nils-herrmann/clean_scirepeval huggingface dataset:
A Flexible Millimeter-Wave Channel Sounder With Absolute Timing

This paper presents a novel ultrawideband wireless spread spectrum millimeter-wave (mmWave) channel sounder that supports both a wideband sliding correlator mode and a real-time spread spectrum mode, also known as wideband correlation or direct correlation. Both channel sounder modes are capable of absolute propagation delay (time of flight) measurements with up to 1 GHz of radio frequency null-to-null bandwidth, and can measure multipath with a 2-ns time resolution. The sliding correlator configuration facilitates long-distance measurements with angular spread and delay spread for up to 185 dB of maximum measurable path loss. The real-time spread spectrum mode is shown to support short-range, small-scale temporal, and Doppler measurements (minimum snapshot sampling interval of  $32.753~\mu \text{s}$ ) with a substantial dynamic fading range of 40 dB for human blockage and dynamic urban scenarios. The channel sounder uses field programmable gate arrays, analog-to-digital converters, digital-to-analog converters, and low-phase-noise rubidium standard references for frequency/time synchronization and absolute time delay measurements. Using propagation theory, several methods are presented here to calibrate and verify the accuracy of the channel sounder, and an improved diffraction model for human blockage, based on the METIS model but now including directional antenna gains, is developed from measurements using the channel sounder. The mmWave channel sounder described here may be used for accurate spatial and temporal ray-tracing calibration, to identify individual multipath components, to measure antenna patterns, for constructing spatial profiles of mmWave channels, and for developing statistical channel impulse response models in time and space.
Improvements in LED-based fluorescence analysis systems

This paper presents results from an electronic interface that significantly improves the stability, power output, and spectral flexibility of light emitting diode (LED)-based systems used to excite fluorescence or other forms of luminescence. LEDs are an attractive alternative to conventional white-light sources used in fluorescence analysis because of reduced power of operation, enhanced modularity, reduced optical loss, fewer imaging artifacts, and increased flexibility in spectral control without the need for high overhead optics. Drawbacks of previously presented LED-based systems include insufficient light output, instability (poor lifetime), and limited flexibility in broadband spectral output. T ld increase i le spectral o on in power, s appropriate o igns. ©
Two cases of death due to plastic bag suffocation.

Deaths due to plastic bag suffocation or plastic bag asphyxia are not reported in Malaysia. In the West many suicides by plastic bag asphyxia, particularly in the elderly and those who are chronically and terminally ill, have been reported. Accidental deaths too are not uncommon in the West, both among small children who play with shopping bags and adolescents who are solvent abusers. Another well-known but not so common form of accidental death from plastic bag asphyxia is sexual asphyxia, which is mostly seen among adult males. Homicide by plastic bag asphyxia too is reported in the West and the victims are invariably infants or adults who are frail or terminally ill and who cannot struggle. Two deaths due to plastic bag asphyxia are presented. Both the autopsies were performed at the University Hospital Mortuary, Kuala Lumpur. Both victims were 50-year old married Chinese males. One death was diagnosed as suicide and the other as sexual asphyxia. Sexual asphyxia is generally believed to be a problem associated exclusively with the West. Specific autopsy findings are often absent in deaths due to plastic bag asphyxia and therefore such deaths could be missed when some interested parties have altered the scene and most importantly have removed the plastic bag. A visit to the scene of death is invariably useful.
Can vaccines interact with drug metabolism?

Vaccines are safe and efficacious in reducing the burden of several serious infections affecting children and adults. Due to their efficacy, vaccines are often administered in patients with chronic diseases, likely to be under poly-therapy. Because of several case reports indicating changes in drug metabolism after vaccination, the hypothesis of an interaction between vaccines and specific drugs has been put forward. These interactions are conceivably of great concern, especially in patients treated with molecules characterised by a narrow therapeutic index. Herein, we review and systematise the available evidence on vaccine-drug interactions. The picture that emerges indicates that reduction in the activity of specific CYPs following vaccination may occur, most likely via interferon γ overproduction, and for specific drugs such as anticonvulsivant and theophylline may have significant clinical relevance. Clinical interaction between vaccines and drugs that are metabolised by cytochromes uninfluenced by INFγ levels, such as warfarin, are instead unlikely to happen. Further studies are however needed to gain a complete picture of vaccine-drug interactions and define their relevance in terms of possible negative clinical impact.
Finite Sample Properties of Semiparametric Estimators of Average Treatment Effects ∗

We explore the finite sample properties of several semiparametric estimators of average treatment effects, including propensity score reweighting, matching, double robust, and control function estimators. When there is good overlap in the distribution of propensity scores for treatment and control units, reweighting estimators are preferred on bias grounds and attain the semiparametric efficiency bound even for samples of size 100. Pair matching exhibits similarly good performance in terms of bias, but has notably higher variance. Local linear and ridge matching are competitive with reweighting in terms of bias and variance, but only once n = 500. Nearest-neighbor, kernel, and blocking matching are not competitive. When overlap is close to failing, none of the estimators examined perform well and √ n -asymptotics may be a poor guide to finite sample performance. Trimming rules, commonly used in the face of problems with overlap, are effective only in settings with homogeneous treatment effects.
Deletion of L-Selectin Increases Atherosclerosis Development in ApoE−/− Mice

Atherosclerosis is an inflammatory disease characterized by accumulation of leukocytes in the arterial intima. Members of the selectin family of adhesion molecules are important mediators of leukocyte extravasation. However, it is unclear whether L-selectin (L-sel) is involved in the pathogenesis of atherosclerosis. In the present study, mice deficient in L-selectin (L-sel(-/-)) animals were crossed with mice lacking Apolipoprotein E (ApoE(-/-)). The development of atherosclerosis was analyzed in double-knockout ApoE/L-sel (ApoE(-/-)L-sel(-/-)) mice and the corresponding ApoE(-/-) controls fed either a normal or a high cholesterol diet (HCD). After 6 weeks of HCD, aortic lesions were increased two-fold in ApoE(-/-)L-sel(-/-) mice as compared to ApoE(-/-) controls (2.46%±0.54% vs 1.28%±0.24% of total aortic area; p<0.05). Formation of atherosclerotic lesions was also enhanced in 6-month-old ApoE(-/-)L-sel(-/-) animals fed a normal diet (10.45%±2.58% vs 1.87%±0.37%; p<0.05). In contrast, after 12 weeks of HCD, there was no difference in atheroma formation between ApoE(-/-)L-sel(-/-) and ApoE(-/-) mice. Serum cholesterol levels remained unchanged by L-sel deletion. Atherosclerotic plaques did not exhibit any differences in cellular composition assessed by immunohistochemistry for CD68, CD3, CD4, and CD8 in ApoE(-/-)L-sel(-/-) as compared to ApoE(-/-) mice. Leukocyte rolling on lesions in the aorta was similar in ApoE(-/-)L-sel(-/-) and ApoE(-/-) animals. ApoE(-/-)L-sel(-/-) mice exhibited reduced size and cellularity of peripheral lymph nodes, increased size of spleen, and increased number of peripheral lymphocytes as compared to ApoE(-/-) controls. These data indicate that L-sel does not promote atherosclerotic lesion formation and suggest that it rather protects from early atherosclerosis.
MINE : Mutual Information Neural Estimation MINE : M UTUAL I NFORMATION N EURAL E STIMATION

We argue that the estimation of the mutual information between high dimensional continuous random variables is achievable by gradient descent over neural networks. This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE is backpropable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results demonstrate substantial added flexibility and improvement in these settings.
Biomechatronics design of a robotic arm for rehabilitation

Rehabilitation is an important process to restore muscle strength and joint's range of motion. This paper proposes a biomechatronic design of a robotic arm that is able to mimic the natural movement of the human shoulder, elbow and wrist joint. In a preliminary experiment, a subject was asked to perform four different arm movements using the developed robotic arm for a period of two weeks. The experimental results were recorded and can be plotted into graphical results using Matlab. Based on the results, the robotic arm shows encouraging effect by increasing the performance of rehabilitation process. This is proven when the result in degree value are accurate when being compared with the flexion of both shoulder and elbow joints. This project can give advantages on research if the input parameter needed in the flexion of elbow and wrist.
Internal States and Behavioral Decision-Making: Toward an Integration of Emotion and Cognition.

Social interactions, such as an aggressive encounter between two conspecific males or a mating encounter between a male and a female, typically progress from an initial appetitive or motivational phase, to a final consummatory phase. This progression involves both changes in the intensity of the animals' internal state of arousal or motivation and sequential changes in their behavior. How are these internal states, and their escalating intensity, encoded in the brain? Does this escalation drive the progression from the appetitive/motivational to the consummatory phase of a social interaction and, if so, how are appropriate behaviors chosen during this progression? Recent work on social behaviors in flies and mice suggests possible ways in which changes in internal state intensity during a social encounter may be encoded and coupled to appropriate behavioral decisions at appropriate phases of the interaction. These studies may have relevance to understanding how emotion states influence cognitive behavioral decisions at higher levels of brain function.
Domain randomization for transferring deep neural networks from simulation to the real world

Bridging the ‘reality gap’ that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability. This paper explores domain randomization, a simple technique for training models on simulated images that transfer to real images by randomizing rendering in the simulator. With enough variability in the simulator, the real world may appear to the model as just another variation. We focus on the task of object localization, which is a stepping stone to general robotic manipulation skills. We find that it is possible to train a real-world object detector that is accurate to 1.5 cm and robust to distractors and partial occlusions using only data from a simulator with non-realistic random textures. To demonstrate the capabilities of our detectors, we show they can be used to perform grasping in a cluttered environment. To our knowledge, this is the first successful transfer of a deep neural network trained only on simulated RGB images (without pre-training on real images) to the real world for the purpose of robotic control.
Ontology Matching: A Machine Learning Approach

This chapter studies ontology matching : the problem of finding the semantic mappings between two given ontologies. This problem lies at the heart of numerous information processing applications. Virtually any application that involves multiple ontologies must establish semantic mappings among them, to ensure interoperability. Examples of such applications arise in myriad domains, including e-commerce, knowledge management, e-learning, information extraction, bio-informatics, web services, and tourism (see Part D of this book on ontology applications). Despite its pervasiveness, today ontology matching is still largely conducted by hand, in a labor-intensive and error-prone process. The manual matching has now become a key bottleneck in building large-scale information management systems. The advent of technologies such as the WWW, XML, and the emerging Semantic Web will further fuel information sharing applications and exacerbate the problem. Hence, the development of tools to assist in the ontology matching process has become crucial for the success of a wide variety of information management applications. In response to the above challenge, we have developed GLUE, a system that employs learning techniques to semi-automatically create semantic mappings between ontologies. We shall begin the chapter by describing a motivating example: ontology matching on the Semantic Web. Then we present our GLUE solution. Finally, we describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings.
Tools for probing local circuits: high-density silicon probes combined with optogenetics

To understand how function arises from the interactions between neurons, it is necessary to use methods that allow the monitoring of brain activity at the single-neuron, single-spike level and the targeted manipulation of the diverse neuron types selectively in a closed-loop manner. Large-scale recordings of neuronal spiking combined with optogenetic perturbation of identified individual neurons has emerged as a suitable method for such tasks in behaving animals. To fully exploit the potential power of these methods, multiple steps of technical innovation are needed. We highlight the current state of the art in electrophysiological recording methods, combined with optogenetics, and discuss directions for progress. In addition, we point to areas where rapid development is in progress and discuss topics where near-term improvements are possible and needed.
Multi-dimensional Data Visualization Techniques for Exploring Financial Performance Data

In this paper, we review nine visualization techniques that can be used for visual exploration of multidimensional financial data. We illustrate the use of these techniques by studying the financial performance of companies from the pulp and paper industry. We also illustrate the use of visualization techniques for detecting multivariate outliers, and other patterns in financial performance data in the form of clusters, relationships, and trends. We provide a subjective comparison between different visualization techniques as to their capabilities for providing insight into financial performance data. The strengths of each technique and the potential benefits of using multiple visualization techniques for gaining insight into financial performance data are highlighted.
Head circumference reference data: birth to 18 years.

Highly reliable data from a sample of 888 white US children, measured serially in a single study, have been used to provide reference data for head circumference from birth to 18 years of age. The present data differ little from those already available for the age range from birth to 36 months of age, but they are considerably higher (about 0.5 cm) at older ages for boys and tend to be slightly higher for girls. These new reference data are smoother across age than those used currently for screening and evaluation. Percentiles for 6-month increments from birth to 6 years have been provided.
Novel SLC 12 A 1 ( NKCC 2 ) Mutations in Two Families with Bartter Syndrome Type 1

Bartter syndrome (BS) type 1, also referred to antenatal BS, is a genetic tubulopathy with hypokalemic metabolic alkalosis and prenatal onset of polyuria leading to polyhydramnios. It has been shown that BS type 1 is caused by mutations in the SLC12A1 gene encoding bumetanide-sensitive Na-K-2Cl– cotransporter (NKCC2). We had the opportunity to care for two unrelated Japanese patients of BS type 1 with typical manifestations including polyhydramnios, prematurity, hypokalemia, alkalosis, and infantile-onset nephrocalcinosis. Analysis of the SLC12A1 gene demonstrated four novel mutations: N117X, G257S, D792fs and N984fs. N117X mutation is expected to abolish most of the NKCC2 protein, whereas G257, which is evolutionary conserved, resides in the third transmemebrane domain. The latter two frameshift mutations reside in the intra-cytoplasmic C-terminal domain, which illustrates the importance of this domain for the NKCC2 function. In conclusion, we found four novel SLC12A1 mutations in two BS type 1 patients. Development of effective therapy for hypercalciuria is mandatory to prevent nephrocalcinosis and resultant renal failure.
3D Wire mesh photonic crystals.

We have investigated the electromagnetic properties of a 3D wire mesh in a geometry rese covalently bonded diamond. The frequency and wave vector dispersion show forbidden bands a frequenciesn0, corresponding to the lattice spacing, just as dielectric photonic crystals do. But have a new forbidden band which commences at zero frequency and extends, in our geome , 12 n0, acting as a type of plasma cutoff frequency. Wire mesh photonic crystals appear to sup longitudinal plane wave, as well as two transverse plane waves. We identify an important new r for microwave photonic crystals, an effective medium limit, in which electromagnetic waves pene deeply into the wire mesh through the aid of an impurity band.
The techniques for face recognition with support vector machines

The development of automatic visual control system is a very important research topic in computer vision. This face identification system must be robust to the various quality of the images such as light, face expression, glasses, beards, moustaches etc. We propose using the wavelet transformation algorithms for reduction the source data space. We have realized the method of the expansion of the values of pixels to the whole intensity range and the algorithm of the equalization of histogram to adjust image intensity values. The support vector machines (SVM) technology has been used for the face recognition in our work.
Discovering security vulnerabilities and leaks in ASP.NET websites

Websites written in ASP.NET might contain security vulnerabilities that are not seen to the owner of the website. This paper describes an algorithm that aims in the detection of security vulnerabilities. The suggested algorithm performs a scanning process for all website/ application files. Our scanner tool relies on studying the source code of the application depending on ASP.NET files and the code behind files (Visual Basic VB and C sharp C#). A program written for this purpose is to generate a report that describes most leaks and vulnerabilities types (by mentioning the file name, leak description and its location). The suggested algorithm will help organization to fix the vulnerabilities and improve the overall security.
CollAFL: Path Sensitive Fuzzing

Coverage-guided fuzzing is a widely used and effective solution to find software vulnerabilities. Tracking code coverage and utilizing it to guide fuzzing are crucial to coverage-guided fuzzers. However, tracking full and accurate path coverage is infeasible in practice due to the high instrumentation overhead. Popular fuzzers (e.g., AFL) often use coarse coverage information, e.g., edge hit counts stored in a compact bitmap, to achieve highly efficient greybox testing. Such inaccuracy and incompleteness in coverage introduce serious limitations to fuzzers. First, it causes path collisions, which prevent fuzzers from discovering potential paths that lead to new crashes. More importantly, it prevents fuzzers from making wise decisions on fuzzing strategies. In this paper, we propose a coverage sensitive fuzzing solution CollAFL. It mitigates path collisions by providing more accurate coverage information, while still preserving low instrumentation overhead. It also utilizes the coverage information to apply three new fuzzing strategies, promoting the speed of discovering new paths and vulnerabilities. We implemented a prototype of CollAFL based on the popular fuzzer AFL and evaluated it on 24 popular applications. The results showed that path collisions are common, i.e., up to 75% of edges could collide with others in some applications, and CollAFL could reduce the edge collision ratio to nearly zero. Moreover, armed with the three fuzzing strategies, CollAFL outperforms AFL in terms of both code coverage and vulnerability discovery. On average, CollAFL covered 20% more program paths, found 320% more unique crashes and 260% more bugs than AFL in 200 hours. In total, CollAFL found 157 new security bugs with 95 new CVEs assigned.
Multivariate Outlier Detection Using Independent Component Analysis

The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in outlier detection , data clustering and multivariate data visualization etc . Accurate identification of outliers plays an important role in statistical analysis. If classical statistical models are blindly applied to data containing outliers, the results can be misleading at best. In addition, outliers themselves are often the special points of interest in many practical situations and their identification is the main purpose of the investigation. This paper takes an attempt a new and novel method for multivariate outlier detection using ICA and compares with different outlier detect ion techniques in the literature.
Pre-processing online financial text for sentiment classification: A natural language processing approach

Online financial textual information contains a large amount of investor sentiment, i.e. subjective assessment and discussion with respect to financial instruments. An effective solution to automate the sentiment analysis of such large amounts of online financial texts would be extremely beneficial. This paper presents a natural language processing (NLP) based pre-processing approach both for noise removal from raw online financial texts and for organizing such texts into an enhanced format that is more usable for feature extraction. The proposed approach integrates six NLP processing steps, including a developed syntactic and semantic combined negation handling algorithm, to reduce noise in the online informal text. Three-class sentiment classification is also introduced in each system implementation. Experimental results show that the proposed pre-processing approach outperforms other pre-processing methods. The combined negation handling algorithm is also evaluated against three standard negation handling approaches.
Probabilistic Encryption and How to Play Mental Poker Keeping Secret All Partial Information

This paper proposes an Encryption Scheme that possess the following property : An adversary, who knows the encryption algorithm and is given the cyphertext, cannot obtain any information about the clear-text.
 Any implementation of a Public Key Cryptosystem, as proposed by Diffie and Hellman in , should possess this property.
 Our Encryption Scheme follows the ideas in the number theoretic implementations of a Public Key Cryptosystem due to Rivest, Shamir and Adleman [13], and Rabin .