nils-herrmann/clean_scirepeval huggingface dataset:
Wideband planar monopole antenna for LTE, GSM, Bluetooth, WiMAX, DCS, PCS, and GPS mobile terminals

A simple planar antenna of high gain for long term evolution (LTE), global system for mobile communications (GSM), Bluetooth, worldwide interoperability for microwave access (WiMAX), digital cellular services (DCS), personal communication services (PCS), wireless local area network (WLAN), and global positioning system (GPS) mobile terminals is presented. The antenna has wide -6 dB reflection coefficient bandwidth of 2.17 GHz (1.33 - 3.5 GHz). The antenna has a compact size of 44.9 mm ×35.5 mm × 0.87 mm placed one side of the substrate of dimension 35.5 mm × 75.5 mm. The antenna has high gain of 3.85 dBi at 2.7 GHz, maximum directivity is 5.04 dBi at 2.7 GHz and total efficiency of the antenna varies between 61% and 95% within the wide impedance bandwidth. In addition, the antenna gain and efficiency is almost constant in individual operating band of operation. Dimensions of the antenna are optimized for wide impedance band. Antenna radiation patterns are presented at 1.5, 2, 2.5, and 3 GHz.
Communities from seed sets

Expanding a seed set into a larger community is a common procedure in link-based analysis. We show how to adapt recent results from theoretical computer science to expand a seed set into a community with small conductance and a strong relationship to the seed, while examining only a small neighborhood of the entire graph. We extend existing results to give theoretical guarantees that apply to a variety of seed sets from specified communities. We also describe simple and flexible heuristics for applying these methods in practice, and present early experiments showing that these methods compare favorably with existing approaches.
Investigating the Agility Bias in DNS Graph Mining

The concept of agile domain name system (DNS) refers to dynamic and rapidly changing mappings between domain names and their Internet protocol (IP) addresses. This empirical paper evaluates the bias from this kind of agility for DNS-based graph theoretical data mining applications. By building on two conventional metrics for observing malicious DNS agility, the agility bias is observed by comparing bipartite DNS graphs to different subgraphs from which vertices and edges are removed according to two criteria. According to an empirical experiment with two longitudinal DNS datasets, irrespective of the criterion, the agility bias is observed to be severe particularly regarding the effect of outlying domains hosted and delivered via content delivery networks and cloud computing services. With these observations, the paper contributes to the research domains of cyber security and DNS mining. In a larger context of applied graph mining, the paper further elaborates the practical concerns related to the learning of large and dynamic bipartite graphs.
A Spread Spectrum Clock Generator for DisplayPort Main Link

This brief presents a spread spectrum clock generator (SSCG) with a process variation compensator for DisplayPort main link. The process variation compensator reduces the error of spread ratio and guarantees reliable operation of an SSCG. The test chip has been implemented in 0.18-μm complementary metal-oxide-semiconductor process. The SSCG supports 10-phase 270- and 162-MHz clocks. The phase noise of an output clock at 270 MHz without spread spectrum clocking is -97.7 and -120.4 dBc/Hz at 1- and 10-MHz offset, respectively. The peak reduction is 8.75 dBm, and the spread ratio of 5000 ppm is achieved with a process variation compensator.
Ensembling neural networks: Many could be better than all

Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its component neural networks is analyzed from the context of both regression and classification, which reveals that it may be better to ensemble many instead of all of the neural networks at hand. This result is interesting because at present, most approaches ensemble all the available neural networks for prediction. Then, in order to show that the appropriate neural networks for composing an ensemble can be effectively selected from a set of available neural networks, an approach named GASEN is presented. GASEN trains a number of neural networks at first. Then it assigns random weights to those networks and employs genetic algorithm to evolve the weights so that they can characterize to some extent the fitness of the neural networks in constituting an ensemble. Finally it selects some neural networks based on the evolved weights to make up the ensemble. A large empirical study shows that, comparing with some popular ensemble approaches such as Bagging and Boosting, GASEN can generate neural network ensembles with far smaller sizes but stronger generalization ability. Furthermore, in order to understand the working mechanism of GASEN, the bias-variance decomposition of the error is provided in this paper, which shows that the success of GASEN may lie in that it can significantly reduce the bias as well as the variance.
Preserved orthographic length and transitional probabilities in written spelling in a case of acquired dysgraphia

We present the performance of a patient with acquired dysgraphia, DS, who has intact oral spelling (100% correct) but severely impaired written spelling (7% correct). Her errors consisted entirely of well-formed letter substitutions. This striking dissociation is further characterized by consistent preservation of orthographic, as opposed to phonological, length in her written output. This pattern of performance indicates that DS has intact graphemic representations, and that her errors are due to a deficit in letter shape assignment. We further interpret the occurrence of a small percentage of lexical errors in her written responses and a significant effect of letter frequencies and transitional probabilities on the pattern of letter substitutions as the result of a repair mechanism that locally constrains DS' written output.
An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints

Recently, there emerged revived interests of designing automatic programs (e.g., using genetic/evolutionary algorithms) to optimize the structure of Convolutional Neural Networks (CNNs)  for a specific task. The challenge in designing such programs lies in how to balance between large search space of the network structures and high computational costs. Existing works either impose strong restrictions on the search space or use enormous computing resources. In this paper, we study how to design a genetic programming approach for optimizing the structure of a CNN for a given task under limited computational resources yet without imposing strong restrictions on the search space. To reduce the computational costs, we propose two general strategies that are observed to be helpful: (i) aggressively selecting strongest individuals for survival and reproduction, and killing weaker individuals at a very early age; (ii) increasing mutation frequency to encourage diversity and faster evolution. The combined strategy with additional optimization techniques allows us to explore a large search space but with affordable computational costs. Our results on standard benchmark datasets (MNIST , SVHN , CIFAR-10 , CIFAR-100 ) are competitive to similar approaches with significantly reduced computational costs.
Pathologic features of fatal shark attacks.

To examine the pattern of injuries in cases of fatal shark attack in South Australian waters, the authors examined the files of their institution for all cases of shark attack in which full autopsies had been performed over the past 25 years, from 1974 to 1998. Of the seven deaths attributed to shark attack during this period, full autopsies were performed in only two cases. In the remaining five cases, bodies either had not been found or were incomplete. Case 1 was a 27-year-old male surfer who had been attacked by a shark. At autopsy, the main areas of injury involved the right thigh, which displayed characteristic teeth marks, extensive soft tissue damage, and incision of the femoral artery. There were also incised wounds of the right wrist. Bony injury was minimal, and no shark teeth were recovered. Case 2 was a 26-year-old male diver who had been attacked by a shark. At autopsy, the main areas of injury involved the left thigh and lower leg, which displayed characteristic teeth marks, extensive soft tissue damage, and incised wounds of the femoral artery and vein. There was also soft tissue trauma to the left wrist, with transection of the radial artery and vein. Bony injury was minimal, and no shark teeth were recovered. In both cases, death resulted from exsanguination following a similar pattern of soft tissue and vascular damage to a leg and arm. This type of injury is in keeping with predator attack from underneath or behind, with the most severe injuries involving one leg. Less severe injuries to the arms may have occurred during the ensuing struggle. Reconstruction of the damaged limb in case 2 by sewing together skin, soft tissue, and muscle bundles not only revealed that no soft tissue was missing but also gave a clearer picture of the pattern of teeth marks, direction of the attack, and species of predator.
Endocannabinoids and stress.

Endogenous cannabinoids play an important role in the physiology and behavioral expression of stress responses. Activation of the hypothalamic-pituitary-adrenal (HPA) axis, including the release of glucocorticoids, is the fundamental hormonal response to stress. Endocannabinoid (eCB) signaling serves to maintain HPA-axis homeostasis, by buffering basal activity as well as by mediating glucocorticoid fast feedback mechanisms. Following chronic stressor exposure, eCBs are also involved in physiological and behavioral habituation processes. Behavioral consequences of stress include fear and stress-induced anxiety as well as memory formation in the context of stress, involving contextual fear conditioning and inhibitory avoidance learning. Chronic stress can also lead to depression-like symptoms. Prominent in these behavioral stress responses is the interaction between eCBs and the HPA-axis. Future directions may differentiate among eCB signaling within various brain structures/neuronal subpopulations as well as between the distinct roles of the endogenous cannabinoid ligands. Investigation into the role of the eCB system in allostatic states and recovery processes may give insight into possible therapeutic manipulations of the system in treating chronic stress-related conditions in humans.
Measuring classifier performance: a coherent alternative to the area under the ROC curve

The area under the ROC curve (AUC) is a very widely used measure of performance for classification and diagnostic rules. It has the appealing property of being objective, requiring no subjective input from the user. On the other hand, the AUC has disadvantages, some of which are well known. For example, the AUC can give potentially misleading results if ROC curves cross. However, the AUC also has a much more serious deficiency, and one which appears not to have been previously recognised. This is that it is fundamentally incoherent in terms of misclassification costs: the AUC uses different misclassification cost distributions for different classifiers. This means that using the AUC is equivalent to using different metrics to evaluate different classification rules. It is equivalent to saying that, using one classifier, misclassifying a class 1 point is p times as serious as misclassifying a class 0 point, but, using another classifier, misclassifying a class 1 point is P times as serious, where p≠P. This is nonsensical because the relative severities of different kinds of misclassifications of individual points is a property of the problem, not the classifiers which happen to have been chosen. This property is explored in detail, and a simple valid alternative to the AUC is proposed.
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction

We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to directly report a global set of distinguishable dimensions to assist with further data exploration and hypothesis generation. MGM extracts distinguishing features on real-world datasets of animal features, recipes ingredients, and disease co-occurrence. It also maintains or improves performance when compared to related approaches. We perform a user study with domain experts to show the MGM’s ability to help with dataset exploration.
On the Complexity of Some Enumeration Problems for Matroids

We present an incremental polynomial-time algorithm for enumerating all circuits of a matroid or, more generally, all minimal spanning sets for a flat. We also show the NP-hardness of several related enumeration problems. †RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway NJ 08854-8003; ({boros,elbassio,gurvich}@rutcor.rutgers.edu). ‡Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway NJ 08854-8003; (leonid@cs.rutgers.edu). ∗This research was supported in part by the National Science Foundation Grant IIS0118635. The research of the first and third authors was also supported in part by the Office of Naval Research Grant N00014-92-J-1375. The second and third authors are also grateful for the partial support by DIMACS, the National Science Foundation’s Center for Discrete Mathematics and Theoretical Computer Science.
Beyond View Transformation: Cycle-Consistent Global and Partial Perception Gan for View-Invariant Gait Recognition

Cross-view gait recognition is a challenging problem when view-interval and pose variation are relatively large. In this paper, we propose Cycle-consistent Attentive Generative Adversarial Networks (CA-GAN) to map different views' gait images to view-consistent and photorealistic gait images for cross-view gait recognition. In CA-GAN, the generative network is composed of two branches, which simultaneously perceives human's global contexts and local body parts information respectively. Moreover, we design a novel Attentive Adversarial Network (AAN) to adaptively learn different weights for the discriminator's receptive fields with attention mechanism. Furthermore, as it is hard to collect the pose-aligned gait image pairs from different views for training CA-GAN’ we combine forward cycle-consistency loss and adver-sarial loss to learn the transformation relationship from source views to target view. The combined loss function can also preserve the discriminative gait structures of different identities at the training stage. Finally, we directly exploit the synthesized view-consistent gait images for cross-view gait recognition task. Experimental results on CASIA-B demonstrate that our method not only outperforms the state-of-the-art methods in cross-view gait recognition, but also presents compelling perceptual results even across the large view-interval.
Towards quantum chemistry on a quantum computer.

Exact first-principles calculations of molecular properties are currently intractable because their computational cost grows exponentially with both the number of atoms and basis set size. A solution is to move to a radically different model of computing by building a quantum computer, which is a device that uses quantum systems themselves to store and process data. Here we report the application of the latest photonic quantum computer technology to calculate properties of the smallest molecular system: the hydrogen molecule in a minimal basis. We calculate the complete energy spectrum to 20 bits of precision and discuss how the technique can be expanded to solve large-scale chemical problems that lie beyond the reach of modern supercomputers. These results represent an early practical step toward a powerful tool with a broad range of quantum-chemical applications.
Early detection: The power and the promise of DNA methylation markers

The past few years have seen an explosion of interest in the epigenetics of cancer. This has been a consequence of both the exciting coalescence of the chromatin and DNA methylation fields, and the realization that DNA methylation changes are involved in human malignancies. The ubiquity of DNA methylation changes has opened the way to a host of innovative diagnostic and therapeutic strategies. Recent advances attest to the great promise of DNA methylation markers as powerful future tools in the clinic.
Document-to-Sentence Level Technique for Novelty Detection

Novelty identification is accustomed to distinguishing novel data from an approaching stream of documents. In this study, we proposed a novel methodology for document-level novelty identification by utilizing document-to-sentence-level strategy. This work first splits a document into sentences, decides the novelty of every sentence, then registers the record-level novelty score in view of an altered limit. Exploratory results on an arrangement of document demonstrate that our methodology beats standard document-level novelty discovery as far as repetition exactness and excess review. This work applies on the document-level information from an arrangement of documents. It is valuable in identifying novel data in information with a high rate of new documents. It has been effectively incorporated in a true novelty identification framework in the zone of information retrieval.
Mobile Human Airbag System for Fall Protection Using MEMS Sensors and Embedded SVM Classifier

This paper introduces a mobile human airbag system designed for fall protection for the elderly. A Micro Inertial Measurement Unit ( muIMU) of 56 mm times 23 mm times 15 mm in size is built. This unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU). It records human motion information, and, through the analysis of falls using a high-speed camera, a lateral fall can be determined by gyro threshold. A human motion database that includes falls and other normal motions (walking, running, etc.) is set up. Using a support vector machine (SVM) training process, we can classify falls and other normal motions successfully with a SVM filter. Based on the SVM filter, an embedded digital signal processing (DSP) system is developed for real-time fall detection. In addition, a smart mechanical airbag deployment system is finalized. The response time for the mechanical trigger is 0.133 s, which allows enough time for compressed air to be released before a person falls to the ground. The integrated system is tested and the feasibility of the airbag system for real-time fall protection is demonstrated.
A 10GHz CMOS RX frontend with spatial cancellation of co-channel interferers for MIMO/digital beamforming arrays

An architecture for low-noise spatial cancellation of co-channel interferer (CCI) at RF in a digital beamforming (DBF)/MIMO receiver (RX) array is presented. The proposed RF cancellation can attenuate CCI prior to the ADC in a DBF/MIMO RX array while preserving a field-of-view (FoV) in each array element, enabling subsequent DSP for multi-beamforming. A novel hybrid-coupler/polyphase-filter based input coupling scheme that simplifies spatial selection of CCI and enables low-noise cancellation is described. A 4-element 10GHz prototype is implemented in 65nm CMOS that achieves >20dB spatial cancellation of CCI while adding <;1.5dB output noise.
Mutational heterogeneity in cancer and the search for new cancer genes

Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour–normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false-positive findings that overshadow true driver events. We show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumour–normal pairs and discover extraordinary variation in mutation frequency and spectrum within cancer types, which sheds light on mutational processes and disease aetiology, and in mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and enable the identification of genes truly associated with cancer.
Guided Music Synthesis with Variable Markov Oracle

In this work the problem of guided improvisation is approached and elaborated; then a new method, Variable Markov Oracle, for guided music synthesis is proposed as the first step to tackle the guided improvisation problem. Variable Markov Oracle is based on previous results from Audio Oracle, which is a fast indexing and recombination method of repeating sub-clips in an audio signal. The newly proposed Variable Markov Oracle is capable of identifying inherent datapoint clusters in an audio signal while tracking the sequential relations among clusters at the same time. With a target audio signal indexed by Variable Markov Oracle, a query-matching algorithm is devised to synthesize new music materials by recombination of the target audio matched to a query audio. This approach makes the query-matching algorithm a solution to the guided music synthesis problem. The query-matching algorithm is efficient and intelligent since it follows the inherent clusters discovered by Variable Markov Oracle, creating a query-by-content result which allows numerous applications in concatenative synthesis, machine improvisation and interactive music system. Examples of using Variable Markov Oracle to synthesize new musical materials based on given music signals in the style of
Water from (waste)water--the dependable water resource.

Water reclamation and reuse provides a unique and viable opportunity to augment traditional water supplies. As a multi-disciplined and important element of water resources development and management, water reuse can help to close the loop between water supply and wastewater disposal. Effective water reuse requires integration of water and reclaimed water supply functions. The successful development of this dependable water resource depends upon close examination and synthesis of elements from infrastructure and facilities planning, wastewater treatment plant siting, treatment process reliability, economic and financial analyses, and water utility management. In this paper, fundamental concepts of water reuse are discussed including definitions, historical developments, the role of water recycling in the hydrologic cycle, categories of water reuse, water quality criteria and regulatory requirements, and technological innovations for the safe use of reclaimed water. The paper emphasizes the integration of this alternative water supply into water resources planning, and the emergence of modern water reclamation and reuse practices from wastewater to reclaimed water to repurified water.