Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating computational Bayesian inference for stochastic biochemical reaction network models using multilevel Monte Carlo sampling

Investigating the behavior of stochastic models of biochemical reactionnetworks generally relies upon numerical stochastic simulation methods to generate many realizations of the model. For many practical applications, such numerical simulation can be computationally expensive. The statistical inference of reaction rate parameters based on observed data is, however, a significantly greater computational challenge; often relying upon likelihood-free methods such as approximate Bayesian computation, that requirethe generation of millions of individual stochastic realizations. In this study, we investigate a new approach to computational inference, based on multilevel Monte Carlo sampling: we approximate the posterior cumulative distribution function through a combination of model samples taken over a range of acceptance thresholds. We demonstrate this approach using a variety of discrete-state, continuous-time Markov models of biochemical reactionnetworks. Results show that a computational gain over standard rejection schemes of up to an order of magnitude is achievable without significant loss in estimator accuracy. Author Summary We develop a new method to infer the reaction rate parameters for stochastic models of biochemical reaction networks. Standard computational approaches, based on numerical simulations, are often used to estimate parameters. These computational approaches, however, are extremely expensive, potentially requiring millions of simulations. To alleviate this issue, we apply a different method of sampling allowing us to find an optimal trade-off between performance and accuracy. Our approach is approximately one order of magnitude faster than standard methods, without significant loss in accuracy.
Exact routing for micro-electrode-dot-array digital microfluidic biochips

Digital microfluidics is an emerging technology that provide fluidic-handling capabilities on a chip. One of the most important issues to be considered when conducting experiments on the corresponding biochips is the routing of droplets. A recent variant of biochips uses a micro-electrode-dot-array (MEDA) which yields a finer controllability of the droplets. Although this new technology allows for more advanced routing possibilities, it also poses new challenges to corresponding CAD methods. In contrast to conventional microfluidic biochips, droplets on MEDA biochips may move diagonally on the grid and are not bound to have the same shape during the entire experiment. In this work, we present an exact routing method that copes with these challenges while, at the same time, guarantees to find the minimal solution with respect to completion time. For the first time, this allows for evaluating the benefits of MEDA biochips compared to their conventional counterparts as well as a quality assessment of previously proposed routing methods in this domain.
Analysis of Web Crawling Algorithms

The web today is huge and enormous collection of data today and it goes on increasing day by day. Thus, searching for some particular data in this collection has a significant impact. Researches taking place give prominence to the relevancy and relatedness of the data that is found. Inspite of their relevance pages for any search topic, the results are still huge to be explored. Another important issue to be kept in mind is the users’ standpoint differs from time to time from topic to topic. Effective relevance prediction can help avoid downloading and visiting many irrelevant pages. The performance of a crawler depends mostly on the opulence of links in the specific topic being searched. This paper reviews the researches on web crawling algorithms used for searching. Keywords— Web Crawling Algorithms, Crawling Algorithm Survey, Search Algorithms, Lexical Database, Metadata, Semantic. __________________________________________________*****_________________________________________________
Handwritten-Word Spotting Using Biologically Inspired Features

For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language, and collection. We propose a biologically inspired whole-word recognition method that is used to incrementally elicit word labels in a live Web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neurophysiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows us to classify text images that have a low frequency of occurrence. Typically, these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually, standard pattern-recognition technology cannot deal with these text images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.
Communications in Binary Fat Trees

Fat trees are built around complete b-ary trees but have processing nodes only at the leaf level and may have different branch capacities in different levels. In this paper we study the communication capabilities of binary fat trees (including the simple binary tree) with respect to five major communication operations: broadcasting, multinode broadcasting, scattering, gathering and total exchange. We present and analyse optimal and nearly optimal algorithms for the five operations.
Malicious LUT: A stealthy FPGA Trojan injected and triggered by the design flow

We present a novel type of Trojan trigger targeted at the field-programmable gate array (FPGA) design flow. Traditional triggers base on rare events, such as rare values or sequences. While in most cases these trigger circuits are able to hide a Trojan attack, exhaustive functional simulation and testing will reveal the Trojan due to violation of the specification. Our trigger behaves functionally and formally equivalent to the hardware description language (HDL) specification throughout the entire FPGA design flow, until the design is written by the place-and-route tool as bitstream configuration file. From then, Trojan payload is always on. We implement the trigger signal using a 4-input lookup table (LUT), each of the inputs connecting to the same signal. This lets us directly address the least significant bit (LSB) and most significant bit (MSB) of the LUT. With the remaining 14 bits, we realize a “magic” unary operation. This way, we are able to implement 16 different Triggers. We demonstrate the attack with a simple example and discuss the effectiveness of the recent detection techniques unused circuit identification (UCI), functional analysis for nearly-unused circuit identification (FANCI) and VeriTrust in order to reveal our trigger.
Repeated Games, Entry in The New Palgrave Dictionary of Economics, 2nd Edition

This entry shows why self-interested agents manage to cooperate in a long-term relationship. When agents interact only once, they often have an incentive to deviate from cooperation. In a repeated interaction, however, any mutually beneficial outcome can be sustained in an equilibrium. This fact, known as the folk theorem, is explained under various information structures. This entry also compares repeated games with other means to achieve efficiency and briefly discuss the scope for potential applications.
Rising Public College Tuition and College Entry: How Well Do Public Subsidies Promote Access to College?

Though economists have spent the past decade analyzing the rising payoff to schooling, we know much less about the responses of youth or the effectiveness of policies aimed at influencing those decisions. States and the federal government currently spend more than $53 billion annually, hoping to promote greater access to college. This paper evaluates the price sensitivity of youth, using several sources of non-experimental variation in costs. The bulk of the evidence points to large enrollment impacts, particularly for low-income students and for those attending two-year colleges. The states have chosen to promote college enrollment by keeping tuition low through across-the-board subsidies rather than using more targeted, means-tested aid. As public enrollments increase, this has become an expensive strategy. Means-tested aid may be better targeted. However, the evidence of enrollment responses to such targeted aid is much weaker. After a federal means-tested grant program was established in 1973, there was no disproportionate increase in enrollment by low-income youth. Given the number of public dollars at stake, the two sets of results should be reconciled.
Hand gesture recognition: An overview

Hand gesture recognition has been applied to many fields in recent years, especially in man-machine interaction (MMI) area, which is regarded as a more natural and flexible input. In this paper, an overview of hand gesture recognition research up to date is presented, which includes common stages of hand gesture recognition, common methods and technique of each stage, the state of the recent research and summaries of some successful hand gesture recognition models.
Developing Novel and Effective Approach for Association Rule Mining Using Progressive Sampling

A challenging task in data mining is the process of discovering association rules from a large database. Most of the existing association rule mining algorithms make repeated passes over the entire database to determine the frequent itemsets, which is likely to incur an extremely high I/O overhead. A simple but an effective way to overcome this problem is to sample the database, such that, it produces rules with highest achievable accuracy on the large database. Numerous researchers have proposed sampling approaches for faster and efficient mining of association rules. In this paper, we propose a novel and effective progressive sampling-based approach for mining association rules from a large database. Initially, the frequent patterns are extracted using Apriori algorithm from an initial sample that is selected based on the temporal characteristics and the size of the database. Using the frequent itemsets generated, the negative border of the initial sample is obtained and sorted. Subsequently, the midpoint itemset in the sorted negative border is scanned in the concrete database to check if it is frequent. Based on the support level computed for the midpoint itemset, the sample size is either progressively increased for determining an optimal sample or association rules are mined by considering it as an optimal sample. The experimental results demonstrate the efficiency of the proposed progressive sampling approach in effective mining of association rules.
Natural Antioxidant Betanin Protects Rats from Paraquat-Induced Acute Lung Injury Interstitial Pneumonia

The effect of betanin on a rat paraquat-induced acute lung injury (ALI) model was investigated. Paraquat was injected intraperitoneally at a single dose of 20 mg/kg body weight, and betanin (25 and 100 mg/kg/d) was orally administered 3 days before and 2 days after paraquat administration. Rats were sacrificed 24 hours after the last betanin dosage, and lung tissue and bronchoalveolar lavage fluid (BALF) were collected. In rats treated only with paraquat, extensive lung injury characteristic of ALI was observed, including histological changes, elevation of lung : body weight ratio, increased lung permeability, increased lung neutrophilia infiltration, increased malondialdehyde (MDA) and myeloperoxidase (MPO) activity, reduced superoxide dismutase (SOD) activity, reduced claudin-4 and zonula occluden-1 protein levels, increased BALF interleukin (IL-1) and tumor necrosis factor (TNF)-α levels, reduced BALF IL-10 levels, and increased lung nuclear factor kappa (NF-κB) activity. In rats treated with betanin, paraquat-induced ALI was attenuated in a dose-dependent manner. In conclusion, our results indicate that betanin attenuates paraquat-induced ALI possibly via antioxidant and anti-inflammatory mechanisms. Thus, the potential for using betanin as an auxilliary therapy for ALI should be explored further.
Modeling of Reusability of Object Oriented Software System

Automatic reusability appraisal is helpful in evaluating the quality of developed or developing reusable software components and in identification of reusable components from existing legacy systems; that can save cost of developing the software from scratch. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this research work, structural attributes of software components are explored using software metrics and quality of the software is inferred by different Neural Network based approaches, taking the metric values as input. The calculated reusability value enables to identify a good quality code automatically. It is found that the reusability value determined is close to the manual analysis used to be performed by the programmers or repository managers. So, the developed system can be used to enhance the productivity and quality of software development. Keywords—Neural Network, Software Reusability, Software Metric, Accuracy, MAE, RMSE.
LU: A Best First Search to Process Single-Origin Multiple-Destination Route Query in a Graph

A single-origin multiple-destination route (SOMDR)query is to retrieve the minimum-cost routes, each of which starts from the same origin and ends at one of a set of destinations. The query is typically used for route assignment in transportation networks in GEOProcessing. In this paper,LU, a fundamental best first search algorithm and framework,is proposed to process SOMDR queries in a graph. It uses a heuristic, h_LU, estimated based on destinations yet-to-be reached to expedite the search process following a best first way. It is a framework that can adopt different heuristics to provide optimal and sub-optimal solutions. The paper discusses how to incorporate heuristic information obtained from the problem domain into a formal mathematical theory of graph searching and how a family of search strategies can demonstrate an optimality property in a sub graph. As an example, the Euclidean distance between two vertices is used as the basis to provide a consistent heuristic, h_LU, for LU to process SOMDR queries in network distance in a transportation network and a set of experiments is performed accordingly. The result demonstrates that LU is much more efficient than Dijkstra’s algorithm when the number of destinations is relatively smaller than the total number of vertices in a graph. On average, Dijkstra’s algorithm expands0.2~4.7 times more vertices and is 0.2~20.6 times slower than LU to retrieve optimal solutions when the number of destinations is up to 100.As a best first search, LU extends the capability of existing best first searches from single-destination query processing to multi-destination query processing. For SOMDR query processing, Dijkstra's algorithm is a special case of LU when no heuristic is adopted during the search process, and A* is a special case of LU when the number of destinations is one.
DEVELOPMENT OF A UAV-BASED REMOTE SENSING SYSTEM FOR UNPAVED ROAD CONDITION ASSESSMENT

This paper discusses an Unmanned Aviation Vehicle (UAV)-based remote sensing system for unpaved road condition assessment. The reported work has been part of the project “Monitoring of Unpaved Road Condition using Remote Sensing and Other Technology”, sponsored by the US Department of Transportation. The system is based on a low cost model helicopter equipped with a GPS/IMU and a geomagnetic sensor to detect the position, attitude and velocity of the helicopter. An autonomous controller was employed to control helicopter to fly along a predefined flight path and reach the desired positions. At the ground station, a computer was used to communicate with the helicopter in real-time to monitor flight parameters and send out control commands. The entire processing system includes camera calibration, integrated sensor orientation, digital 3D road surface model and orthoimage generation, automated feature extraction and measurement for road condition assessment. In this paper, we introduce our general strategy for road monitoring and the configuration of the UAV mapping system. The initial results of the analysis of road imagery collected by the UAV for detection of road distress in a test site are also presented.
Botulinum Neurotoxins A and E Undergo Retrograde Axonal Transport in Primary Motor Neurons

The striking differences between the clinical symptoms of tetanus and botulism have been ascribed to the different fate of the parental neurotoxins once internalised in motor neurons. Tetanus toxin (TeNT) is known to undergo transcytosis into inhibitory interneurons and block the release of inhibitory neurotransmitters in the spinal cord, causing a spastic paralysis. In contrast, botulinum neurotoxins (BoNTs) block acetylcholine release at the neuromuscular junction, therefore inducing a flaccid paralysis. Whilst overt experimental evidence supports the sorting of TeNT to the axonal retrograde transport pathway, recent findings challenge the established view that BoNT trafficking is restricted to the neuromuscular junction by highlighting central effects caused by these neurotoxins. These results suggest a more complex scenario whereby BoNTs also engage long-range trafficking mechanisms. However, the intracellular pathways underlying this process remain unclear. We sought to fill this gap by using primary motor neurons either in mass culture or differentiated in microfluidic devices to directly monitor the endocytosis and axonal transport of full length BoNT/A and BoNT/E and their recombinant binding fragments. We show that BoNT/A and BoNT/E are internalised by spinal cord motor neurons and undergo fast axonal retrograde transport. BoNT/A and BoNT/E are internalised in non-acidic axonal carriers that partially overlap with those containing TeNT, following a process that is largely independent of stimulated synaptic vesicle endo-exocytosis. Following intramuscular injection in vivo, BoNT/A and TeNT displayed central effects with a similar time course. Central actions paralleled the peripheral spastic paralysis for TeNT, but lagged behind the onset of flaccid paralysis for BoNT/A. These results suggest that the fast axonal retrograde transport compartment is composed of multifunctional trafficking organelles orchestrating the simultaneous transfer of diverse cargoes from nerve terminals to the soma, and represents a general gateway for the delivery of virulence factors and pathogens to the central nervous system.
Automatic segmentation of zona pellucida in HMC images of human embryos

An important prognostic parameter for assessing the success of an in vitro fertilization treatment is the variation in thickness of the zona pellucida. Zona pellucida, the envelope of the human embryo, is usually visualized using Hoffman modulation contrast microscopy (HMC). This paper addresses the problem of segmenting the zona pellucida in HMC images of embryos. We propose a variational method based on an image model for the zona which takes advantage of the characteristic appearance of HMC images. The simple topology of the embryo allows us to focus on parametric models. Our approach is partly inspired by the works of Chan and Vese, to which it has some similarities.
Fish Biomass Structure at Pristine Coral Reefs and Degradation by Fishing

Coral reefs around the world have experienced a dramatic decline during the past 25 years. Overfishing is believed to play a major role. There is significant interest in stabilizing and restoring damaged reefs, and first steps include understanding the functioning of reefs in their natural state and examining the effects of fishing. The isolated Kingman and Palmyra reefs are believed to provide a baseline for the natural state of coral reefs. At Kingman, it was recently discovered that apex predators constitute 85% of the total fish biomass. This is in sharp contrast to most reefs, where the prey biomass substantially dominates the total fish biomass. The recent study at the two pristine reefs also indicates that the predator:prey fish biomass ratio is an increasing function of reef cover. Based on these field observations, we model the fish biomass structure at a pristine coral reef. Since the prey hide from predators in the coral, a key component of our model is a refuge. Our model yields both the inverted biomass pyramid and the increasing dependence of the predator:prey biomass ratio on reef cover. Thus, refuge may provide a general new mechanism in ecology to create an inverted biomass pyramid, that does not require mass action interactions between predators and prey. We add various forms of fishing to our model, and show that sufficiently high fishing pressure with quite general types of fishing transforms the inverted biomass pyramid to be bottom heavy. We also show that prey fishing alone has the same effect.
A Promising Direction for Web Tracking Countermeasures

Web tracking continues to pose a vexing policy problem. Surveys have repeatedly demonstrated substantial consumer demand for control mechanisms, and policymakers worldwide have pressed for a Do Not Track system that effectuates user preferences. At present, however, consumers are left in the lurch: existing control mechanisms and countermeasures have spotty effectiveness and are difficult to use. We argue in this position paper that machine learning could enable tracking countermeasures that are effective and easy to use. Moreover, by distancing human expert judgments, machine learning approaches are both easy to maintain and palatable to browser vendors. We briefly explore some of the promise and challenges of machine learning for tracking countermeasures, and we close with preliminary results from a prototype implementation.
Cover sheet Title : Real-time health monitoring of historic buildings with wireless sensor networks

This paper describes the application of a wireless sensor network (WSN) in Torre Aquila, a 31 meter-tall medieval tower located in the city of Trento (Italy). Special attention was paid to monitoring and preservation of an artistic treasure: the fresco of the “Cycle of the Months” on the second floor. The various sensors installed include accelerometers, thermometers and strain gauges, arranged to record both structural response and external effects (road traffic vibration, temperature change), in order to real-time calibrate the structural model parameters and to identify any possible occurrence of abnormal situations. Strain sensors include prototypes of new Fiber Optic Sensors (FOS) in view of their long-term stability and durability. Based on the first 8 months of operation in assessing the stability of the tower, the wireless system is seen to be an effective tool thanks to its customized hardware and dedicated software. The whole system is reliable and energy efficient. The comparison between the acquired measurements and simulated numerical results shows good agreement.