nils-herrmann/clean_scirepeval huggingface dataset:
Architectural implications of quantum computing technologies

In this article we present a classification scheme for quantum computing technologies that is based on the characteristics most relevant to computer systems architecture. The engineering trade-offs of execution speed, decoherence of the quantum states, and size of systems are described. Concurrency, storage capacity, and interconnection network topology influence algorithmic efficiency, while quantum error correction and necessary quantum state measurement are the ultimate drivers of logical clock speed. We discuss several proposed technologies. Finally, we use our taxonomy to explore architectural implications for common arithmetic circuits, examine the implementation of quantum error correction, and discuss cluster-state quantum computation.
Stochastic Hill Climbing with Learning by Vectors of NormalDistributionsSecond corrected and enhanced

This paper describes a stochastic hill climbing algorithm named SHCLVND to optimize arbitrary vectorial < n ! < functions. It needs less parameters. It uses normal (Gaussian) distributions to represent probabilities which are used for generating more and more better argument vectors. The-parameters of the normal distributions are changed by a kind of Hebbian learning. Kvasnicka et al. KPP95] used algorithm Stochastic Hill Climbing with Learning (HCwL) to optimize a highly multimodal vectorial function on real numbers. We have tested proposed algorithm by optimizations of the same and a similar function and show the results in comparison to HCwL. In opposite to it algorithm SHCLVND desribed here works directly on vectors of numbers instead their bit-vector representations and uses normal distributions instead of numbers to represent probabilities. 1 Overview In Section 2 we give an introduction with the way to the algorithm. Then we describe it exactly in Section 3. There is also given a compact notation in pseudo PASCAL-code, see Section 3.4. After that we give an example: we optimize highly multimodal functions with the proposed algorithm and give some visualisations of the progress in Section 4. In Section 5 there are a short summary and some ideas for future works. At last in Section 6 we give some hints for practical use of the algorithm. 2  This paper describes a hill climbing algorithm to optimize vectorial functions on real numbers.  Flexible algorithms for optimizing any vectorial function are interesting if there is no or only a very diicult mathematical solution known, e.g. parameter adjustments to optimize with respect to some relevant property the recalling behavior of a (trained) neuronal net HKP91, Roj93], or the resulting image of some image-processing lter.
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients

Distributed model training is vulnerable to byzantine system failures and adversarial compute nodes, i.e., nodes that use malicious updates to corrupt the global model stored at a parameter server (PS). To guarantee some form of robustness, recent work suggests using variants of the geometric median as an aggregation rule, in place of gradient averaging. Unfortunately, median-based rules can incur a prohibitive computational overhead in large-scale settings, and their convergence guarantees often require strong assumptions. In this work, we present DRACO, a scalable framework for robust distributed training that uses ideas from coding theory. In DRACO, each compute node evaluates redundant gradients that are used by the parameter server to eliminate the effects of adversarial updates. DRACO comes with problemindependent robustness guarantees, and the model that it trains is identical to the one trained in the adversary-free setup. We provide extensive experiments on real datasets and distributed setups across a variety of large-scale models, where we show that DRACO is several times, to orders of magnitude faster than median-based approaches.
Logic and linear algebra: an introduction

We give an introduction to computation and logic tailored for algebraists, and use this as a springboard to discuss geometric models of computation and the role of cut-elimination in these models, following Girard's geometry of interaction program. We discuss how to represent programs in the λ-calculus and proofs in linear logic as linear maps between infinite-dimensional vector spaces. The interesting part of this vector space semantics is based on the cofree cocommutative coalgebra of Sweedler [71] and the recent explicit computations of liftings in [62].
Elevated salivary C-reactive protein levels are associated with active and passive smoking in healthy youth: A pilot study

We examined salivary C-reactive protein (CRP) levels in the context of tobacco smoke exposure (TSE) in healthy youth. We hypothesized that there would be a dose-response relationship between TSE status and salivary CRP levels. This work is a pilot study (N = 45) for a larger investigation in which we aim to validate salivary CRP against serum CRP, the gold standard measurement of low-grade inflammation. Participants were healthy youth with no self-reported periodontal disease, no objectively measured obesity/adiposity, and no clinical depression, based on the Beck Depression Inventory (BDI-II). We assessed tobacco smoking and confirmed smoking status (non-smoking, passive smoking, and active smoking) with salivary cotinine measurement. We measured salivary CRP by the ELISA method. We controlled for several potential confounders. We found evidence for the existence of a dose-response relationship between the TSE status and salivary CRP levels. Our preliminary findings indicate that salivary CRP seems to have a similar relation to TSE as its widely used serum (systemic inflammatory) biomarker counterpart.
Dispelling the stigma of schizophrenia: II. The impact of information on dangerousness.

This study addressed a relatively neglected topic in schizophrenia: identifying methods to reduce stigma directed toward individuals with this disorder. The study investigated whether presentation of information describing the association between violent behavior and schizophrenia could affect subjects' impressions of the dangerousness of both a target person with schizophrenia and individuals with mental illness in general. Subjects with and without previous contact with individuals with a mental illness were administered one of four "information sheets" with varying information about schizophrenia and its association with violent behavior. Subjects then read a brief vignette of a male or female target individual with schizophrenia. Results showed that subjects who reported previous contact with individuals with a mental illness rated the male target individual and individuals with mental illness in general as less dangerous than did subjects without previous contact. Subjects who received information summarizing the prevalence rates of violent behavior among individuals with schizophrenia and other psychiatric disorders (e.g., substance abuse) rated individuals with a mental illness as less dangerous than did subjects who did not receive this information. Implications of the findings for public education are discussed.
Personality & video game genre preferences

This research has been conducted to ascertain whether people with certain personality types exhibit preferences for particular game genres. Four hundred and sixty-six participants completed an online survey in which they described their preference for various game genres and provided measures of personality. Personality types were measured using the five-factor model of personality. Significant relationships between personality types and game genres were found. The results are interpreted in the context of the features of particular game genres and possible matches between personality traits and these features.
A Comparative Study of Off-Line Deep Learning Based Network Intrusion Detection

Network intrusion detection systems (NIDS) are essential security building-blocks for today's organizations to ensure safe and trusted communication of information. In this paper, we study the feasibility of off-line deep learning based NIDSes by constructing the detection engine with multiple advanced deep learning models and conducting a quantitative and comparative evaluation of those models. We first introduce the general deep learning methodology and its potential implication on the network intrusion detection problem. We then review multiple machine learning solutions to two network intrusion detection tasks (NSL-KDD and UNSW-NB15 datasets). We develop a TensorFlow-based deep learning library, called NetLearner, and implement a handful of cutting-edge deep learning models for NIDS. Finally, we conduct a quantitative and comparative performance evaluation of those models using NetLearner.
Search-and-Compute on Encrypted Data

Private query processing on encrypted databases allows users to obtain data from encrypted databases in such a way that the user’s sensitive data will be protected from exposure. Given an encrypted database, the users typically submit queries similar to the following examples: – How many employees in an organization make over $100,000? – What is the average age of factory workers suffering from leukemia? Answering the above questions requires one to search and then compute over the encrypted databases in sequence. In the case of privately processing queries with only one of these operations, many efficient solutions have been developed using a special-purpose encryption scheme (e.g., searchable encryption). In this paper, we are interested in efficiently processing queries that need to perform both operations on fully encrypted databases. One immediate solution is to use several special-purpose encryption schemes at the same time, but this approach is associated with a high computational cost for maintaining multiple encryption contexts. The other solution is to use a privacy homomorphism (or fully homomorphic encryption) scheme. However, no secure solutions have been developed that meet the efficiency requirements. In this work, we construct a unified framework so as to efficiently and privately process queries with “search” and “compute” operations. To this end, the first part of our work involves devising some underlying circuits as primitives for queries on encrypted data. Second, we apply two optimization techniques to improve the efficiency of the circuit primitives. One technique is to exploit SIMD techniques to accelerate their basic operations. In contrast to general SIMD approaches, our SIMD implementation can be applied even when one basic operation is executed. The other technique is to take a large integer ring (e.g., Z2t) as a message space instead of a binary field. Even for an integer of k bits with k ą t, addition can be performed with degree 1 circuits with lazy carry operations. As a result, search queries including a conjunctive or disjunctive query on encrypted databases of N tuples with μ-bit attributes require OpN logμq homomorphic operations with depth Oplogμq circuits. Search-and-compute queries, such as a conjunctive query with aggregate functions in the same conditions, are processed using OpμNq homomorphic operations with at most depth Oplogμ logNq circuits. Further, we can process search-and-compute queries using only OpN logμq homomorphic operations with depth Oplogμq circuits, even in the large domain. Finally, we present various experiments by varying the parameters, such as the query type and the number of tuples.
Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks

Previous algorithms for supervised sequence learning are based on dynamic recurrent networks. This paper describes an alternative class of gradient-based systems consisting of two feedforward nets that learn to deal with temporal sequences using fast weights: The first net learns to produce context-dependent weight changes for the second net whose weights may vary very quickly. The method offers the potential for STM storage efficiency: A single weight (instead of a full-fledged unit) may be sufficient for storing temporal information. Various learning methods are derived. Two experiments with unknown time delays illustrate the approach. One experiment shows how the system can be used for adaptive temporary variable binding.
Robust Low-Rank Matrix Completion by Riemannian Optimization

Low-rank matrix completion is the problem where one tries to recover a low-rank matrix from noisy observations of a subset of its entries. In this paper, we propose RMC, a new method to deal with the problem of robust low-rank matrix completion, i.e., matrix completion where a fraction of the observed entries are corrupted by non-Gaussian noise, typically outliers. The method relies on the idea of smoothing the `1 norm and using Riemannian optimization to deal with the low-rank constraint. We first state the algorithms as the successive minimization of smooth approximations of the `1 norm and we analyze its convergence by showing the strict decrease of the objective function. We then perform numerical experiments on synthetic data and demonstrate the effectiveness on the proposed method on the Netflix dataset.
Cooperative navigation in robotic swarms

We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm’s intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths.
Two-gun suicide by simultaneous shots to the head: interdisciplinary reconstruction on the basis of scene investigation, autopsy findings, GSR analysis and examination of firearms, bullets and cartridge cases

Suicidal shots fired simultaneously to the head from two handguns are rare. The authors report about a recent case in which a Smith & Wesson cal. 9 mm pistol and a Smith & Wesson cal. .357 Magnum revolver were used. Sitting on a sofa, a 33-year-old man (member of a shooting club) fired two simultaneous shots to the head; the pistol held in the left hand was discharged into the left temple, and the revolver held in the right hand was fired into the mouth. Both weapons remained in the respective hands. An upside-down muzzle imprint in the left temporal region and recoil injuries of a mandibular incisor, and the lower lip indicated that both the pistol and the revolver had been held in an inverted manner at the time of discharge. Blood stains (backspatter) and gunshot residues were present on both firing hands, whereas forward spatter originating from the exit wounds was deposited on the wall behind the suicide’s head.
An unusual accidental death from positional asphyxia.

Death from postural or positional asphyxia takes place in circumstances when the victim's body assumes an abnormal position compromising the process of respiration. The diagnosis is usually based on circumstantial evidence in conjunction with excluding other significant underlying causes of death. This case report is about a 37-year-old man who had been drinking the previous night and was found dead in the morning in a knee-chest position. The forensic medical examiner had the opportunity to examine and photograph the scene of death while the body was still in its original position. Apart from a blood alcohol level of 290 mg/100 mL, marked congestion of the face, and petechial hemorrhages on the conjunctivae, autopsy findings were unremarkable. There were no injuries or pathological findings to account for his death. Death was certified as due to postural asphyxia secondary to intoxication by alcohol.
Application of summative content analysis to a postal questionnaire.

Content analysis of replies to closed questions in questionnaires can be undertaken to understand remarks that may explain the responses, provide illustrative examples of issues raised in the questionnaire, define new issues or issues of importance that were not covered in the questionnaire and inform the design of new questions in future surveys.



To discuss the usefulness of summative content analysis to free text in postal questionnaires.



Content analysis provides useful comparative insights between two respondent groups in the case example provided. Five themes emerged: poor understanding of the concept of 'patient lateral transfer work technique' and the direct instrument nursing observation (DINO) instrument's key directions; outcomes of patient transfer; positive responses; manual handling risk; and poor translation into English of DINO.



Respondents need an opportunity to clarify their responses to questionnaires using free text, to provide insight into their understanding of the question being asked, understanding of the concept or construct being discussed, and data triangulation through the confirmation of item responses and free-text comments.



Responses to questions in a postal questionnaire and the opportunity for free-text commentary by respondents enable the identification of hidden meanings behind tickbox responses to questions.
Validation of the Greek translation of the Dundee Ready Education Environment Measure (DREEM).

The educational environment makes an important contribution to student learning. The DREEM questionnaire is a validated tool assessing the environment.



To translate and validate the DREEM into Greek.



Forward translations from English were produced by three independent Greek translators and then back translations by five independent bilingual translators. The Greek DREEM.v0 that was produced was administered to 831 undergraduate students from six Greek medical schools. Cronbach's alpha and test-retest correlation were used to evaluate reliability and factor analysis was used to assess validity. Questions that increased alpha if deleted and/or sorted unexpectedly in factor analysis were further checked through two focus groups.



Questionnaires were returned by 487 respondents (59%), who were representative of all surveyed students by gender but not by year of study or medical school. The instrument's overall alpha was 0.90, and for the learning, teachers, academic, atmosphere and social subscales the alphas were 0.79 (expected 0.69), 0.78 (0.67), 0.69 (0.60), 0.68 (0.69), 0.48 (0.57), respectively. In a subset of the whole sample, test and retest alphas were both 0.90, and mean item scores highly correlated (p<0.001). Factor analysis produced meaningful subscales but not always matching the original ones. Focus group evaluation revealed possible misunderstanding for questions 17, 25, 29 and 38, which were revised in the DREEM.Gr.v1. The group mean overall scale score was 107.7 (SD 20.2), with significant differences across medical schools (p<0.001).



Alphas and test-retest correlation suggest the Greek translated and validated DREEM scale is a reliable tool for assessing the medical education environment and for informing policy. Factor analysis and focus group input suggest it is a valid tool. Reasonable school differences suggest the instrument's sensitivity.
Swallowing in sleep and wakefulness in adult cats.

Clinical evidence indicates that swallowing, a vital function, may be impaired in sleep. To address this issue, we elicited swallows in awake and sleeping adult cats by injecting water through a nasopharyngeal tube. Our results indicate that swallowing occurs not only in non-rapid eye movement (NREM) sleep, but also in rapid eye movement (REM) sleep. In NREM sleep, the injections often caused arousal followed by swallowing, but, in the majority of cases, swallowing occurred in NREM sleep before arousal. These swallows in NREM sleep were entirely comparable to swallows in wakefulness. In contrast, the injections in REM sleep were less likely to cause arousal, and the swallows occurred as hypotonic events. Furthermore, apneas were sometimes elicited by the injections in REM sleep, and there was repetitive swallowing upon arousal. These results suggest that the hypotonic swallows of REM sleep were ineffective.
Entity Linking with a Paraphrase Flavor

The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the main challenges in the linking process. We propose a novel approach that combines two state-of-the-art models — for entity disambiguation and for paraphrase detection — to overcome these challenges. We consider name variations as paraphrases of the same entity mention and adopt a paraphrase model for this task. Our approach utilizes a graph-based disambiguation model based on Personalized Page Rank, and then refines and clusters its output using the paraphrase similarity between entity mention strings. It achieves a competitive performance of 80.5% in B+F clustering score on diagnostic TAC EDL 2014 data.
Design of a Reflection-Type Phase Shifter With Wide Relative Phase Shift and Constant Insertion Loss

reflection-type phase shifter with constant insertion loss over a wide relative phase-shift range is presented. This important feature is attributed to the salient integration of an impedance-transforming quadrature coupler with equalized series-resonated varactors. The impedance-transforming quadrature coupler is used to increase the maximal relative phase shift for a given varactor with a limited capacitance range. When the phase is tuned, the typical large insertion-loss variation of the phase shifter due to the varactor parasitic effect is minimized by shunting the series-resonated varactor with a resistor Rp. A set of closed-form equations for predicting the relative phase shift, insertion loss, and insertion-loss variation with respect to the quadrature coupler and varactor parameters is derived. Three phase shifters were implemented with a silicon varactor of a restricted capacitance range of Cv,min = 1.4 pF and Cv,max = 8 pF, wherein the parasitic resistance is close to 2 Omega. The measured insertion-loss variation is 0.1 dB over the relative phase-shift tuning range of 237deg at 2 GHz and the return losses are better than 20 dB, excellently agreeing with the theoretical and simulated results.