous tubules, which contained the same number of Sertoli cells, spermatogonia, and early spermatocytes as wild-type siblings. The number of late spermatocytes, on the other hand, was moderately decreased, the number of round spermatids was dramatically decreased, and elongated spermatids were completely absent. These changes appear to be due to an increase in apoptosis in spermatocytes. While the number of Leydig cells progressively increased from birth to 60 days of age in wild-type animals, they remained unchanged in null animals. Consequently, 60-day-old null animals contained only a few Leydig cells of fetal type. The age-dependent increase in testicular macrophages lagged behind in null animals compared with wild-type siblings. Orchidopexy indicated that −/− testicular phenotype was not due to abdominal location. Rather, it was mostly due to androgen deficiency, as 21-day testosterone replacement therapy stimulated the growth of seminiferous tubules, decreased apoptosis, and increased the number of late spermatocytes and round spermatids and their subsequent differentiation into mature sperm. The therapy, however, failed to restore adult-type Leydig cells and testicular macrophage numbers to the wild-type levels. In summary, our data support the concept that FSH signaling alone can maintain the proliferation and development of Sertoli cells, spermatogonia, and early spermatocytes. LH actions mediated by testosterone are required for completion of spermatogenesis, and finally, androgen-independent actions of LH are required for the formation of adult-type Leydig cells and recruitment of macrophages into the testes.
Improved PBTI Reliability in Junction-Less FET Fabricated at Low Thermal Budget for 3-D Sequential Integration

Junction-less FETs are used as top-tier devices in a 3-D sequential integration. Due to the low thermal budget allowed in the 3-D integration, conventional inversion mode FETs show extremely poor BTI reliability. In contrast, a junction-less FET shows improved BTI reliability, which is attributed to the reduced oxide electric field of operation. We observe that the reliability of junction-less FETs can be further improved by increasing the channel doping and/or the channel thickness. Correspondingly, a tradeoff exists between performance (subthreshold slope, carrier mobility), reliability, and variability. This tradeoff is verified in both planar/FinFET structures and can serve as a device optimization matrix. Furthermore, we use the non-radiative multi-phonon (NMP) theory, as implemented in the imec/T.U. Vienna BTI simulation framework “Comphy,” to investigate the degradation kinetics and show that the stress/recovery traces measured in inversion mode and junction-less nFETs can be reproduced with the same set of oxide defect parameters. This observation confirms that the reliability improvement in junction-less devices is inherent to their specific operation mode and not related to the different fabrication flows compared to standard inversion mode devices. Based on the calibrated Comphy model, we perform BTI lifetime projections, exposing for junction-less devices a substantial deviation from the commonly used power-law voltage acceleration.
Index-Based Optimal Algorithm for Computing K-Cores in Large Uncertain Graphs

Uncertainty in graph data occurs for a variety of reasons, such as noise and measurement errors. Recently, uncertain graph management and analysis have attracted many research attentions. Among them, computing k-cores in uncertain graphs (aka, (k, η)-cores) is an important problem and has emerged in many applications, for example, community detection, protein-protein interaction network analysis and influence maximization. Given an uncertain graph, the (k, η)-cores can be derived by iteratively removing the vertex with an η-degree of less than k and updating the η-degrees of its neighbors. However, the results heavily depend on the two input parameters k and η, and the settings for these parameters are unique to the specific graph structure and the user's subjective requirements. Additionally, computing and updating the η-degree for each vertex is the most costly component of the algorithm, and that cost is high. To overcome these drawbacks, we have developed an index-based solution for computing (k, η)-cores in this paper. The size of the index is well bounded by O(m), where m is the number of edges in the graph. Based on this index, queries for any k and η can be answered in optimal time. Further, the method is accompanied by several different optimizations to speed up construction of the index. We conduct extensive experiments on eight real-world datasets to practically evaluate the performance of all the proposed algorithms. The results demonstrate that this index-based approach is several orders of magnitude faster at processing queries than the traditional online approaches.
Canonicalization of Open Knowledge Bases with Side Information from the Source Text

Nowadays Open Information Extraction (Open IE) approaches, which extract triples from unstructured text, contribute to the construction of large Open Knowledge Bases (Open KBs). However, one crucial problem is that the noun phrases and relation phrases in the extracted triples are not well canonicalized, which leads to a large number of redundant and ambiguous facts. For example, both and may be extracted and stored in Open KBs. Recent research proposes to solve this problem by clustering over manually-defined feature spaces based on the similarity of the noun phrases and relation phrases. However, the performance of such techniques is limited, since only the information contained in the triples is utilized to measure their similarity. In this paper, we propose to perform canonicalization over Open IE triples by incorporating the side information from the original data sources, including the candidate entities of the noun phrases detected in the source text, the types of the candidate entities and the domain knowledge of the source text. We model the canonicalization problem of noun phrases and relation phrases jointly based on such side information, and demonstrate the effectiveness of our approach through extensive experiments on two real-world datasets.
Presto: SQL on Everything

Presto is an open source distributed query engine that supports much of the SQL analytics workload at Facebook. Presto is designed to be adaptive, flexible, and extensible. It supports a wide variety of use cases with diverse characteristics. These range from user-facing reporting applications with sub-second latency requirements to multi-hour ETL jobs that aggregate or join terabytes of data. Presto's Connector API allows plugins to provide a high performance I/O interface to dozens of data sources, including Hadoop data warehouses, RDBMSs, NoSQL systems, and stream processing systems. In this paper, we outline a selection of use cases that Presto supports at Facebook. We then describe its architecture and implementation, and call out features and performance optimizations that enable it to support these use cases. Finally, we present performance results that demonstrate the impact of our main design decisions.
Synaptic responses of substantia gelatinosa neurones to dorsal column stimulation in rat spinal cord in vitro.

1. To study the mechanism of dorsal column stimulation‐induced depression of nociceptive transmission in the spinal cord, synaptic responses evoked in dorsal horn neurones by dorsal column and dorsal root stimulations were examined in a horizontal spinal cord slice of the adult rat. Intracellular recordings were made from substantia gelatinosa (SG) neurones. 2. All SG neurones examined received monosynaptic inputs and/or polysynpatic inputs from both dorsal column and dorsal root. A delta fibres were probably responsible for the synaptic responses. The responses evoked by dorsal column stimulation were similar to those evoked by primary afferent A delta fibre stimulation. 3. Monosynaptic excitatory postsynaptic potentials (EPSPs) evoked by dorsal column A delta fibres were depressed by 6‐cyano‐7‐nitroquinoxaline‐2,3‐dione, suggesting that these fibres released L‐glutamate or a related amino acid as a transmitter. 4. In 38 of 101 SG neurones, dorsal column stimulation evoked an initial EPSP followed by fast and/or slow inhibitory postsynaptic potentials (IPSPs). These IPSPs reversed polarity at a membrane potential of ‐73 +/‐ 2 mV. The fast IPSPs observed in 16 of the SG neurones (42%) that received inhibitory inputs were depressed by strychnine, while the slow IPSPs observed in 22 SG neurones were depressed by bicuculline. In a few cells, a long‐lasting slow IPSP with a much slower time course was detected; this IPSP was insensitive to strychnine and bicuculline, and reversed polarity at a membrane potential near ‐90 mV. 5. Repetitive stimulation of the dorsal column depressed the amplitude of monosynaptic EPSPs evoked by dorsal root stimulation. 6. The responses of SG neurones to dorsal column stimulation had configurations and durations similar to responses to dorsal root stimulation, and may be mediated largely by the same A delta fibres. However, a C fibre‐mediated response could not be detected in SG neurones from dorsal column stimulation, although dorsal root stimulation could evoke C fibre‐mediated monosynaptic EPSPs in 18 of 88 SG neurones (20%). 7. These observations suggest that SG neurones receive abundant A delta but not C fibre inputs from the dorsal column and that dorsal column stimulation inhibits primary afferent transmission in the spinal cord both by reducing transmitter release from primary A delta fibres and by hyperpolarizing SG neurones.
Monitoring the Antiviral Effect of Alpha Interferon on Individual Cells

ABSTRACT An infectious hepatitis C virus (HCV) cDNA clone (JFH1) was generated recently. However, quantitative analysis of HCV infection and observation of infected cells have proved to be difficult because the yield of HCV in cell cultures is fairly low. We generated infectious HCV clones containing the convenient reporters green fluorescent protein (GFP) and Renilla luciferase in the NS5a-coding sequence. The new viruses responded to antiviral agents in a dose-dependent manner. Responses of individual cells containing HCV to alpha interferon (IFN-α) were monitored using GFP-tagged HCV and time-lapse confocal microscopy. Marked variations in the response to IFN-α were observed among HCV-containing cells.