nils-herrmann/clean_scirepeval huggingface dataset:
Wavelet Trees: From Theory to Practice

The \emph{wavelet tree} data structure is a space-efficient technique for rank and select queries that generalizes from binary characters to an arbitrary multicharacter alphabet. It has become a key tool in modern full-text indexing and data compression because of its capabilities in compressing, indexing, and searching. We present a comparative study of its practical performance regarding a wide range of options on the dimensions of different coding schemes and tree shapes. Our results are both theoretical and experimental: (1)~We show that the run-length $\delta$ coding size of wavelet trees achieves the 0-order empirical entropy size of the original string with leading constant 1, when the string's 0-order empirical entropy is asymptotically less than the logarithm of the alphabet size. This result complements the previous works that are dedicated to analyzing run-length $\gamma$-encoded wavelet trees. It also reveals the scenarios when run-length $\delta$ encoding becomes practical. (2)~We introduce a full generic package of wavelet trees for a wide range of options on the dimensions of coding schemes and tree shapes. Our experimental study reveals the practical performance of the various modifications.
The clinical spectrum of sport-related traumatic brain injury

Acute and chronic sports-related traumatic brain injuries (TBIs) are a substantial public health concern. Various types of acute TBI can occur in sport, but detection and management of cerebral concussion is of greatest importance as mismanagement of this syndrome can lead to persistent or chronic postconcussion syndrome (CPCS) or diffuse cerebral swelling. Chronic TBI encompasses a spectrum of disorders that are associated with long-term consequences of brain injury, including chronic traumatic encephalopathy (CTE), dementia pugilistica, post-traumatic parkinsonism, post-traumatic dementia and CPCS. CTE is the prototype of chronic TBI, but can only be definitively diagnosed at autopsy as no reliable biomarkers of this disorder are available. Whether CTE shares neuropathological features with CPCS is unknown. Evidence suggests that participation in contact–collision sports may increase the risk of neurodegenerative disorders such as Alzheimer disease, but the data are conflicting. In this Review, the spectrum of acute and chronic sport-related TBI is discussed, highlighting how examination of athletes involved in high-impact sports has advanced our understanding of pathology of brain injury and enabled improvements in detection and diagnosis of sport-related TBI.
Persistence-Based Clustering in Riemannian Manifolds

We present a clustering scheme that combines a mode-seeking phase with a cluster merging phase in the corresponding density map. While mode detection is done by a standard graph-based hill-climbing scheme, the novelty of our approach resides in its use of topological persistence to guide the merging of clusters. Our algorithm provides additional feedback in the form of a set of points in the plane, called a persistence diagram (PD), which provably reflects the prominences of the modes of the density. In practice, this feedback enables the user to choose relevant parameter values, so that under mild sampling conditions the algorithm will output the correct number of clusters, a notion that can be made formally sound within persistence theory. In addition, the output clusters have the property that their spatial locations are bound to the ones of the basins of attraction of the peaks of the density.
 The algorithm only requires rough estimates of the density at the data points, and knowledge of (approximate) pairwise distances between them. It is therefore applicable in any metric space. Meanwhile, its complexity remains practical: although the size of the input distance matrix may be up to quadratic in the number of data points, a careful implementation only uses a linear amount of memory and takes barely more time to run than to read through the input.
Nuclear magnetic resonance gyroscope: For DARPA's micro-technology for positioning, navigation and timing program

Information on the Nuclear Magnetic Resonance Gyro under development by Northrop Grumman Corporation is presented. The basics of Operation are summarized, a review of the completed phases is presented, and the current state of development and progress in phase 4 is discussed. Many details have been left out for the sake of brevity, but the principles are still complete.
Defining the Ethereum Virtual Machine for Interactive Theorem Provers

Smart contracts in Ethereum are executed by the Ethereum Virtual Machine (EVM). We defined EVM in Lem, a language that can be compiled for a few interactive theorem provers. We tested our definition against a standard test suite for Ethereum implementations. Using our definition, we proved some safety properties of Ethereum smart contracts in an interactive theorem prover Isabelle/HOL. To our knowledge, ours is the first formal EVM definition for smart contract verification that implements all instructions. Our definition can serve as a basis for further analysis and generation of Ethereum smart contracts.
Sanskrit Machine Translation Systems: A Comparative Analysis

Machine Translation is area of research since six decades. It is gaining popularity since last decade due to better computational facilities available at personal computer systems. This paper presents different Machine Translation system where Sanskrit is involved as source, target or key support language. Researchers employ various techniques like Rule based, Corpus based, Direct for machine translation. The main aim to focus on Sanskrit in Machine Translation in this paper is to uncover the language suitability, its morphology and employ appropriate MT techniques.
28/38GHz dual band slotted patch antenna with proximity-coupled feed for 5G communication

This paper presents a slotted-rectangular patch antenna with proximity-coupled feed operated at dual band of millimeter-wave (mmV) frequencies, 28GHz and 38GHz. The antenna was built in multilayer substrate construct by 10-layers Low temperature Co-fiber Ceramic (LTCC) with 5 mils thickness each. The slotted-patch and thick substrate are configured to enhance the bandwidth and obtain a good result in gain as well. The bandwidth using are 21.3% at 28GHz and 13.0% at 38GHz with the direction gains are 8.63dBi and 8.62dBi at 28GHz and 38GHz respectively.
Traumatic brain injury: pathophysiology for neurocritical care.

Severe cases of traumatic brain injury (TBI) require neurocritical care, the goal being to stabilize hemodynamics and systemic oxygenation to prevent secondary brain injury. It is reported that approximately 45 % of dysoxygenation episodes during critical care have both extracranial and intracranial causes, such as intracranial hypertension and brain edema. For this reason, neurocritical care is incomplete if it only focuses on prevention of increased intracranial pressure (ICP) or decreased cerebral perfusion pressure (CPP). Arterial hypotension is a major risk factor for secondary brain injury, but hypertension with a loss of autoregulation response or excess hyperventilation to reduce ICP can also result in a critical condition in the brain and is associated with a poor outcome after TBI. Moreover, brain injury itself stimulates systemic inflammation, leading to increased permeability of the blood-brain barrier, exacerbated by secondary brain injury and resulting in increased ICP. Indeed, systemic inflammatory response syndrome after TBI reflects the extent of tissue damage at onset and predicts further tissue disruption, producing a worsening clinical condition and ultimately a poor outcome. Elevation of blood catecholamine levels after severe brain damage has been reported to contribute to the regulation of the cytokine network, but this phenomenon is a systemic protective response against systemic insults. Catecholamines are directly involved in the regulation of cytokines, and elevated levels appear to influence the immune system during stress. Medical complications are the leading cause of late morbidity and mortality in many types of brain damage. Neurocritical care after severe TBI has therefore been refined to focus not only on secondary brain injury but also on systemic organ damage after excitation of sympathetic nerves following a stress reaction.
All Shook Up: Fluctuations, Maxwell's Demon and the Thermodynamics of Computation

The most successful exorcism of Maxwell’s demon is Smoluchowski’s 1912 observation that thermal fluctuations would likely disrupt the operation of any molecular-scale demonic machine. A later tradition sought to exorcise Maxwell’s demon by assessing the entropic cost of the demon’s processing of information. This later tradition fails since these same thermal fluctuations invalidate the molecular-scale manipulations upon which the thermodynamics of computation is based. A new argument concerning conservation of phase space volume shows that all Maxwell’s demons must fail.
A survey of depth and inertial sensor fusion for human action recognition

A number of review or survey articles have previously appeared on human action recognition where either vision sensors or inertial sensors are used individually. Considering that each sensor modality has its own limitations, in a number of previously published papers, it has been shown that the fusion of vision and inertial sensor data improves the accuracy of recognition. This survey article provides an overview of the recent investigations where both vision and inertial sensors are used together and simultaneously to perform human action recognition more effectively. The thrust of this survey is on the utilization of depth cameras and inertial sensors as these two types of sensors are cost-effective, commercially available, and more significantly they both provide 3D human action data. An overview of the components necessary to achieve fusion of data from depth and inertial sensors is provided. In addition, a review of the publicly available datasets that include depth and inertial data which are simultaneously captured via depth and inertial sensors is presented.
The association of sleep and late-night cell phone use among adolescents.

This study aims to assess the relationship of late-night cell phone use with sleep duration and quality in a sample of Iranian adolescents.



The study population consisted of 2400 adolescents, aged 12-18 years, living in Isfahan, Iran. Age, body mass index, sleep duration, cell phone use after 9p.m., and physical activity were documented. For sleep assessment, the Pittsburgh Sleep Quality Index questionnaire was used.



The participation rate was 90.4% (n=2257 adolescents). The mean (SD) age of participants was 15.44 (1.55) years; 1270 participants reported to use cell phone after 9p.m. Overall, 56.1% of girls and 38.9% of boys reported poor quality sleep, respectively. Wake-up time was 8:17 a.m. (2.33), among late-night cell phone users and 8:03a.m. (2.11) among non-users. Most (52%) late-night cell phone users had poor sleep quality. Sedentary participants had higher sleep latency than their peers. Adjusted binary and multinomial logistic regression models showed that late-night cell users were 1.39 times more likely to have a poor sleep quality than non-users (p-value<0.001).



Late-night cell phone use by adolescents was associated with poorer sleep quality. Participants who were physically active had better sleep quality and quantity. As part of healthy lifestyle recommendations, avoidance of late-night cell phone use should be encouraged in adolescents.
A Quantitative Comparison of Reactive and Proactive Replicated Storage Systems

Replicated storage systems allow their stored data objects to outlive the life of the nodes storing them through replication. In this paper, we focus on durability, and more specifically on the concept of an object's lifetime, i.e., the duration of time between the creation of an object and when it is permanently irretrievable from the system. We analyze two main replication strategies: reactive, in which replication occurs in response to failures, and proactive, in which replication occurs in anticipation of failures. Our work presents a quantitative analysis that compares reactive and proactive through analytical models and simulations, considering exponentially distributed failures and reactive repairs, and periodic proactive replications. We also present a derivation of the analytical formula for the variance of the lifetime in the reactive model. Our results indicate that a proactive strategy leads to multiple times higher storage requirements than a reactive strategy. In addition, reactive systems are only moderately bursty in terms of bandwidth consumption, with rare peaks of at most five times the bandwidth consumption in proactive systems (given input parameter values that are compatible with real systems). Finally, for both strategies, the standard deviation is very close to the expected lifetime, and consequently, the lifetimes close to being exponentially distributed.
Models of facial aging and implications for treatment.

In this article, which focuses on concepts rather than techniques, the author emphasizes that the best predictor of a good facelift outcome is an already attractive face that has good enough tissue quality to maintain a result past the swelling stage. The author notes that too often, surgeons gravitate toward a particular facial support technique and use it all the time, to often unsatisfactory results. He singles out different areas (the brows, the tear trough, the cheeks, and so forth) and shows how the addition of volume may give results better than traditional methods. As he points out, a less limited and ritualistic approach to the face seems to be how cosmetic surgery is evolving; all factors that might make a face better are reasonable to entertain.
A little Information Theory

T HE recent development of various methods of modulation such as PCM and PPM which exchange bandwidth for signal-to-noise ratio has intensified the interest in a general theory of communication. A basis for such a theory is contained in the important papers of Nyquist 1 and Hartley2 on this subject. In the present paper we will extend the theory to include a number of new factors, in particular the effect of noise in the channel, and the savings possible due to the statistical structure of the original message and due to the nature of the final destination of the information. The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages. The system must be designed to operate for each possible selection, not just the one which will actually be chosen since this is unknown at the time of design. If the number of messages in the set is finite then this number or any monotonic function of this number can be regarded as a measure of the information produced when one message is chosen from the set, all choices being equally likely. As was pointed out by Hartley the most natural choice is the logarithmic function. Although this definition must be generalized considerably when we consider the influence of the statistics of the message and when we have a continuous range of messages, we will in all cases use an essentially logarithmic measure. The logarithmic measure is more convenient for various reasons:
Accelerating the Nonuniform Fast Fourier Transform Using FPGAs

We present an FPGA accelerator for the Non-uniform Fast Fourier Transform, which is a technique to reconstruct images from arbitrarily sampled data. We accelerate the compute-intensive interpolation step of the NuFFT Gridding algorithm by implementing it on an FPGA. In order to ensure efficient memory performance, we present a novel FPGA implementation for Geometric Tiling based sorting of the arbitrary samples. The convolution is then performed by a novel Data Translation architecture which is composed of a multi-port local memory, dynamic coordinate-generator and a plug-and-play kernel pipeline. Our implementation is in single-precision floating point and has been ported onto the BEE3 platform. Experimental results show that our FPGA implementation can generate fairly high performance without sacrificing flexibility for various data-sizes and kernel functions. We demonstrate up to 8X speedup and up to 27 times higher performance-per-watt over a comparable CPU implementation and up to 20% higher performance-per-watt when compared to a relevant GPU implementation.
A Spectral Clustering Approach To Finding Communities in Graph

Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan [9] recently proposed an objective function for graph clustering called the Q function which allows automatic selection of the number of clusters. Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings. In this paper we show how optimizing the Q function can be reformulated as a spectral relaxation problem and propose two new spectral clustering algorithms that seek to maximize Q. Experimental results indicate that the new algorithms are efficient and effective at finding both good clusterings and the appropriate number of clusters across a variety of real-world graph data sets. In addition, the spectral algorithms are much faster for large sparse graphs, scaling roughly linearly with the number of nodes n in the graph, compared to O(n) for previous clustering algorithms using the Q function.
Slim Embedding Layers for Recurrent Neural Language Models

Recurrent neural language models are the state-of-the-art models for language modeling. When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of recurrent neural language models. In this paper, we introduce a simple space compression method that randomly shares the structured parameters at both the input and output embedding layers of the recurrent neural language models to significantly reduce the size of model parameters, but still compactly represent the original input and output embedding layers. The method is easy to implement and tune. Experiments on several data sets show that the new method can get similar perplexity and BLEU score results while only using a very tiny fraction of parame-
A single-input dual-output Boost converter with PI controller

In this paper, the proposed SIMO (Single input multiple outputs) DC-DC converter based on coupled inductor. The required controllable high DC voltage and intermediate DC voltage with high voltage gain from low input voltage sources, like renewable energy, can be achieved easily from the proposed converter. The high voltage DC bus can be used as the leading power for a DC load and intermediate voltage DC output terminals can charge supplementary power sources like battery modules. This converter operates simply with one power switch. It incorporates the techniques of voltage clamping (VC) and zero current switching (ZCS). The simulation result in PSIM software shows that the aims of high efficiency, high voltage gain, several output voltages with unlike levels, are achieved.
The Futility of DNSSec

The lack of data authentication and integrity guarantees in the Domain Name System (DNS) facilitates a wide variety of malicious activity on the Internet today. DNSSec, a set of cryptographic extensions to DNS, has been proposed to address these threats. While DNSSec does provide certain security guarantees, here we argue that it does not provide what users really need, namely end-to-end authentication and integrity. Even worse, DNSSec makes DNS much less efficient and harder to administer, thus significantly compromising DNS’s availability—arguably its most important characteristic. In this paper we explain the structure of DNS, examine the threats against it, present the details of DNSSec, and analyze the benefits of DNSSec relative to its costs. This cost-benefit analysis clearly shows that DNSSec deployment is a futile effort, one that provides little long-term benefit yet has distinct, perhaps very significant costs.
Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems U+0028 ADASs U+0029. A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.
Definition of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage as an outcome event in clinical trials and observational studies: proposal of a multidisciplinary research group.

In clinical trials and observational studies there is considerable inconsistency in the use of definitions to describe delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage. A major cause for this inconsistency is the combining of radiographic evidence of vasospasm with clinical features of cerebral ischemia, although multiple factors may contribute to DCI. The second issue is the variability and overlap of terms used to describe each phenomenon. This makes comparisons among studies difficult.



An international ad hoc panel of experts involved in subarachnoid hemorrhage research developed and proposed a definition of DCI to be used as an outcome measure in clinical trials and observational studies. We used a consensus-building approach.



It is proposed that in observational studies and clinical trials aiming to investigate strategies to prevent DCI, the 2 main outcome measures should be: (1) cerebral infarction identified on CT or MRI or proven at autopsy, after exclusion of procedure-related infarctions; and (2) functional outcome. Secondary outcome measure should be clinical deterioration caused by DCI, after exclusion of other potential causes of clinical deterioration. Vasospasm on angiography or transcranial Doppler can also be used as an outcome measure to investigate proof of concept but should be interpreted in conjunction with DCI or functional outcome.



The proposed measures reflect the most relevant morphological and clinical features of DCI without regard to pathogenesis to be used as an outcome measure in clinical trials and observational studies.