nils-herrmann/clean_scirepeval huggingface dataset:
Spatial-Based Predictive Control and Geometric Corridor Planning for Adaptive Cruise Control Coupled With Obstacle Avoidance

This paper presents an integrated control approach for autonomous driving comprising a corridor path planner that determines constraints on vehicle position, and a linear time-varying model predictive controller combining path planning and tracking in a road-aligned coordinate frame. The capabilities of the approach are illustrated in obstacle-free curved road-profile tracking, in an application coupling adaptive cruise control (ACC) with obstacle avoidance (OA), and in a typical driving maneuver on highways. The vehicle is modeled as a nonlinear dynamic bicycle model with throttle, brake pedal position, and steering angle as control inputs. Proximity measurements are assumed to be available within a given range field surrounding the vehicle. The proposed general feedback control architecture includes an estimator design for fusion of database information (maps), exteroceptive as well as proprioceptive measurements, a geometric corridor planner based on graph theory for the avoidance of multiple, potentially dynamically moving objects, and a spatial-based predictive controller. Switching rules for transitioning between four different driving modes, i.e., ACC, OA, obstacle-free road tracking (RT), and controlled braking (Brake), are discussed. The proposed method is evaluated on test cases, including curved and highway two-lane road tracks with static as well as moving obstacles.
Humphrey Ridley (1653-1708): 17th century evolution in neuroanatomy and selective cerebrovascular injections for cadaver dissection.

Humphrey Ridley, M.D. (1653-1708), is a relatively unknown historical figure, belonging to the postmedieval era of neuroanatomical discovery. He was born in the market town of Mansfield, 14 miles from the county of Nottinghamshire, England. After studying at Merton College, Oxford, he pursued medicine at Leiden University in the Netherlands. In 1688, he was incorporated as an M.D. at Cambridge. Ridley authored the first original treatise in English language on neuroanatomy, The Anatomy of the Brain Containing its Mechanisms and Physiology: Together with Some New Discoveries and Corrections of Ancient and Modern Authors upon that Subject. Ridley described the venous anatomy of the eponymous circular sinus in connection with the parasellar compartment. His methods were novel, unique, and effective. To appreciate the venous anatomy, he preferred to perform his anatomical dissections on recently executed criminals who had been hanged. These cadavers had considerable venous engorgement, which made the skull base venous anatomy clearer. To enhance the appearance of the cerebral vasculature further, he used tinged wax and quicksilver in the injections. He set up experimental models to answer questions definitively, in proving that the arachnoid mater is a separate meningeal layer. The first description of the subarachnoid cisterns, blood-brain barrier, and the fifth cranial nerve ganglion with its branches are also attributed to Ridley. This historical vignette revisits Ridley's life and academic work that influenced neuroscience and neurosurgical understanding in its infancy. It is unfortunate that most of his novel contributions have gone unnoticed and uncited. The authors hope that this article will inform the neurosurgical community of Ridley's contributions to the field of neurosurgery.
Personalized Effect of Health Behavior on Blood Pressure: Machine Learning Based Prediction and Recommendation

Blood pressure (BP) is one of the most important indicator of human health. In this paper, we investigate the relationship between BP and health behavior (e.g. sleep and exercise). Using the data collected from off-the-shelf wearable devices and wireless home BP monitors, we propose a data driven personalized model to predict daily BP level and provide actionable insight into health behavior and daily BP. In the proposed machine learning model using Random Forest (RF), trend and periodicity features of BP time-series are extracted to improve prediction. To further enhance the performance of the prediction model, we propose RF with Feature Selection (RFFS), which performs RF-based feature selection to filter out unnecessary features. Our experimental results demonstrate that the proposed approach is robust to different individuals and has smaller prediction error than existing methods. We also validate the effectiveness of personalized recommendation of health behavior generated by RFFS model.
Centralized and Distributed Anonymization for High-Dimensional Healthcare Data

Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients’ privacy. In this article, we study the privacy concerns of sharing patient information between the Hong Kong Red Cross Blood Transfusion Service (BTS) and the public hospitals. We generalize their information and privacy requirements to the problems of centralized anonymization and distributed anonymization, and identify the major challenges that make traditional data anonymization methods not applicable. Furthermore, we propose a new privacy model called LKC-privacy to overcome the challenges and present two anonymization algorithms to achieve LKC-privacy in both the centralized and the distributed scenarios. Experiments on real-life data demonstrate that our anonymization algorithms can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large datasets.
Design, development, and performance evaluation of a low-cost, low-power wake-up radio system for wireless sensor networks

Energy-efficient operation is a challenge for wireless sensor networks (WSNs). A common method employed for this purpose is duty-cycled operation, which extends battery lifetime yet incurs several types of energy wastes and challenges. A promising alternative to duty-cycled operation is the use of wake-up radio (WuR), where the main microcontroller unit (MCU) and transceiver, that is, the two most energy-consuming elements, are kept in energy-saving mode until a special signal from another node is received by an attached, secondary, ultra-low power receiver. Next, this so-called wake-up receiver generates an interrupt to activate the receiver node's MCU and, consequently, the main radio. This article presents a complete wake-up radio design that targets simplicity in design for the monetary cost and flexibility concerns, along with a good operation range and very low power consumption. Both the transmitter (WuTx) and the receiver (WuRx) designs are presented with the accompanying physical experiments for several design alternatives. Detailed analysis of the end system is provided in terms of both operational distance (more than 10 m) and current consumption (less than 1 μA). As a reference, a commercial WuR system is analyzed and compared to the presented system by expressing the trade-offs and advantages of both systems.
Object Substitution Without Reentry ?

G. Francis and F. Hermens (2002) used computer simulations to claim that many current models of metacontrast masking can account for the findings of V. Di Lollo, J. T. Enns, and R. A. Rensink (2000). They also claimed that notions of reentrant processing are not necessary because all of V. Di Lollo et al. 's data can be explained by feed-forward models. The authors show that G. Francis and F. Hermens's claims are vitiated by inappropriate modeling of attention and by ignoring important aspects of V. Di Lollo et al. 's results.
Network Security

In this paper I present a review of the different forms of network security in place in the world today. It is a tutorial type of paper and it especially deals with cryptographic algorithms, security protocols, authentication issues, end to end security solutions with a host of other network security issues. I compile these into a general purview of this topic and then I go in detail regarding the issues involved. I first focus on the state of Network security in the world today after explaining the need for network security. After highlighting these, I will be looking into the different types of Network security used. This part is quite an extensive coverage into the various forms of Network security. Then, I will be highlighting the problems still facing computer networks followed by the latest research done in the areas of Computer and Network Security.
Discriminating Stress From Cognitive Load Using a Wearable EDA Device

The inferred cost of work-related stress call for prevention strategies that aim at detecting early warning signs at the workplace. This paper goes one step towards the goal of developing a personal health system for detecting stress. We analyze the discriminative power of electrodermal activity (EDA) in distinguishing stress from cognitive load in an office environment. A collective of 33 subjects underwent a laboratory intervention that included mild cognitive load and two stress factors, which are relevant at the workplace: mental stress induced by solving arithmetic problems under time pressure and psychosocial stress induced by social-evaluative threat. During the experiments, a wearable device was used to monitor the EDA as a measure of the individual stress reaction. Analysis of the data showed that the distributions of the EDA peak height and the instantaneous peak rate carry information about the stress level of a person. Six classifiers were investigated regarding their ability to discriminate cognitive load from stress. A maximum accuracy of 82.8% was achieved for discriminating stress from cognitive load. This would allow keeping track of stressful phases during a working day by using a wearable EDA device.
Sonographic diagnosis of pneumoperitoneum using the ‘enhancement of the peritoneal stripe sign.’ A prospective study

The objective of this study was to validate the Enhanced Peritoneal Stripe Sign (EPSS) in diagnosing pneumoperitoneum in patients presenting with acute abdomen. The EPSS was described as a specific sonographic sign of pneumoperitoneum in an animal model and few patients who had undergone laparoscopy (Muradali et al.  1999). This is the first large-scale study in patients to detect the efficacy of EPSS. Six hundred consecutive patients with acute abdominal pain presenting to the author over a period of 3 months in the emergency ultrasonography department were prospectively studied for the presence of the EPSS. As part of their clinical work up, patients also underwent plain radiographs and/or a computed tomography (CT) of the abdomen. The author was unaware of the results of other imaging studies at the time of the sonographic examination. In all cases, the final diagnosis was based on the intra-operative findings, results of other imaging techniques and clinical follow-up. Based on the final diagnosis, 21 out of 600 patients had pneumoperitoneum. The EPSS was found to be positive in all 21 of these patients. Another three patients were found to have the sign false positive. There were no false negatives in this study. The EPSS thus had a sensitivity of 100%, a specificity of 99%, a positive predictive value of 87.5% and a negative predictive value of 100%. The EPSS is a reliable and accurate sonographic sign for the diagnosis of pneumoperitoneum. It should be looked for in all patients presenting with acute abdominal pain.
Bioinformatics and medicine in the era of deep learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.
A Novel Robot Fish With Wire-Driven Active Body and Compliant Tail

In the past two decades, a large number of robot fishes have been built, though their performances are still far inferior compared with that of the real fish. One of the key reasons is the design. This paper presents a novel robot fish. The key innovation is the combination of an active wire-driven body with a soft compliant tail to accomplish undulatory swimming (i.e., flapping in “S” motion). First, the design is presented. Second, the design and analysis model is given. In particular, it is shown that the compliant tail can be modeled through the proposed multipseudolink model. Then, a prototype and experimental results are presented. The experiment results demonstrate that the kinematics of the robot fish can be predicted by the presented model with reasonable errors. Moreover, it has several advantages; simple in structure, easy to control, capable of high-speed swimming (the maximum speed is 2.15 body lengths/s), and good maneuverability (the average turning speed is 63°/s). Most importantly, it is highly efficient; the swimming performance of the robot fish is close to that of a real fish.
"Hunger Hurts but Starving Works: " Characterizing the Presentation of Eating Disorders Online

Within the CSCW community, little has been done to systematically analyze online eating disorder (ED) user generated content. In this paper, we present the results of a cross-platform content analysis of ED-related posts. We analyze the way that hashtags are used in ad-hoc ED- focused networks and present a comprehensive corpus of ED-terminology that frequently accompanies ED activities online. We provide exemplars of the types of ED-related content found online. Through this characterization of activities, we draw attention to the increasingly important role that these platforms play and how they are used and misappropriated for negative health purposes. We also outline specific challenges associated with researching these types of networks online.  This paper includes media that could potentially be a trigger to those dealing with an eating disorder or with other self-injury illnesses. Please use caution when reading, printing, or disseminating this paper.
A highly manufacturable 28nm CMOS low power platform technology with fully functional 64Mb SRAM using dual/tripe gate oxide process

For the first time, we present good yielding 64 Mb SRAM test-chip with the smallest cell using dual/triple gate oxide process flow in 28 nm node. The low power technology platform continues scaling trend and extends SiON/poly technology beyond 32 nm node with gate density of 2.3times higher than that of 45 nm, and integrates high density (0.127 um2) and low Vccmin (0.155 um2) 6-T SRAM cells, low power transistors, analog/RF components and Cu-low-k interconnect. Simultaneously available low standby (LSTP) and low operating power (LOP) transistors provide 25-40% speed improvement or 30-50% active power reduction over prior 45 nm technology. Competitive mismatch (AVt of 2.86 mV.um) and 1/f noise characteristics, and enhanced MOM unit capacitance of 4.4 fF/um2 (4 metal layers) with Q factor >100 at 2.4 GHz are also achieved.
Survey : IoT Vehicular Information Network

Inter vehicular communication is a technology where vehicles act as different nodes to form a network. In a vehicular network different vehicles communicate among each other via wireless access .Authentication is very crucial security service for inter vehicular communication (IVC) in Vehicular Information Network. It is because, protecting vehicles from any attempt to cause damage (misuse) to their private data and the attacks on their privacy. In this survey paper, we investigate the authentication issues for vehicular information network architecture based on the communication principle of named data networking (NDN). This paper surveys the most emerging paradigm of NDN in vehicular information network. So, we aims this survey paper helps to improve content naming, addressing, data aggregation and mobility for IVC in the vehicular information network.
p53 status determines the role of autophagy in pancreatic tumour development

Macroautophagy (hereafter referred to as autophagy) is a process in which organelles termed autophagosomes deliver cytoplasmic constituents to lysosomes for degradation. Autophagy has a major role in cellular homeostasis and has been implicated in various forms of human disease. The role of autophagy in cancer seems to be complex, with reports indicating both pro-tumorigenic and tumour-suppressive roles. Here we show, in a humanized genetically-modified mouse model of pancreatic ductal adenocarcinoma (PDAC), that autophagy’s role in tumour development is intrinsically connected to the status of the tumour suppressor p53. Mice with pancreases containing an activated oncogenic allele of Kras (also called Ki-Ras)—the most common mutational event in PDAC—develop a small number of pre-cancerous lesions that stochastically develop into PDAC over time. However, mice also lacking the essential autophagy genes Atg5 or Atg7 accumulate low-grade, pre-malignant pancreatic intraepithelial neoplasia lesions, but progression to high-grade pancreatic intraepithelial neoplasias and PDAC is blocked. In marked contrast, in mice containing oncogenic Kras and lacking p53, loss of autophagy no longer blocks tumour progression, but actually accelerates tumour onset, with metabolic analysis revealing enhanced glucose uptake and enrichment of anabolic pathways, which can fuel tumour growth. These findings provide considerable insight into the role of autophagy in cancer and have important implications for autophagy inhibition in cancer therapy. In this regard, we also show that treatment of mice with the autophagy inhibitor hydroxychloroquine, which is currently being used in several clinical trials, significantly accelerates tumour formation in mice containing oncogenic Kras but lacking p53.
Collision avoidance in the presence of a virtual agent in small-scale virtual environments

Computer-controlled, human-like virtual agents (VAs), are often embedded into immersive virtual environments (IVEs) in order to enliven a scene or to assist users. Certain constraints need to be fulfilled, e.g., a collision avoidance strategy allowing users to maintain their personal space. Violating this flexible protective zone causes discomfort in real-world situations and in IVEs. However, no studies on collision avoidance for small-scale IVEs have been conducted yet. Our goal is to close this gap by presenting the results of a controlled user study in a CAVE. 27 participants were immersed in a small-scale office with the task of reaching the office door. Their way was blocked either by a male or female VA, representing their co-worker. The VA showed different behavioral patterns regarding gaze and locomotion. Our results indicate that participants preferred collaborative collision avoidance: they expect the VA to step aside in order to get more space to pass while being willing to adapt their own walking paths.
Image and video decomposition and editing

My research has centered around understanding the colorful appearance of physical and digital paintings and images. My work focuses on decomposing images or videos into more editable data structures called layers, to enable efficient image or video re-editing. Given a time-lapse painting video, we can recover translucent layer strokes from every frame pairs by maximizing translucency of layers for its maximum re-usability, under either digital color compositing model or a physically inspired nonlinear color layering model, after which, we apply a spatial-temporal clustering on strokes to obtain semantic layers for further editing, such as global recoloring and local recoloring, spatial-temporal gradient recoloring and so on. With a single image input, we use the convex shape geometry intuition of color points distribution in RGB space, to help extract a small size palette from a image and then solve an optimization to extract translucent RGBA layers, under digital alpha compositing model. The translucent layers are suitable for global and local image recoloring and new object insertion as layers efficiently. Alternatively, we can apply an alternating least square optimization to extract multi-spectral physical pigment parameters from a single digitized physical painting image, under a physically inspired nonlinear color mixing model, with help of some multi-spectral pigment parameters priors. With these multi-spectral pigment parameters and their mixing layers, we demonstrate tonal adjustments, selection masking, recoloring, physical pigment understanding, palette summarization and edge enhancement. Our recent ongoing work introduces an extremely scalable and efficient yet simple palette-based image decomposition algorithm to extract additive mixing layers from single image. Our approach is based on the geometry of images in RGBXY-space. This new geometric approach is orders of magnitude more efficient than previous work and requires no numerical optimization. We demonstrate a real-time layer updating GUI. We also present a palette-based framework for color composition for visual applications, such as image and video harmonization, color transfer and so on.
A Robust FSM Watermarking Scheme for IP Protection of Sequential Circuit Design

Finite state machines (FSMs) are the backbone of sequential circuit design. In this paper, a new FSM watermarking scheme is proposed by making the authorship information a non-redundant property of the FSM. To overcome the vulnerability to state removal attack and minimize the design overhead, the watermark bits are seamlessly interwoven into the outputs of the existing and free transitions of state transition graph (STG). Unlike other transition-based STG watermarking, pseudo input variables have been reduced and made functionally indiscernible by the notion of reserved free literal. The assignment of reserved literals is exploited to minimize the overhead of watermarking and make the watermarked FSM fallible upon removal of any pseudo input variable. A direct and convenient detection scheme is also proposed to allow the watermark on the FSM to be publicly detectable. Experimental results on the watermarked circuits from the ISCAS'89 and IWLS'93 benchmark sets show lower or acceptably low overheads with higher tamper resilience and stronger authorship proof in comparison with related watermarking schemes for sequential functions.
SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers

This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.