nils-herrmann/clean_scirepeval huggingface dataset:
Learning to track 3D human motion from silhouettes

We describe a sparse Bayesian regression method for recovering 3D human body motion directly from silhouettes extracted from monocular video sequences. No detailed body shape model is needed, and realism is ensured by training on real human motion capture data. The tracker estimates 3D body pose by using Relevance Vector Machine regression to combine a learned autoregressive dynamical model with robust shape descriptors extracted automatically from image silhouettes. We studied several different combination methods, the most effective being to learn a nonlinear observation-update correction based on joint regression with respect to the predicted state and the observations. We demonstrate the method on a 54-parameter full body pose model, both quantitatively using motion capture based test sequences, and qualitatively on a test video sequence.
Analysis of composition of microbiomes: a novel method for studying microbial composition

Understanding the factors regulating our microbiota is important but requires appropriate statistical methodology. When comparing two or more populations most existing approaches either discount the underlying compositional structure in the microbiome data or use probability models such as the multinomial and Dirichlet-multinomial distributions, which may impose a correlation structure not suitable for microbiome data.



To develop a methodology that accounts for compositional constraints to reduce false discoveries in detecting differentially abundant taxa at an ecosystem level, while maintaining high statistical power.



We introduced a novel statistical framework called analysis of composition of microbiomes (ANCOM). ANCOM accounts for the underlying structure in the data and can be used for comparing the composition of microbiomes in two or more populations. ANCOM makes no distributional assumptions and can be implemented in a linear model framework to adjust for covariates as well as model longitudinal data. ANCOM also scales well to compare samples involving thousands of taxa.



We compared the performance of ANCOM to the standard t-test and a recently published methodology called Zero Inflated Gaussian (ZIG) methodology (1) for drawing inferences on the mean taxa abundance in two or more populations. ANCOM controlled the false discovery rate (FDR) at the desired nominal level while also improving power, whereas the t-test and ZIG had inflated FDRs, in some instances as high as 68% for the t-test and 60% for ZIG. We illustrate the performance of ANCOM using two publicly available microbial datasets in the human gut, demonstrating its general applicability to testing hypotheses about compositional differences in microbial communities.



Accounting for compositionality using log-ratio analysis results in significantly improved inference in microbiota survey data.
Efficient Inference and Structured Learning for Semantic Role Labeling

We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-theshelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBankand FrameNet-annotated corpora.
Talisman: Commodity Realtime 3D Graphics for the PC

A new 3D graphics and multimedia hardware architecture, cod named Talisman, is described which exploits both spatial and temporal coherence to reduce the cost of high quality animatio Individually animated objects are rendered into independent image layers which are composited together at video refresh ra to create the final display. During the compositing process, a fu affine transformation is applied to the layers to allow translatio rotation, scaling and skew to be used to simulate 3D motion of objects, thus providing a multiplier on 3D rendering performan and exploiting temporal image coherence. Image compression broadly exploited for textures and image layers to reduce imag capacity and bandwidth requirements. Performance rivaling hi end 3D graphics workstations can be achieved at a cost point two to three hundred dollars.
Optimistic crash consistency

We introduce optimistic crash consistency, a new approach to crash consistency in journaling file systems. Using an array of novel techniques, we demonstrate how to build an optimistic commit protocol that correctly recovers from crashes and delivers high performance. We implement this optimistic approach within a Linux ext4 variant which we call OptFS. We introduce two new file-system primitives, osync() and dsync(), that decouple ordering of writes from their durability. We show through experiments that OptFS improves performance for many workloads, sometimes by an order of magnitude; we confirm its correctness through a series of robustness tests, showing it recovers to a consistent state after crashes. Finally, we show that osync() and dsync() are useful in atomic file system and database update scenarios, both improving performance and meeting application-level consistency demands.
GPU simulator of multilayer neural network based on multi-valued neurons

In this paper, we consider principles of design and basic fundamentals of a GPU simulator of the multilayer neural network with multi-valued neurons (MLMVN). Slowing down a learning process due to a big learning dataset and/or a big neural network needed for solving a certain problem is a potential bottleneck preventing the use of neural networks for solving some challenging problems. The same is related to deep learning. MLMVN is a feedforward complex-valued neural network, which has a number of advantages when compared to real-valued neural networks. These advantages include derivative-free learning and significantly better generalization capability. To extend applicability of MLMVN, its GPU-based software implementation shall be considered. We present basic principles of the GPU simulator of MLMVN and how matrix algebra operations are specifically employed there. It is shown that the bigger the network is, the more beneficial is its GPU implementation. It is shown that up to 32× acceleration can be achieved for the MLMVN learning process. Some applications, which could not be even considered without a GPU simulator, are also presented.
Fixed-Point CORDIC-Based QR Decomposition by Givens Rotations on FPGA

This paper presents a parallel architecture of an QR decomposition systolic array based on the Givens rotations algorithm on FPGA. The proposed architecture adopts a direct mapping by 21 fixed-point CORDIC-based process units that can compute the QR decomposition for an 4×4 real matrix. In order to achieve a comprehensive resource and performance evaluation, the computational error analysis, the resource utilized, and speed achieved on Virtex5 XC5VTX150T FPGA, are evaluated with the different precision of the intermediate word lengthes. The evaluation results show that 1) the proposed systolic array satisfies 99.9% correct 4×4 QR decomposition for the 2-13 accuracy requirement when the word length of the data path is lager than 25-bit, 2) occupies about 2, 810 (13%) slices, and achieves about 2.06 M/sec updates by running at the maximum frequency 111 MHz.
Rectangular waveguide based polarizer for mm-wave antenna measurements

A polarizer for mm-wave antenna measurements based on standard rectangular waveguide technology is presented. The polarizer features a compact design and exhibits full band coverage with an excellent return loss |S11| ≤ -20 dB and a transmission coefficient |S21| ≥ -2 dB. Furthermore, it has identical amplitude and phase responses in both the horizontal and vertical polarization states. This unique property enables accurate measurements in applications, which require dual-polarized signal acquisition such as near-field antenna or polarimetric radar cross section (RCS) measurements. Measurement results of a manually rotated prototype operating in the D-band (110-170 GHz) are shown. The design can be scaled to even higher mm-wave bands.
Subcutaneous phaeohyphomycosis due to Phialemoniopsis ocularis successfully treated by voriconazole

We report a case of subcutaneous infection in a 67 year-old Cambodian man who presented with a 5-month history of swelling of the right foot. Histopathology was compatible with phaeohyphomycosis and the hyphomycete Phialemoniopsis ocularis was identified by the means of morphological and molecular techniques. The patient responded well to a 6-month oral treatment with voriconazole alone.
Doodle Master: A Doodle Beautification System Based on Auto-encoding Generative Adversarial Networks

For those people without artistic talent, they can only draw rough or even awful doodles to express their ideas. We propose a doodle beautification system named Doodle Master, which can transfer a rough doodle to a plausible image and also keep the semantic concepts of the drawings. The Doodle Master applies the VAE/GAN model to decode and generate the beautified result from a constrained latent space. To achieve better performance for sketch data which is more like discrete distribution, a shared-weight method is proposed to improve the learnt features of the discriminator with the aid of the encoder. Furthermore, we design an interface for the user to draw with basic drawing tools and adjust the number of reconstruction times. The experiments show that the proposed Doodle Master system can successfully beautify the rough doodle or sketch in real-time.
PortAudio - an Open Source Cross Platform Audio API

This paper describes a new open-source cross-platform 'C' library for audio input and output. It is designed to simplify the porting of audio applications between various platforms, and also to simplify the development of audio programs in general by hiding the complexities of device interfacing. The API was worked out through community discussions on the music-dsp mailing list. A number of people have contributed to the development of the API and are listed on the web-site. Implementations of PortAudio for Windows MME and DirectSound, the Macintosh Sound Manager, and Unix OSS have been developed and are freely available on the web. Support for other platforms is being planned. The paper describes the use of PortAudio and discusses the issues involved in its development including the design philosophy, latency, callbacks versus blocking read/write calls, and efficiency .
Smartphones and Cognition: A Review of Research Exploring the Links between Mobile Technology Habits and Cognitive Functioning

While smartphones and related mobile technologies are recognized as flexible and powerful tools that, when used prudently, can augment human cognition, there is also a growing perception that habitual involvement with these devices may have a negative and lasting impact on users' ability to think, remember, pay attention, and regulate emotion. The present review considers an intensifying, though still limited, area of research exploring the potential cognitive impacts of smartphone-related habits, and seeks to determine in which domains of functioning there is accruing evidence of a significant relationship between smartphone technology and cognitive performance, and in which domains the scientific literature is not yet mature enough to endorse any firm conclusions. We focus our review primarily on three facets of cognition that are clearly implicated in public discourse regarding the impacts of mobile technology - attention, memory, and delay of gratification - and then consider evidence regarding the broader relationships between smartphone habits and everyday cognitive functioning. Along the way, we highlight compelling findings, discuss limitations with respect to empirical methodology and interpretation, and offer suggestions for how the field might progress toward a more coherent and robust area of scientific inquiry.
Stabilization at upright equilibrium position of a double inverted pendulum with unconstrained bat optimization

A double inverted pendulum plant has been in the domain of control researchers as an established model for studies on stability. The stability of such as a system taking the linearized plant dynamics has yielded satisfactory results by many researchers using classical control techniques. The established model that is analyzed as part of this work was tested under the influence of time delay, where the controller was fine tuned using a BAT algorithm taking into considering the fitness function of square of error. This proposed method gave results which were better when compared without time delay wherein the calculated values indicated the issues when incorporating time delay.
DigSig: Runtime Authentication of Binaries at Kernel Level

This paper presents a Linux kernel module, DigSig, which helps system administrators control Executable and Linkable Format (ELF) binary execution and library loading based on the presence of a valid digital signature. By preventing attackers from replacing libraries and sensitive, privileged system daemons with malicious code, DigSig increases the difficulty of hiding illicit activities such as access to compromised systems. DigSig provides system administrators with an efficient tool which mitigates the risk of running malicious code at run time. This tool adds extra functionality previously unavailable for the Linux operating system: kernel level RSA signature verification with caching and revocation of signatures.
Mathematical Methods in Linguistics

1. All other texts on the mathematics of language are now obsolete. Therefore, instead of going on about what a wonderful job Partee, ter Meulen, and Wall (henceforth, PMW) have done in some ways (breadth of coverage, much better presentation of formal semantics than is usual in books on mathematics of language, etc.), I will leave the lily ungilded, and focus on some points where the book under review could be made far better than it actually is. 2. Perhaps my main complaint concerns the treatment of the connections between the mathematical methods and the linguistics. This whole question is dealt with rather unevenly, and this is reflected in the very structure of the book. The major topics covered, corresponding to the book's division into parts (which are then subdivided into chapters) are set theory, logic and formal systems, algebra, "English as a formal language" (this is the heading under which compositionality, lambda-abstraction, generalized quantifiers, and intensionality are discussed), and finally formal language and automata theory. Now, the "English as a formal language" part deals with a Montague-style treatment of this language, but it does not go into contemporary syntactic analyses of English, not even ones that are mathematically precise and firmly grounded in formal language theory. Having praised the book for its detailed discussion of the uses of formal semantics in linguistics, I must damn its cavalier treatment of the uses of formal syntax. Thus, there is no mention anywhere in it of generalized phrase structure grammar or X-bar syntax or almost anything else of relevance to modern syntactic theory. Likewise, although the section on set theory deals at some length with nondenumerable sets, there is no mention of the argument of Langendoen and Postal (1984) that NLs are not denumerable. Since this is perhaps the one place in the literature where set theory and linguistics meet, one does not have to be a fan of Langendoen and Postal to see that this topic should be broached. 3. Certain important theoretical topics, usually ones at the interface of mathematics and linguistics, are presented sketchily and even misleadingly; for example, the compositionality of formal semantics, the generative power of transformational grammar, the nonregularity and noncontext freeness of NLs, and (more generally) the question of what kinds of objects one can prove things about. Let us begin with the principle of compositionality (i.e., that "the meaning of a complex expression is a function of the meanings of its parts and of the syntactic rules by which they are combined"). PMW claim that "construed broadly and vaguely
H 5 Spark : Bridging the I / O Gap between Spark and Scientific Data Formats on HPC Systems

The Spark framework has been tremendously powerful for performing Big Data analytics in distributed data centers. However, using Spark to analyze large-scale scientific data on HPC systems has several challenges. For instance, parallel file systems are shared among all computing nodes, in contrast to shared-nothing architectures. Additionally, accessing data stored in commonly used scientific data formats, such as HDF5 and netCDF, is not natively supported in Spark. Our study focuses on improving I/O performance of Spark on HPC systems when reading and writing scientific data stored in HDF5/netCDF. We select several scientific use cases to drive the design of an efficient parallel I/O API for Spark on HPC systems, called H5Spark, which optimizes I/O performance and takes into account Lustre file system striping. We evaluate the performance of H5Spark on Cori, a Cray XC40 system located at NERSC.
Text Detection and Recognition from Scene Images using MSER and CNN

Detection and recognition of text from natural images is very important for extracting information from images but is an extensively challenging task. This paper proposes an approach for detection of text area from natural scene images using Maximally Stable Extremal Regions (MSER) and recognizing the text using a self-trained Neural Network. Some preprocessing is applied to the image then MSER and canny edge is used to locate the smaller areas that may more likely contain text. The text is individually isolated as single characters by simple algorithms on the binary image and then passed through the recognition model specially designed for hazy and unaligned characters.
Adaptive software-based Feedback Acquisition: A Persona-based design

Users' feedback is vital to improve software quality and it provides developers with a rich knowledge on how software meets users' requirements in practice. Feedback informs how software should adapt, or be adapted, at runtime and what evolutionary actions to take in the next release. However, studies have noted that accommodating the different preferences of users on how feedback should be requested is a complex task and requires a careful engineering process. This calls for an adaptive feedback acquisition mechanisms to cater for such variability. In this paper, we tackle this problem by employing the concept of Persona to aid software engineers understand the various users' behaviours and improve their ability to design feedback acquisition techniques more efficiently. We create a set of personas based on a mixture of qualitative and quantitative studies and propose PAFA, a Persona-based method for Adaptive Feedback Acquisition.
Social Media Forensics For Hate Speech Opinion Mining

Social Media Hate Speech has continued to grow both locally and globally due to the increase of Online Social Media web forums like Facebook, Twitter and blogging. This has been propelled even further by smartphones and mobile data penetration locally. Global and Local terrorism has posed a vital question for technologists to investigate, prosecute, predict and prevent Social Media Hate Speech. This study provides a social media digital forensics tool through the design, development and implementation of a software application. The study will develop an application using Linux Apache MySQL PHP and Python. The application will use Scrapy Python page ranking algorithm to perform web crawling and the data will be placed in a MySQL database for data mining. The application used Agile Software development methodology with twenty websites being the subject of interest. The websites will be the sample size to demonstrate how the application
Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.
The Relationship Between Creatine and Whey Protein Supplements Consumption and Anesthesia in Rats.

Because the trend of pharmacotherapy is toward controlling diet rather than administration of drugs, in our study we examined the probable relationship between Creatine (Cr) or Whey (Wh) consumption and anesthesia (analgesia effect of ketamine). Creatine and Wh are among the most favorable supplements in the market. Whey is a protein, which is extracted from milk and is a rich source of amino acids. Creatine is an amino acid derivative that can change to ATP in the body. Both of these supplements result in Nitric Oxide (NO) retention, which is believed to be effective in N-Methyl-D-aspartate (NMDA) receptor analgesia.



The main question of this study was whether Wh and Cr are effective on analgesic and anesthetic characteristics of ketamine and whether this is related to NO retention or amino acids' features.



We divided 30 male Wistar rats to three (n = 10) groups; including Cr, Wh and sham (water only) groups. Each group was administered (by gavage) the supplements for an intermediate dosage during 25 days. After this period, they became anesthetized using a Ketamine-Xylazine (KX) and their time to anesthesia and analgesia, and total sleep time were recorded.



Data were analyzed twice using the SPSS 18 software with Analysis of Variance (ANOVA) and post hoc test; first time we expunged the rats that didn't become anesthetized and the second time we included all of the samples. There was a significant P-value (P < 0.05) for total anesthesia time in the second analysis. Bonferroni multiple comparison indicated that the difference was between Cr and Sham groups (P < 0.021).



The data only indicated that there might be a significant relationship between Cr consumption and total sleep time. Further studies, with rats of different gender and different dosage of supplement and anesthetics are suggested.