nils-herrmann/clean_scirepeval huggingface dataset:
Systems biology 101—what you need to know

Systems biology has spurred interest in thousands of researchers, some just starting their careers, others well established but interested in learning more about it. What is the best plan for scientists and students interested in a career in systems biology? Why the excitement? The use of systematic genomic, proteomic and metabolomic technologies to construct models of complex biological systems and diseases is becoming increasingly commonplace. These endeavors, collectively known as systems biology 1,2 , establish an approach by which to interrogate and iteratively refine our knowledge of the cell. In so doing, systems biology integrates knowledge from diverse biological components and data into models of the system as a whole. Although the notion of systems science has existed for some time 3 , these approaches have recently become far more powerful because of a host of new experimental technologies that are high-throughput, quantitative and large-scale 4. As evidence of the impact 'systems' thinking has had on biology , consider the explosive growth of new research institutes, companies, conferences and academic departments that have the words 'systems biology' in the title or mission statement. Several journals are now either entirely devoted to reporting systems biology research or are sponsoring regular sections devoted to current issues in systems or computational biology, such as this inaugural section in  And under the leadership of Elias Zerhouni, the National Institutes of Health (Bethesda, MD, USA) has released a new 'roadmap' that includes interdisciplinary science and inte-grative systems biology as core focus areas 5 ; the UK's Biotechnology and Biological Sciences Research Council has also targeted predictive and integrated biology as a strategic aim over the next five years 6. Where to start Because of the need to couple computational analysis techniques with systematic biological experimentation, more and more universities are offering PhD programs that integrate both computational and biological subject matter (Table 1). Several of these programs, such as those recently initiated by 'systems biology' directly in the name. Others offer courses of study from within physics, engineering or biology departments (e.g., the systems biology syllabus within the Apart from PhD programs with course offerings in systems biology, a number of institutions offer intensive short courses (Table 2). These include the Institute for Systems Biology (Seattle, WA, USA), Oxford University (Oxford, UK) and Biocentrum Amsterdam (Holland). There are also several other emerging initiatives and educational programs around the globe (Table 3). Given the …
A comparative study of elasticsearch and CouchDB document oriented databases

With the advent of large complex datasets, NOSQL databases have gained immense popularity for their efficiency to handle such datasets in comparison to relational databases. There are a number of NOSQL data stores for e.g. Mongo DB, Apache Couch DB etc. Operations in these data stores are executed quickly. In this paper we aim to get familiar with 2 most popular NoSQL databases: Elasticsearch and Apache CouchDB. This paper also aims to analyze the performance of Elasticsearch and CouchDB on image data sets. This analysis is based on the results carried out by instantiate, read, update and delete operations on both document-oriented stores and thus justifying how CouchDB is more efficient than Elasticsearch during insertion, updation and deletion operations but during selection operation Elasticsearch performs much better than CouchDB. The implementation has been done on LINUX platform.
Compact 5.8-GHz Rectenna Using Stepped-Impedance Dipole Antenna

This letter describes a compact 5.8-GHz rectenna using a stepped-impedance dipole antenna. In comparison to the conventional uniform half-wavelength dipole antenna, the stepped-impedance dipole antenna shows a 23% length reduction. This stepped-impedance dipole antenna is then used to receive the microwave power for a rectenna. The rectenna shows a maximum conversion efficiency of 76% at 5.8 GHz with a load resistance of 250 Omega. Furthermore, the conversion efficiency is better than 67% from 5.725 to 5.875 GHz, which covers the entire 5.8-GHz ISM band.
Low correlation MIMO antenna for LTE 700MHz band

A key feature of upcoming 4G wireless communication standards is multiple-input-multiple-output (MIMO) technology. To make the best use of MIMO, the antenna correlation between adjacent antennas must be low (&#60; 0.5). In this context, we propose a new correlation reduction technique suitable for closely spaced antennas (distance, d &#60; λ/40). This technique reduces mutual coupling between antennas and concurrently uncorrelates the antennas' radiation characteristic by inducing negative group delay in the target frequency band. The validity of the technique is demonstrated with a USB dongle MIMO antenna designed for LTE 700MHz band. Measurement results show that the antenna correlation is reduced more than 37% using the proposed technique.
Treatment of depression with transcranial direct current stimulation (tDCS): A Review

Major Depression Disorder (MDD) is usually accompanied by alterations of cortical activity and excitability, especially in prefrontal areas. These are reflections of a dysfunction in a distributed cortico-subcortical, bihemispheric network. Therefore it is reasonable to hypothesize that altering this pathological state with techniques of brain stimulation may offer a therapeutic target. Besides repetitive transcranial magnetic stimulation, tonic stimulation with weak direct currents (tDCS) modulates cortical excitability for hours after the end of stimulation, thus, it is a promising non-invasive therapeutic option. Early studies from the 1960s suggested some efficacy of DC stimulation to reduce symptoms in depression, but mixed results and development of psychotropic drugs resulted in an early abandonment of this technique. In the last years tDCS protocols have been optimized. Application of the newly developed stimulation protocols in patients with major depression has shown promise in few pilot studies. Further studies are needed to identify the optimal parameters of stimulation and the clinical and patient characteristics that may condition response to tDCS.
Advancements in Question Answering Systems Towards Indic Languages

Limited work has been reported for automated Question and Answering (Q&A) systems in vernacular languages like Punjabi. This was validated from this systematic study on Q&A systems. This work discusses the typical architecture and issues/challenges associated in building multiple types of Q&A systems. Future directions as well as a new metric for selection of most probable answers have been discussed based on the limitations of previous Q&A systems.
Morphological Smoothing and Extrapolation of Word Embeddings

Languages with rich inflectional morphology exhibit lexical data sparsity, since the word used to express a given concept will vary with the syntactic context. For instance, each count noun in Czech has 12 forms (where English uses only singular and plural). Even in large corpora, we are unlikely to observe all inflections of a given lemma. This reduces the vocabulary coverage of methods that induce continuous representations for words from distributional corpus information. We solve this problem by exploiting existing morphological resources that can enumerate a word’s component morphemes. We present a latentvariable Gaussian graphical model that allows us to extrapolate continuous representations for words not observed in the training corpus, as well as smoothing the representations provided for the observed words. The latent variables represent embeddings of morphemes, which combine to create embeddings of words. Over several languages and training sizes, our model improves the embeddings for words, when evaluated on an analogy task, skip-gram predictive accuracy, and word similarity.
A Generalized Delayed Signal Cancellation Method for Detecting Fundamental-Frequency Positive-Sequence Three-Phase Signals

A novel scheme for obtaining the fundamental-frequency positive-sequence grid voltage vector based on a generalization of the delayed signal cancellation method is proposed in this paper. The technique is implemented by sampling and storing the instantaneous αβ voltage vector. A mathematical transformation is then proposed through which the current and delayed voltage vectors are combined. It is shown that the proposed transformation has unity gain for the fundamental-frequency positive-sequence voltage vector, while its gain is equal to zero for some chosen components. Cascaded transformations can then be used for eliminating the fundamental-frequency negative-sequence vector, as well as chosen positive- and negative-sequence harmonic vector components and, thus, for accurately obtaining the fundamental-frequency positive-sequence voltage vector. The output of the last transformation block is input to a synchronous reference frame phase-locked loop for detecting frequency and position of the positive-sequence vector. A proposal for making the scheme frequency adaptive is also presented. The good performance of the proposed method is verified with simulations and experiments by using distorted and unbalanced signals, containing fundamental-frequency as well as positive- and negative-sequence harmonic components. The proposed method frequency adaptation capability is also verified.
ANALYTIC PROBLEMS FOR ELLIPTIC CURVES

We consider some problems of analytic number theory for elliptic curves which can be considered as analogues of classical questions around the distribution of primes in arithmetic progressions to large moduli, and to the question of twin primes. This leads to some local results on the distribution of the group structures of elliptic curves defined over a prime finite field, exhibiting an interesting dichotomy for the occurence of the possible groups.
Fraud Detection From Taxis' Driving Behaviors

Taxi is a major transportation in the urban area, offering great benefits and convenience to our daily life. However, one of the major business fraud in taxis is the charging fraud, specifically overcharging for the actual distance. In practice, it is hard for us to always monitor taxis and detect such fraud. Due to the Global Positioning System (GPS) embedded in taxis, we can collect the GPS reports from the taxis' locations, and thus, it is possible for us to retrieve their traces. Intuitively, we can utilize such information to construct taxis' trajectories, compute the actual service distance on the city map, and detect fraudulent behaviors. However, in practice, due to the extremely limited reports, notable location errors, complex city map, and road networks, our task to detect taxi fraud faces significant challenges, and the previous methods cannot work well. In this paper, we have a critical and interesting observation that fraudulent taxis always play a secret trick, i.e., modifying the taximeter to a smaller scale. As a result, it not only makes the service distance larger but also makes the reported taxi speed larger. Fortunately, the speed information collected from the GPS reports is accurate. Hence, we utilize the speed information to design a system, which is called the Speed-based Fraud Detection System (SFDS), to model taxi behaviors and detect taxi fraud. Our method is robust to the location errors and independent of the map information and road networks. At the same time, the experiments on real-life data sets confirm that our method has better accuracy, scalability, and more efficient computation, compared with the previous related methods. Finally, interesting findings of our work and discussions on potential issues are provided in this paper for future city transportation and human behavior research.
Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders.

This study was designed to determine the effectiveness of a group stress reduction program based on mindfulness meditation for patients with anxiety disorders.



The 22 study participants were screened with a structured clinical interview and found to meet the DSM-III-R criteria for generalized anxiety disorder or panic disorder with or without agoraphobia. Assessments, including self-ratings and therapists' ratings, were obtained weekly before and during the meditation-based stress reduction and relaxation program and monthly during the 3-month follow-up period.



Repeated measures analyses of variance documented significant reductions in anxiety and depression scores after treatment for 20 of the subjects--changes that were maintained at follow-up. The number of subjects experiencing panic symptoms was also substantially reduced. A comparison of the study subjects with a group of nonstudy participants in the program who met the initial screening criteria for entry into the study showed that both groups achieved similar reductions in anxiety scores on the SCL-90-R and on the Medical Symptom Checklist, suggesting generalizability of the study findings.



A group mindfulness meditation training program can effectively reduce symptoms of anxiety and panic and can help maintain these reductions in patients with generalized anxiety disorder, panic disorder, or panic disorder with agoraphobia.
A case against convexity in conceptual spaces

The notion of conceptual space, proposed by Gärdenfors as a framework for the representation of concepts and knowledge, has been highly influential over the last decade or so. One of the main theses involved in this approach is that the conceptual regions associated with properties, concepts, verbs, etc. are convex. The aim of this paper is to show that such a constraint—that of the convexity of the geometry of conceptual regions—is problematic; both from a theoretical perspective and with regard to the inner workings of the theory itself. On the one hand, all the arguments provided in favor of the convexity of conceptual regions rest on controversial assumptions. Additionally, his argument in support of a Euclidean metric, based on the integral character of conceptual dimensions, is weak, and under non-Euclidean metrics the structure of regions may be non-convex. Furthermore, even if the metric were Euclidean, the convexity constraint could be not satisfied if concepts were differentially weighted. On the other hand, Gärdenfors’ convexity constraint is brought into question by the own inner workings of conceptual spaces because: (i) some of the allegedly convex properties of concepts are not convex; (ii) the conceptual regions resulting from the combination of convex properties can be non-convex; (iii) convex regions may co-vary in non-convex ways; and (iv) his definition of verbs is incompatible with a definition of properties in terms of convex regions. Therefore, the mandatory character of the convexity requirement for regions in a conceptual space theory should be reconsidered.
Impedance control of VToL UAVs with a momentum-based external generalized forces estimator

An estimator of external generalized forces (force plus moments) acting on aerial platforms, and based on the momentum of the mechanical system, is proposed for the control of VToL UAVs together with a hierarchical architecture separating the translational and rotational dynamics of the vehicle. The closed-loop system equations are shaped as mechanical impedances, programmable through the controller gains, and forced by the residuals given by the estimation error. This arrangement allows the VToL UAVs to perform hovering and tracking tasks without a precise knowledge of the vehicle dynamics and in presence of external disturbances and unmodeled aerodynamic effects. Experiments are presented to evaluate the performance of the proposed control design.
Contrastive Learning Using Spectral Methods

In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another. For example, given a background corpus of news articles together with writings of a particular author, one may want a topic model that explains word patterns and themes specific to the author. Another example comes from genomics, in which biological signals may be collected from different regions of a genome, and one wants a model that captures the differential statistics observed in these regions. This paper formalizes this notion of contrastive learning for mixture models, and develops spectral algorithms for inferring mixture components specific to a foreground data set when contrasted with a background data set. The method builds on recent moment-based estimators and tensor decompositions for latent variable models, and has the intuitive feature of using background data statistics to appropriately modify moments estimated from foreground data. A key advantage of the method is that the background data need only be coarsely modeled, which is important when the background is too complex, noisy, or not of interest. The method is demonstrated on applications in contrastive topic modeling and genomic sequence analysis.
Barriers to Providing Physical Education and Physical Activity in Victorian State Secondary Schools

An on-line questionnaire was completed by 115 physical education teachers to establish the barriers to their implementation of physical education in Victorian state secondary schools. In addition, the barriers perceived by teachers to impact on students’ participation in school-based physical education and physical activity were examined. The barriers to the provision of physical education were found to be largely institutional, although two-thirds of respondents recognised their own difficulties in engaging students when teaching as potential obstacles to student participation. Students were also perceived to be influenced by their own (45 per cent) and their peers (62 per cent) low levels of interest when choosing to participate. An awareness of these barriers has implications for physical education teaching, curriculum design, teacher training and adolescent participation in the school environment.
Adaptive Fraud Detection

One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.
Boilerplate detection using shallow text features

In addition to the actual content Web pages consist of navigational elements, templates, and advertisements. This boilerplate text typically is not related to the main content, may deteriorate search precision and thus needs to be detected properly. In this paper, we analyze a small set of shallow text features for classifying the individual text elements in a Web page. We compare the approach to complex, state-of-the-art techniques and show that competitive accuracy can be achieved, at almost no cost. Moreover, we derive a simple and plausible stochastic model for describing the boilerplate creation process. With the help of our model, we also quantify the impact of boilerplate removal to retrieval performance and show significant improvements over the baseline. Finally, we extend the principled approach by straight-forward heuristics, achieving a remarkable detection accuracy.
An efficient active ripple filter for use in DC-DC conversion

When low ripple is required from a switched mode dc-dc converter, dissipative active filters offer an alternative to passive LC and coupled inductor filters. Analytical and experimental results are presented for a simple active ripple filter. The filter employs a pair of current transformers as sensors for feedforward and feedback control and two metal-oxide-semiconductor field-effect transistors (MOSFETs) as cancellation current drivers. Measurements demonstrate good ripple attenuation up to 5 MHz, more than 70 dB being obtained at 100 kHz, the switching frequency of a test converter. The overall efficiency was measured as 95%, with room for further improvement. The filter is suitable for input and output smoothing in dc-dc converters for aerospace and other critical applications.
A Fast Hybrid k-Means Level Set Algorithm For Segmentation

In this paper, we first draw a connection between a level set algorithm and k-Means plus nonlinear diffusion preprocessing. Then, we exploit this link to develop a new hybrid numerical technique for segmentation that draws on the speed and simplicity of k-Means procedures, and the robustness of level set algorithms. The proposed method retains spatial coherence on initial data characteristic of curve evolution techniques, as well as the balance between a pixel/voxel’s proximity to the curve and its intention to cross over the curve from the underlying energy. However, it is orders of magnitude faster than standard curve evolutions. Moreover, it does not suffer from the limitations of k-Means due to inaccurate local minima and allows for segmentation results ranging from k-Means clustering type partitioning to level set partitions.
Massively-Parallel Lossless Data Decompression

Today's exponentially increasing data volumes and the high cost of storage make compression essential for the Big Data industry. Although research has concentrated on efficient compression, fast decompression is critical for analytics queries that repeatedly read compressed data. While decompression can be parallelized somewhat by assigning each data block to a different process, break-through speed-ups require exploiting the massive parallelism of modern multi-core processors and GPUs for data decompression within a block. We propose two new techniques to increase the degree of parallelism during decompression. The first technique exploits the massive parallelism of GPU and SIMD architectures. The second sacrifices some compression efficiency to eliminate data dependencies that limit parallelism during decompression. We evaluate these techniques on the decompressor of the DEFLATE scheme, called Inflate, which is based on LZ77 compression and Huffman encoding. We achieve a 2× speed-up in a head-to-head comparison with several multi core CPU-based libraries, while achieving a 17% energy saving with comparable compression ratios.
Optimization of average and cogging torque in 3-phase IPM motor drives

In this paper, an interior permanent magnet (IPM) brushless DC motor for traction applications is analyzed. The effect of magnetization direction, number of stator slots and current waveform on torque pulsation are examined. A three-phase, four-pole IPM motor is considered for the study. The finite element method is used to calculate the torque, reluctance torque, back iron flux density, tooth flux density, detent torque and back-EMF of the motor. It is shown that because of the reluctance torque resulting from rotor saliency, the peak point in the torque-angle curve is shifted to the left. Therefore, it is not possible to find the switching instants just by considering the direction of stator and rotor MMF and keeping the right angle between them. A Matlab program has been developed to rind the switching intervals, which will produce the maximum average torque and minimum cogging torque. Experimental results to support the simulation findings are included in the paper.