nils-herrmann/clean_scirepeval huggingface dataset:
Space-alternating generalized expectation-maximization algorithm

The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all parameters simultaneously, which has two drawbacks: 1) slow convergence, and 2) difficult maximization steps due to coupling when smoothness penalties are used. This paper describes the space-alternating generalized EM (SAGE) method, which updates the parameters sequentially by alternating between several small hidden-data spaces defined by the algorithm designer. We prove that the sequence of estimates monotonically increases the penalized-likelihood objective, we derive asymptotic convergence rates, and we provide sufficient conditions for monotone convergence in norm. Two signal processing applications illustrate the method: estimation of superimposed signals in Gaussian noise, and image reconstruction from Poisson measurements. In both applications, our SAGE algorithms easily accommodate smoothness penalties and converge faster than the EM algorithms.
Success Factors for Agile Software Development - A Case Study from Sri Lanka

Agile software development methods provide great flexibility to adapt to changing requirements and rapidly market products. Sri Lankan software organizations too are embracing these methods to develop software products. Being an iterative an incremental software engineering methodology, agile philosophy promotes working software over comprehensive documentation and heavily relies on continuous customer collaboration throughout the life cycle of the product. Hence characteristics of the people involved with the project and their working environment plays an important role in the success of an agile project compared to any other software engineering methodology. This study investigated the factors that lead to the success of a project that adopts agile methodology in Sri Lanka. An online questionnaire was used to collect data to identify people and organizational factors that lead to project success. The sample consisted of Sri Lankan software professionals with several years of industry experience in developing projects using agile methods. According to the statistical data analysis, customer satisfaction, customer commitment, team size, corporate culture, technical competency, decision time, customer commitment and training and learning have a influence on the success of the project.
Cactus Pear : A Fruit of Nutraceutical and Functional Importance

The constantly increasing demand for nutraceuticals is paralleled by a more pronounced request for natural ingredients and health-promoting foods. The multiple functional properties of cactus pear fit well this trend. Recent data revealed the high content of some chemical constituents, which can give added value to this fruit on a nutritional and technological functionality basis. High levels of betalains, taurine, calcium, magnesium, and antioxidants are noteworthy.
Nuclear Dynamics of Heterochromatin Repair.

Repairing double-strand breaks (DSBs) is particularly challenging in pericentromeric heterochromatin, where the abundance of repeated sequences exacerbates the risk of ectopic recombination and chromosome rearrangements. Recent studies in Drosophila cells revealed that faithful homologous recombination (HR) repair of heterochromatic DSBs relies on the relocalization of DSBs to the nuclear periphery before Rad51 recruitment. We summarize here the exciting progress in understanding this pathway, including conserved responses in mammalian cells and surprising similarities with mechanisms in yeast that deal with DSBs in distinct sites that are difficult to repair, including other repeated sequences. We will also point out some of the most important open questions in the field and emerging evidence suggesting that deregulating these pathways might have dramatic consequences for human health.
Corner detection based on gradient correlation matrices of planar curves

An efficient and novel technique is developed for detecting and localizing corners of planar curves. This paper discusses the gradient feature distribution of planar curves and constructs gradient correlation matrices (GCMs) over the region of support (ROS) of these planar curves. It is shown that the eigenstructure and determinant of the GCMs encode the geometric features of these curves, such as curvature features and the dominant points. The determinant of the GCMs is shown to have a strong corner response, and is used as a ‘‘cornerness’’ measure of planar curves. A comprehensive performance evaluation of the proposed detector is performed, using the ACU and localization error criteria. Experimental results demonstrate that the GCM detector has a strong corner position response, along with a high detection rate and good localization performance. & 2009 Elsevier Ltd. All rights reserved.
Chemical wizardry ? The generation of diversity in terpenoid biosynthesis *

Terpene synthases catalyze the conversion of linear prenyl-diphosphates to a multitude of hydrocarbon skeletons with often high regioand stereoselectivity. These remarkable enzymes all rely on a shared fold for activity, namely, the class I terpene cyclase fold. Recent work has illuminated the catalytic strategy used by these enzymes to catalyze the arguably most complex chemical reactions found in  Terpene synthases catalyze the formation of a reactive carbocation and provide a template for the cyclization reactions while at the same time providing the necessary stability of the carbocationic reaction intermediates as well as strictly controlling water access.
Novel approach to separation of musical signal sources by NMF

This paper proposes a method to separate polyphonic music signal into signals of each musical instrument by NMF: Non-negative Matrix Factorization based on preservation of spectrum envelope. Sound source separation is taken as a fundamental issue in music signal processing and NMF is becoming common to solve it because of its versatility and compatibility with music signal processing. Our method bases on a common feature of harmonic signal: spectrum envelopes of musical signal in close pitches played by the harmonic music instrument would be similar. We estimate power spectrums of each instrument by NMF with restriction to synchronize spectrum envelope of bases which are allocated to all possible center frequencies of each instrument. This manipulation means separation of components which refers to tones of each instrument and realizes both of separation without pre-training and separation of signal including harmonic and non-harmonic sound. We had an experiment to decompose mixture sound signal of MIDI instruments into each instrument and evaluated the result by SNR of single MIDI instrument sound signals and separated signals. As a result, SNR of lead guitar and drums approximately marked 3.6 and 6.0 dB and showed significance of our method.
Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF

We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain stateof-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.
Talk the Walk: Navigating New York City through Grounded Dialogue

We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are described in detail, are challenging and their full solution is an open problem that we pose to the community. We (i) focus on the task of tourist localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding tourist utterances into the guide’s map, (ii) show it yields significant improvements for both emergent and natural language communication, and (iii) using this method, we establish non-trivial baselines on the full task.
Reducing peak electricity demand in building climate control using real-time pricing and model predictive control

A method to reduce peak electricity demand in building climate control by using real-time electricity pricing and applying model predictive control (MPC) is investigated. We propose to use a newly developed time-varying, hourly-based electricity tariff for end-consumers, that has been designed to truly reflect marginal costs of electricity provision, based on spot market prices as well as on electricity grid load levels, which is directly incorporated into the MPC cost function. Since this electricity tariff is only available for a limited time window into the future we use least-squares support vector machines for electricity tariff price forecasting and thus provide the MPC controller with the necessary estimated time-varying costs for the whole prediction horizon. In the given context, the hourly pricing provides an economic incentive for a building controller to react sensitively with respect to high spot market electricity prices and high grid loading, respectively. Within the proposed tariff regime, grid-friendly behaviour is rewarded. It can be shown that peak electricity demand of buildings can be significantly reduced. The here presented study is an example for the successful implementation of demand response (DR) in the field of building climate control.
Guillain-Barré syndrome and Zika virus outbreaks.

Arboviruses have been associated with central and peripheral nervous system injuries, in special the flaviviruses. Guillain-Barré syndrome (GBS), transverse myelitis, meningoencephalitis, ophthalmological manifestations, and other neurological complications have been recently associated to Zika virus (ZIKV) infection. In this review, we aim to analyze the epidemiological aspects, possible pathophysiology, and what we have learned about the clinical and laboratory findings, as well as treatment of patients with ZIKV-associated neurological complications.



In the last decades, case series have suggested a possible link between flaviviruses and development of GBS. Recently, large outbreaks of ZIKV infection in Asia and the Americas have led to an increased incidence of GBS in these territories. Rapidly, several case reports and case series have reported an increase of all clinical forms and electrophysiological patterns of GBS, also including cases with associated central nervous system involvement. Finally, cases suggestive of acute transient polyneuritis, as well as acute and progressive postinfectious neuropathies associated to ZIKV infection have been reported, questioning the usually implicated mechanisms of neuronal injury.



The recent ZIKV outbreaks have triggered the occurrence of a myriad of neurological manifestations likely associated to this arbovirosis, in special GBS and its variants.
Mathematical Modeling and Simulation of the Kidney Hemodialysis

For an organism to survive, it needs to get rid of metabolic waste materials, as well as maintain proper concentrations of various necessary materials. As these materials are metabolized or broken down, certain waste products are produced, such as carbon dioxide, water, urea and related nitrogenous compounds, salts, and various minerals. If these metabolic waste products remain in the body, they rapidly change the body’s homeostasis. To avoid this change in homeostasis, the body must get rid of (excrete) the waste products quickly and efficiently. In a healthy human being, kidney is the organ which maintains homeostasis thereby ensures the process of removal of these toxic wastes from the body. For a patient with severe loss of kidney function dialysis is the only means of preventing excessive fluid gain and accumulation of these toxic chemicals. Currently, the painstaking hemodialysis procedure is necessary for patients with kidney failure; where patients undergo frequent extensive treatments. In an effort to improve and reduce the duration of dialysis, in this work, a mathematical model of kidney hemodialysis procedure was developed in which the kidney was modeled using two-compartment model. The model was evaluated using MATLAB based on the clinical data obtained from sixteen (16) kidney patients in Aminu Kano Teaching hospital (AKTH), Kano State, Nigeria. The model was validated using the collected data by comparing goodness of fit of data collected to the model predictions. Hemodialysis adequacy was also investigated based on the results of the model to determine the best duration of dialysis. It was found that dialysis procedure twice per week with five hours per session is the optimum.
Posterior Reversible Encephalopathy Syndrome Due to Hyponatremia

Posterior reversible encephalopathy syndrome (PRES) is characterized by variable associations of seizure activity, consciousness impairment, headaches, visual abnormalities, nausea/vomiting, and focal neurological signs. The PRES may occur in diverse situations. The findings on neuroimaging in PRES are often symmetric and predominate edema in the white matter of the brain areas perfused by the posterior brain circulation, which is reversible when the underlying cause is treated. We report the case of PRES in normotensive patient with hyponatremia.
Emotion-based music recommendation by association discovery from film music

With the growth of digital music, the development of music recommendation is helpful for users. The existing recommendation approaches are based on the users' preference on music. However, sometimes, recommending music according to the emotion is needed. In this paper, we propose a novel model for emotion-based music recommendation, which is based on the association discovery from film music. We investigated the music feature extraction and modified the affinity graph for association discovery between emotions and music features. Experimental result shows that the proposed approach achieves 85% accuracy in average.
Chapter 0 Fractal Dimension Estimation Methods for Biomedical Images

The use of medical images has its main aim in the detection of potential abnormalities. This goal is accurately achieved with the synergy between the ability in recognizing unique image patterns and finding the relationship between them and possible diagnoses. One of the methods used to aid this process is the extrapolation of important features from the images called texture; texture is an important source of visual information and is a key component in image analysis.
Principles of Layered Attestation

Systems designed with measurement and attestation in mind are often layered, with the lower layers measuring the layers above them. Attestations of such systems, which we call layered attestations, must bundle together the results of a diverse set of application-specific measurements of various parts of the system. Some methods of layered at-testation are more trustworthy than others, so it is important for system designers to understand the trust consequences of different system configurations. This paper presents a formal framework for reasoning about layered attestations, and provides generic reusable principles for achieving trustworthy results.
PI3K and cancer: lessons, challenges and opportunities

The central role of phosphoinositide 3-kinase (PI3K) activation in tumour cell biology has prompted a sizeable effort to target PI3K and/or downstream kinases such as AKT and mammalian target of rapamycin (mTOR) in cancer. However, emerging clinical data show limited single-agent activity of inhibitors targeting PI3K, AKT or mTOR at tolerated doses. One exception is the response to PI3Kδ inhibitors in chronic lymphocytic leukaemia, where a combination of cell-intrinsic and -extrinsic activities drive efficacy. Here, we review key challenges and opportunities for the clinical development of inhibitors targeting the PI3K–AKT–mTOR pathway. Through a greater focus on patient selection, increased understanding of immune modulation and strategic application of rational combinations, it should be possible to realize the potential of this promising class of targeted anticancer agents.
The Intestinal Bacterial Community in the Food Waste-Reducing Larvae of Hermetia illucens

As it is known that food waste can be reduced by the larvae of Hermetia illucens (Black soldier fly, BSF), the scientific and commercial value of BSF larvae has increased recently. We hypothesised that the ability of catabolic degradation by BSF larvae might be due to intestinal microorganisms. Herein, we analysed the bacterial communities in the gut of BSF larvae by pyrosequencing of extracting intestinal metagenomic DNA from larvae that had been fed three different diets. The 16S rRNA sequencing results produced 9737, 9723 and 5985 PCR products from larval samples fed food waste, cooked rice and calf forage, respectively. A BLAST search using the EzTaxon program showed that the bacterial community in the gut of larvae fed three different diets was mainly composed of the four phyla with dissimilar proportions. Although the composition of the bacterial communities depended on the different nutrient sources, the identified bacterial strains in the gut of BSF larvae represented unique bacterial species that were unlike the intestinal microflora of other insects. Thus, our study analysed the structure of the bacterial communities in the gut of BSF larvae after three different feedings and assessed the application of particular bacteria for the efficient degradation of organic compounds.
Accessible photo album: enhancing the photo sharing experience for people with visual impairment

While a photograph is a visual artifact, studies reveal that a number of people with visual impairments are also interested in being able to share their memories and experiences with their sighted counterparts in the form of a photograph. We conducted an online survey to better understand the challenges faced by people with visual impairments in sharing and organizing photos, and reviewed existing tools and their limitations. Based on our analysis, we developed an accessible mobile application that enables a visually impaired user to capture photos along with audio recordings for the ambient sound and memo description and to browse through them eyes-free. Five visually impaired participants took part in a study in which they used our app to take photographs in naturalistic settings and to share them later with a sighted viewer. The participants were able to use our app to identify each photograph on their own during the photo sharing session, and reported high satisfaction in having been able to take the initiative during the process.
Cross-Domain Sentiment Classification by Capsule Network With Semantic Rules

Sentiment analysis is an important but challenging task. Remarkable success has been achieved on domains where sufficient labeled training data is available. Nevertheless, annotating sufficient data is labor-intensive and time-consuming, establishing significant barriers for adapting the sentiment classification systems to new domains. In this paper, we introduce a Capsule network for sentiment analysis in domain adaptation scenario with semantic rules (CapsuleDAR). CapsuleDAR exploits capsule network to encode the intrinsic spatial part-whole relationship constituting domain invariant knowledge that bridges the knowledge gap between the source and target domains. Furthermore, we also propose a rule network to incorporate the semantic rules into the capsule network to enhance the comprehensive sentence representation learning. Extensive experiments are conducted to evaluate the effectiveness of the proposed CapsuleDAR model on a real world data set of four domains. Experimental results demonstrate that CapsuleDAR achieves substantially better performance than the strong competitors for the cross-domain sentiment classification task.
Design of a dual-mode substrate integrated waveguide filter with slot line perturbation

A single-cavity dual-mode substrate integrated waveguide (SIW) bandpass filter (BPF) for X-band application is presented in this paper. Coplanar waveguide (CPW) is used as SIW-microstrip transition in this design. Two slots of the CPW with unequal lengths are used to excite two degenerate modes, i.e. TE102 and TE201. A slot line is etched on the ground plane of the SIW cavity for perturbation. Its size and position are related to the effect of mode-split, namely the coupling between the two degenerate modes. Due to the cancellation of the two modes, a transmission zero in the lower stopband of the BPF is achieved, which improves the selectivity of the proposed BPF. And the location of the transmission zero can be controlled by adjusting the position and the size of the slot line perturbation properly. By introducing source-load coupling, an additional transmission zero is produced in the upper stopband of the BPF, it enhances the stopband performance of the BPF. Influences of the slot line perturbation on the BPF have been studied. A dual-mode BPF for X-band application has been designed, fabricated and measured. A good agreement between simulation and measurement verifies the validity of this design methodology.
A force-feedback exoskeleton for upper-limb rehabilitation in virtual reality

This paper presents the design and the clinical validation of an upper-limb force-feedback exoskeleton, the L-EXOS, for robotic-assisted rehabilitation in virtual reality (VR). The L-EXOS is a five degrees of freedom exoskeleton with a wearable structure and anthropomorphic workspace that can cover the full range of motion of human arm. A specific VR application focused on the reaching task was developed and evaluated on a group of eight post-stroke patients, to assess the efficacy of the system for the rehabilitation of upper limb. The evaluation showed a significant reduction of the performance error in the reaching task (paired t-test, p < 0.02).
A Frequency Adaptive Phase Shift Modulation Control Based LLC Series Resonant Converter for Wide Input Voltage Applications

This paper presents an isolated LLC series resonant DC/DC converter with novel frequency adaptive phase shift modulation control, which suitable for wide input voltage (200–400 V) applications. The proposed topology integrates two half-bridge in series on the primary side to reduce the switching stress to half of the input voltage. Unlike the conventional converter, this control strategy increases the voltage gain range with zero-voltage-switching (ZVS) to all switches under all operating voltage and load variations. Adaptive frequency control is used to secure ZVS in the primary bridge with regards to load change. To do so, the voltage gain becomes independent of the loaded quality factor. In addition, the phase shift control is used to regulate the output voltage as constant under all possible inputs. The control of these two variables also significantly minimizes the circulating current, especially from the low-voltage side, which increases the efficiency as compared to a conventional converter. Experimental results of a 1-Kw prototype converter with 200–400-V input and 48-V output are presented to verify all theoretical analysis and characteristics.