Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved pseudorandom generators from pseudorandom multi-switching lemmas

We give the best known pseudorandom generators for two touchstone classes in unconditional derandomization: an $\varepsilon$-PRG for the class of size-$M$ depth-$d$ $\mathsf{AC}^0$ circuits with seed length $\log(M)^{d+O(1)}\cdot \log(1/\varepsilon)$, and an $\varepsilon$-PRG for the class of $S$-sparse $\mathbb{F}_2$ polynomials with seed length $2^{O(\sqrt{\log S})}\cdot \log(1/\varepsilon)$. These results bring the state of the art for unconditional derandomization of these classes into sharp alignment with the state of the art for computational hardness for all parameter settings: improving on the seed lengths of either PRG would require breakthrough progress on longstanding and notorious circuit lower bounds. The key enabling ingredient in our approach is a new \emph{pseudorandom multi-switching lemma}. We derandomize recently-developed \emph{multi}-switching lemmas, which are powerful generalizations of H{\aa}stad's switching lemma that deal with \emph{families} of depth-two circuits. Our pseudorandom multi-switching lemma---a randomness-efficient algorithm for sampling restrictions that simultaneously simplify all circuits in a family---achieves the parameters obtained by the (full randomness) multi-switching lemmas of Impagliazzo, Matthews, and Paturi [IMP12] and H{\aa}stad [H{\aa}s14]. This optimality of our derandomization translates into the optimality (given current circuit lower bounds) of our PRGs for $\mathsf{AC}^0$ and sparse $\mathbb{F}_2$ polynomials.
Rhein induces apoptosis of human gastric cancer SGC-7901 cells via an intrinsic mitochondrial pathway

Rhein is a primary anthraquinone found in the roots of a traditional Chinese herb, rhubarb, and has been shown to have some anticancer effects. The aim of the present study was to investigate the effect of rhein on the apoptosis of the human gastric cancer line SGC-7901 and to identify the mechanism involved. SGC-7901 cells were cultured and treated with rhein (0, 50, 100, 150, and 200 µM) for 24, 48, or 72 h. Relative cell viability assessed by the MTT assay after treatment was 100, 99, 85, 79, 63% for 24 h; 100, 98, 80, 51, 37% for 48 h, and 100, 97, 60, 36, 15% for 72 h, respectively. Cell apoptosis was detected with TUNEL staining and quantified with flow cytometry using annexin FITC-PI staining at 48 h after 100, 200 and 300 µm rhein. The percentage of apoptotic cells was 7.3, 21.9, 43.5%, respectively. We also measured the mRNA levels of caspase-3 and -9 using real-time PCR. Treatment with 100 µM rhein for 48 h significantly increased mRNA expression of caspase-3 and -9. The levels of apoptosis-related proteins including Bcl-2, Bax, Bcl-xL, and pro-caspase-3 were evaluated in rhein-treated cells. Rhein increased the Bax:Bcl-2 ratio but decreased the protein levels of Bcl-xL and pro-caspase-3. Moreover, rhein significantly increased the expression of cytochrome c and apoptotic protease activating factor 1, two critical components involved in mitochondrial pathway-mediated apoptosis. We conclude that rhein inhibits SGC-7901 proliferation by inducing apoptosis and this antitumor effect of rhein is mediated in part by an intrinsic mitochondrial pathway.
An introduction to Stream Data Management on Large Information Networks ∗

In recent times there has been a surge of large scale information networks arising in various application domains, ranging from communication networks, cellphone call networks, social networks, email networks, road traffic networks, financial transaction networks, to name a few. In such applications there is a need to manage and process large data streams in near-real time. Examples of such queries include finding breaking news in Twitter stream, detecting network intrusion from network communication data, detecting congestion from traffic information, and so on. Some of the key data management challenges in supporting such real-time stream processing are very high update rates, dynamic structural changes in the underlying networks, complex relationships between data items, low latency requirements for processing queries. In this paper we present an introduction to the problem of stream data management in large scale information networks. We present some of the related works in the area of stream data management, continuous aggregation queries in sensor networks, intrusion detection, and processing social network data streams. We also present a glimpse of our ongoing work to manage large graphs and efficiently evaluate real-time aggregation queries on them.
Genome Sequence of a Lancefield Group C Streptococcus zooepidemicus Strain Causing Epidemic Nephritis: New Information about an Old Disease

Outbreaks of disease attributable to human error or natural causes can provide unique opportunities to gain new information about host-pathogen interactions and new leads for pathogenesis research. Poststreptococcal glomerulonephritis (PSGN), a sequela of infection with pathogenic streptococci, is a common cause of preventable kidney disease worldwide. Although PSGN usually occurs after infection with group A streptococci, organisms of Lancefield group C and G also can be responsible. Despite decades of study, the molecular pathogenesis of PSGN is poorly understood. As a first step toward gaining new information about PSGN pathogenesis, we sequenced the genome of Streptococcus equi subsp. zooepidemicus strain MGCS10565, a group C organism that caused a very large and unusually severe epidemic of nephritis in Brazil. The genome is a circular chromosome of 2,024,171 bp. The genome shares extensive gene content, including many virulence factors, with genetically related group A streptococci, but unexpectedly lacks prophages. The genome contains many apparently foreign genes interspersed around the chromosome, consistent with the presence of a full array of genes required for natural competence. An inordinately large family of genes encodes secreted extracellular collagen-like proteins with multiple integrin-binding motifs. The absence of a gene related to speB rules out the long-held belief that streptococcal pyrogenic exotoxin B or antibodies reacting with it singularly cause PSGN. Many proteins previously implicated in GAS PSGN, such as streptokinase, are either highly divergent in strain MGCS10565 or are not more closely related between these species than to orthologs present in other streptococci that do not commonly cause PSGN. Our analysis provides a comparative genomics framework for renewed appraisal of molecular events underlying APSGN pathogenesis.
CCT: Connect and Control Things: A novel mobile application to manage M2M devices and endpoints

This paper presents a novel application that allows mobile clients to interact with M2M devices and endpoints in real time. The application "Connect and Control Things" (CCT) is designed to discover things, receive data from the sensors, control the actuators and generate alarms in real time. The novel capabilities of CCT are: (i) dynamic discovery of device and endpoint, (ii) real time interaction with sensors and actuators associated to M2M devices, (iii) benefit from Sensor Markup Language (SenML) representation, (iv) supporting extension of SenML capabilities for actuators and (v) learning actuators' resources and control them. The architectural design, prototypes implementation, flow of network operations and a real-life test scenario are illustrated. The prototype Android application registers higher CPU usage and power consumption due to intense network operations and parsing sensor metadata repeatedly. We have proposed several optimization techniques to reduce the CPU load, network data usage and overall power consumption. Two use cases of the application have been discussed. Finally the paper summarizes the contributions and concludes with the future research directions.
Epigenetics of Brain Disorders: The Paradigm of Alzheimers Disease

Over 80% of brain disorders are associated with multiple genomic defects in conjunction with environmental factors and epigenetic phenomena. Classical epigenetic mechanisms, including DNA methylation, histone modifications, and microRNAs (miRNAs) regulation, are among the major regulatory elements that control metabolic pathways at the molecular level, with epigenetic modifications controlling gene expression transcriptionally and miRNAs suppressing gene expression post-transcriptionally. Epigenetic modifications are related to disease development, environmental exposure, drug treatment and aging. Epigenetic changes are reversible and can be potentially targeted by pharmacological intervention. Both hypermethylation and hypomethylation of DNA, chomatin changes and miRNA dysregulation are common in age-related disorders and in many neuropsychiatric, neurodevelopmental and neurodegenerative disorders. Major epigenetic mechanisms may contribute to Alzheimer’s disease (AD) pathology. Several pathogenic genes and many other AD-related susceptibility genes contain methylated CpG sites. AD brains exhibit a genome-wide decrease in DNA methylation. Pathogenic histone modifications are present in AD. Alterations in epigentically regulated miRNAs may contribute to the abnormal expression of pathogenic genes in AD. Epigenetic drugs can reverse epigenetic changes in gene expression and might open future avenues in AD therapeutics. Individual differences in drug response are associated with genetic and epigenetic variability and disease determinants. Pharmacoepigenomics deals with the influence that epigenetic alterations may exert on genes involved in the pharmacogenomic network (pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes) responsible for the pharmacokinetics and pharmacodynamics of drugs (efficacy and safety), as well as the effects that drugs may have on the epigenetic machinery.
Storing the Web in Memory: Space Efficient Language Models with Constant Time Retrieval

We present three novel methods of compactly storing very large n-gram language models. These methods use substantially less space than all known approaches and allow n-gram probabilities or counts to be retrieved in constant time, at speeds comparable to modern language modeling toolkits. Our basic approach generates an explicit minimal perfect hash function, that maps all n-grams in a model to distinct integers to enable storage of associated values. Extensions of this approach exploit distributional characteristics of n-gram data to reduce storage costs, including variable length coding of values and the use of tiered structures that partition the data for more efficient storage. We apply our approach to storing the full Google Web1T n-gram set and all 1-to-5 grams of the Gigaword newswire corpus. For the 1.5 billion n-grams of Gigaword, for example, we can store full count information at a cost of 1.66 bytes per n-gram (around 30% of the cost when using the current state-of-the-art approach), or quantized counts for 1.41 bytes per n-gram. For applications that are tolerant of a certain class of relatively innocuous errors (where unseen n-grams may be accepted as rare n-grams), we can reduce the latter cost to below 1 byte per n-gram.
Nosocomial Outbreak Caused by Multidrug-Resistant Pseudomonas aeruginosa Producing IMP-13 Metallo-β-Lactamase

ABSTRACT An outbreak of Pseudomonas aeruginosa showing a multidrug-resistant (MDR) phenotype (including carbapenems, ceftazidime, cefepime, gentamicin, tobramycin, and fluoroquinolones) was observed, during a 5-month period, in a general intensive care unit of a large tertiary care and clinical research hospital in southern Italy. The outbreak involved 15 patients, with a total of 87 isolates, mostly from lower respiratory tract specimens. Analysis of isolates involved in the outbreak revealed production of metallo-β-lactamase (MBL) activity, and genotyping by pulsed-field gel electrophoresis of genomic DNA digested by SpeI revealed clonal relatedness among isolates. Molecular analysis of the MBL determinant showed the presence of a blaIMP-13 gene carried on a gene cassette inserted in a class 1 integron which also contained an aacA4 aminoglycoside resistance cassette encoding an AAC(6′)-Ib enzyme. The blaIMP-13-containing integron and its genetic environment appeared to be similar to those found in P. aeruginosa isolates producing IMP-13 from a hospital in Rome. The blaIMP-13 gene was not transferable by conjugation and was apparently carried on the chromosome. The outbreak was coincidental with a shortage of nursing personnel, and resolution was apparently associated with reinstatement of nursing personnel and reinforcement of general infection control practices within the intensive care unit. To our best knowledge this is the first description of a nosocomial outbreak of relatively large size caused by an IMP-producing gram-negative pathogen in Europe.
Second Order Cone Programming for Sensor Network Localization with Anchor Position Uncertainty

Node localization is a difficult task in sensor networks in which the ranging measurements are subject to errors and anchor positions are subject to uncertainty. In this paper, the robust localization problem is formulated using the maximum likelihood criterion under an unbounded uncertainty model for the anchor positions. To overcome the non-convexity of the resulting optimization problem, a convex relaxation leading to second order cone programming (SOCP) is devised. Furthermore, an analysis is performed in order to identify the set of nodes which are accurately positioned using robust SOCP, and to establish a relation between the solution of the proposed robust SOCP optimization and the existing robust optimization using semidefinite programming (SDP). Based on this analysis, a mixed robust SDP-SOCP localization framework is proposed which benefits from the better accuracy of SDP and the lower complexity of SOCP. Since the centralized optimization involves a high computational complexity in large networks, we also derive the distributed implementation of the proposed robust SOCP convex relaxation. Finally, we propose an iterative optimization based on the expectation maximization (EM) algorithm for the cases where anchor uncertainty parameters are unavailable. Simulations confirm that the robust SOCP and mixed robust SDP-SOCP provide tradeoffs between localization accuracy and computational complexity that render them attractive solutions, especially in networks with a large number of nodes.
Deep belief networks for predicting corporate defaults

This paper provides a new perspective on the default prediction problem using deep learning algorithms. Via the advantages of deep learning, the representable factors of input data will no longer need to be explicitly extracted, but can be implicitly learned by the deep learning algorithms. We consider the stock returns of both default and solvent companies as input signals and adopt one of the deep learning architecture, Deep Belief Networks (DBN), to train the prediction models. The preliminary results show that the proposed approach outperforms traditional machine learning algorithms.
Arabic sentiment analysis of YouTube comments

With the current level of ubiquity of social media websites, obtaining the users preferences automatically became a crucial task to assess their tendencies and behaviors online. Arabic language as one of the most spoken languages in the world and the fastest growing language on the Internet motivates us to provide reliable automated tools that can perform sentiment analysis to reveal users opinions. In this paper, we present our work of Arabic comments classification based on our collected and manually annotated YouTube Arabic comments. We share our classification results utilizing the most commonly used supervised classifiers: SVM-RBF, KNN, and Bernoulli NB classifiers. Experiments were performed using both raw and language-normalized datasets. We show that SVM-RBF outperformed other classification methods with an f-measure of 88.8% using a normalized dataset with two polarities.
Introduction of Improved Repairing Locality into Secret Sharing Schemes with Perfect Security

Repairing locality is an appreciated feature for distributed storage, in which a damaged or lost data share can be repaired by accessing a subset of other shares much smaller than is required for decoding the complete data. However for Secret Sharing (SS) schemes, it has been proven theoretically that local repairing can not be achieved with perfect security for the majority of threshold SS schemes, where all the shares are equally regarded in both secret recovering and share repairing. In this paper we make an attempt on decoupling the two processes to make secure local repairing possible. Dedicated repairing redundancies only for the repairing process are generated, which are random numbers to the original secret Through this manner a threshold SS scheme with improved repairing locality is achieved on the condition that security of repairing redundancies is ensured, or else our scheme degenerates into a perfect access structure that is equivalent to the best existing schemes can do. To maximize security of the repairing redundancies, a random placement mechanism is also proposed.
Oxidative Stress in Aging-Matters of the Heart and Mind

Oxidative damage is considered to be the primary cause of several aging associated disease pathologies. Cumulative oxidative damage tends to be pervasive among cellular macromolecules, impacting proteins, lipids, RNA and DNA of cells. At a systemic level, events subsequent to oxidative damage induce an inflammatory response to sites of oxidative damage, often contributing to additional oxidative stress. At a cellular level, oxidative damage to mitochondria results in acidification of the cytoplasm and release of cytochrome c, causing apoptosis. This review summarizes findings in the literature on oxidative stress and consequent damage on cells and tissues of the cardiovascular system and the central nervous system, with a focus on aging-related diseases that have well-documented evidence of oxidative damage in initiation and/or progression of the disease. The current understanding of the cellular mechanisms with a focus on macromolecular damage, impacted cellular pathways and gross morphological changes associated with oxidative damage is also reviewed. Additionally, the impact of calorific restriction with its profound impact on cardiovascular and neuronal aging is addressed.
An Empirical Study of an Extended Theory of Planned Behaviour Model for Pirated Software Purchase

In recent years, the widespread use of computer and Internet becomes more ubiquitous in our everyday life, but the situation of software piracy has become increasingly serious. In this study, Ajzen’s (1991) Theory of Planned Behaviour (TPB) is used as a theoretical framework to extend prior research pirated software purchasing behavior among students in Malaysia. Data were collected through self administered questionnaire. The hypothesis model incorporates social factors and habits into the basic TPB model. This paper uses multiple regression analysis to indentify the factors affecting pirated software purchasing behavior with a questionnaire of 419 students. The multiple regression analysis results indicate that all factors except perceived behavioural control have positive and significant impact on pirated software purchasing behavior of Malaysian students. Field of Research: Marketing
Towards area classification for large-scale fingerprint-based system

In spacious and multi-area buildings, fingerprint-based localization often suffers from expensive location search. Besides, context knowledge like inside/outside-region and floor area is important for complete location service. To address above issues, beyond the algorithms finding the exact location point, we study accurate and efficient indoor area classification for large-scale fingerprint-based system. We first study leveraging the one-class classification to conduct inside/outside-region detection given only the inside fingerprints. Then we discuss different area determination algorithms, and compare their detection accuracy and deployment efficiency. To further enhance accuracy, we also discuss rejecting unclassifiable signals and calibrating heterogeneous devices. We have implemented different algorithms on Android platforms. Experimental trials (totally over 30,000 fingerprints and 15,000 test data) at an international airport, a business building, a premium shopping mall and a university campus have evaluated practicability and deployability of different classification schemes. Our studies can also serve as design guidelines for area classification.
Chapter 9 Progress , History and Promise of Ovarian Cryopreservation and Transplantation for Pediatric Cancer Patients

Ovarian cryopreservation followed by transplantation is one of the potential ways fertility can be preserved and endocrine function restored in women who are at risk for ovarian failure, early menopause, or loss of fertility. While much of the progress in this area has occurred in the past couple of decades, the concept of transplantation of reproductive organs has been present since the nineteenth century. The first account of ovarian grafting was published in 1863 in a thesis by Paul Bert. Results were disappointing, hence the loss of interest over the next 30 years. In 1895, New York surgeon Robert Morris performed the first human ovarian implant in a woman with ovarian failure who reportedly became pregnant post-implant, though the pregnancy ended in a spontaneous abortion. Further experiments in ovarian grafting resulted in pregnancy in rabbits, dogs, and sheep from 1895 to 1899. However, because of the low success rate and limited clinical applicability of fresh ovary transplantation at that time, interest in this field waned [see review in 1]. The first attempts to cryopreserve ovarian tissue from rodents in the 1950s were largely unsuccessful, with only a 5% follicle survival rate due to several factors, including lack of effective cryoprotectants, automated cryopreservation machines, and optimized cryopreservation protocols [2–4]. By the 1970s, more effective cryoprotectants such as propanediol, ethylene glycol, and DMSO became available [5]. Slow cooling protocols were subsequently developed and successfully applied to ovarian tissue [6,7]. These developments resulted in restoration of fertility in up to 86% of rodents by the 1990s [8–12]. Despite these advances, doubt remained as to the applicability of these methods in humans since the rodent ovary is different from the bulkier, more fibrous human ovary with characteristically widely dispersed primordial follicles. Encouraging findings came in 1994, when Gosden demonstrated that ovarian cortical strips from sheep were able to survive cryopreservation and revascularize upon transplantation [8,13]. This was a significant step forward, as the sheep ovary is more similar to that of humans. By 1999, Baird showed that ovarian function was maintained for a limited though long-term (nearly 2 years) period in autotransplanted sheep [14]. Transplantation of the whole ovary after cryopreservation resulted in even longer-term function of more than 36 months [15]. In 2004, Lee reported the first live birth after heterotopic transplantation of fresh autologous ovarian tissue in the monkey [16]. There is less