nils-herrmann/clean_scirepeval huggingface dataset:
An Experimental Evaluation of a Monte-Carlo Algorithm for Singular Value Decomposition

We demonstrate that an algorithm proposed by Drineas et. al. in  to approximate the singular vectors/values of a matrix A, is not only of theoretical interest but also a fast, viable alternative to traditional algorithms. The algorithm samples a small number of rows (or columns) of the matrix, scales them appropriately to form a small matrix S and computes the singular value decomposition (SVD) of S, which is a good approximation to the SVD of the original matrix. We experimentally evaluate the accuracy and speed of this randomized algorithm using image matrices and three different sampling schemes. Our results show that our approximations of the singular vectors of A span almost the same space as the corresponding exact singular vectors of A.
Analytical detection and characterization of biopharmaceutical glycosylation by MS.

Glycosylation plays an important role in ensuring the proper structure and function of most biotherapeutic proteins. Even small changes in glycan composition, structure, or location can have a drastic impact on drug safety and efficacy. Recently, glycosylation has become the subject of increased focus as biopharmaceutical companies rush to create not only biosimilars, but also biobetters based on existing biotherapeutic proteins. Against this backdrop of ongoing biopharmaceutical innovation, updated methods for accurate and detailed analysis of protein glycosylation are critical for biopharmaceutical companies and government regulatory agencies alike. This review summarizes current methods of characterizing biopharmaceutical glycosylation, including compositional mass profiling, isomer-specific profiling and structural elucidation by MS and hyphenated techniques.
Deciphering the Mechanisms of Endophyte-Mediated Biofortification of Fe and Zn in Wheat

An investigation was carried out to understand the mechanism(s) underlying enhanced Fe or Zn uptake in low Fe–Zn accumulator wheat genotype 4HPYT-414, due to inoculation of siderophore-producing and zinc-solubilizing endophytes—Arthrobacter sulfonivorans DS-68 and Arthrobacter sp. DS-179. Root anatomical features, using transmission electron microscopy (TEM), qualitative and quantitative aspects of production of organic acids and sugars in root exudates, and expression of TaZIP genes were analysed to relate to endophyte-mediated higher concentrations of Fe and Zn in the roots and shoots of wheat plants. TEM studies revealed that the endodermis, cortical region, root hair extension, xylem and xylem vessels, pericycle and vascular bundles were more pronounced and thicker in inoculated treatments, as compared to control. The organic acid profile of root exudates revealed five types of organic acids, with citric acid being predominant. Inoculation of A. sulfonivorans and Arthrobacter sp. brought about 5- and eightfold increases in the amounts of acids, respectively, as compared to control, particularly citric acid, succinic acid and acetic acid. Among the four TaZIP genes targeted, expression was achieved only for TaZIP3 and TaZIP7 genes, which showed 1–2 fold increases in the inoculated treatments. The results clearly indicated that the endophyte-mediated overexpression of TaZIP3 and TaZIP7 genes in roots and shoots, and the observed anatomical and exudate changes were acting synergistically in facilitating higher Fe and Zn translocation in roots and shoots.
A pole-placement approach for buck converter based PV array Emulator

This paper presents the Photovoltaic (PV) array Emulator based on DC-DC buck converter control by the pole placement technique. A photovoltaic array emulator has the similar electrical characteristics to a photovoltaic panel or module. The photovoltaic array emulator independent from the environment condition so it generate and maintain same characteristics of real photovoltaic system. Here pole placement technique is used to control photovoltaic emulator's voltage and current characteristics which obtain by PV model. This whole system has been evaluated in MATLAB with linear and nonlinear loads. The simulation results shows that the PV array emulator gives similar behavior like real PV system.
Learning to Find Good Correspondences

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outliers, while simultaneously using them to recover the relative pose, as encoded by the essential matrix. Our architecture is based on a multi-layer perceptron operating on pixel coordinates rather than directly on the image, and is thus simple and small. We introduce a novel normalization technique, called Context Normalization, which allows us to process each data point separately while embedding global information in it, and also makes the network invariant to the order of the correspondences. Our experiments on multiple challenging datasets demonstrate that our method is able to drastically improve the state of the art with little training data.
SCADA Systems in the Railway Domain: Enhancing Reliability through Redundant MultipathTCP

Reliability is a key research issue to promote the role of rail in the transportation system. An important set of critical and vital railway services, such as Traction Power Supply and Supervision Systems, relay currently on modern Supervisory Control and Data Acquisition (SCADA) systems. At the same time, these large scale and networked SCADA systems are deployed over heterogeneous wide area networks. Current strategies to warrant reliability on these systems focus on costly equipment redundancy or retransmission techniques with associated delay penalization. In this paper, we propose a MultiPath strategy with redundant policy to increase reliability in railway SCADA over TCP/IP systems. We built a hybrid simulation tool with real Modbus over TCP/IP equipment and validate our approach comparing the obtained results with three alternative strategies (default TCP/IP, backup and default delivery policy).
Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of preand post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions.  2014, 6 471
Demo Abstract: Cyber-Physical Fingerprinting for Internet of Things Authentication

Current security and privacy solution to fail to meet the IoT requirements due to computational restrictions and portable nature of IoT objects. In this demo, a novel authentication framework is proposed that exploits the device-specific information to authenticate each object in the IoT. The framework is shown to effectively track the physical environment effects on objects. The experiment shows a sample IoT environment consisting of multiple Raspberry PI units operating as IoT objects. The proposed framework monitors the changes of the physical environment surrounding objects by examining the changes in the device-specific information of each IoT object. The scenario of an emulation attacker is presented in the demo as a case study. The attacker is capable of replicating all the transmitted messages and security keys of one of the IoT objects. Our proposed framework is able to effectively detect such an attack and improve the authentication accuracy for all IoT objects.
Adult polyglucosan body disease associated with lewy bodies and tremor.

Adult polyglucosan body disease (PGBD) is rare and typically presents with upper and lower motor neuron involvement and neurogenic bladder. Extrapyramidal features are unusual in PGBD and are presumed secondary to widespread pathology that includes the basal ganglia. There are no prior reports of Lewy bodies in PGBD.



To report a unique finding of Lewy bodies in a patient with PGBD.  The tremor responded to levodopa therapy. Several months later, she developed upper and lower motor neuron involvement and other clinical features of PGBD. A sural nerve biopsy specimen revealed intra-axonal polyglucosan bodies that confirmed the clinical diagnosis. Bulbar and limb weakness progressed, and she developed dementia. She died 6 years after onset. At autopsy, extensive polyglucosan body formation was found in many regions of the central nervous system. In addition, numerous alpha-synuclein staining Lewy bodies were observed in the substantia nigra, accompanied by marked neuron depopulation.



To our knowledge, this is the first report of adult PGBD associated with Lewy bodies and levodopa-responsive tremor. Although polyglucosan bodies were seen in substantia nigra, it is most likely that our patient had coexisting Parkinson disease.
Jammer signal effectiveness against a conical-scan missile seeker

Infrared (IR) guided missiles remain a threat to both military and civilian aircraft, and as such, the development of effective countermeasures against this threat remains vital. A simulation has been developed to assess the effectiveness of a jammer signal against a conical-scan seeker by testing critical jammer parameters. The critical parameters of a jammer signal are the jam-to-signal (J/S) ratio, the jammer frequency and the jammer duty cycle. It was found that the most effective jammer signal is one with a modulated envelope.
A comparative analysis of machine learning techniques for botnet detection

Day by day more and more devices are getting connected to the Internet and with the advent of the Internet of Things, this rate has had an exponential growth. The lack of security in devices connected to the IoT is making them hot targets for cyber-criminals and strength of botnet attacks have increased drastically. Botnets are the technological backbones of multitudinous attacks including Distributed Denial of Service (DDoS), SPAM, identity theft and organizational spying. The 2016 Dyn cyber attack involved multiple DDoS attacks with an estimated throughput of 1.2 terabits per second; the attack is the largest DDoS attack on record. In this paper, we compare three different techniques for botnet detection with each having its unique use cases. The results of the detection methods were verified using ISCX Intrusion Detection Dataset and the CTU-13 Dataset.
Unsupervised learning algorithms for intrusion detection

This work summarizes our research on the topic of the application of unsupervised learning algorithms to the problem of intrusion detection, and in particular our main research results in network intrusion detection. We proposed a novel, two tier architecture for network intrusion detection, capable of clustering packet payloads and correlating anomalies in the packet stream. We show the experiments we conducted on such architecture, we give performance results, and we compare our achievements with other comparable existing systems.
Assessment of features technology

Features encapsulate the engineering significance of portions of the geometry of a part or assembly, and, as such, are important in product design, product definition, and reasoning, for a variety of applications. Feature-based systems have demonstrated some potential in creating attractive design environments and in automating the geometric reasoning required in applications such as process planning and manufacturability evaluation. The paper reviews the major concepts and approaches that are in use in feature-based modeling. Several methodologies are prevalent for creating feature models and databases. These fall broadly into the categories of interactive definition, automatic recognition/extraction, and design by features. Within each, there are several subcategories, which are discussed and compared in the paper. Also presented are several schemes popular for representing features. They include augmented graphs, syntactic strings in grammars, and objects in objectoriented programming. Feature interactions and validation issues are outlined. Attempts at developing feature taxonomies are also summarized.
Design and Optimal Research of a Non-Contact Adjustable Magnetic Adhesion Mechanism for a Wall-Climbing Welding Robot

Wall‐climbing  welding  robots  (WCWRs)  can replace  workers  in  manufacturing  and  maintaining large  unstructured  equipment,  such  as  ships.  The adhesion mechanism  is  the key component of WCWRs. As  it  is directly related  to  the robot’s ability  in relation to  adsorbing, moving  flexibly  and  obstacle‐passing.  In this  paper,  a  novel  non‐contact  adjustably  magnetic adhesion mechanism  is  proposed. The magnet  suckers are mounted under the robot’s axils and the sucker and wall  are  in non‐contact.  In order  to pass obstacles,  the sucker and the wheel unit can be pulled up and pushed down  by  a  lifting mechanism.  The magnetic  adhesion force  can  be  adjusted  by  changing  the  height  of  the gap  between  the  sucker  and  the  wall  by  the  lifting mechanism.  In  order  to  increase  the  adhesion  force, the  value  of  the  sucker’s  magnetic  energy  density (MED) is maximized by optimizing the magnet sucker’s structure  parameters  with  a  finite  element  method. Experiments  prove  that  the  magnetic  adhesion mechanism  has  enough  adhesion  force  and  that  the WCWR can complete wall‐climbing work within a large unstructured environment.
A compliant self-adaptive gripper with proprioceptive haptic feedback

Grippers and robotic hands are an important field in robotics. Recently, the combination of grasping devices and haptic feedback has been a promising avenue for many applications such as laparoscopic surgery and spatial telemanipulation. This paper presents the work behind a new selfadaptive, a.k.a. underactuated, gripper with a proprioceptive haptic feedback in which the apparent stiffness of the gripper as seen by its actuator is used to estimate contact location. This system combines many technologies and concepts in an integrated mechatronic tool. Among them, underactuated grasping, haptic feedback, compliant joints and a differential seesaw mechanism are used. Following a theoretical modeling of the gripper based on the virtual work principle, the authors present numerical data used to validate this model. Then, a presentation of the practical prototype is given, discussing the sensors, controllers, and mechanical architecture. Finally, the control law and the experimental validation of the haptic feedback are presented.
Determinants of Default in P2P Lending

This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans' data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower's debt level.
Polarizers on sections of square waveguides with inner corner ridges

The optimization and analysis of new waveguide polarizers have been carried out on the basis of rigorous full-wave model. These polarizers transform the dominant mode of input rectangular waveguide into an elliptically polarized wave of output square waveguide. The phase-shifting module is realized on the basis of one or two sections of a square waveguide having two diagonally placed square ridges. It has been found out that polarizers with single-section phase shifter can provide the bandwidth from 11% to 15% at the axial ratio level of r < 2 dB and the return loss level of LR > 20 dB, whereas the two-section ones have the bandwidths more 23% at r < 1 dB and LR > 23 dB
GRASS-RaPlaT − an Open-Source Tool for Radio Coverage Calculations

network is a complex and expensive task which requires careful planning to achieve the required radio signal coverage. Commercial radio network planning tools are available, but they are expensive and inflexible in the sense that they are often limited to a particular radio network technology, its frequency band(s) and a fixed set of channel models. This led us to develop an open-source radio coverage simulation tool with user-extendible set of radio propagation models, which is especially suitable for research work but at the same time also for professional communication network planning. The tool, GRASS-RaPlaT, is based on the open-source Geographical Resources Analysis Support System (GRASS) and currently includes modules for a number of channel models, a module for sectorization according to given antenna radiation patterns, a module for calculating and storing the complete radio network coverage data, and a number of supporting modules, e.g. for adapting input data and analyzing simulation results. Its computation has been tested with existing real GSM network data, and the accuracy of results evaluated by comparing with those from a professional radio network planning tool.
REDUCTION OF WORK IN PROCESS INVENTORY AND PRODUCTION LEAD TIME IN A BEARING INDUSTRY USING VALUE STREAM MAPPING TOOL

Manufacturers always face cost-reduction and efficiency challenges in their operations. Industries require improvement in Production Lead Times, costs and customer service levels to survive. Because of this, companies have become more customers focused. The result is that companies have been putting in significant effort to improve their efficiency. In this paper Value Stream Mapping (VSM) tool is used in bearing manufacturing industry by focusing both on processes and their cycle times for a product UC208 INNER which is used in plumber block. In order to use the value stream mapping, relevant data has been collected and analyzed. After collecting the data customer need was identified. Current state map was draw by defining the resources and activities needed to manufacture, deliver the product. The study of current state map shows the areas for improvement and identifying the different types of wastes. From the current state map, it was noticeable that Annealing and CNC Machining processing have higher cycle time and work in process. The lean principles and techniques implemented or suggested and future state map was created and the total lead time was reduced from 7.3 days to 3.8 days. The WIP at each work station has also been reduced. The production lead time was reduced from 409 seconds to 344 seconds.
LiDAR-Camera Calibration using 3D-3D Point correspondences

With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors. They both provide rich and complementary data which can be used by various algorithms and machine learning to sense and make vital inferences about the surroundings. We propose a novel pipeline and experimental setup to find accurate rigid-body transformation for extrinsically calibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences in LiDAR and camera frame and gives a closed form solution. We further show the accuracy of the estimate by fusing point clouds from two stereo cameras which align perfectly with the rotation and translation estimated by our method, confirming the accuracy of our method’s estimates both mathematically and visually. Taking our idea of extrinsic Work was done during an internship at Robotics Research Center at IIIT-H ar X iv :1 70 5. 09 78 5v 1 [ cs .R O ] 2 7 M ay 2 01 7 2 A. Dhall, K. Chelani, V. Radhakrishnan, K.M. Krishna LiDAR-camera calibration forward, we demonstrate how two cameras with no overlapping field-of-view can also be calibrated extrinsically using 3D point correspondences. The code has been made available as open-source software in the form of a ROS package.
Severe disturbance of higher cognition after bilateral frontal lobe ablation: patient EVR.

After bilateral ablation of orbital and lower mesial frontal cortices, a patient had profound changes of behavior that have remained stable for 8 years. Although he could not meet personal and professional responsibilities, his "measurable" intelligence was superior, and he was therefore considered a "malingerer." Neurologic and neuropsychological examinations were otherwise intact. CT, MRI, and SPET revealed a localized lesion of the orbital and lower mesial frontal cortices. All other cerebral areas had normal structure and radioactivity patterns. Such impairments of motivation and complex social behavior were not seen in control cases with superior mesial or dorsolateral frontal lesions.