Miracle-dz/UniFAR-FTUs huggingface dataset:
Exniffer: Learning to Prioritize Crashes by Assessing the Exploitability from Memory Dump

An important component of software reliability is the assurance of certain security guarantees, such as absence of low-level bugs that may result in code exploitation, for example. A program crash is an early indicator of possible errors in the program like memory corruption, access violation or division by zero. In particular, a crash may indicate the presence of safety or security critical errors. A safety-error crash does not result in any exploitable condition, whereas a security-error crash allows an attacker to exploit a vulnerability. However, distinguishing one from the other is a non-trivial task. This exacerbates the problem in cases where we get hundreds of crashes and programmers have to make choices which crash to patch first! In this work, we present a technique to identify security critical crashes by applying machine learning on a set of features derived from core-dump files and runtime information obtained from hardware assisted monitoring such as the last branch record (LBR) register. We implement the proposed technique in a prototype called Exniffer. Our empirical results, obtained by experimenting Exniffer on several crashes on real-world applications show that proposed technique is able to classify a given crash as exploitable or not-exploitable with high accuracy.
A New Algorithm for Surface Currents Inversion With High-Frequency Over-the-Horizon Radar

The conventional method of extracting ocean surface currents by high-frequency over-the-horizon radar is based on the fixed first-order Bragg frequency formula and ignores the effects caused by the environment, especially in near-shore areas. In this letter, a current inversion model based on 2-D Fourier series expansion was developed. The first-order Bragg frequency and the Doppler offset induced by radial current are dealt with the bivariate functions of group distance and azimuth angle in the proposed method. By solving an overdetermined matrix equation with the least-square fitting method, the current at each detection grid can be estimated. As the Bragg frequency obtained by this new algorithm is adaptive to the environment, the accuracy of current measurement will be improved. The feasibility and effectiveness of the new method are verified with simulations and experimental results. The currents estimated by the traditional method and the new algorithm are compared with two in situ buoys. Results indicate that the new algorithm possesses comparable accuracy for far-shore areas and better accuracy for near-shore areas when compared with the conventional method.
Upregulated NNT-AS1, a long noncoding RNA, contributes to proliferation and migration of colorectal cancer cells in vitro and in vivo

The expression patterns of the long non-coding RNA Nicotinamide Nucleotide Transhydrogenase-antisense RNA1 (NNT-AS1) have not been investigated in the context of cancer. In this study, we aim to investigate the NNT-AS1 expression level in colorectal cancer (CRC) patients and its potential roles in tumor biology. We measured the expression of NNT-AS1 in 70 paired tumor tissues and adjacent normal tissues. NNT-AS1 was expressed higher in tumor tissues than that in adjacent noncancer tissues, and higher expression of NNT-AS1 was significantly correlated with lymph node metastasis (Yes vs. No, P=0.004), TNM stage (I/II vs. III/IV, P=0.004), vessel invasion (Yes vs. No, P=0.002) and differentiation (well and moderate vs. poor, P=0.008). Multivariate analyses revealed that NNT-AS1 expression was an independent predictor of overall survival (P=0.0174) and progression free survival (P=0.0132) for CRC. Knockdown of NNT-AS1 using small interfering RNA (siRNA) significantly impaired CRC cell proliferation, migration and invasion in vitro and silencing NNT-AS1 also suppressed tumor growth and metastasis in nude mice. The western blot experiments revealed that silencing NNT-AS1 inhibited epithelial-mesenchymal transition (EMT) and inactivated MAPK/Erk signaling pathway in CRC cell lines. In conclusion, our studies implied that NNT-AS1 may involve in the development and progression of CRC via its regulation of cell proliferation, migration, and invasion by NNT-AS1-mediated activating of MAPK/Erk signaling pathway and EMT. NNT-AS1 may be a useful diagnostic and prognostic biomarker and a potential therapeutic target in CRC patients.
A Wideband, Unidirectional Circularly Polarized Antenna for Full-Duplex Applications

A wideband planar unidirectional circularly polarized (CP) antenna with simultaneous transmit and receive (STAR) capability is presented for full-duplex applications. The proposed STAR antenna consists of four radiating elements and is fed with a custom-designed feed network. The radiating elements are identical CP patch antennas, which are sequentially rotated along the center of the STAR antenna. The four radiating elements are symmetrically distributed along the perimeter of a circle. The feed network is composed of two identical 180° power dividers. Each pair of CP elements, placed opposite to each other on one of the axes of symmetry of the STAR antenna, is excited together using one of the 180° power dividers. This way, a null is created along the other axis of symmetry of the STAR antenna causing near-field cancellation at the location of the other two antennas. A prototype of the proposed STAR antenna operating in the 6.0–7.2 GHz frequency range is designed, fabricated, and experimentally characterized. Measurement results show that the antenna provides an isolation better than 40 dB, an axial ratio better than 3 dB, and a gain higher than 8.7 dBic over this entire band of operation.
Optimal Encoding in Stochastic Latent-Variable Models

In this work we explore encoding strategies learned by statistical models of sensory coding in noisy spiking networks. Early stages of sensory communication in neural systems can be viewed as encoding channels in the information-theoretic sense. However, neural populations face constraints not commonly considered in communications theory. Using restricted Boltzmann machines as a model of sensory encoding, we find that networks with sufficient capacity learn to balance precision and noise-robustness in order to adaptively communicate stimuli with varying information content. Mirroring variability suppression observed in sensory systems, informative stimuli are encoded with high precision, at the cost of more variable responses to frequent, hence less informative stimuli. Curiously, we also find that statistical criticality in the neural population code emerges at model sizes where the input statistics are well captured. These phenomena have well-defined thermodynamic interpretations, and we discuss their connection to prevailing theories of coding and statistical criticality in neural populations.
Phosphoproteome of the Oleaginous Green Alga, Chlorella vulgaris UTEX 395, under Nitrogen-Replete and -Deplete Conditions

The unicellular green alga, Chlorella vulgaris UTEX 395, represents a promising biocatalyst for renewable biofuel production due to its relatively rapid growth rate and high lipid accumulation capacity (Guarnieri et al., 2011, 2012; Gerken et al., 2013; Griffiths et al., 2014; Zuniga et al., 2016). Prior analyses have unveiled the global proteome dynamics of C. vulgaris following nitrogen depletion, which induces a high lipid accumulation phenotype (Guarnieri et al., 2011, 2013). More recently, we have reported a draft genome, genome-scale model, and nitrosoproteome for this alga (Zuniga et al., 2016; Henard et al., 2017)1 providing further insight into lipid biosynthetic-, nutrient response-, and post-transcriptional-regulatory mechanisms. To further our understanding of these regulatory mechanisms and expand the knowledge base surrounding this organism, comparative phosphoproteomic analyses were conducted under nitrogen-replete and -deplete conditions to identify differentially phosphorylated proteins that will aid in the evaluation of the potential role of phosphoregulation in lipogenesis.
Changes in ventilation of the Mediterranean Sea during the past 25 year

Abstract. Significant changes in the overturning circulation of the Mediterranean Sea has been observed during the last few decades, the most prominent phenomena being the Eastern Mediterranean Transient (EMT) in the early 1990s and the Western Mediterranean Transition (WMT) during the mid-2000s. During both of these events unusually large amounts of deep water were formed, and in the case of the EMT, the deep water formation area shifted from the Adriatic to the Aegean Sea. Here we synthesize a unique collection of transient tracer (CFC-12, SF6 and tritium) data from nine cruises conducted between 1987 and 2011 and use these data to determine temporal variability of Mediterranean ventilation. We also discuss biases and technical problems with transient tracer-based ages arising from their different input histories over time; particularly in the case of time-dependent ventilation. We observe a period of low ventilation in the deep eastern (Levantine) basin after it was ventilated by the EMT so that the age of the deep water is increasing with time. In the Ionian Sea, on the other hand, we see evidence of increased ventilation after year 2001, indicating the restarted deep water formation in the Adriatic Sea. This is also reflected in the increasing age of the Cretan Sea deep water and decreasing age of Adriatic Sea deep water since the end of the 1980s. In the western Mediterranean deep basin we see the massive input of recently ventilated waters during the WMT. This signal is not yet apparent in the Tyrrhenian Sea, where the ventilation seems to be fairly constant since the EMT. Also the western Alboran Sea does not show any temporal trends in ventilation.
Attribute weighted Naive Bayes for remote sensing image classification based on cuckoo search algorithm

The Naive Bayes classifier(NB) is an effective and simple classification method for remote sensing image classification which is based on probability theory. However, in general, the contribution of each feature is different for classification and its attribute independence assumption is often invalid in the real world. The attribute weighted Naive Bayes(WNB) classifier might have better performance compared to NB, nevertheless, it is a hard and time-consuming work to learn the weight values for all features. Cuckoo search is a newly proposed meta-heuristic optimization algorithm which has been successfully applied for many parameter optimization problems. In the paper, a remote image classification approach is proposed, the attribute weight of which is learnt through cuckoo search algorithm (CSWNB in brief). In order to testify the performance of the proposed method, it is compared to some other evolutionary algorithms, such as attributed weighted Naive Bayes based on Genetic Algorithm (GAWNB), attributed weighted Naive Bayes based on Particle Swarm Optimization (PSOWNB) and attributed weighted Naive Bayes based on Water Wave Optimization (WWOWNB) etc. Experimental results demonstrate that the proposed approach has higher classification accuracy and more stable performance.
Bifidobacterium breve UCC2003 Employs Multiple Transcriptional Regulators To Control Metabolism of Particular Human Milk Oligosaccharides

ABSTRACT Bifidobacterial carbohydrate metabolism has been studied in considerable detail for a variety of both plant- and human-derived glycans, particularly involving the bifidobacterial prototype strain Bifidobacterium breve UCC2003. We recently elucidated the metabolic pathways by which the human milk oligosaccharide (HMO) constituents lacto-N-tetraose (LNT), lacto-N-neotetraose (LNnT) and lacto-N-biose (LNB) are utilized by B. breve UCC2003. However, to date, no work has been carried out on the regulatory mechanisms that control the expression of the genetic loci involved in these HMO metabolic pathways. In this study, we describe the characterization of three transcriptional regulators and the corresponding operator and associated (inducible) promoter sequences, with the latter governing the transcription of the genetic elements involved in LN(n)T/LNB metabolism. The activity of these regulators is dependent on the release of specific monosaccharides, which are believed to act as allosteric effectors and which are derived from the corresponding HMOs targeted by the particular locus. IMPORTANCE Human milk oligosaccharides (HMOs) are a key factor in the development of the breastfed-infant microbiota. They function as prebiotics, selecting for a specific range of microbes, including a number of infant-associated species of bifidobacteria, which are thought to provide a range of health benefits to the infant host. While much research has been carried out on elucidating the mechanisms of HMO metabolism in infant-associated bifidobacteria, to date there is very little understanding of the transcriptional regulation of these pathways. This study reveals a multicomponent transcriptional regulation system that controls the recently identified pathways of HMO metabolism in the infant-associated Bifidobacterium breve prototype strain UCC2003. This not only provides insight into the regulatory mechanisms present in other infant-associated bifidobacteria but also provides an example of a network of sequential steps regulating microbial carbohydrate metabolism.
Determining the Minimal Clinically Important Difference for 6-Minute Walk Distance in Fibromyalgia

ObjectiveThe aim of this study was to estimate the minimal clinically important difference (MCID) for 6-min walk distance (6MWD) in patients with fibromyalgia. DesignData from a recently completed trial that included 187 patients who completed the 6-min walk test, Fibromyalgia Impact Questionnaire (FIQ), and Short-Form 36 (SF36) at 12 and 36 wks were used to examine longitudinal changes in 6MWD. An anchor-based approach that used linear regression analyses was used to determine the MCID for 6MWD, using the total FIQ score (FIQ-Total) and SF36-physical function domain as clinical anchors. ResultsThe mean (SD) change in 6MWD from baseline to week 36 was 34.4 (65.2) m (P < 0.001). The anchor-based MCIDs for the 6MWD were 156 and 167 m for the FIQ and SF36-physical function domain, respectively. These MCIDs correspond with clinically meaningful improvements in FIQ (14% reduction) and SF36-physical function domain (10-point increase). ConclusionThe MCID for 6MWD in patients with fibromyalgia was 156 to 167 m. These findings provide the first evidence of the change in 6MWD that is perceived by patients to be clinically meaningful. Further research using other MCID calculation methods is needed to refine estimates of the MCID for 6MWD in patients with fibromyalgia.
Higher resolution cine imaging with compressed sensing for accelerated clinical left ventricular evaluation

To assess the clinical feasibility of a compressed sensing cine magnetic resonance imaging (MRI) sequence of both high temporal and spatial resolution (CS_bSSFP) in comparison to a balanced steady‐state free precession cine (bSSFP) sequence for reliable quantification of left ventricular (LV) volumes and mass.
High-Speed and Lightweight Humanoid Robot Arm for a Skillful Badminton Robot

Sports, especially badminton, require participants to perform dynamic and skillful motions. Previous robots have had difficulty in performing like a human because of their severe limitations of low operating speed, heavy bodies, and simplistic mechanisms. In this letter, we propose a new robot design that consists of a structure integrated with pneumatic actuators and noninterfering many-degree-of-freedom joints, for the realization of a high-speed and lightweight humanoid robot. We made a four-degree-of-freedom robot arm for badminton, which is an especially dynamic sport, aiming for maximum speed while meeting geometric requirements. The robot swung with a racket-head speed of 21 m/s, which is a value higher than speeds achieved by previous robotic arms. The robot also realized a skillful shot, namely the spin net shot, which cannot be performed by previous badminton robots having simple mechanisms. A pneumatic robot is considered difficult to control, especially in terms of feedback control. We found that the reproducibility of the robot was as fine as 10–40 mm at the racket head for four kinds of strong swings. Using feedforward control, we also conducted an experiment in which the robot hits a flying shuttle, and achieved a high hitting rate of 69.7% for powerful swings. We believe that this research expands the possibilities of the pneumatic robot and is the first step toward developing a skillful humanoid badminton robot.
Image retrieval by dense caption reasoning

Humans tend to understand image scene by recognizing visual elements, then conjecturing and inferring based on them, hence are able to search relevant images. In this paper, we concern about the problem of complex image retrieval by reasoning image dense captions, which is similar to the way of human perception for searching images. Specifically, we transform the problem of complex image retrieval into a dense captioning and scene graph matching issue by using structured language descriptions for retrieval. Experimental results on a novel proposed large-scale content-based image retrieval dataset demonstrate the rationality and effectiveness of our method.
Using the National Institutes of Health Stroke Scale: A Cautionary Tale

The National Institutes of Health Stroke Scale (NIHSS) is the most widely used deficit rating scale in modern neurology: over 500 000 healthcare professionals have been certified to administer it using a web-based platform. Every clinical trial in vascular neurology—prevention, acute treatment, recovery—requires a severity assessment, and the NIHSS became the gold standard for stroke severity rating after the first successful trial in acute stroke therapy, the NINDS r-tPA (National Institute of Neurological Disorders and Stroke recombinant tissue-type plasminogen activator) for Acute Stroke Trial (the Trial). 1 As part of the Trial, detailed and rigorous training/certification procedures were created for the NIHSS that facilitate wider use of the scale outside of research.2 Today, payers and regulators demand reportable data on patient outcomes, and such outcomes must be adjusted for baseline severity: the NIHSS has become the de facto metric for regulatory compliance. The Joint Commission, as part of its certification program for Primary Stroke Centers, now requires an NIHSS score within 12 hours of admission for all stroke patients; this assessment is to be done by a certified examiner.3,4 Federal agencies also require outcomes adjusted for baseline stroke severity—using the NIHSS.5 Despite widening regulatory requirements, considerable problems may arise in using the NIHSS in clinical practice because the scale was designed for research purposes.6 Given that the scale was not designed for such widespread—and determinative—application, anyone using (or mandating use of) the NIHSS must understand its development history, clinimetric properties, and its proper bedside administration. During the late 1980s, several stroke-deficit rating scales were in use.7–10 For use in a National Institutes of Health–sponsored trial of naloxone for acute stroke, investigators combined scales that had been developed at the University of Cincinnati, Canadian neurological scale, the Edinburgh-2 coma scale, and the Oxbury …
The Healing Power of Poison: Helpful Non-relevant Documents in Feedback

The use of feedback information is an effective approach to address the vocabulary gap between a user's query and the relevant documents. It has been shown that some relevant documents act like "poison pills," i.e. they hurt the performance of feedback systems despite the fact that they are relevant. In this paper, we study the positive counterpart of this by investigating the helpfulness of nonrelevant documents in feedback. In general, we find that although documents that are explicitly judged as non-relevant are normally assumed to be poisonous for feedback systems, sometimes considering high-scored non-relevant documents as a positive feedback helps to improve the performance of retrieval. In our experimental data, we observe a considerable fraction of non-relevant documents in higher ranked positions of the initial retrieval run, for most of the topics. Hence, by ignoring the potential value of non-relevant documents, we may loose a lot of useful information. We investigate the potential contribution of non-relevant documents using existing state-of-the-art feedback methods. Our main findings are the following. First, we find that some of the nonrelevant documents are exclusively helpful, they improve retrieval on their own, and others are complementary helpful, they lead to further improvement when added to a set of relevant documents. Second, we discover that, on average, exclusively helpful non-relevant documents have a higher contribution to the performance improvement, compared to the complementary ones. Third, we show that non-relevant documents in topics with poor average precision in the initial retrieval are more likely to help in the feedback.
Obfuscated malicious javascript detection scheme using the feature based on divided URL

On web application services, detecting obfuscated malicious JavaScript utilized for the attacks such as Drive-by-Download is an urgent demand. Obfuscation is a technique that modifies some elements of program codes and is used to evade the pattern matching of traditional anti-virus softwares. In particular, encode obfuscation is adopted in almost all malicious JavaScript codes as the most effective technique to hide their malicious intents. Therefore, many approaches focus on encode obfuscation to detect malicious JavaScript. However, we point out that malicious JavaScript obfuscated by the techniques except for encode obfuscation can easily evade those approaches. Motivated by the above, in this paper, we first investigated the malicious files that previous schemes cannot detect, and found that some files contain divided URL in their codes. In order to detect such JavaScript codes as malicious, we propose obfuscated malicious JavaScript detection scheme using the feature based on divided URL. We focus on the fact that the segments of URL are declared as variables and connected later. Our scheme stores variables and their contents in the dictionary type object and in the connection parts, verifies that malicious URL can be reconstructed. By the computer simulation with real dataset, we show that our scheme improves the detection effectiveness of the conventional scheme.
The half-life of MOOC knowledge: a randomized trial evaluating knowledge retention and retrieval practice in MOOCs

Retrieval practice has been established in the learning sciences as one of the most effective strategies to facilitate robust learning in traditional classroom contexts. The cognitive theory underpinning the "testing effect" states that actively recalling information is more effective than passively revisiting materials for storing information in long-term memory. We document the design, deployment, and evaluation of an Adaptive Retrieval Practice System (ARPS) in a MOOC. This push-based system leverages the testing effect to promote learner engagement and achievement by intelligently delivering quiz questions from prior course units to learners throughout the course. We conducted an experiment in which learners were randomized to receive ARPS in a MOOC to track their performance and behavior compared to a control group. In contrast to prior literature, we find no significant effect of retrieval practice in this MOOC environment. In the treatment condition, passing learners engaged more with ARPS but exhibited similar levels of knowledge retention as non-passing learners.