nils-herrmann/clean_scirepeval huggingface dataset:
Seasonal freeze resistance of rainbow smelt (Osmerus mordax) is generated by differential expression of glycerol-3-phosphate dehydrogenase, phosphoenolpyruvate carboxykinase, and antifreeze protein genes.

In winter, rainbow smelt (Osmerus mordax) accumulate glycerol and produce an antifreeze protein (AFP), which both contribute to freeze resistance. The role of differential gene expression in the seasonal pattern of these adaptations was investigated. First, cDNAs encoding smelt and Atlantic salmon (Salmo salar) phosphoenolpyruvate carboxykinase (PEPCK) and smelt glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were cloned so that all sequences required for expression analysis would be available. Using quantitative PCR, expression of beta actin in rainbow smelt liver was compared with that of GAPDH in order to determine its validity as a reference gene. Then, levels of glycerol-3-phosphate dehydrogenase (GPDH), PEPCK, and AFP relative to beta actin were measured in smelt liver over a fall-winter-spring interval. Levels of GPDH mRNA increased in the fall just before plasma glycerol accumulation, implying a driving role in glycerol synthesis. GPDH mRNA levels then declined during winter, well in advance of serum glycerol, suggesting the possibility of GPDH enzyme or glycerol conservation in smelt during the winter months. PEPCK mRNA levels rose in parallel with serum glycerol in the fall, consistent with an increasing requirement for amino acids as metabolic precursors, remained elevated for much of the winter, and then declined in advance of the decline in plasma glycerol. AFP mRNA was elevated at the onset of fall sampling in October and remained elevated until April, implying separate regulation from GPDH and PEPCK. Thus, winter freezing point depression in smelt appears to result from a seasonal cycle of GPDH gene expression, with an ensuing increase in the expression of PEPCK, and a similar but independent cycle of AFP gene expression.
Mental Health Services for Individuals with High Functioning Autism Spectrum Disorder

Adolescents and adults with an autism spectrum disorder (ASD) who do not have an intellectual impairment or disability (ID), described here as individuals with high-functioning autism spectrum disorder (HFASD), represent a complex and underserved psychiatric population. While there is an emerging literature on the mental health needs of children with ASD with normal intelligence, we know less about these issues in adults. Of the few studies of adolescents and adults with HFASD completed to date, findings suggest that they face a multitude of cooccurring psychiatric (e.g., anxiety, depression), psychosocial, and functional issues, all of which occur in addition to their ASD symptomatology. Despite this, traditional mental health services and supports are falling short of meeting the needs of these adults. This review highlights the service needs and the corresponding gaps in care for this population. It also provides an overview of the literature on psychiatric risk factors, identifies areas requiring further study, and makes recommendations for how existing mental health services could include adults with HFASD.
Biometric Recognition in Automated Border Control: A Survey

The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas.
Mathematical and Computational Modeling in Complex Biological Systems

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
PyMT: a post-WIMP multi-touch user interface toolkit

Multi-touch and tabletop input paradigms open novel doors for post-WIMP (Windows, Icons, Menus, Pointer) user interfaces. Developing these novel interfaces and applications poses unique challenges for designers and programmers alike. We present PyMT (Python Multi-Touch), a toolkit aimed at addressing these challenges. We discuss PyMT's architecture and sample applications to demonstrate how it enables rapid development of prototypes and interaction techniques while being accessible to novice programmers and providing great flexibility and creative freedom to advanced users. We share experiences gathered in the open source development of PyMT to explore design and programming challenges posed by multi-touch tabletop and post-WIMP interfaces. Specifically, we discuss changes to the event model and the implementation of development and debugging tools that we found useful along the way.
Deep Neural Network Hyper-Parameter Setting for Classification of Obstructive Sleep Apnea Episodes

The wide availability of sensing devices in the medical domain causes the creation of large and very large data sets. Hence, tasks as the classification in such data sets becomes more and more difficult. Deep Neural Networks (DNNs) are very effective in classification, yet finding the best values for their hyper-parameters is a difficult and time-consuming task. This paper introduces an approach to decrease execution times to automatically find good hyper-parameter values for DNN through Evolutionary Algorithms when classification task is faced. This decrease is obtained through the combination of two mechanisms. The former is constituted by a distributed version for a Differential Evolution algorithm. The latter is based on a procedure aimed at reducing the size of the training set and relying on a decomposition into cubes of the space of the data set attributes. Experiments are carried out on a medical data set about Obstructive Sleep Anpnea. They show that sub-optimal DNN hyper-parameter values are obtained in a much lower time with respect to the case where this reduction is not effected, and that this does not come to the detriment of the accuracy in the classification over the test set items.
Prenatal ultrasound and urological anomalies.

Prenatal ultrasound is an integral part of caring for pregnant women in the United States. Although surprisingly few data exist to support the clinical benefit of screening ultrasound during pregnancy, its use continues to rise. Urologic anomalies are among the most commonly identified, with overall detection sensitivity approaching 90%. Prenatal hydronephrosis is the most frequently identified finding and predicting postnatal pathology based on its presence can be difficult. As the degree of fetal hydronephrosis increases so does the risk of true urinary tract pathology. Diagnoses that require more urgent care include causes of lower urinary tract obstruction and bladder and cloacal exstrophy.
Evolution, challenge, and outlook of TSV, 3D IC integration and 3d silicon integration

3D integration consists of 3D IC packaging, 3D IC integration, and 3D Si integration. They are different and in general the TSV (through-silicon via) separates 3D IC packaging from 3D IC/Si integrations since the latter two use TSV but 3D IC packaging does not. TSV (with a new concept that every chip or interposer could have two surfaces with circuits) is the heart of 3D IC/Si integrations and is the focus of this investigation. The origin of 3D integration is presented. Also, the evolution, challenges, and outlook of 3D IC/Si integrations are discussed as well as their road maps are presented. Finally, a few generic, low-cost, and thermal-enhanced 3D IC integration system-in-packages (SiPs) with various passive TSV interposers are proposed.
Rover control based on an optimal torque distribution - Application to 6 motorized wheels passive rover

The capability to overcome terrain irregularities or obstacles, named terrainability, is mostly dependant on the suspension mechanism of the rover and its control. For a given wheeled robot, the terrainability can be improved by using a sophisticated control, and is somewhat related to minimizing wheel slip. The proposed control method, named torque control, improves the rover terrainability by taking into account the whole mechanical structure. The rover model is based on the Newton-Euler equations and knowing the complete state of the mechanical structures allows us to compute the force distribution in the structure, and especially between the wheels and the ground. Thus, a set of torques maximizing the traction can be used to drive the rover. The torque control algorithm is presented in this paper, as well as tests showing its impact and improvement in terms of terrainability. Using the CRAB rover platform, we show that the torque control not only increases the climbing performance but also limits odometric errors and reduces the overall power consumption.
Generation of Fock states in a superconducting quantum circuit

Spin systems and harmonic oscillators comprise two archetypes in quantum mechanics. The spin-1/2 system, with two quantum energy levels, is essentially the most nonlinear system found in nature, whereas the harmonic oscillator represents the most linear, with an infinite number of evenly spaced quantum levels. A significant difference between these systems is that a two-level spin can be prepared in an arbitrary quantum state using classical excitations, whereas classical excitations applied to an oscillator generate a coherent state, nearly indistinguishable from a classical state. Quantum behaviour in an oscillator is most obvious in Fock states, which are states with specific numbers of energy quanta, but such states are hard to create. Here we demonstrate the controlled generation of multi-photon Fock states in a solid-state system. We use a superconducting phase qubit, which is a close approximation to a two-level spin system, coupled to a microwave resonator, which acts as a harmonic oscillator, to prepare and analyse pure Fock states with up to six photons. We contrast the Fock states with coherent states generated using classical pulses applied directly to the resonator.
iCAP: Interactive Prototyping of Context-Aware Applications

Although numerous context-aware applications have been developed and there have been technological advances for acquiring contextual information, it is still difficult to develop and prototype interesting context-aware applications. This is largely due to the lack of programming support available to both programmers and end-users. This lack of support closes off the context-aware application design space to a larger group of users. We present iCAP, a system that allows end-users to visually design a wide variety of context-aware applications, including those based on if-then rules, temporal and spatial relationships and environment personalization. iCAP allows users to quickly prototype and test their applications without writing any code. We describe the study we conducted to understand end-users’ mental models of context-aware applications, how this impacted the design of our system and several applications that demonstrate iCAP’s richness and ease of use. We also describe a user study performed with 20 end-users, who were able to use iCAP to specify every application that they envisioned, illustrating iCAP’s expressiveness and usability.
A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy

Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi-automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real-time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp-MRI and ultrasound data. In addition to using convolutional networks for extracting spatial features, the proposed approach employs recurrent networks to exploit the temporal information among a series of ultrasound images. One of the key contributions in the architecture is to use residual convolution in the recurrent networks to improve optimization. We also exploit recurrent connections within and across different layers of the deep networks to maximize the utilization of the temporal information. Furthermore, we perform dense and sparse sampling of the input ultrasound sequence to make the network robust to ultrasound artifacts. Our architecture is trained on 2,238 labeled transrectal ultrasound images, with an additional 637 and 1,017 unseen images used for validation and testing, respectively. We obtain a mean Dice similarity coefficient of 93%, a mean surface distance error of 1.10 mm and a mean Hausdorff distance error of 3.0 mm. A comparison of the reported results with those of a state-of-the-art technique indicates statistically significant improvement achieved by the proposed approach.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs. We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples. We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet.
Structural insight into the mechanism of stabilization of the 7SK small nuclear RNA by LARP7

The non-coding RNA 7SK is the scaffold for a small nuclear ribonucleoprotein (7SKsnRNP) which regulates the function of the positive transcription elongation factor P-TEFb in the control of RNA polymerase II elongation in metazoans. The La-related protein LARP7 is a component of the 7SKsnRNP required for stability and function of the RNA. To address the function of LARP7 we determined the crystal structure of its La module, which binds a stretch of uridines at the 3'-end of 7SK. The structure shows that the penultimate uridine is tethered by the two domains, the La-motif and the RNA-recognition motif (RRM1), and reveals that the RRM1 is significantly smaller and more exposed than in the La protein. Sequence analysis suggests that this impacts interaction with 7SK. Binding assays, footprinting and small-angle scattering experiments show that a second RRM domain located at the C-terminus binds the apical loop of the 3' hairpin of 7SK, while the N-terminal domains bind at its foot. Our results suggest that LARP7 uses both its N- and C-terminal domains to stabilize 7SK in a closed structure, which forms by joining conserved sequences at the 5'-end with the foot of the 3' hairpin and has thus functional implications.
Gamifying Education and Research on ICS Security: Design, Implementation and Results of S3

In this work, we consider challenges relating to security for Industrial Control Systems (ICS) in the context of ICS security education and research targeted both to academia and industry. We propose to address those challenges through gamified attack training and countermeasure evaluation. We tested our proposed ICS security gamification idea in the context of the (to the best of our knowledge) first Capture-The-Flag (CTF) event targeted to ICS security called SWaT Security Showdown (S3). Six teams acted as attackers in a security competition leveraging an ICS testbed, with several academic defense systems attempting to detect the ongoing attacks. The event was conducted in two phases. The online phase (a jeopardy-style CTF) served as a training session. The live phase was structured as an attack-defense CTF. We acted as judges and we assigned points to the attacker teams according to a scoring system that we developed internally based on multiple factors, including realistic attacker models. We conclude the paper with an evaluation and discussion of the S3, including statistics derived from the data collected in each phase of S3.
Trusting Digital Technologies Correctly

Trust is a facilitator of interactions among the members of a system, whether these be human agents, artificial agents or a combination of both (a hybrid system). Elsewhere, I have argued that the occurrences of trust are related to, and affect, preexisting relations, like purchasing, negotiation, communication, and delegation (Taddeo 2010a, b). Trust is not to be considered a relation itself but a property of relations, something that changes the way relations occur. Consider, for example, a case of communication. Alice talks to Bob and she informs him that the grocery store down the road is closed for the day As Bob trusts Alice, he believes her and decides not to walk to the shop to double check, instead he starts searching for an alternative place to shop for his groceries. Between Alice and Bob there is a firstorder relation, the communication, which ranges over the two agents, and there is the second-order property of trust that ranges over the first-order-relation and affects the way it occurs. As a property of relations, trust changes the way relations occur by minimising the effort and commitment for the achievement of a given goal of the agents who decides to trust (the trustor). It does so in two ways. First, the trustor can avoid performing the action necessary to achieve her/his goal her/himself, because s/he can count on the trustee to do it—Bob does not walk to the shop to check whether it is actually closed. Second, the trustor can decide not to supervise the trustee’s performance. Bob does not ask Alice how she knows about the opening times of the shop. Delegation without supervision characterises the presence of trust (Taddeo 2010a, b). I define trust as follows:
When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data

User modeling is critical for understanding user intents, while it is also challenging as user intents are so diverse and not directly observable. Most existing works exploit specific types of behavior signals for user modeling, e.g., opinionated data or network structure; but the dependency among different types of user-generated data is neglected.
 We focus on self-consistence across multiple modalities of user-generated data to model user intents. A probabilistic generative model is developed to integrate two companion learning tasks of opinionated content modeling and social network structure modeling for users. Individual users are modeled as a mixture over the instances of paired learning tasks to realize their behavior heterogeneity, and the tasks are clustered by sharing a global prior distribution to capture the homogeneity among users. Extensive experimental evaluations on large collections of Amazon and Yelp reviews with social network structures confirm the effectiveness of the proposed solution. The learned user models are interpretable and predictive: they enable more accurate sentiment classification and item/friend recommendations than the corresponding baselines that only model a singular type of user behaviors.
A standardized test battery for the study of synesthesia

Synesthesia is an unusual condition in which stimulation of one modality evokes sensation or experience in another modality. Although discussed in the literature well over a century ago, synesthesia slipped out of the scientific spotlight for decades because of the difficulty in verifying and quantifying private perceptual experiences. In recent years, the study of synesthesia has enjoyed a renaissance due to the introduction of tests that demonstrate the reality of the condition, its automatic and involuntary nature, and its measurable perceptual consequences. However, while several research groups now study synesthesia, there is no single protocol for comparing, contrasting and pooling synesthetic subjects across these groups. There is no standard battery of tests, no quantifiable scoring system, and no standard phrasing of questions. Additionally, the tests that exist offer no means for data comparison. To remedy this deficit we have devised the Synesthesia Battery. This unified collection of tests is freely accessible online (http://www.synesthete.org). It consists of a questionnaire and several online software programs, and test results are immediately available for use by synesthetes and invited researchers. Performance on the tests is quantified with a standard scoring system. We introduce several novel tests here, and offer the software for running the tests. By presenting standardized procedures for testing and comparing subjects, this endeavor hopes to speed scientific progress in synesthesia research.