nils-herrmann/clean_scirepeval huggingface dataset:
FocusVR: Effective 8 Usable VR Display Power Management

In this paper, we present the design and implementation of FocusVR, a system for effectively and efficiently reducing the power consumption of Virtual Reality (VR) devices by smartly dimming their displays. These devices are becoming increasingly common with large companies such as Facebook (Oculus Rift), and HTC and Valve (Vive), recently releasing high quality VR devices to the consumer market. However, these devices require increasingly higher screen resolutions and refresh rates to be effective, and this in turn, leads to high display power consumption costs. We show how the use of smart dimming techniques, vignettes and color mapping, can significantly reduce the power consumption of VR displays with minimal impact on usability. In particular, we describe the implementation of FocusVR in both Android and the Unity game engine and then present detailed measurement results across 3 different VR devices -- the Gear VR, the DK2, and the Vive. In addition, we present the results of 3 user studies, with 68 participants in total, that tested the usability of FocusVR. Overall, we show that FocusVR is able to save up to 80% of the display power and up to 50% of the overall system power, with negligible impact to usability.
Understanding individual contribution and collaboration in student software teams

Software development is an inherently team-based activity, and many software-engineering courses are structured around team projects, in order to provide students with an authentic learning experience. The collaborative-development tools through which student developers define, share and manage their tasks generate a detailed record in the process. Albeit not designed for this purpose, this record can provide the instructor with insights into the students' work, the team's progress over time, and the individual team-member's contributions. In this paper, we describe an analysis and visualization toolkit that enables instructors to interactively explore the trace of the team's collaborative work, to better understand the team dynamics, and the tasks of the individual team developers. We also discuss our grounded-theory analysis of one team's work, based on their email exchanges, questionnaires and interviews. Our analyses suggest that the inferences supported by our toolkit are congruent with the developers' feedback, while there are some discrepancies with the reflections of the team as a whole.
Interpersonal forgiving in close relationships: II. Theoretical elaboration and measurement.

Interpersonal forgiving was conceptualized in the context of a 2-factor motivational system that governs people's responses to interpersonal offenses. Four studies were conducted to examine the extent to which forgiving could be predicted with relationship-level variables such as satisfaction, commitment, and closeness; offense-level variables such as apology and impact of the offense; and social-cognitive variables such as offender-focused empathy and rumination about the offense. Also described is the development of the transgression-related interpersonal motivations inventory--a self-report measure designed to assess the 2-component motivational system (Avoidance and Revenge) posited to underlie forgiving. The measure demonstrated a variety of desirable psychometric properties, commending its use for future research. As predicted, empathy, apology, rumination, and several indexes of relationship closeness were associated with self-reported forgiving.
[Nanopsychiatry. The potential role of nanotechnologies in the future of psychiatry. A systematic review].

Nanomedicine is defined as the area using nanotechnology's concepts for the benefit of human beings, their health and well being. The field of nanotechnology opened new unsuspected fields of research a few years ago.



To provide an overview of nanotechnology application areas that could affect care for psychiatric illnesses.



We conducted a systematic review using the PRISMA criteria (preferred reporting items for systematic reviews and meta-analysis). Inclusion criteria were specified in advance: all studies describing the development of nanotechnology in psychiatry. The research paradigm was: "(nanotechnology OR nanoparticles OR nanomedicine) AND (central nervous system)" Articles were identified in three research bases, Medline (1966-present), Web of Science (1975-present) and Cochrane (all articles). The last search was carried out on April 2, 2012. Seventy-six items were included in this qualitative review.



The main applications of nanotechnology in psychiatry are (i) pharmacology. There are two main difficulties in neuropharmacology. Drugs have to pass the blood brain barrier and then to be internalized by targeted cells. Nanoparticles could increase drugs' bioavailability and pharmacokinetics, especially improving safety and efficacy of psychotropic drugs. Liposomes, nanosomes, nanoparticle polymers, nanobubbles are some examples of this targeted drug delivery. Nanotechnologies could also add new pharmacological properties, like nanohells and dendrimers; (ii) living analysis. Nanotechnology provides technical assistance to in vivo imaging or metabolome analysis; (iii) central nervous system modeling. Research teams have modelized inorganic synapses and mimicked synaptic behavior, essential for further creation of artificial neural systems. Some nanoparticle assemblies present the same small world and free-scale network architecture as cortical neural networks. Nanotechnologies and quantum physics could be used to create models of artificial intelligence and mental illnesses.



Even if nanotechnologies are promising, their safety is still tricky and this must be kept in mind.



We are not about to see a concrete application of nanomedicine in daily psychiatric practice. However, it seems essential that psychiatrists do not forsake this area of research the perspectives of which could be decisive in the field of mental illness.
Mastering Erosion of Software Architecture in Automotive Software Product Lines

Most automobile manufacturers maintain many vehicle types to keep a successful position on the market. Through the further development all vehicle types gain a diverse amount of new functionality. Additional features have to be supported by the car’s software. For time efficient accomplishment, usually the existing electronic control unit (ECU) code is extended. In the majority of cases this evolutionary development process is accompanied by a constant decay of the software architecture. This effect known as software erosion leads to an increasing deviation from the requirements specifications. To counteract the erosion it is necessary to continuously restore the architecture in respect of the specification. Automobile manufacturers cope with the erosion of their ECU software with varying degree of success. Successfully we applied a methodical and structured approach of architecture restoration in the specific case of the brake servo unit (BSU). Software product lines from existing BSU variants were extracted by explicit projection of the architecture variability and decomposition of the original architecture. After initial application, this approach was capable to restore the BSU architecture recurrently.
BiTouch and BiPad: designing bimanual interaction for hand-held tablets

Despite the demonstrated benefits of bimanual interaction, most tablets use just one hand for interaction, to free the other for support. In a preliminary study, we identified five holds that permit simultaneous support and interaction, and noted that users frequently change position to combat fatigue. We then designed the BiTouch design space, which introduces a support function in the kinematic chain model for interacting with hand-held tablets, and developed BiPad, a toolkit for creating bimanual tablet interaction with the thumb or the fingers of the supporting hand. We ran a controlled experiment to explore how tablet orientation and hand position affect three novel techniques: bimanual taps, gestures and chords. Bimanual taps outperformed our one-handed control condition in both landscape and portrait orientations; bimanual chords and gestures in portrait mode only; and thumbs outperformed fingers, but were more tiring and less stable. Together, BiTouch and BiPad offer new opportunities for designing bimanual interaction on hand-held tablets.
Gestational diabetes mellitus: clinical predictors and long-term risk of developing type 2 diabetes: a retrospective cohort study using survival analysis.

We sought to determine the long-term risk of type 2 diabetes following a pregnancy complicated by gestational diabetes mellitus (GDM) and assess what maternal antepartum, postpartum, and neonatal factors are predictive of later development of type 2 diabetes.



This was a retrospective cohort study using survival analysis on 5,470 GDM patients and 783 control subjects who presented for postnatal follow-up at the Mercy Hospital for Women between 1971 and 2003.



Risk of developing diabetes increased with time of follow-up for both groups and was 9.6 times greater for patients with GDM. The cumulative risk of developing type 2 diabetes for the GDM patients was 25.8% at 15 years postdiagnosis. Predictive factors for the development of type 2 diabetes were use of insulin (hazard ratio 3.5), Asian origin compared with Caucasian (2.1), and 1-h blood glucose (1.3 for every 1 mmol increase above 10.1 mmol). BMI was associated with an increased risk of developing type 2 diabetes but did not meet the assumption of proportional hazards required for valid inference when using Cox proportional hazards.



While specific predictive factors for the later development of type 2 diabetes can be identified in the index pregnancy, women with a history of GDM, as a group, are worthy of long-term follow-up to ameliorate their excess cardiovascular risk.
Recursive Feature Generation for Knowledge-based Learning

When humans perform inductive learning, they often enhance the process with background knowledge. With the increasing availability of well-formed collaborative knowledge bases, the performance of learning algorithms could be significantly enhanced if a way were found to exploit these knowledge bases. In this work, we present a novel algorithm for injecting external knowledge into induction algorithms using feature generation. Given a feature, the algorithm defines a new learning task over its set of values, and uses the knowledge base to solve the constructed learning task. The resulting classifier is then used as a new feature for the original problem. We have applied our algorithm to the domain of text classification using large semantic knowledge bases. We have shown that the generated features significantly improve the performance of existing learning algorithms.
Learning from Examples as an Inverse Problem

Many works related learning from examples to regularization techniques for inverse problems, emphasizing the strong algorithmic and conceptual analogy of certain learning algorithms with regularization algorithms. In particular it is well known that regularization schemes such as Tikhonov regularization can be effectively used in the context of learning and are closely related to algorithms such as support vector machines. Nevertheless the connection with inverse problem was considered only for the discrete (finite sample) problem which is solved in practice and the probabilistic aspects of learning from examples were not taken into account. In this paper we provide a natural extension of such analysis to the continuous (population) case and analyse the interplay between the discrete and continuous problems. From a theoretical point of view, this allows to draw a clear connection between the consistency approach imposed in learning theory, and the stability convergence property used in ill-posed inverse problems. The main mathematical result of the paper is a new probabilistic bound for the regularized least-squares algorithm. By means of standard results on the approximation term, the consistency of the algorithm easily follows.
Cosmetic use of polylactic acid for hand rejuvenation: report on 27 patients.

Polylactic acid (PLA) is effectively used on the face. In the Author's opinion it can also be applied successfully to other body areas. The aim of the present retrospective study is to estimate the safety and results of this new technique in order to improve the hand skeletal status in old patients.



Twenty-seven patients were treated in the period from January 2004 to April 2005 (a total of 109 sessions). Their age averaged 65.9 years. In all cases the hand treatment was associated with a face or neck treatment. 150 mg polylactic acid was diluted with 0.5 mL of 3% Carbocain and water for injectable preparations (from 5 to 8 mL). Intermetacarpal spaces were injected with this solution in dosages ranging from 2 to 4 mL with a particular and rigorous technique. The protocol, consisting of 3-6 consecutive sessions, is discussed below.



The results have been evaluated by a Definitive Graduated Score (DGS) based on the patient's and doctor's satisfaction scores (from 1 to 10) and by the photograph score. They have ranged from 4 to 9 (average of 6.55). No important side effects have been detected. There has been one case of fine unnoticeable nodulations. In six cases the result has not been satisfactory. In seven cases the DGS was higher than 8.



The fibro-connectival restoration of hands is an important step in the global aesthetic treatment of old patients. This technique can give good results, particularly if associated with peelings and sclerotherapy. Side effects have been acceptable.
SI/PI/EMI Analysis of Through-Via Effects on Power/Ground Plane using High Dielectric Material

In this paper, we simulated and analyzed about through-via's signal integrity, (SI)/power integrity, and (PI)/electromagnetic interference (EMI) that goes through the power/ground plane which was caused by the high dielectric material that supports the embedded high value capacitors. In order to evaluate through-via's effectiveness, the simulation condition was operated on the LTCC module for mixed signal system. For the circumstance SI, delay time of signal line and signal quality significantly decrease because of higher parasitic capacitance between through-via's and anti-pads. However, in a situation where the dielectric material is chosen, the EMI's characteristic power/ground plan with embedded high dielectric material shows a better characteristic than when the low dielectric material was chosen. As a result, if the high dielectric material is applied on LTCC module, the mixed module packaging that is made with the digital IC and RF component will be realized as the optimistic design. The simulation structure takes the LTCC process designer guidebook as a basic structure and uses the HFSS/designer tool. When the dielectric constant uses 7.8 and 500, the through-via's that pass through the LTCC module are delay time of 41.4 psec and 56, respectively. When the dielectric constant of 500 is compared with 7.8, the power/ground plane impedance shows a trait lower than several GHz range and effectiveness in the rejection of the resonance mode. When uses the dielectric constant is 500, the EMI level is 7.8 and it is prove that the EMI level improves at maximum 20 dB V/m.
Hashing as Tie-Aware Learning to Rank

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based optimization with deep neural networks. Our results establish the new state-of-the-art for image retrieval by Hamming ranking in common benchmarks.
The Transaction Graph for Modeling Blockchain Semantics

The advent of Bitcoin paved the way for a plethora of blockchain systems supporting diverse applications beyond cryptocurrencies. Although in-depth studies of the protocols, security, and privacy of blockchains are available, there is no formal model of the transaction semantics that a blockchain is supposed to guarantee. In this work, we fill this gap, motivated by the observation that the semantics of transactions in blockchain systems can be captured by a directed acyclic graph. Such a transaction graph, or TDAG, generally consists of the states and the transactions as transitions between the states, together with conditions for the consistency and validity of transactions. We instantiate the TDAG model for three prominent blockchain systems: Bitcoin, Ethereum, and Hyperledger Fabric. We specify the states and transactions as well as the validity conditions of the TDAG for each one. This demonstrates the applicability of the model and formalizes the transaction-level semantics that these systems aim for.
Evolving virtual creatures

This paper describes a novel system for creating virtual creatures that move and behave in simulated three-dimensional physical worlds. The morphologies of creatures and the neural systems for controlling their muscle forces are both generated automatically using genetic algorithms. Different fitness evaluation functions are used to direct simulated evolutions towards specific behaviors such as swimming, walking, jumping, and following.
A genetic language is presented that uses nodes and connections as its primitive elements to represent directed graphs, which are used to describe both the morphology and the neural circuitry of these creatures. This genetic language defines a hyperspace containing an indefinite number of possible creatures with behaviors, and when it is searched using optimization techniques, a variety of successful and interesting locomotion strategies emerge, some of which would be difficult to invent or built by design.
Ranking Outliers Using Symmetric Neighborhood Relationship

Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Besides classical outlier analysis algorithms, recent studies have focused on mining local outliers, i.e., the outliers that have density distribution significantly different from their neighborhood. The estimation of density distribution at the location of an object has so far been based on the density distribution of its k-nearest neighbors [2, 11]. However, when outliers are in the location where the density distributions in the neighborhood are significantly different, for example, in the case of objects from a sparse cluster close to a denser cluster, this may result in wrong estimation. To avoid this problem, here we propose a simple but effective measure on local outliers based on a symmetric neighborhood relationship. The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution. As a result, outliers so discovered are more meaningful. To compute such local outliers efficiently, several mining algorithms are developed that detects top-n outliers based on our definition. A comprehensive performance evaluation and analysis shows that our methods are not only efficient in the computation but also more effective in ranking outliers.
The Impact of Ebook Distribution on Print Sales : Analysis of a Natural Experiment

Digital distribution channels introduce several new strategic questions for the creative industries. Two notable questions are (1) how will participation in digital channels impact physical sales, and (2) where should digital releases occur relative to existing “physical” release dates. These questions are particularly salient for book publishing where firms have expressed concern that making ebooks available alongside hardcover titles may cannibalize hardcover sales. We analyze the impact of ebook availability on physical sales using a natural experiment that occurred between April and June 2010. Across all titles we find that delaying ebook availability results in a small (and statistically insignificant) increase in print sales and a large decrease in total ebook sales. However, we also find this effect is moderated by consumers’ brand awareness and digital channel preference: delaying Kindle availability for books with strong brand awareness and weak digital channel preference results in a statistically significant increase in print sales for those titles. These results contribute to a growing academic literature analyzing the impact of digitization on marketing strategies in the media industries. Our results also highlight the usefulness of natural experiments derived from rapidly changing media distribution environments for identifying the impact of digital distribution channels on sales.
A fabric-based wearable band for hand gesture recognition based on filament strain sensors: A preliminary investigation

A wearable system based on a breathable cloth wristband equipped with stretchable strain gauge sensors were assembled and tested to detect a set of 16 different hand gestures. The sensors embedded on the wristband prototype do not require a direct contact with the skin, thus maximizing comfort. To evaluate the performance of the developed band, different gestures were labelled by using grasping information detected in real-time by commercial Force-Sensing Resistor (FSR) sensors. Signals recorded by the wristband were processed through two machine-learning algorithms, i.e. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), reaching accuracies of 87% and 95% respectively.
Neurological complications of dengue virus infection

Dengue is the second most common mosquito-borne disease affecting human beings. In 2009, WHO endorsed new guidelines that, for the first time, consider neurological manifestations in the clinical case classification for severe dengue. Dengue can manifest with a wide range of neurological features, which have been noted--depending on the clinical setting--in 0·5-21% of patients with dengue admitted to hospital. Furthermore, dengue was identified in 4-47% of admissions with encephalitis-like illness in endemic areas. Neurological complications can be categorised into dengue encephalopathy (eg, caused by hepatic failure or metabolic disorders), encephalitis (caused by direct virus invasion), neuromuscular complications (eg, Guillain-Barré syndrome or transient muscle dysfunctions), and neuro-ophthalmic involvement. However, overlap of these categories is possible. In endemic countries and after travel to these regions, dengue should be considered in patients presenting with fever and acute neurological manifestations.