nils-herrmann/clean_scirepeval huggingface dataset:
Ageing and Parkinson's disease: substantia nigra regional selectivity.

The micro-architecture of the substantia nigra was studied in control cases of varying age and patients with parkinsonism. A single 7 mu section stained with haematoxylin and eosin was examined at a specific level within the caudal nigra using strict criteria. The pars compacta was divided into a ventral and a dorsal tier, and each tier was further subdivided into 3 regions. In 36 control cases there was a linear fallout of pigmented neurons with advancing age in the pars compacta of the caudal substantia nigra at a rate of 4.7% per decade. Regionally, the lateral ventral tier was relatively spared (2.1% loss per decade) compared with the medial ventral tier (5.4%) and the dorsal tier (6.9%). In 20 Parkinson's disease (PD) cases of varying disease duration there was an exponential loss of pigmented neurons with a 45% loss in the first decade. Regionally, the pattern was opposite to ageing. Loss was greatest in the lateral ventral tier (average loss 91%) followed by the medial ventral tier (71%) and the dorsal tier (56%). The presymptomatic phase of PD from the onset of neuronal loss was estimated to be about 5 yrs. This phase is represented by incidental Lewy body cases: individuals who die without clinical signs of PD or dementia, but who are found to have Lewy bodies at post-mortem. In 7 cases cell loss was confined to the lateral ventral tier (average loss 52%) congruent with the lateral ventral selectivity of symptomatic PD. It was calculated that at the onset of symptoms there was a 68% cell loss in the lateral ventral tier and a 48% loss in the caudal nigra as a whole. The regional selectivity of PD is relatively specific. In 15 cases of striatonigral degeneration the distribution of cell loss was similar, but the loss in the dorsal tier was greater than PD by 21%. In 14 cases of Steele-Richardson-Olszewski syndrome (SRO) there was no predilection for the lateral ventral tier, but a tendency to involve the medial nigra and spare the lateral. These findings suggest that age-related attrition of pigmented nigral cells is not an important factor in the pathogenesis of PD.
A 0 . 5m CMOS 4 . 0-Gbit / s Serial Link Transceiver with Data Recovery Using Oversampling

A 4-Gbit/s serial link transceiver is fabricated in a MOSIS 0.5m HPCMOS process. To achieve the high data rate without speed critical logic on chip, the data are multiplexed when transmitted and immediately demultiplexed when received. This parallelism is achieved by using multiple phases tapped from a PLL using the phase spacing to determine the bit time. Using an 8 : 1 multiplexer yields 4 Gbits/s, with an on-chip VCO running at 500 MHz. The internal logic runs at 250 MHz. For robust data recovery, the input is sampled at 3 the bit rate and uses a digital phase-picking logic to recover the data. The digital phase picking can adjust the sample at the clock rate to allow high tracking bandwidth. With a 3.3-V supply, the chip has a measured bit error rate (BER) of <10 14.
The structural biology of CRISPR-Cas systems.

Prokaryotic CRISPR-Cas genomic loci encode RNA-mediated adaptive immune systems that bear some functional similarities with eukaryotic RNA interference. Acquired and heritable immunity against bacteriophage and plasmids begins with integration of ∼30 base pair foreign DNA sequences into the host genome. CRISPR-derived transcripts assemble with CRISPR-associated (Cas) proteins to target complementary nucleic acids for degradation. Here we review recent advances in the structural biology of these targeting complexes, with a focus on structural studies of the multisubunit Type I CRISPR RNA-guided surveillance and the Cas9 DNA endonuclease found in Type II CRISPR-Cas systems. These complexes have distinct structures that are each capable of site-specific double-stranded DNA binding and local helix unwinding.
Sparsity-based DOA estimation using co-prime arrays

In this paper, we propose co-prime arrays for effective direction-of-arrival (DOA) estimation. To fully utilize the virtual aperture achieved in the difference co-array constructed from a co-prime array structure, sparsity-based spatial spectrum estimation technique is exploited. Compared to existing techniques, the proposed technique achieves better utilization of the co-array aperture and thus results in increased degrees-of-freedom as well as improved DOA estimation performance.
Modeling Evolving Relationships Between Characters in Literary Novels

Studying characters plays a vital role in computationally representing and interpreting narratives. Unlike previous work, which has focused on inferring character roles, we focus on the problem of modeling their relationships. Rather than assuming a fixed relationship for a character pair, we hypothesize that relationships temporally evolve with the progress of the narrative, and formulate the problem of relationship modeling as a structured prediction problem. We propose a semisupervised framework to learn relationship sequences from fully as well as partially labeled data. We present a Markovian model capable of accumulating historical beliefs about the relationship and status changes. We use a set of rich linguistic and semantically motivated features that incorporate world knowledge to investigate the textual content of narrative. We empirically demonstrate that such a framework outperforms competitive baselines.
Coupling analysis and equivalent circuit model of the IC stripline method

As portable electronic devices are widely used in wireless communication, analysis of RF interference becomes an essential step for IC designers. In order to test electromagnetic compatibility (EMC) of IC operating at high frequencies, IC stripline method is proposed in IEC standard. This method can be applied up to 3 GHz and covers the testing of ICs and small component. This paper represents simulation results of the open version of IC stripline in 3D EM solver. Also, the coupling effect of IC stripline method is analyzed with S-parameter results. The distributed lumped-element equivalent model is presented for explaining coupling relation between IC stripline and package. This model can be used for quick analysis for EMC of ICs.
Improving Ontology-Based User Profiles

Personalized Web browsing and search hope to provide Web information that matches a user’s personal interests and thus provide more effective and efficient information access. A key feature in developing successful personalized Web applications is to build user profiles that accurately represent a user’ s interests. The main goal of this research is to investigate techniques that implicitly build ontology-based user profiles. We build the profiles without user interaction, automatically monitoring the user’s browsing habits. After building the initial profile from visited Web pages, we investigate techniques to improve the accuracy of the user profile. In particular, we focus on how quickly we can achieve profile stability, how to identify the most important concepts, the effect of depth in the concept-hierarchy on the importance of a concept, and how many levels from the hierarchy should be used to represent the user. Our major findings are that ranking the concepts in the profiles by number of documents assigned to them rather than by accumulated weights provides better profile accuracy. We are also able to identify stable concepts in the profile, thus allowing us to detect long-term user interests. We found that the accuracy of concept detection decreases as we descend them in the concept hierarchy, however this loss of accuracy must be balanced against the detailed view of the user available only through the inclusion of lower-level concepts.
Evolutionary Programming in Electromagnetic Optimization: A Review

In this paper, we review recent advances in evolutionary programming (EP) and its implementation in various antenna, microwave, frequency selective surfaces and RF circuit optimization problems. EP, unlike the other two paradigms of evolutionary computational techniques, namely, genetic algorithms (GA) and evolution strategies (ES), uses a mutation only variation operator where adaptive and/or self-adaptive techniques exist, or can easily be designed, for adapting the parameters of mutation operator during the evolution process. We present the so-called meta-EP and design of its mutation operator, using Gaussian, Cauchy and Poisson mutations as well as a hybrid of these mutations, for continuous, discrete and mixed parameter electromagnetic optimization problems. In addition, an efficient hybrid use of EP, gradient search methods and cluster analysis, as well as a novel hybrid EP-GA algorithm are discussed. Examples presented include optimizations of corrugated horn antennas, multilayered stacked microstrip antennas, Yagi-Uda arrays, partially reflective surfaces, dielectric filters, meander-line polarizer and RF duplexers
Ontology-Based Prediction and Prioritization of Gene Functional Annotations

Genes and their protein products are essential molecular units of a living organism. The knowledge of their functions is key for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. The association of a gene or protein with its functions, described by controlled terms of biomolecular terminologies or ontologies, is named gene functional annotation. Very many and valuable gene annotations expressed through terminologies and ontologies are available. Nevertheless, they might include some erroneous information, since only a subset of annotations are reviewed by curators. Furthermore, they are incomplete by definition, given the rapidly evolving pace of biomolecular knowledge. In this scenario, computational methods that are able to quicken the annotation curation process and reliably suggest new annotations are very important. Here, we first propose a computational pipeline that uses different semantic and machine learning methods to predict novel ontology-based gene functional annotations; then, we introduce a new semantic prioritization rule to categorize the predicted annotations by their likelihood of being correct. Our tests and validations proved the effectiveness of our pipeline and prioritization of predicted annotations, by selecting as most likely manifold predicted annotations that were later confirmed.
Technical Aspects of Cyber Kill Chain

Recent trends in targeted cyber-attacks has increased the interest of research in the field of cyber securit y. Such attacks have massive disruptive effects on organizati ons, enterprises and governments. Cyber kill chain is a model to describe cyber-attacks so as to develop incident response a nd analysis capabilities. Cyber kill chain in simple terms is an attack chain, the path that an intruder takes to penetrate information systems over time to execute an attack on the target. This paper broadly categories the methodologies, techniques an d tools involved in cyber-attacks. This paper intends to help a cybe r security researcher to realize the options available to an a ttacker at every stage of a cyber-attack. Reconnaissance, RAT, Exploit, Cyber Attack, Persistence, Command & Control
E-hubs: the new B2B (business-to-business) marketplaces.

Electronic hubs--Internet-based intermediaries that host electronic marketplaces and mediate transactions among businesses--are generating a lot of interest. Companies like Ariba, Chemdex, and Commerce One have already attained breathtaking stock market capitalizations. Venture capitalists are pouring money into more business-to-business start-ups. Even industrial stalwarts like GM and Ford are making plans to set up their own Web markets. As new entrants with new business models pour into the business-to-business space, it's increasingly difficult to make sense of the landscape. This article provides a blueprint of the e-hub arena. The authors start by looking at the two dimensions of purchasing: what businesses buy--manufacturing inputs or operating inputs--and how they buy--through systematic sourcing or spot sourcing. They classify B2B e-hubs into four categories: MRO hubs, yield managers, exchanges, and catalog hubs, and they discuss each type in detail. Drilling deeper into this B2B matrix, the authors look at how e-hubs create value--through aggregation and matching--and explain when each mechanism works best. They also examine the biases of e-hubs. Although many e-hubs are neutral--they're operated by independent third parties--some favor the buyers or sellers. The authors explain the differences and discuss the pros and cons of each position. The B2B marketplace is changing rapidly. This framework helps buyers, sellers, and market makers navigate the landscape by explaining what the different hubs do and how they add the most value.
Employing traditional machine learning algorithms for big data streams analysis: The case of object trajectory prediction

In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4’, 10’, 20’ and 40’ time intervals. We explore the necessary tradeoffs between accuracy, performance and resource utilization are explored given the large volume and update rates of input data. We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a single-pass predictive approach, under real-time constraints. The results are measured in terms of accuracy and performance and are compared against the baseline kinematic equations. Results show that it is possible to efficiently model the trajectory of multiple vessels using a single model, which is trained and evaluated using an adequately large, static dataset, thus achieving a significant gain in terms of resource usage while not compromising accuracy.
Optimal Auction for Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach

Blockchain has recently been applied in many applications such as bitcoin, smart grid, and Internet of Things (IoT) as a public ledger of transactions. However, the use of blockchain in mobile environments is still limited because the mining process consumes too much computing and energy resources on mobile devices. Edge computing offered by the Edge Computing Service Provider (ECSP) can be adopted as a viable solution for offloading the mining tasks from the mobile devices, i.e., miners, in the mobile blockchain environment. However, a mechanism for edge resource allocation to maximize the revenue for the ECSP and to ensure incentive compatibility and individual rationality is still open. In this paper, we develop an optimal auction based on deep learning for the edge resource allocation. Specifically, we construct a multi-layer neural network architecture based on an analytical solution of the optimal auction. The neural networks first perform monotone transformations of the miners' bids. Then, they calculate allocation and conditional payment rules for the miners. We use valuations of the miners as the training data to adjust parameters of the neural networks so as to optimize the loss function which is the expected, negated revenue of the ECSP.We show the experimental results to confirm the benefits of using the deep learning for deriving the optimal auction for mobile blockchain with high revenue.
FPTree: A Hybrid SCM-DRAM Persistent and Concurrent B-Tree for Storage Class Memory

The advent of Storage Class Memory (SCM) is driving a rethink of storage systems towards a single-level architecture where memory and storage are merged. In this context, several works have investigated how to design persistent trees in SCM as a fundamental building block for these novel systems. However, these trees are significantly slower than DRAM-based counterparts since trees are latency-sensitive and SCM exhibits higher latencies than DRAM. In this paper we propose a novel hybrid SCM-DRAM persistent and concurrent B-Tree, named Fingerprinting Persistent Tree (FPTree) that achieves similar performance to DRAM-based counterparts. In this novel design, leaf nodes are persisted in SCM while inner nodes are placed in DRAM and rebuilt upon recovery. The FPTree uses Fingerprinting, a technique that limits the expected number of in-leaf probed keys to one. In addition, we propose a hybrid concurrency scheme for the FPTree that is partially based on Hardware Transactional Memory. We conduct a thorough performance evaluation and show that the FPTree outperforms state-of-the-art persistent trees with different SCM latencies by up to a factor of 8.2. Moreover, we show that the FPTree scales very well on a machine with 88 logical cores. Finally, we integrate the evaluated trees in memcached and a prototype database. We show that the FPTree incurs an almost negligible performance overhead over using fully transient data structures, while significantly outperforming other persistent trees.
A cloud robot system using the dexterity network and berkeley robotics and automation as a service (Brass)

In support of Cloud Robotics, Robotics and Automation as a Service (RAaaS) frameworks have the potential to reduce the complexity of software development, simplify software installation and maintenance, and facilitate data sharing for machine learning. In this proof-of-concept paper, we describe Berkeley Robotics and Automation as a Service (Brass), a RAaaS prototype that allows robots to access a remote server that hosts a robust grasp-planning system (Dex-Net 1.0) that maintains data on hundreds of candidate grasps on thousands of 3D object meshes and uses perturbation sampling to estimate and update a stochastic robustness metric for each grasp. Results suggest that such a system can increase grasp reliability over naive locally-computed grasping strategies with network latencies of 30 and 200 msec for servers 500 and 6000 miles away, respectively. We also study how the system can use execution reports from robots in the field to update grasp recommendations over time.
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures

Belief Spans • A text span to record Informable Slots and Requestable Slots to enable a RNN to decode it. For example: <Inf>Chinese; Expensive<\Inf> <Req>Address; Phone<\Req> • Roles: knowledge base search and response conditioning. Sequicity Formalization  Source-target sequence pair: {B0R0U1, B1R1}, {B1R1U2, B2R2}, ..., {Bt-1Rt-1Ut, BtRt}.  Two-stage decoding: – Bt=Seq2seq(Bt-1Rt-1Ut) – Rt=Seq2seq(Bt-1Rt-1Ut|Bt, KB search results)
Velodyne-based curb detection up to 50 meters away

Long range curb detection is crucial for an Autonomous Land Vehicle (ALV) navigation in urban environments. This paper presents a novel curb detection algorithm which can detect the curbs up to 50 meters away with Velodyne LIDAR. Instead of building a Digital Elevation Map (DEM) and utilizing geometric features (like normal direction) to extract candidate curb points, we take each scan line of Velodyne LIDAR as a processing unite directly. Some feature points, which are extracted from individual scan lines, are selected as the initial curb points by the distance criterion and Hough Transform (HT). Eventually, iterative Gaussian Process Regression (GPR), which utilizes the above initial curb points as the initial seeds, is exploited to represent both the curved and straight-line curb model. In order to verify the effectiveness of our algorithm quantitatively, 2934 Velodyne scans are collected in various urban scenes with our ALV, and 566 of them are labelled manually1. Our algorithm is also compared with two other curb detection techniques. The experimental results on the dataset show promising performance.
Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease.

Magnetic resonance imaging (MRI) examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Height-adjusted TKV (HtTKV) has become the gold-standard imaging biomarker for ADPKD progression at early stages of the disease when estimated glomerular filtration rate (eGFR) is still normal. However, HtTKV does not take advantage of the wealth of information provided by MRI. Here we tested whether image texture features provide additional insights into the ADPKD kidney that may be used as complementary information to existing biomarkers. A retrospective cohort of 122 patients from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study was identified who had T2-weighted MRIs and eGFR values over 70 mL/min/1.73m2 at the time of their baseline scan. We computed nine distinct image texture features for each patient. The ability of each feature to predict subsequent progression to CKD stage 3A, 3B, and 30% reduction in eGFR at eight-year follow-up was assessed. A multiple linear regression model was developed incorporating age, baseline eGFR, HtTKV, and three image texture features identified by stability feature selection (Entropy, Correlation, and Energy). Including texture in a multiple linear regression model (predicting percent change in eGFR) improved Pearson correlation coefficient from -0.51 (using age, eGFR, and HtTKV) to -0.70 (adding texture). Thus, texture analysis offers an approach to refine ADPKD prognosis and should be further explored for its utility in individualized clinical decision making and outcome prediction.
Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems

Feature extraction is one of the most important steps in any brain-computer interface (BCI) system. In particular, spatio-spectral feature extraction for motor-imagery BCIs (MI-BCI) has been the focus of several works in the past decade. This paper proposes a novel method, called separable common spatio-spectral patterns (SCSSP), for extraction of discriminant spatio-spectral EEG features in MI-BCIs.  Assuming a binary classification problem, SCSSP uses a heteroscedastic matrix-variate Gaussian model for the multiband EEG rhythms, and seeks the spatio-spectral features whose variance is maximized for one brain task and minimized for the other task. Therefore, SCSSP can be considered as a spatio-spectral generalization of the conventional common spatial patterns (CSP) algorithm.  The experimental results on two-class and multiclass motor-imagery data from publicly available BCI Competition datasets demonstrate that the proposed computationally efficient method competes closely with filter-bank CSP (FBCSP), and can even outperform the FBCSP if enough training data are available. Furthermore, SCSSP provides us with a simple measure for ranking the discriminant power of extracted spatio-spectral features, which is not possible in FBCSP.  The matrix-variate Gaussian assumption allows the SCSSP method to jointly process the EEG data in both spatial and spectral domains. As a result, compared to the similar solutions in the literature such as FBCSP, the proposed SCSSP method requires significantly lower computations.  The proposed computationally efficient spatio-spectral feature extractor is particularly suitable for applications in which the computational power is limited, such as emerging wearable mobile BCI systems.