Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Method of Software Vulnerability Detection based on Fuzzing Technique

Buffer overflow vulnerabilities can cause attacks that result in serious consequences. However the techniques of buffer overflow vulnerability detection are limited to manual analysis, binary-patch comparison, fuzzing and so on. They rely on manual analysis, thus cause high overhead. In this paper, we propose a novel method of detection of buffer overflow vulnerabilities, which is based on fuzzing, data-flow dynamic analysis and automated exception analysis. This new method effectively improves the detection of unknown security vulnerabilities (0 Day). Moreover, it is more automated and has better performance in finding new security vulnerabilities.
Scalable mammogram retrieval using Anchor Graph Hashing

Mammogram analysis is known to provide early-stage diagnosis of breast cancer in reducing its morbidity and mortality. In this paper, we propose a scalable content-based image retrieval (CBIR) framework for digital mammograms. CBIR is of great significance for breast cancer diagnosis as it can provide doctors image-guided avenues to access relevant cases. Clinical decisions based on such cases offer a reliable and consistent supplement for doctors. In our framework, we employ an unsupervised algorithm, Anchor Graph Hashing (AGH), to compress the mammogram features into compact binary codes, and then perform searching in the Hamming space. In addition, we also propose to fuse different features in AGH to improve its search accuracy. Experiments on the Digital Database for Screening Mammography (DDSM) demonstrate that our system is capable of providing content-based accesses to proven diagnosis, and aiding doctors to make reliable clinical decisions. What's more, our system is applicable to large-scale mammogram database, such that high number analogical cases would be retrieved as clinical references.
A NEW PROBE OF DARK MATTER AND HIGH-ENERGY UNIVERSE USING MICROLENSING

We propose the existence of ultracompact minihalos as a new type of massive compact halo object (MACHO) and suggest an observational test to discover them. These new MACHOs are a powerful probe into the nature of dark matter and physics in the high-energy universe. Non-Gaussian energy-density fluctuations produced at phase transitions (e.g., QCD) or by features in the inflation potential can trigger primordial black hole (PBH) formation if their amplitudes are δ ≳ 30%. We show that a PBH accumulates over time a sufficiently massive and compact minihalo to be able to modify or dominate its microlensing magnification light curve. Perturbations of amplitude 0.03% ≲ δ ≲ 30% are too small to form PBHs, but can nonetheless seed the growth of ultracompact minihalos. Thus, the likelihood of ultracompact minihalos as MACHOs is greater than that of PBHs. In addition, depending on their mass, they may be sites of formation of the first Population III stars. Ultracompact minihalos and PBHs produce a microlensing light curve that can be distinguished from that of a “point-like” object if high-quality photometric data are taken for a sufficiently long time after the peak of the magnification event. This enables them to be detected below the stellar-lensing “background” toward both the Magellanic Clouds and the Galactic bulge.
Methods and Statistics for Combining Motif Match Scores

Position-specific scoring matrices are useful for representing and searching for protein sequence motifs. A sequence family can often be described by a group of one or more motifs, and an effective search must combine the scores for matching a sequence to each of the motifs in the group. We describe three methods for combining match scores and estimating the statistical significance of the combined scores and evaluate the search quality (classification accuracy) and the accuracy of the estimate of statistical significance of each. The three methods are: 1) sum of scores, 2) sum of reduced variates, 3) product of score p-values. We show that method 3) is superior to the other two methods in both regards, and that combining motif scores indeed gives better search accuracy. The MAST sequence homology search algorithm utilizing the product of p-values scoring method is available for interactive use and downloading at URL http:/(/)www.sdsc. edu/MEME.
Maintain history information in DHT based P2P system under churn

Peer-to-Peer (P2P) systems receive growing acceptance, and the need of maintain the history information of nodes appears increasingly in a variety of P2P based applications. In this paper, we propose an algorithm to efficiently maintain the history information of all nodes in Chord like network under churn. In spirit to successor duplication, the duplications of load history are placed in its successors, and are updated in time. If there are some nodes depart from the network, the load history can be retrieved from the successors of the departed one. Simulation results show that, if the network has high churn ratio, great duplication number and large stabilization duration will lead to reliable history information maintenance.
Detection of Urinary Excreted Fungal Galactomannan-like Antigens for Diagnosis of Invasive Aspergillosis

Mortality associated with invasive aspergillosis (IA) remains high, partly because of delayed diagnosis. Detection of microbial exoantigens, released in serum and other body fluids during infection, may help timely diagnosis. In course of IA, Aspergillus galactomannan (GM), a well established polysaccharide biomarker, is released in body fluids including urine. Urine is an abundant, safely collected specimen, well-suited for point-of-care (POC) testing, which could play an increasing role in screening for early disease. Our main objective was to demonstrate GM antigenuria as a clinically relevant biological phenomenon in IA and establish proof-of-concept that it could be translated to POC diagnosis. Utilizing a novel IgM monoclonal antibody (MAb476) that recognizes GM-like antigens from Aspergillus and other molds, we demonstrated antigenuria in an experimental animal IA model (guinea pig), as well as in human patients. In addition, we investigated the chemical nature of the urinary excreted antigen in human samples, characterized antigen detection in urine by immunoassays, described a putative assay inhibitor in urine, and indicated means of alleviation of the inhibition. We also designed and used a lateral flow immunochromatographic assay to detect urinary excreted antigen in a limited number of IA patient urine samples. In this study, we establish that POC diagnosis of IA based on urinary GM detection is feasible. Prospective studies will be necessary to establish the performance characteristics of an optimized device and define its optimal clinical use.
Notes on the Nesting and Breeding of Delichon urbica (Linnaeus, 1758) (Aves: Passeriformes) near Köprüköy (Kızılırmak, Turkey)

The migration and some bio-ecological characteristics of a house martin (Delichon urbica) population, which has established colonies under bridges over the K›z›l›rmak River near Koprukoy village (K›r›kkale), were investigated in 2003 and 2004. These house martins were observed from the beginning of April to the first week of September, when they migrated south. According to the specimens examined, mean measurements of total length, wingspan, and weight were recorded as 133.5 mm, 279.5 mm, and 15.5 g, respectively. Mean measurements and weights of the nests were 12.5 mm long × 9 mm wide × 6 mm high, and 425 g. The first clutch was observed in May, the first offspring appeared during the first week of June, and litter size varied from 1 to 4. The mean weight of eggs was 1.6 g; the total body length and weight of a hatchling were 6 mm and 0.83 g. It was also found that the house martin had a maximum of 2 clutches during the breeding season.
Federal Reserve Bank of San Francisco Working Paper Series New Evidence Using Regional Data Why Has the U.s. Beveridge Curve Shifted Back? New Evidence Using Regional Data* Why Has the U.s. Beveridge Curve Shifted Back? New Evidence Using Regional Data

The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Federal Reserve Bank of San Francisco or the Board of Governors of the Federal Reserve System. ABSTRACT The Beveridge curve depicts the empirical relationship between job vacancies and unemployment, which in turn reflects the underlying efficiency of the job matching process. Previous analyses of the Beveridge curve suggested deterioration in match efficiency during the 1970s and early 1980s, followed by improved match efficiency beginning in the late 1980s. This paper combines aggregate and regional data on job vacancies and unemployment to estimate the U.S. aggregate and regional Beveridge curves, focusing on the period 1976-2005. Using new data on job vacancies from the U.S. Bureau of Labor Statistics, the help-wanted advertising series that formed the basis of past work are modified to form synthetic job vacancy series at the national and regional level. The results suggest that a decline in the dispersion of employment growth across geographic areas contributed to a pronounced inward shift in the Beveridge curve since the late 1980s, reversing the earlier pattern identified by Abraham (1987) and reinforcing findings of favorable labor market trends in the 1990s (e.g., Katz and Krueger 1999). * The author thanks Geoff MacDonald and especially Jaclyn Hodges for expert research assistance and Ken Goldstein of the Conference Board for providing the help-wanted index data. He also thanks Mark Schweitzer and other participants at the 2004 Federal Reserve System Regional Research meetings for comments. The views expressed in this paper are those of the author and should not be attributed to the Federal Reserve Bank of San Francisco or the Federal Reserve System.
Science achievement within the United States: a view through affective and demographic lenses

ABSTRACT This study investigated the predictive effects of science self-efficacy, value in learning science, science attitudes, gender, and race on science achievement, among United States middle school students utilising Trends in International Math and Science Study 2011 data. A series of preliminary analyses using several descriptive statistics, Cronbach alpha, and exploratory factor analysis, were conducted. The main analysis of the study was Structural Equation Modelling (SEM), utilising both measurement models and structural models. Findings indicated that science self-efficacy, value in learning science, gender and race were significant positive predictors of science achievement, while attitude was not a predictor. Male students showed higher science achievement, science self-efficacy and science attitude than females. White and Asian students showed significantly higher science achievement, science self-efficacy, value in science learning, and science attitudes than Black and Hispanic students. These findings may be utilised to implement new science programs and encourage and train educators.
Hierarchical Layouts for Photo Libraries

We use an annotated digital photo collection to demonstrate a two-level auto-layout technique consisting of a central primary region with secondary regions surrounding it. Because the object sizes within regions can only be changed in discrete units, we refer to them as quantum content. Our real-time algorithms enable a compelling interactive display as users resize the canvas, or move and resize the primary region
Shrinking the Metabolic Solution Space Using Experimental Datasets

Constraint-based models of metabolism have been used in a variety of studies on drug discovery, metabolic engineering, evolution, and multi-species interactions. These genome-scale models can be generated for any sequenced organism since their main parameters (i.e., reaction stoichiometry) are highly conserved. Their relatively low parameter requirement makes these models easy to develop; however, these models often result in a solution space with multiple possible flux distributions, making it difficult to determine the precise flux state in the cell. Recent research efforts in this modeling field have investigated how additional experimental data, including gene expression, protein expression, metabolite concentrations, and kinetic parameters, can be used to reduce the solution space. This mini-review provides a summary of the data-driven computational approaches that are available for reducing the solution space and thereby improve predictions of intracellular fluxes by constraint-based models.
New Object Detection for On-board Robot Vision by Lifting Complex Wavelet Transforms

This paper aims to develop a fast algorithm for detecting a new object from video sequences captured by on-board robot vision. We first propose lifting complex wavelet, which is a new method for extracting local features in an image. The proposed lifting complex wavelet transforms can be detected the features faster than the conventional SIFT algorithm. Our new object detection is performed by using an overlap ratio between the local features of current- and past-frames. In experiments, we show that new objects can be detected fast from on-board robot vision.
symMMU: symbolically executed runtime libraries for symbolic memory access

Symbolic execution calls for specialized address translation. Unlike a pointer on a traditional machine model, which corresponds to a single address, a symbolic pointer may represent multiple feasible addresses. A symbolic pointer dereference manipulates symbolic state, potentially submitting many theorem prover requests in the process. Hence, design and management of symbolic accesses critically affects symbolic executor performance, complexity, and completeness. We demonstrate a symbolic execution extension, the symMMU, which separates access dispatch mechanism from policy by servicing memory accesses with symbolically executed runtime software handlers. This runtime code concisely represents access policies including pointer concretization, address forking, and symbolic indexing. These policies are competitive with a baseline hard-coded memory policy. Furthermore, the symMMU cleanly supports handlers for profiling, heap analysis, and demand allocated symbolic buffers. In practice, the symMMU flags hardware-validated bugs for over a thousand Linux program binaries.
Learning From State Differences: STD( )

TD() with function approximation has proved empirically successful for some complex reinforcement learning problems. For linear approximation, TD() has been shown to minimise the squared error between the approximate value of each state and the true value. However, as far as policy is concerned, it is error in the relative ordering of states that is critical, rather than error in the state values. We illustrate this point, both in simple two-state and three-state systems in which TD()—starting from an optimal policy—converges to a sub-optimal policy, and also in backgammon. We then present a modified form of TD(), called STD(), in which function approximators are trained with respect to relative state values on binary decision problems. A theoretical analysis, including a proof of monotonic policy improvement for STD() in the context of the two-state system, is presented , along with a comparison with Bertsekas' differential training method [1]. This is followed by successful demonstrations of STD() on the two-state system and a variation on the well known acrobot problem.
Dendritic Excitation Dependence on Synaptic Democracy in CA 1 Pyramidal Neurons

12 Synaptic democracy is believed to be an important effect that enables distal 13 synapses to contribute information to the soma. Synaptic democracy is found in 14 hippocampal CA1 pyramidal neurons, which is an important area of the brain 15 for learning and memory. Previous computational work by Sterratt et al [1] 16 show that synaptic democracy arises in a model of a CA1 pyramidal neuron 17 when sufficient synapses are synchronously activated given that the calcium 18 concentration is homeostatically maintained. However, in this work only a 5Hz 19 excitatory postsynaptic potential (EPSP) was tested. We explored how different 20 input vectors might affect the dynamics of synaptic democracy and the neuron 21 as a whole. We found that high frequency stimulation compensated for a sub22 threshold number of synapses allowing for synaptic democracy. Interestingly, in 23 the cases with supra-threshold synapse numbers, higher frequency synaptic 24 inputs abolished synaptic democracy. 25
Media Independence through Routine Press-State Relations: Immigration and Government Statistics in the British Press

When and how does press coverage maintain independence from governments’ preferred language? Leading scholarship argues that elites shape media content, especially in foreign affairs settings where journalists rely on official sources. But do media push back in domestic policy contexts? Focusing on immigration in Britain, we find press coverage exhibits signs of autonomy that rely on the state’s administrative branches. Our evidence comes from automated linguistic analysis of 190,000 items of migration coverage in nineteen national British newspapers from 2006 to 2015, and press releases published by the U.K. Home Office between 2010 and 2015. We show that the press increasingly portrayed immigration in terms of its scale. Then, by comparing the dynamics of a key government policy—lowering “net migration”—in press and Home Office rhetoric, we illustrate the limits of the government to insert its desired language into the press. Finally, we argue routine press interactions with the nonpolitical Office for National Statistics enabled coverage that diverged from politicians’ preferred lines. Our study contributes to press-state theory by providing evidence of media semiautonomy in a domestic policy arena, and highlighting the often-overlooked role of routine, bureaucratic procedure in supporting that autonomy.
Lower bound on training-based channel estimation error for frequency-selective block-fading Rayleigh MIMO channels

A lower bound on the error correlation matrix of training-based channel estimators is derived for multiple-input multiple-output (MIMO) systems over block-fading frequency-selective channels with symbol-spaced receivers. The bound is obtained in a constructive way by evaluating the asymptotic performance of an estimator that fully exploits the algebraic structure of the multipath channel. In particular, the estimator is assumed to be able to estimate the long-term features of the channel consistently (e.g., second order statistics of fading, delays, angles) while tracking the fast-varying fading fluctuations by Wiener filtering. Known estimators that are able to attain the bound under simplified settings are referred to, and general guidelines for designing novel estimators are discussed. Based on the simple analytical expression of the bound, the impact of channel estimation error on the link capacity is investigated for different system parameters and channel characteristics (e.g., Doppler shift, spatial correlation of fading). Numerical results are provided to corroborate the analysis.
PTSD Symptom Severities, Interpersonal Traumas, and Benzodiazepines Are Associated with Substance-Related Problems in Trauma Patients

Background: Trauma is commonly associated with substance-related problems, yet associations between specific substances and specific posttraumatic stress disorder symptoms (PTSSs) are understudied. We hypothesized that substance-related problems are associated with PTSS severities, interpersonal traumas, and benzodiazepine prescriptions. Methods: Using a cross-sectional survey methodology in a consecutive sample of adult outpatients with trauma histories (n = 472), we used logistic regression to examine substance-related problems in general (primary, confirmatory analysis), as well as alcohol, tobacco, and illicit drug problems specifically (secondary, exploratory analyses) in relation to demographics, trauma type, PTSSs, and benzodiazepine prescriptions. Results: After adjusting for multiple testing, several factors were significantly associated with substance-related problems, particularly benzodiazepines (AOR = 2.78; 1.99 for alcohol, 2.42 for tobacco, 8.02 for illicit drugs), DSM-5 PTSD diagnosis (AOR = 1.92; 2.38 for alcohol, 2.00 for tobacco, 2.14 for illicit drugs), most PTSSs (especially negative beliefs, recklessness, and avoidance), and interpersonal traumas (e.g., assaults and child abuse). Conclusion: In this clinical sample, there were consistent and strong associations between several trauma-related variables and substance-related problems, consistent with our hypotheses. We discuss possible explanations and implications of these findings, which we hope will stimulate further research, and improve screening and treatment.
Short and Long-Term Analysis and Comparison of Neurodegeneration and Inflammatory Cell Response in the Ipsilateral and Contralateral Hemisphere of the Neonatal Mouse Brain after Hypoxia/Ischemia

Understanding the evolution of neonatal hypoxic/ischemic is essential for novel neuroprotective approaches. We describe the neuropathology and glial/inflammatory response, from 3 hours to 100 days, after carotid occlusion and hypoxia (8% O2, 55 minutes) to the C57/BL6 P7 mouse. Massive tissue injury and atrophy in the ipsilateral (IL) hippocampus, corpus callosum, and caudate-putamen are consistently shown. Astrogliosis peaks at 14 days, but glial scar is still evident at day 100. Microgliosis peaks at 3–7 days and decreases by day 14. Both glial responses start at 3 hours in the corpus callosum and hippocampal fissure, to progressively cover the degenerating CA field. Neutrophils increase in the ventricles and hippocampal vasculature, showing also parenchymal extravasation at 7 days. Remarkably, delayed milder atrophy is also seen in the contralateral (CL) hippocampus and corpus callosum, areas showing astrogliosis and microgliosis during the first 72 hours. This detailed and long-term cellular response characterization of the ipsilateral and contralateral hemisphere after H/I may help in the design of better therapeutic strategies.
Form classification using DP matching

In order to process many kinds of form document, form classification must be performed in the first place. However, a substantial amount of time is required to analyze and recognize such form documents according to the increase of t, lte number of registered models. To solve this problem, w,' suggest document understanding with a partial matchmg method in which form structure recognition and form (:lassification are performed for only some areas of the input form. In this case, the definition of the matching areas and ibrm classification are performed by using a DP matching algorithm. By using this approach, the recognition of an input form and the extraction of field items can be protessed at a high recognition rate in a reasonable time span. in terms of al)plicability, the experimental recognition rate and processing time seem to be encouraging.