Miracle-dz/UniFAR-FTUs huggingface dataset:
D RA FT Quantitative Verification Techniques for Biological Processes

Probabilistic model checking is a formal verification framework for systems which exhibit stochastic behaviour. It has been successfully applied to a wide range of domains, including security and communication protocols, distributed algorithms and power management. In this chapter we demonstrate its applicability to the analysis of biological pathways and show how it can yield a better understanding of the dynamics of these systems. Through a case study of the MAP (Mitogen–Activated Protein) Kinase cascade, we explain how biological pathways can be modelled in the probabilistic model checker PRISM and how this enables the analysis of a rich selection of quantitative properties.
GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS)

s of papers presented at the 9th IEEE International Workshop on GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS) November 10–November 12, 2010 Cold Spring Harbor Laboratory Cold Spring Harbor, New York Abstracts of papers presented at the 9th IEEE International Workshop ons of papers presented at the 9th IEEE International Workshop on GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS) November 10–November 12, 2010
Revolutionizing Ritual Interaction in the Classroom: Constructing the Chinese Renaissance of the 21st Century

Over the past decade, educational leaders in China have maintained that the pattern of social interactions in Chinese classrooms is not conducive to the cultivation of innovativeness and creativity, and that this lack of creativity is a major barrier to China’s global competitiveness. One key response has been the implementation of the 2001 “New Curriculum Reforms” in basic education. In this study, I draw on qualitative classroom observation and in-depth interview data, and quantitative survey data, from Gansu Province to investigate the extent to which classroom interactions differ substantially in Chinese primary school classrooms that are implementing the “New Curriculum” reforms, compared to those that are not. To the extent that individuals are constructed by the interactions in which they participate, changes in classrooms could have far-reaching implications for contemporary youth socialized differently from those of previous generations, and for the future social, cultural and political order of China.
Elongating under Stress

In response to extracellular stimuli, mitogen-activated protein kinases (MAPKs) modulate gene expression to maximize cell survival. Exposure of yeast to high osmolarity results in activation of the p38-related MAPK Hog1, which plays a key role in reprogramming the gene expression pattern required for cell survival upon osmostress. Hog1 not only regulates initiation but also modulates other steps of the transcription process. Recent work indicates that other yeast signalling MAPKs such as Mpk1 modulate transcriptional elongation in response to cell wall stress. Similarly, mammalian MAPKs have also been found associated to coding regions of stress-responsive genes. In this paper, significant progress in MAPK-regulated events that occur during the transcriptional elongation step is summarized, and future directions are discussed. We expect that the principles learned from these studies will provide a new understanding of the regulation of gene expression by signalling kinases.
On a Mathematical Model for Low-Rate Shrew DDoS

The shrew distributed denial of service (DDoS) attack is very detrimental for many applications, since it can throttle TCP flows to a small fraction of their ideal rate at very low attack cost. Earlier works mainly focused on empirical studies of defending against the shrew DDoS, and very few of them provided analytic results about the attack itself. In this paper, we propose a mathematical model for estimating attack effect of this stealthy type of DDoS. By originally capturing the adjustment behaviors of victim TCPs congestion window, our model can comprehensively evaluate the combined impact of attack pattern (i.e., how the attack is configured) and network environment on attack effect (the existing models failed to consider the impact of network environment). Henceforth, our model has higher accuracy over a wider range of network environments. The relative error of our model remains around 10% for most attack patterns and network environments, whereas the relative error of the benchmark model in previous works has a mean value of 69.57%, and it could be more than 180% in some cases. More importantly, our model reveals some novel properties of the shrew attack from the interaction between attack pattern and network environment, such as the minimum cost formula to launch a successful attack, and the maximum effect formula of a shrew attack. With them, we are able to find out how to adaptively tune the attack parameters (e.g., the DoS burst length) to improve its attack effect in a given network environment, and how to reconfigure the network resource (e.g., the bottleneck buffer size) to mitigate the shrew DDoS with a given attack pattern. Finally, based on our theoretical results, we put forward a simple strategy to defend the shrew attack. The simulation results indicate that this strategy can remarkably increase TCP throughput by nearly half of the bottleneck bandwidth (and can be higher) for general attack patterns.
MEANING NEGOTIATION IN ACADEMIC DISCOURSE: A TYPOLOGY OF CITATIONS AS SEMANTIC MARKERS

In academic discourse, citations constitute a multifunctional pragmatic act: they acknowledge the property rights of writers, create intellectual or conceptual linkages, and enhance the persuasiveness of a text. Citations can, furthermore, serve to negotiate term meaning within the text. In this capacity, they acquire a dual dialogic quality: as citations, they participate in the social construction of knowledge by engaging in a ‘dialogue’ with the writers of previous texts but, as semantic markers, they also interact with the text’s readers by responding to plausible needs for semantic clarification. The article’s main focus is to present a typology of citations used as semantic markers. An analysis of the rhetoricity of semantic markers in academic discourse is also included in the discussion.
Three-axis magnetic attitude control algorithms for small satellites in the presence of noise

This paper studies the three-axis attitude stabilization problem for a nadir-pointing spacecraft using only magnetic torquers as actuators. Since magnetic control systems are relatively lightweight, require low power and are inexpensive, they are attractive for small, inexpensive satellites in low Earth orbits. In this paper we present three-axis attitude control algorithms for small satellites using only magnetic torquers in the presence of noise and investigate their performance through simulations. These algorithms combine ideas from averaging and linear quadratic regulation as well as sliding mode design. Computer simulations are included to illustrate the effectiveness of the proposed control laws.
Amazon S3 for science grids: a viable solution?

Amazon.com has introduced the Simple Storage Service (S3), a commodity-priced storage utility. S3 aims to provide storage as a low-cost, highly available service, with a simple 'pay-as-you-go' charging model. This article makes three contributions. First, we evaluate S3's ability to provide storage support to large-scale science projects from a cost, availability, and performance perspective. Second, we identify a set of additional functionalities that storage services targeting data-intensive science applications should support. Third, we propose unbundling the success metrics for storage utility performance as a solution, to reduce storage costs.
MODE-INDEPENDENT H∞ CONTROL OF DISCRETE-TIME MARKOVIAN JUMP LINEAR SYSTEMS

Abstract This paper addresses the problem of H ∞ control of discrete-time linear systems with Markovian jumping parameters. The main contribution of the paper is to provide a method for designing a state-feedback H ∞ control for systems where the jumping parameter is not accessible. The cases where the transition probability matrix of the Markov process is either exactly known, or unknown but belongs to a given polytope, are treated. The robust H ∞ control problem for systems with polytopic-type uncertain state-space model matrices is also considered and a state-feedback control design based on a Lyapunov function that depends on the uncertain parameters is developed. The proposed control designs are given in terms of linear matrix inequalities. Illustrative numerical examples are provided to demonstrate the effectiveness of the derived results.
Model Fusion for Multimodal Depression Classification and Level Detection

Audio-visual emotion and mood disorder cues have been recently explored to develop tools to assist psychologists and psychiatrists in evaluating a patient's level of depression. In this paper, we present a number of different multimodal depression level predictors using a model fusion approach, in the context of the AVEC14 challenge. We show that an i-vector based representation for short term audio features contains useful information for depression classification and prediction. We also employed a classification step prior to regression to allow having different regression models depending on the presence or absence of depression. Our experiments show that a combination of our audio-based model and two other models based on the LGBP-TOP video features lead to an improvement of 4% over the baseline model proposed by the challenge organizers.
ID3 Classifier for Pupils' Status Prediction

Predicting the pupil’s status is the primary goal. Many studies have been made by a large number of scientists to explore the prediction of their research. One best solution is predicting the results based on the data source by applying some data mining techniques. This research work is to identify the prediction results by means of applying classification technique on the data source being available. There are many approaches in classification technique but this paper implements ID3 (Iterative Dichotomiser 3) Decision Tree concept which provides higher accuracy rates. This model extracts highly useful, reliable patterns from the database to ensure pupil’s to achieve a higher academic output.
Recursive Autonomy Identification for Bayesian Network Structure Learning

We propose a constraint-based algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition into autonomous substructures. In comparison to other constraintbased algorithms d-separating structures and then directing the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. Learning using the RAI algorithm renders smaller condition sets thus requires a smaller number of high order CI tests. This reduces complexity and run-time as well as increases accuracy since diminishing the curse-of-dimensionality. When evaluated on synthetic and "real-world" databases as well as the ALARM network, the RAI algorithm shows better structural correctness, run-time reduction along with accuracy improvement compared to popular constraint-based structure learning algorithms. Accuracy improvement is also demonstrated when compared to a common search-and-score structure learning algorithm.
Black ring with a positive cosmological constant

We consider a black ring with a cosmological constant in the five-dimensional $\mathcal{N}=4$ de Sitter supergravity theory. Our solution preserves half of the de Sitter supersymmetries and has one rotation symmetry. Unlike the flat case, there is no angular momentum and the stability against gravitational self-attraction is balanced by the cosmological repulsion due to the cosmological constant. Our solution describes a singular black ring since, although it has horizons of topology ${S}^{1}\ifmmode\times\else\texttimes\fi{}{S}^{2}$, the horizons are singular. Despite the singularity, our solution displays some interesting regular physical properties: it carries a dipole charge and this charge contributes to the first law of thermodynamics; it has an entropy and mass which conform to the entropic $N$-bound proposal and the maximal mass conjecture. We conjecture that the Gregory-Laflamme instability leads to a resolution of the singularity and results in a regular black ring.
A Lightweight Medium Access Protocol (LMAC) for Wireless Sensor Networks: Reducing Preamble Transmissions and Transceiver State Switches

In this paper, we present an energy-efficient medium access protocol designed for wireless sensor networks. Although the protocol uses TDMA to give nodes in the WSN the opportunity to communicate collision-free, the network is self-organizing in terms of time slot assignment and synchronization. The main goal of the medium access protocol is to minimize overhead of the physical layer. The protocol reduces the number of transceiver state switches and hence the energy wasted in preamble transmissions. The protocol is compared to SMAC and EMACs by simulation. The LMAC protocol is able to extend the network lifetime by a factor 2.4 and 3.8, compared to EMACs and SMAC respectively.
Local Structure in the Web

The web graph has been widely adopted as the core describing the web structure [4]. However, little attention has been paid to the relationship betweenthe web graph and the location of the pages. It has already been noticed that links are often local (i.e. from a page to another page of the same server) and this can be used for efficient encoding of the web graph [9,7]. Locality in the web can be further modelled by the clustered graph induced by the prefix tree of URLs. The web tree's internal nodes are the commonprefixes of URLs and its leaves are the URLs themselves. A prefix ordering of URLs according to this tree allows to observe local structure in the web directly on the adjacency matrix M of the web graph. M splits in two terms : M = D + S, where D is diagonal by blocks and S is a very sparse matrix. The blocks of D that can be observed along the diagonal are sets of pages strongly related together.
Adiponectin Protects against Glutamate-Induced Excitotoxicity via Activating SIRT1-Dependent PGC-1α Expression in HT22 Hippocampal Neurons

Glutamate- (Glu-) induced excitotoxicity plays a critical role in stroke. This study aimed to investigate the effects of APN on Glu-induced injury in HT22 neurons. HT22 neurons were treated with Glu in the absence or the presence of an APN peptide. Cell viability was assessed using the MTT assay, while cell apoptosis was evaluated using TUNEL staining. Levels of LDH, MDA, SOD, and GSH-Px were detected using the respective kits, and ROS levels were detected using dichlorofluorescein diacetate. Western blot was used to detect the expression levels of silent information regulator 1 (SIRT1), peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), cleaved caspase-3, Bax, and Bcl-2. In addition to the western blot, immunofluorescence was used to investigate the expression levels of SIRT1 and PGC-1α. Our results suggest that APN peptide increased cell viability, SOD, and GSH-Px levels and decreased LDH release, ROS and MDA levels, and cell apoptosis. APN peptide upregulated the expression of SIRT1, PGC-1α, and Bcl-2 and downregulated the expression of cleaved caspase-3 and Bax. Furthermore, the protective effects of the APN peptide were abolished by SIRT1 siRNA. Our findings suggest that APN peptide protects HT22 neurons against Glu-induced injury by inhibiting neuronal apoptosis and activating SIRT1-dependent PGC-1α signaling.
Embodied EFL Reading Activity: Let's Produce Comics.

While theories of embodied cognition have been investigated in lab experiments with proficient readers, currently no studies have applied these theories to improving reading comprehension for low-proficiency readers. Using an embodied cognition approach, this study investigated producing comics as an embodied reading activity. To compare effects on English as a foreign language (EFL) reading comprehension of narrative texts, 71 lowproficiency tertiary EFL readers were randomly assigned to one of two collaborative post-reading activity groups: comics production or translation. Before the activities, the participants were given background knowledge instruction for the narrative texts. Reading comprehension was assessed by a true-false test, followed by a semi-structured focus group interview. The results show that the comics production group outperformed the translation group in reading comprehension. Moreover, evidence from interviews shows the comics production activity assisted low-proficiency EFL readers in constructing multimodal representations of what they read, improving the depth of their reading comprehension.
Fine-grained parallelization of similarity search between protein sequences

This report presents the implementation of a protein sequence comparison algorithm specifically designed for speeding up time consuming part on parallel hardware such as SSE instructions, multicore architectures or graphic boards. Three programs have been developed: PLAST-P, TPLAST-N and PLAST-X. They provide equivalent results compared to the NCBI BLAST family programs (BLAST-P, TBLAST-N and BLAST-X) with a speed-up factor ranging from 5 to 10.
HSRA: Hindi stopword removal algorithm

In the last few years, electronic documents have been the main source of data in many research areas like Web Mining, Information Retrieval, Artificial Intelligence, Natural Language Processing etc. Text Processing plays a vital role for processing structured or unstructured data from the web. Preprocessing is the main step in any text processing systems. One significant preprocessing technique is the elimination of functional words, also known as stopwords, which affects the performance of text processing tasks. An efficient stopword removal technique is required in all text processing tasks. In this paper, we are proposing a stopword removal algorithm for Hindi Language which is using the concept of a Deterministic Finite Automata (DFA). A large number of available works on stopword removal techniques are based on dictionary containing stopword lists. Then pattern matching technique is applied and the matched patterns, which is a stopword, is removed from the document. It is a time consuming task as searching process takes a long time. This makes the method inefficient and very expensive. In comparison of that, our algorithm has been tested on 200 documents and achieved 99% accuracy and also time efficient.
Online Celebrity Bashing: Wrecking Ball or Good for You? Adolescent Girls’ Attitudes Toward the Media and Public Bashing of Miley Cyrus and Selena Gomez

Journalists and readers of celebrity news regularly bash celebrities online, a behavior that is easily accepted among adolescents. This study investigates whether these attitudes of acceptance differ according to the perpetrator of the bashing (media versus public) and the likeability of the involved celebrity (liked versus disliked). Using a vignette study, we examine adolescent girls’ attitudes toward media (journalists’) and public (readers’) bashing of a generally disliked celebrity (Miley Cyrus) and a generally liked celebrity (Selena Gomez). All participants read an identical negative news story (media bashing) and two related negative reader comments (public bashing). Participants were randomly assigned to read this information about either Miley Cyrus or Selena Gomez. Results of a mixed-design ANOVA showed that the girls had less negative attitudes toward media bashing compared with public bashing. Moreover, they more easily accepted the bashing of a disliked celebrity than the bashing of a liked celebrity.
MP74-10 DEEP LEARNING FOR SEMI-AUTOMATED PIRADSV2 SCORING ON MULTIPARAMETRIC PROSTATE MRI

INTRODUCTION AND OBJECTIVES: Multiparametric MRI (mpMRI) of the prostate has demonstrated great promise in aiding diagnosis of clinically significant prostate cancer. The Prostate Imaging Reporting and Data System (PIRADS)v2 was designed to provide standardized interpretation and reporting but inter-reader agreement remains low. In this study, we use a deep learning (DL) approach to develop a semi-automated PIRADSv2 scoring system. METHODS: Patients undergoing mpMRI for assessment of localized disease from February – October 2018 were considered for study inclusion. mpMRI scans were acquired at 3T with T2-weighted (T2W), diffusion-weighted (ADC map, high b-value DWI acquisition), and dynamic-contrast enhanced (DCE) MRI sequences and were prospectively interpreted by a single expert reader with >10years experience. All detected lesions were manually contoured on T2W MRI with reference to full mpMRI series to capture full extent of lesion boundaries. Bounding boxes derived from lesion contours were fed through a convolutional neural network with a ResNet101 backbone from the fast.ai library as three-channel images (T2W, ADC, high-b). Binary image classification was evaluated comparing PIRADS 2-3 versus PIRADS 4-5. Model performance was further assessed when training with all mpMRI slices encompassing lesion volumes or single mpMRI slice containing largest diameter of each lesion. Data were partitioned to 80% training and 20% validation. Outcome was measured as percent correct classification on the validation dataset. RESULTS: In total, 196 patients with contoured lesions were included in preliminary cohort had a PIRADS score of 2 or higher (n=24 PIRADS 2, n=73 PIRADS 3, n=58 PIRADS 4, n=41 PIRADS 5). When each slice of the tumors were treated as a unique sample, there were a total of 444 segmented tumor slices: n=33 PIRADS 2, n=113 PIRADS 3, n=108 PIRADS 4, n=190 PIRADS 5. Correct classification rate was 60% when the largest slice for each tumor was used. Correct classification rate increased to 71% when all 444 tumor slices were utilized. The PPV, NPV, sensitivity, specificity for correct depiction of PIRADS 4/5 lesions were 87%, 38%, 74%, 58%, respectively. CONCLUSIONS: We have demonstrated the possibility of more accurate and consistent classification of PIRADSv2 scores of lesions detected at mpMRI using an in house-made DL based algorithm. The accuracy of the system showed improvement as the size of the dataset increased in size. Future attempts will focus on further increasing of the dataset size and optimization of the system. Source of Funding: NIH Intramural Research Program. Also, this project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
Object-Oriented Reinforcement Learning in Cooperative Multiagent Domains

Although Reinforcement Learning methods have successfully been applied to increasingly large problems, scalability remains a central issue. While Object-Oriented Markov Decision Processes (OO-MDP) are used to exploit regularities in a domain, Multiagent System (MAS) methods are used to divide workload amongst multiple agents. In this work we propose a novel combination of OO-MDP and MAS, called Multiagent Object-Oriented Markov Decision Process (MOO-MDP), so as to accrue the benefits of both strategies and be able to better address scalability issues. We present an algorithm to solve deterministic cooperative MOO-MDPs, and prove that it learns optimal policies while reducing the learning space by exploiting state abstractions. We experimentally compare our results with earlier approaches and show advantages with regard to discounted cumulative reward, number of steps to fulfill the task, and Q-table size.