Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in perceptual sensitivity related to spatial cues depends on subcortical activity

Significance Our paper addresses an important question about the brain circuits for visual spatial attention: what are the mechanisms that make it possible to focus on some visual objects and ignore others? Recent work suggests that cortical and subcortical areas might make distinct contributions to attention: some cortical areas may be responsible for changes in perceptual sensitivity whereas other cortical and subcortical areas may control how subjects select between visual objects. Contrary to this suggestion, our results demonstrate that activity from a subcortical structure in the primate, the superior colliculus, is also necessary to produce changes in perceptual sensitivity in an attention-cueing paradigm. Thus, attention-related changes in perceptual sensitivity are not accomplished by cortical areas alone but also depend on subcortical signals. Spatial cues allow animals to selectively attend to relevant visual stimuli while ignoring distracters. This process depends on a distributed neuronal network, and an important current challenge is to understand the functional contributions made by individual brain regions within this network and how these contributions interact. Recent findings point to a possible anatomical segregation, with cortical and subcortical brain regions contributing to different functional components of selective attention. Cortical areas, especially visual cortex, may be responsible for implementing changes in perceptual sensitivity by changing the signal-to-noise ratio, whereas other regions, such as the superior colliculus, may be involved in processes that influence selection between competing stimuli without regulating perceptual sensitivity. Such a segregation of function would predict that when activity in the superior colliculus is suppressed by reversible inactivation, animals should still show changes in perceptual sensitivity mediated by the intact cortical circuits. Contrary to this prediction, here we report that inactivation of the primate superior colliculus eliminates the changes in perceptual sensitivity made possible by spatial cues. These findings demonstrate changes in perceptual sensitivity depend not only on neuronal activity in cortex but also require interaction with signals from the superior colliculus.
Anomaly Detection in Log Data using Graph Databases and Machine Learning to Defend Advanced Persistent Threats

Advanced Persistent Threats (APTs) are a main impendence in cyber security of computer networks. In 2015, a successful breach remains undetected 146 days on average, reported by [Fi16]. With our work we demonstrate a feasible and fast way to analyse real world log data to detect breaches or breach attempts. By adapting well-known kill chain mechanisms and a combine of a time series database and an abstracted graph approach, it is possible to create flexible attack profiles. Using this approach, it can be demonstrated that the graph analysis successfully detects simulated attacks by analysing the log data of a simulated computer network. Considering another source for log data, the framework is capable to deliver sufficient performance for analysing real-world data in short time. By using the computing power of the graph database it is possible to identify the attacker and furthermore it is feasible to detect other affected system components. We believe to significantly reduce the detection time of breaches with this approach and react fast to new attack vectors.
How a single chip causes massive power bills GPUSimPow: A GPGPU power simulator

Modern GPUs are true power houses in every meaning of the word: While they offer general-purpose (GPGPU) compute performance an order of magnitude higher than that of conventional CPUs, they have also been rapidly approaching the infamous “power wall”, as a single chip sometimes consumes more than 300W. Thus, the design space of GPGPU microarchitecture has been extended by another dimension: power. While GPU researchers have previously relied on cycle-accurate simulators for estimating performance during design cycles, there are no simulation tools that include power as well. To mitigate this issue, we introduce the GPUSimPow power estimation framework for GPGPUs consisting of both analytical and empirical models for regular and irregular hardware components. To validate this framework, we build a custom measurement setup to obtain power numbers from real graphics cards. An evaluation on a set of well-known benchmarks reveals an average relative error of 11.7% between simulated and hardware power for GT240 and an average relative error of 10.8% for GTX580. The simulator has been made available to the public [1].
The temporal relation between depression and comorbid psychopathology in adolescents at varied risk for depression.

BACKGROUND This study examined the temporal comorbidity of depressive disorders with anxiety, externalizing, and substance use disorders in adolescents who varied in risk for depression. METHODS Participants were 240 adolescents and their mothers who had either a history of depression (high-risk, n = 185) or were lifetime-free of psychiatric disorders (low-risk, n = 55). Children (54.2% females) were first evaluated in 6th grade (mean age = 11.86, SD = .57) with the K-SADS-PL to assess current and lifetime diagnoses, and then annually through 12th grade with the A-LIFE to assess diagnoses since the previous evaluation. RESULTS For girls, the rate of depression was high regardless of prior anxiety, whereas for boys, the odds that those with prior subthreshold anxiety would have subsequent subthreshold depression were 1.5 times those of boys with no prior subthreshold anxiety, controlling for risk. In addition, the odds that girls with prior substance use disorders would have a threshold depressive disorder subsequently were three times those of girls with no prior substance use disorders, controlling for risk. CONCLUSIONS These results highlight the importance of early detection of various forms of psychopathology in youth who then can be targeted for intervention. The prospective paths to comorbidity differed by sex, thus suggesting that interventions need to be constructed with sensitivity to these distinct diagnostic trajectories.
Evolution Equations for Weakly Nonlinear, Quasi-Planar Waves in Isotropic Dielectrics and Elastomers

p. ii Acknowledgements.p. iii Chapter 1: Introduction.p. 1-6 Part I: General Considerations Chapter 2: Statement of Problem.p. 8-11 Chapter 3: Derivation of the General Evolution Equation.p. 12-19 Chapter 4: Summary.p. 20-21 Part II: The Nonlinear Dielectric Chapter 5: Introduction.p. 23 Chapter 6: Formulation-Nonlinear Dielectric.p. 24-26 Section 6.1: Discussion of the Relative Dielectric Constant.p. 26-29 Section 6.2: Derivation of the Final System of Equations.p. 29-33 Chapter 7: Derivation of the Evolution Equation.p. 34-38 Chapter 8: Inclusion of Conduction and Dispersion.p. 39-41 Chapter 9: Summary.p. 42 Part III: The Hyperelastic Solid Chapter 10: Introduction.p. 44 Chapter 11: Formulation-Hyperelastic Solids.p. 45-52 Chapter 12: Stress-Strain Relationships.p. 53-55 Chapter 13: Computation of Eigenvalues and Vectors.p. 56-60 Chapter 14: Nonlinearity Calculations for the Hyperelastic Solid.p. 61 Section 14.1: Determining the Order of Nonlinearity; P-mode.p. 61-63 Section 14.2: Determining the Order of Nonlinearity; S-mode.p. 63-65 Chapter 15: Evaluation of the Evolution Equation for the S-mode.p. 66-69
Sparsity analysis of endoscopy images

Sparsity analysis of images is important to understand the image characteristic and its possible potential in applications. In this paper, normal images and endoscopy image are investigated for their sparsity using K-SVD algorithm that finds a dictionary basis with minimal number of non-zero coefficients in the transformed domain to have minimal prediction error. The results show that the endoscopy image has lower prediction error and lower number of non-zero coefficients in the transformed domain. This indicates the fact that one can develop a better endoscopy image encoder with better prediction mechanism, and a better endoscopy image decoder with better error concealment method to recover data contaminated by the noise, both based on the idea of sparsity.
Structural studies of a hyperthermophilic thymidylate kinase enzyme reveal conformational substates along the reaction coordinate

Thymidylate kinase (TMK) is a key enzyme which plays an important role in DNA synthesis. It belongs to the family of nucleoside monophosphate kinases, several of which undergo structure‐encoded conformational changes to perform their function. However, the absence of three‐dimensional structures for all the different reaction intermediates of a single TMK homolog hinders a clear understanding of its functional mechanism. We herein report the different conformational states along the reaction coordinate of a hyperthermophilic TMK from Aquifex aeolicus, determined via X‐ray diffraction and further validated through normal‐mode studies. The analyses implicate an arginine residue in the Lid region in catalysis, which was confirmed through site‐directed mutagenesis and subsequent enzyme assays on the wild‐type protein and mutants. Furthermore, the enzyme was found to exhibit broad specificity toward phosphate group acceptor nucleotides. Our comprehensive analyses of the conformational landscape of TMK, together with associated biochemical experiments, provide insights into the mechanistic details of TMK‐driven catalysis, for example, the order of substrate binding and the reaction mechanism for phosphate transfer. Such a study has utility in the design of potent inhibitors for these enzymes.
Evaluation of a Quality Improvement Resource for Public Health Practitioners

Objectives: Quality improvement is a critical mechanism to manage public health agency performance and to strengthen accountability for public funds. The objective of this study was to evaluate a relatively new quality improvement resource, the Public Health Quality Improvement Exchange (PHQIX), a free online communication platform dedicated to making public health quality improvement information accessible to practitioners. Methods: We conducted an internet-based survey of registered PHQIX users (n = 536 respondents) in 2013 and key informant interviews with PHQIX frequent users (n = 21) in 2014, in the United States. We assessed use of the PHQIX website, user engagement and satisfaction, communication and knowledge exchange, use of information, and impact on quality improvement capacity and accreditation readiness. Results: Of 462 respondents, 369 (79.9%) browsed quality improvement initiatives, making it the most commonly used site feature, and respondents described PHQIX as a near-unique source for real-world quality improvement examples. Respondents were satisfied with the quality and breadth of topics and relevance to their settings (average satisfaction scores, 3.9-4.1 [where 5 was the most satisfied]). Of 407 respondents, 237 (58.2%) said that they had put into practice information learned on PHQIX, and 209 of 405 (51.6%) said that PHQIX had helped to improve quality improvement capacity. Fewer than half of respondents used the commenting function, the Community Forum, and the Ask an Expert feature. Conclusions: Findings suggest that PHQIX, particularly descriptions of the quality improvement initiatives, is a valued resource for public health practitioners. Users reported sharing information with colleagues and applying what they learned to their own work. These findings may relate to other efforts to disseminate quality improvement knowledge.
The Status of Quantum-Based Long-Term Secure Communication over the Internet

Sensitive digital data, such as health information or governmental archives, are often stored for decades or centuries. The processing of such data calls for long-term security. Secure channels on the Internet require robust key establishment methods. Currently used key distribution protocols are either vulnerable to future attacks based on Shor's algorithm, or vulnerable in principle due to their reliance on computational problems. Quantum-based key distribution protocols are information-theoretically secure and offer long-term security. However, significant obstacles to their real-world use remain. This paper, which results from a multidisciplinary project involving computer scientists and physicists, systematizes knowledge about obstacles to and strategies for the realization of long-term secure Internet communication from quantum-based key distribution. We discuss performance and security particulars, consider the specific challenges arising from multi-user network settings, and identify key challenges for actual deployment.
Adviser Networks: Learning What Question to Ask for Human-In-The-Loop Viewpoint Estimation

Humans have an unparalleled visual intelligence and can overcome visual ambiguities that machines currently cannot. Recent works have shown that incorporating guidance from humans during inference for real-world, challenging tasks like viewpoint-estimation and fine-grained classification, can help overcome difficult cases in which the computer-alone would have otherwise failed. These hybrid intelligence approaches are hence gaining traction. However, deciding what question to ask the human in the loop at inference time remains an unknown for these problems. We address this question by formulating it as what we call the Adviser Problem: can we learn a mapping from the input to a specific question to ask the human in the loop so as to maximize the expected positive impact to the overall task? We formulate a solution to the adviser problem using a deep network and apply it to the viewpoint estimation problem where the question asks for the location of a specific keypoint in the input image. We show that by using the keypoint guidance from the Adviser Network and the human, the model is able to outperform the previous hybrid-intelligence state-of-the-art by 3.27%, and outperform the computer-only state-of-the-art by 10.44% absolute.
Few-Shot Learning with Metric-Agnostic Conditional Embeddings

Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architecture where class representations are conditioned for each few-shot trial based on a target image. We also deviate from traditional metric-learning approaches by training a network to perform comparisons between classes rather than relying on a static metric comparison. This allows the network to decide what aspects of each class are important for the comparison at hand. We find that this flexible architecture works well in practice, achieving state-of-the-art performance on the Caltech-UCSD birds fine-grained classification task.
Secure and resilient scheme for data protection in unattended wireless sensor networks

Unattended Wireless Sensor Networks (UWSN) are usually deployed in human-hostile environments. Such architectures raise a challenge to data protection for two main reasons. First, sensors have limited capacities in terms of performance and memory, so not all cryptographic mechanisms can be applied. Moreover, the measurements cannot be immediately gathered, so they have to be kept inside the devices until a mobile sink comes to collect them. This paper introduces a new method for secure and resilient data protection inside UWSN. It is based on a lightweight fragmentation scheme that transforms data collected by a sensor into multiple secure fragments that are distributed over sensor's neighboring nodes in a way that only a certain amount of these fragments is required for data recovery. Moreover, data security is reinforced by the use of a dynamic key refreshed after each visit of the mobile sink. Authentication and integrity information are dispersed within the fragments to protected data from active attacks. Homomorphic properties of the algorithm allow to significantly reduce storage space inside the nodes. Performance and empirical security evaluation results show that the proposed scheme achieves a good trade-off between performance, data protection and memory occupation.
Comparative effectiveness of oral pharmacologic interventions for knee osteoarthritis: A network meta-analysis

Abstract Objectives: To explore the relative efficacy of oral pharmacologic interventions in the treatment of knee OA. Methods: A systematic literature review was conducted using the MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials databases to identify trials conducted in patients with knee OA with a minimum 6 weeks of follow-up. The standardized mean differences of the change from baseline to week 6 in Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain between the treatment groups were estimated using Bayesian random-effects network meta-analyses. Subgroup analyses of baseline pain status (high, pain score ≥60 mm; low, pain score <60 mm) were performed. Results: Of 4067 manuscripts, 44 were included in the evidence synthesis. Etoricoxib had the highest ranking for improving WOMAC pain (probability of being top ranked, p (best) = . 43) followed by naproxen (p (best) = . 12), acetaminophen (AAP) (p (best) = . 04), and celecoxib (p (best) = . 02). The top three ranked interventions were etoricoxib, celecoxib and aceclofenac in the higher pain group, and tramadol, celecoxib, and diclofenac in the lower pain group. Conclusion: In the overall analysis, etoricoxib, celecoxib, and aceclofenac had the highest rankings for improving WOMAC pain. The ability to improve knee OA symptoms may differ depending on baseline pain and radiologic features.
Power-efficient focal-plane image representation for extraction of enriched Viola-Jones features

This paper describes the use of a reconfigurable focal-plane processing array in order to achieve an image representation which dramatically reduces the computational load of the Viola-Jones object detection framework. Additionally, such representation provides richer information than the simple sum of pixels within rectangular regions originally defined in this framework. As a result, more elaborated features could be devised to speed up the execution of the subsequent attentional cascade, boosting thus the performance of the whole algorithm. The proposed circuitry has been successfully implemented in a CMOS prototype smart imager. Experimental results are given, demonstrating the suitability of the approach presented to efficiently deliver enriched Viola-Jones features.
Non-Convex Sparse and Low-Rank Based Robust Subspace Segmentation for Data Mining

Parsimony, including sparsity and low-rank, has shown great importance for data mining in social networks, particularly in tasks such as segmentation and recognition. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with convex l1-norm or nuclear norm constraints. However, the obtained results by convex optimization are usually suboptimal to solutions of original sparse or low-rank problems. In this paper, a novel robust subspace segmentation algorithm has been proposed by integrating lp-norm and Schatten p-norm constraints. Our so-obtained affinity graph can better capture local geometrical structure and the global information of the data. As a consequence, our algorithm is more generative, discriminative and robust. An efficient linearized alternating direction method is derived to realize our model. Extensive segmentation experiments are conducted on public datasets. The proposed algorithm is revealed to be more effective and robust compared to five existing algorithms.
Sulfur amino acid requirement of broiler chicks from fourteen to thirty-eight days of age. 2. Economic evaluation.

A calculation model was developed that can be used to derive dietary methionine and cystine (TSAA) levels for maximum profitability in growing broilers. Based on dose-response curves from two broiler experiments, the model calculates additional income from improved feed conversion and from increased breast meat yield. From the comparison of additional income to the additional cost associated with increasing dietary TSAA levels, the TSAA content giving maximum profit can be estimated. Under European price conditions, a dietary level of . 95% TSAA was found to be most profitable for broilers grown to 1.7 kg (Experiment 1, 14 to 34 d of age), when only the feed conversion response to dietary TSAA was considered. In broilers grown to 2.2 kg (Experiment 2, 14 to 38 d of age), two situations were simulated. If only the feed conversion response was evaluated, the most profitable TSAA level would be . 85%. If both feed conversion and breast meat responses were considered, a higher level of . 89% TSAA would be optimum. Changing prices of input and output variables (broiler feed, breast meat, DL-methionine) affected the respective economic optimum level of dietary TSAA only slightly.
A generative geometric kernel

We present the design and implementation of a generative geometric kernel. The kernel generator is generic, type-safe, parametrized by many design-level choices and extensible. The resulting code has minimal traces of the design abstractions. We achieve genericity through a layered design deriving concepts from affine geometry, linear algebra and abstract algebra. We achieve parametrization and type-safety by using OCaml's module system, including higher order modules. The cost of abstraction is removed by using MetaOCaml's support for code generation coupled with some annotations atop the code type.
The Structure of Challenging Parenting Behavior and Associations With Anxiety in Dutch and Australian Children

Challenging parenting behavior (CPB), a novel construct involving active physical and verbal behaviors that encourage children to push their limits, has been identified as a potential buffer against child anxiety. This study aimed to (a) evaluate the measurement invariance of the Challenging Parenting Behavior Questionnaire (CPBQ4-6) across Dutch and Australian mothers and fathers of preschoolers, (b) examine differences in levels of CPB across mothers and fathers and across countries, and (c) examine whether parents’ CPB predicts less child anxiety symptoms and disorders. Participants were 312 families—146 Dutch and 166 Australian—with their 3- to 4-year-old child (55.8% girls). Fathers’ and mothers’ CPB was measured using the CPBQ4-6, and child anxiety symptoms and presence of anxiety disorders were assessed using maternal reports. Multigroup confirmatory factor analyses revealed equivalence of factor structure and factor loadings (all significant) of the CPBQ4-6 across mothers and fathers and across countries. Evidence of partial scalar invariance indicated that the groups differed on some subscales of the CPBQ4-6. Australian mothers scored lower on the CPB factor than Australian fathers and Dutch parents. Structural equation models showed that CPB predicted fewer child anxiety symptoms and anxiety disorders for all groups. The study confirms that the CPBQ4-6 is appropriate for use with Dutch and Australian parents of preschool-age children and identifies CPB as a multifaceted and coherent construct. The negative relations between CPB and child anxiety suggest that CPB has a protective role in childhood anxiety and is important to examine in future research and interventions.