Miracle-dz/UniFAR-FTUs huggingface dataset:
Programming social behavior by the maternal fragile X protein

The developing fetus and neonate are highly sensitive to maternal environment. Besides the well‐documented effects of maternal stress, nutrition and infections, maternal mutations, by altering the fetal, perinatal and/or early postnatal environment, can impact the behavior of genetically normal offspring. Mutation/premutation in the X‐linked FMR1 (encoding the translational regulator FMRP) in females, although primarily responsible for causing fragile X syndrome (FXS) in their children, may also elicit such maternal effects. We showed that a deficit in maternal FMRP in mice results in hyperactivity in the genetically normal offspring. To test if maternal FMRP has a broader intergenerational effect, we measured social behavior, a core dimension of neurodevelopmental disorders, in offspring of FMRP‐deficient dams. We found that male offspring of Fmr1+/− mothers, independent of their own Fmr1 genotype, exhibit increased approach and reduced avoidance toward conspecific strangers, reminiscent of ‘indiscriminate friendliness’ or the lack of stranger anxiety, diagnosed in neglected children and in patients with Asperger's and Williams syndrome. Furthermore, social interaction failed to activate mesolimbic/amygdala regions, encoding social aversion, in these mice, providing a neurobiological basis for the behavioral abnormality. This work identifies a novel role for FMRP that extends its function beyond the well‐established genetic function into intergenerational non‐genetic inheritance/programming of social behavior and the corresponding neuronal circuit. As FXS premutation and some psychiatric conditions that can be associated with reduced FMRP expression are more prevalent in mothers than full FMR1 mutation, our findings potentially broaden the significance of FMRP‐dependent programming of social behavior beyond the FXS population.
LearningRx Cognitive Training Effects in Children Ages 8–14: A Randomized Controlled Trial

Summary In a randomized controlled study, we examined the effects of a one‐on‐one cognitive training program on memory, visual and auditory processing, processing speed, reasoning, attention, and General Intellectual Ability (GIA) score for students ages 8–14. Participants were randomly assigned to either an experimental group to complete 60 h of cognitive training or to a wait‐list control group. The purpose of the study was to examine changes in multiple cognitive skills after completing cognitive training with ThinkRx, a LearningRx program. Results showed statistically significant differences between groups on all outcome measures except for attention. Implications, limitations, and suggestions for future research are examined. © 2016 The Authors Applied Cognitive Psychology Published by John Wiley & Sons Ltd.
Exploiting constructive interference for scalable flooding in wireless networks

Exploiting constructive interference in wireless networks is an emerging trend for it allows multiple senders transmit an identical packet simultaneously. Constructive interference based flooding can realize millisecond network flooding latency and sub-microsecond time synchronization accuracy, require no network state information and adapt to topology changes. However, constructive interference has a precondition to function, namely, the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold. We disclose that constructive interference based flooding suffers the scalability problem. The packet reception performances of intermediate nodes degrade significantly as the density or the size of the network increases. We theoretically show that constructive interference based flooding has a packet reception ratio (PRR) lower bound (95:4%) in the grid topology. For a general topology, we propose the spine constructive interference based flooding (SCIF) protocol. With little overhead, SCIF floods the entire network much more reliably than Glossy [1] in high density or large-scale networks. Extensive simulations illustrate that the PRR of SCIF keeps stable above 96% as the network size grows from 400 to 4000 while the PRR of Glossy is only 26% when the size of the network is 4000. We also propose to use waveform analysis to explain the root cause of constructive interference, which is mainly examined in simulations and experiments. We further derive the closed-form PRR formula and define interference gain factor (IGF) to quantitatively measure constructive interference.
CNN-based Commercial Detection in TV Broadcasting

TV is an important advertising media. Information of a piece of TV commercial, such as broadcasting time, the duration, the casting and etc., may reflect the business value of the host company of this commercial. An automatic commercial detection system is needed for third-party by business analysis. Previous works about TV commercial detection just apply to one kind of video, like news, sports. And those methods detect commercials at frame level, which need expensive computing cost. In this paper, we design and implement an automatic commercial detection system for TV broadcasting. This system works at shot level and detects commercials in streaming videos, including TV broadcasting and online videos. It consists of two modules, the shot boundary detection module and the shot classification module. We crawl actual broadcasting videos and split them into shots, classify these shots into two classes, commercial and non-commercial. Then, we extract shot features with deep convolutional neural network, and train a support vector machine classifier to complete shot classification. Combining the state-of-the-art convolutional neural network with traditional machine learning techniques, our system can handle various program types and indiscernible commercials, and get precision 93% and recall 95% in realistic TV programs. Except the TV broadcasting, our system works in other video media like online videos.
Feature Selection and Classification of Post-Graduation Income of College Students in the United States

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. Five groups of machine learning algorithms were applied to the dataset for classification using the best selected attribute subsets. Based on our findings, we discuss the role of neighborhood professional degree attainment, parental income, SAT scores, and family college education in post-graduation incomes and the implications for social stratification.
Vejovis: suggesting fixes for JavaScript faults

JavaScript is used in web applications for achieving rich user interfaces and implementing core functionality. Unfortunately, JavaScript code is known to be prone to faults. In an earlier study, we found that over 65% of such faults are caused by the interaction of JavaScript code with the DOM at runtime (DOM-related faults). In this paper, we first perform an analysis of 190 bug reports to understand fixes commonly applied by programmers to these DOM-related faults; we observe that parameter replacements and DOM element validations are common fix categories. Based on these findings, we propose an automated technique and tool, called Vejovis, for suggesting repairs for DOM-based JavaScript faults. To evaluate Vejovis, we conduct a case study in which we subject Vejovis to 22 real-world bugs across 11 applications. We find that Vejovis accurately suggests repairs for 20 out of the 22 bugs, and in 13 of the 20 cases, the correct fix was the top ranked one.
A 2-Step Approach to Improve Data-driven Parking Availability Predictions

Knowing where to park in advance is a most wished feature by many drivers. In recent years, many research efforts have been spent to analyse massive amount of parking information, to learn availability trends and thus to predict, within a Parking Guidance and Information (PGI) system, where there is the highest chance to find free parking spaces. The most of these solutions exploits raw data coming from stationary sensors or crowd-sensed by mobile probes. In both the cases, these massive amounts of data present a high level of noise. In this paper we propose a 2-step approach to predict parking space availability with the twofold goal to handle the noise in the data and to significantly reduce the space needed to store these models. In particular, in the first step, we smooth the raw parking data by using Support Vector Regressions (SVR) in combination with a specifically defined technique to tune the SVR parameters. In the second step, on top of this smoothed trend curve, we train a multidimensional SVR model, representing parking space availability, and suitable for parking predictions. The proposal has been empirically evaluated on a real-world dataset of on-street parking information from the SFpark project, and compared against a standard, one-step SVR model with different settings. Results show that the predictions obtained with the proposed approach are always by far more accurate, with a statistically significant difference, while requiring a fraction of the storage normally used for raw data.
Practical implementation of a general computer aided design technique for switched capacitor circuits

A general and efficient CAD method for simulation of switched capacitor filters and A/D and D/A converters, is demonstrated. It is based on the direct implementation of controlled switch branches into the widely used modified nodal analysis technique and is therefore in contrast to other methods, directly compatible with existing CAD techniques. It allows for DC, time domain, and frequency response calculations for arbitrary clock cycles and all types of inputs (piecewise constant, sample and hold, and continuous). The circuit can also contain resistors and allows for nonlinear time domain analysis too. As implemented in the Diana program it allows for full top-down design from principle to transistor level, including clock drivers, control logic, etc. Input is directly from the circuit diagram. The method is illustrated by practical design examples.
Challenges and Developments in the Assessment of (Mal)adaptive Personality and Pathological States in Older Adults

Since older adults often show an atypical presentation of (mal)adaptive personality traits and pathological states, the articles in this special issue will concisely discuss some perennial issues in clinical assessment in older adults and thus outline the main challenges this domain faces. By bringing empirical work and meta-analytic studies from leading scholars in the field of geropsychology, the articles will also address these challenges by reporting the latest developments in the field. This way, we hope to reshape the way clinicians and researchers assess (mal)adaptive personality and pathological states in older adults into a more reliable and valid assessment method that integrates the specific biopsychosocial context of older age.
An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

Preoperatively predict the probability of Prostate cancer (PCa) biochemical recurrence (BCR) is of definite clinical relevance. The purpose of this study was to develop an imaging-based approach in the prediction of 3-years BCR through a novel support vector machine (SVM) classification. We collected clinicopathologic and MR imaging datasets in 205 patients pathologically confirmed PCa after radical prostatectomy. Univariable and multivariable analyses were used to assess the association between MR findings and 3-years BCR, and modeled the imaging variables and follow-up data to predict 3-year PCa BCR using SVM analysis. The performance of SVM was compared with conventional Logistic regression (LR) and D'Amico risk stratification scheme by area under the receiver operating characteristic curve (Az) analysis. We found that SVM had significantly higher Az (0.959 vs. 0.886; p = 0.007), sensitivity (93.3% vs. 83.3%; p = 0.025), specificity (91.7% vs. 77.2%; p = 0.009) and accuracy (92.2% vs. 79.0%; p = 0.006) than LR analysis. Performance of popularized D'Amico scheme was effectively improved by adding MRI-derived variables (Az: 0.970 vs. 0.859, p < 0.001; sensitivity: 91.7% vs. 86.7%, p = 0.031; specificity: 94.5% vs. 78.6%, p = 0.001; and accuracy: 93.7% vs. 81.0%, p = 0.007). Additionally, beside pathological Gleason score (hazard ratio [HR] = 1.560, p = 0.008), surgical-T3b (HR = 4.525, p < 0.001) and positive surgical margin (HR = 1.314, p = 0.007), apparent diffusion coefficient (HR = 0.149, p = 0.035) was the only independent imaging predictor of time to PSA failure. Therefore, We concluded that imaging-based approach using SVM was superior to LR analysis in predicting PCa outcome. Adding MR variables improved the performance of D'Amico scheme.
Designing and evaluating two interventions to improve identity theft recovery outcomes

Identity theft is among the most reputationally and financially damaging of all technology crimes. However, global understanding of identity theft is hampered by a lack of empirical research and a complex threat landscape. This paper reports on a four-stage project to improve identity theft outcomes for current and future victims, sponsored by a research grant from the Australian Communications Consumer Action Network (ACCAN). We first analysed 4,000 Australian cases of identity theft in order to understand the processes behind identity theft attacks, victim reporting trends, and opportunities for improving victim and investigation outcomes. Two opportunities for intervention were identified. The first related to understanding long-term victim recovery, and the informational and support mechanisms that improved victim welfare. The second related to developing improved data and threat sharing between telecommunications carriers and banks. From this analysis, we designed and implemented two interventions to improve identity theft outcomes: one at the individual level to improve identity theft recovery, and the second at the organizational level to promote threat sharing. This paper reports on the initial outcomes of implementing these interventions.
Perceiving the Slightest Tag Motion beyond Localization

Existing methods in RFID systems often employ presence or absence fashion to detect the tags' motions, so they cannot meet motion detection requirement in many applications. Our recent observations suggest that the signal strength backscattered from the tag is hypersensitive to its position, inspiring us to perceive the tag motion through its radio signal strength changes. Motion perception is not trivial and challenged by weak stability of strength in that any other interference or noise may incur significant changes as well, resulting in high false positives. To tackle this issue, we propose to model the strength via the Mixture of Gaussian Model (MoG). The problem is thus converted to foreground segment in computer vision with the help of Strength Image, where the technique of MoG based background subtraction is employed. We then implement a prototype using commercial off-the-shelf products. The evaluation results show that the slightest tag motion (~10 cm) can be precisely perceived, and the accuracy is up to 92.34 percent while the false positive is suppressed under 0.5 percent.
Techniques for Shared Resource Management in Systems with Throughput Processors

The continued growth of the computational capability of throughput processors has made throughput processors the platform of choice for a wide variety of high performance computing applications. Graphics Processing Units (GPUs) are a prime example of throughput processors that can deliver high performance for applications ranging from typical graphics applications to general-purpose data parallel (GPGPU) applications. However, this success has been accompanied by new performance bottlenecks throughout the memory hierarchy of GPU-based systems. We identify and eliminate performance bottlenecks caused by major sources of interference throughout the memory hierarchy. We introduce changes to the memory hierarchy for systems with GPUs that allow the memory hierarchy to be aware of both CPU and GPU applications' characteristics. We introduce mechanisms to dynamically analyze different applications' characteristics and propose four major changes throughout the memory hierarchy. We propose changes to the cache management and memory scheduling mechanisms to mitigate intra-application interference in GPGPU applications. We propose changes to the memory controller design and its scheduling policy to mitigate inter-application interference in heterogeneous CPU-GPU systems. We redesign the MMU and the memory hierarchy in GPUs to be aware of ddress-translation data in order to mitigate the inter-address-space interference. We introduce a hardware-software cooperative technique that modifies the memory allocation policy to enable large page support in order to further reduce the inter-address-space interference at the shared Translation Lookaside Buffer (TLB). Our evaluations show that the GPU-aware cache and memory management techniques proposed in this dissertation are effective at mitigating the interference caused by GPUs on current and future GPU-based systems.
Statistical Reconstruction of Class Hierarchies in Binaries

We address a fundamental problem in reverse engineering of object-oriented code: the reconstruction of a program's class hierarchy from its stripped binary. Existing approaches rely heavily on structural information that is not always available, e.g., calls to parent constructors. As a result, these approaches often leave gaps in the hierarchies they construct, or fail to construct them altogether. Our main insight is that behavioral information can be used to infer subclass/superclass relations, supplementing any missing structural information. Thus, we propose the first statistical approach for static reconstruction of class hierarchies based on behavioral similarity. We capture the behavior of each type using a statistical language model (SLM), define a metric for pairwise similarity between types based on the Kullback-Leibler divergence between their SLMs, and lift it to determine the most likely class hierarchy. We implemented our approach in a tool called ROCK and used it to automatically reconstruct the class hierarchies of several real-world stripped C++ binaries. Our results demonstrate that ROCK obtained significantly more accurate class hierarchies than those obtained using structural analysis alone.
The History Heuristic and Alpha-Beta Search Enhancements in Practice

Many enhancements to the alpha-beta algorithm have been proposed to help reduce the size of minimax trees. A recent enhancement, the history heuristic, which improves the order in which branches are considered at interior nodes is described. A comprehensive set of experiments is reported which tries all combinations of enhancements to determine which one yields the best performance. In contrast, previous work on assessing their performance has concentrated on the benefits of individual enhancements or a few combinations. The aim is to find the combination that provides the greatest reduction in tree size. Results indicate that the history heuristic combined with transposition tables significantly outperforms other alpha-beta enhancements in application-generated game trees. For trees up to depth 8, this combination accounts for 99% of the possible reductions in tree size, with the other enhancements yielding insignificant gains. >
Formulation and solution method of tour conducting knapsack problem

Many studies for developing methods of solving have been done in the field of combinatorial optimization. However, it cannot simply apply these methods to real problems. For example, when a tourist decide to traveling schedule within a traveling time limit, he or she needs to select and travel tourist spots in order to be satisfied as far as possible. Such a problem cannot be solved by conventional solving method. In this study, the authors define this problem as TCKP (Tour Conducting Knapsack Problem). And the authors formulate and develop method for solving TCKP. Formulation and Solution method of TCKP are based on those of TSP (Traveling Salesman Problem) and knapsack problem. Numerical experiment is carried out in order to verify the effectiveness of proposed method. Experimental results show that it can be obtained the optimized solution within one minute when the number of tourist spots is 15 and fewer.
Wide band designs of rectangular microstrip antenna using modified ground plane

Wide band microstrip antenna is realized when more than one resonating modes are present in a single patch. This has been more frequently realized by using resonant slots embedded inside the radiating element. In this paper, wide band designs of rectangular microstrip antennas using modified ground plane (defected ground plane) on substrate thickness of 0. 07λ0, are proposed. The defects in ground plane alter higher order TM02 resonant mode frequency in the patch which gives bandwidth of 110 MHz (7.2%). Broadband configuration with slot on patch as well as the ground plane is presented. The two slots optimizes spacing between patch's TM10 mode with respect to TM02 modes on patch and on ground plane, to yield increased bandwidth of 275 MHz (16.6%). The proposed designs yields broadside radiation pattern over bandwidth with antenna gain of more than 6 dBi. The work proposed here clearly brings out and explains an effects of modifications in patch and ground plane in terms of resonant modes that realizes wider bandwidth.
‘Disease, illness, affliction? Don’t know’: Ambivalence and ambiguity in the narratives of young people about having acne

The popular characterisation of acne as a mundane and insignificant feature of adolescence sits at odds with academic studies showing the detrimental impacts of the condition on people of various ages. Drawing from in-depth qualitative interviews with 13- to 25-year olds living in England, this article will consider some of the tensions between two messages about acne through the ambiguities in young people’s narratives. Consistent with existing literature on skin conditions and visual differences more broadly, participants in the study recounted ways in which acne negatively shaped their lives socially and emotionally. However, there were topics around which participants’ accounts held nuances and complications, in relation to: understandings held about acne causes; uncertainty as to whether acne was a medical concern/problem; and comparisons to other people’s acne or broader health experiences. In attending to these aspects, this article will explore how different and potentially conflicting meanings are negotiated by young people, producing ambiguous and ambivalent experiential accounts about living with acne.