nils-herrmann/clean_scirepeval huggingface dataset:
A New Similarity Measure for Taxonomy Based on Edge Counting

This paper introduces a new similarity measure based on edge counting in a taxonomy like WorldNet or Ontology. Measurement of similarity between text segments or concepts is very useful for many applications like information retrieval, ontology matching, text mining, and question answering and so on. Several measures have been developed for measuring similarity between two concepts: out of these we see that the measure given by Wu and Palmer  is simple, and gives good performance. Our measure is based on their measure but strengthens it. Wu and Palmer  measure has a disadvantage that it does not consider how far the concepts are semantically. In our measure we include the shortest path between the concepts and the depth of whole taxonomy together with the distances used in Wu and Palmer . Also the measure has following disadvantage i.e. in some situations, the similarity of two elements of an IS-A ontology contained in the neighbourhood exceeds the similarity value of two elements contained in the same hierarchy. Our measure introduces a penalization factor for this case based upon shortest length between the concepts and depth of whole taxonomy.
Configuring of Spiking Central Pattern Generator Networks for Bipedal Walking Using Genetic Algorthms

In limbed animals, spinal neural circuits responsible for controlling muscular activities during walking are called central pattern generators (CPG). CPG networks display oscillatory activities that actuates individual or groups of muscles in a coordinated fashion so that the limbs of the animal are flexed and extended at the appropriate time and with the required velocity for the animal to efficiently traverse various types of terrain, and to recover from environmental perturbation. Typically, the CPG networks are constructed with many neurons, each of which has a number of control parameters. As the number of muscles increases, it is often impossible to manually, albeit intelligently, select the network parameters for a particular movement. Furthermore, it is virtually impossible to reconfigure the parameters on-line. This paper describes how genetic algorithms (GA) can be used for on-line (re)configuring of CPG networks for a bipedal robot. We show that the neuron parameters and connection weights/network topology of a canonical walking network can be reconfigured within a few of generations of the GA. The networks, constructed with integrate-and-fire-with-adaptation (IFA) neurons, are implemented with a microcontroller and can be reconfigured to vary walking speed from 0.5Hz to 3.5Hz. The phase relationship between the hips and knees can be arbitrarily set (to within 1 degree) and prescribed complex joint angle profiles are realized. This is a powerful approach to generating complex muscle synergies for robots with multiple joints and distributed actuators.
Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation

We motivate and address a human-in-the-loop variant of the monocular viewpoint estimation task in which the location and class of one semantic object keypoint is available at test time. In order to leverage the keypoint information, we devise a Convolutional Neural Network called Click-Here CNN (CH-CNN) that integrates the keypoint information with activations from the layers that process the image. It transforms the keypoint information into a 2D map that can be used to weigh features from certain parts of the image more heavily. The weighted sum of these spatial features is combined with global image features to provide relevant information to the prediction layers. To train our network, we collect a novel dataset of 3D keypoint annotations on thousands of CAD models, and synthetically render millions of images with 2D keypoint information. On test instances from PASCAL 3D+, our model achieves a mean class accuracy of 90.7%, whereas the state-of-the-art baseline only obtains 85.7% mean class accuracy, justifying our argument for human-in-the-loop inference.
AutoMAC: rateless wireless concurrent medium access

Current wireless network design is built on the ethos of avoiding interference. In this paper we question this long-held design principle. We show that with appropriate design, successful concurrent transmissions can be enabled and exploited on both the uplink and downlink. We show that this counter-intuitive approach of encouraging interference can be exploited to increase network capacity significantly and simplify network design. We design and implement name, a novel MAC and PHY protocol that exploits recently proposed rateless coding techniques to provide such concurrency. We show via a prototype implementation and experimental evaluation that name can provide a 60% increase in network capacity on the uplink compared to traditional Wifi that does omniscient rate adaptation and a $35\%$ median throughput gain on the downlink PHY layer as compared to an omniscient scheme that picks the best conventional bitrate.
Big Data and Predictive Analytics Methods for Modeling and Analysis of Semiconductor Manufacturing Processes

Semiconductor manufacturing fabs generate huge amount of data. The big data approaches of data management have increased speed, quality and accessibility of the data. This paper discusses harnessing value from this data using predictive analytics methods. Various aspects predictive analytics in the context of semiconductor manufacturing are discussed. The limitations of standard methods of analysis and the need to adopt robust methods of modeling and analysis are highlighted. The robust prediction modeling method is implemented on wafer sensor data resulting in improved prediction ability of wafer quality characteristics.
Management of ascites in cirrhosis.

Ascites is a common complication of liver cirrhosis associated with a poor prognosis. The treatment of ascites requires dietary sodium restriction and the judicious use of distal and loop diuretics, sequential at an earlier stage of ascites, and a combination at a later stage of ascites. The diagnosis of refractory ascites requires the demonstration of diuretic non-responsiveness, despite dietary sodium restriction, or the presence of diuretic-related complications. Patients with refractory ascites require second-line treatments of repeat large-volume paracentesis (LVP) or the insertion of a transjugular intrahepatic portosystemic shunt (TIPS), and assessment for liver transplantation. Careful patient selection is paramount for TIPS to be successful as a treatment for ascites. Patients not suitable for TIPS insertion should receive LVP. The use of albumin as a volume expander is recommended for LVP of >5-6 L to prevent the development of circulatory dysfunction, although the clinical significance of post-paracentesis circulatory dysfunction is still debated. Significant mortality is still being observed in cirrhotic patients with ascites and relatively preserved liver and renal function, as indicated by a lower Model for End-Stage Liver Disease (MELD) score. It is proposed that patients with lower MELD scores and ascites should receive additional points in calculating their priority for liver transplantation. Potential new treatment options for ascites include the use of various vasoconstrictors, vasopressin V(2) receptor antagonists, or the insertion of a peritoneo-vesical shunt, all of which could possibly improve the management of ascites.
Upward spirals of positive emotions counter downward spirals of negativity: insights from the broaden-and-build theory and affective neuroscience on the treatment of emotion dysfunctions and deficits in psychopathology.

This review integrates Fredrickson's broaden-and-build theory of positive emotions with advances in affective neuroscience regarding plasticity in the neural circuitry of emotions to inform the treatment of emotion deficits within psychopathology. We first present a body of research showing that positive emotions broaden cognition and behavioral repertoires, and in so doing, build durable biopsychosocial resources that support coping and flourishing mental health. Next, by explicating the processes through which momentary experiences of emotions may accrue into self-perpetuating emotional systems, the current review proposes an underlying architecture of state-trait interactions that engenders lasting affective dispositions. This theoretical framework is then used to elucidate the cognitive-emotional mechanisms underpinning three disorders of affect regulation: depression, anxiety, and schizophrenia. In turn, two mind training interventions, mindfulness and loving-kindness meditation, are highlighted as means of generating positive emotions that may counter the negative affective processes implicated in these disorders. We conclude with the proposition that positive emotions may exert a countervailing force on the dysphoric, fearful, or anhedonic states characteristic of psychopathologies typified by emotional dysfunctions.
Context-aware body area networks (CABAN) for interactive smart environments: interference characterization

Body Area Networks are unique in that the large-scale mobility of users allows the network itself to travel across a diverse range of operating domains or even to enter new and unknown environments. This network mobility is unlike node mobility in that sensed changes in inter-network interference level may be used to identify opportunities for intelligent inter-networking, for example, by merging or splitting from other networks, thus providing an extra degree of freedom. This paper introduces the concept of context-aware bodynets for interactive environments using inter-network interference sensing. New ideas are explored at both the physical and link layers with an investigation based on a 'smart' office environment. A series of carefully controlled measurements of the mesh interconnectivity both within and between an ambulatory body area network and a stationary desk-based network were performed using 2.45 GHz nodes. Received signal strength and carrier to interference ratio time series for selected node to node links are presented. The results provide an insight into the potential interference between the mobile and static networks and highlight the possibility for automatic identification of network merging and splitting opportunities.
Aesthetic Correction of Smoker's Lip Using 1064 nm Q-switched Neodymium-doped Yttrium Aluminium Garnet Laser

Journal of Cutaneous and Aesthetic Surgery ¦ Volume 10 ¦ Issue 2 ¦ April‐June 2017 118 2003;9:CS1‐4. 4. Abrahamson TG, Davis DA. Angiolymphoid hyperplasia with eosinophilia responsive to pulsed dye laser.  Kaur T, Sandhu K, Gupta S, Kanwar AJ, Kumar B. Treatment of angiolymphoid hyperplasia with eosinophilia with the carbon dioxide laser. J Dermatolog Treat 2004;15:328‐30. 6. Akdeniz N, Kösem M, Calka O, Bilgili SG, Metin A, Gelincik I. Intralesional bleomycin for angiolymphoid hyperplasia.
Brain anatomy differences in childhood stuttering

Stuttering is a developmental speech disorder that occurs in 5% of children with spontaneous remission in approximately 70% of cases. Previous imaging studies in adults with persistent stuttering found left white matter deficiencies and reversed right-left asymmetries compared to fluent controls. We hypothesized that similar differences might be present indicating brain development differences in children at risk of stuttering. Optimized voxel-based morphometry compared gray matter volume (GMV) and diffusion tensor imaging measured fractional anisotropy (FA) in white matter tracts in 3 groups: children with persistent stuttering, children recovered from stuttering, and fluent peers. Both the persistent stuttering and recovered groups had reduced GMV from normal in speech-relevant regions: the left inferior frontal gyrus and bilateral temporal regions. Reduced FA was found in the left white matter tracts underlying the motor regions for face and larynx in the persistent stuttering group. Contrary to previous findings in adults who stutter, no increases were found in the right hemisphere speech regions in stuttering or recovered children and no differences in right-left asymmetries. Instead, a risk for childhood stuttering was associated with deficiencies in left gray matter volume while reduced white matter integrity in the left hemisphere speech system was associated with persistent stuttering. Anatomical increases in right hemisphere structures previously found in adults who stutter may have resulted from a lifetime of stuttering. These findings point to the importance of considering the role of neuroplasticity during development when studying persistent forms of developmental disorders in adults.
A Novel Method for Unsupervised Anomaly Detection Using  Unlabelled Data

Most current intrusion detection methods cannot process large amounts of audit data for real-time operation. In this paper, anomaly network intrusion detection method based on principal component analysis (PCA) for data reduction and fuzzy adaptive resonance theory (fuzzy ART) for classifier is presented. Moreover, PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Using a set of benchmark data from KDD (knowledge discovery and data mining) competition designed by DARPA for demonstrate to detection intrusions. Experimental results show the proposed model can classify the network connections with satisfying performance.
Depth Estimation for Glossy Surfaces with Light-Field Cameras

Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current lightfield depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. Because light-field cameras have an array of micro-lenses, the captured data allows modification of both focus and perspective viewpoints. In this paper, we develop an iterative approach to use the benefits of light-field data to estimate and remove the specular component, improving the depth estimation. The approach enables light-field data depth estimation to support both specular and diffuse scenes. We present a physically-based method that estimates one or multiple light source colors. We show our method outperforms current state-of-the-art diffuse and specular separation and depth estimation algorithms in multiple real world scenarios.
Torque Ripple Reduction in Interior Permanent Magnet Synchronous Machines Using Stators With Odd Number of Slots Per Pole Pair

This paper develops analytical principles for torque ripple reduction in interior permanent magnet (IPM) synchronous machines. The significance of slot harmonics and the benefits of stators with odd number of slots per pole pair are highlighted. Based on these valuable analytical insights, this paper proposes coordination of the selection of stators with odd number of slots per pole pair and IPM rotors with multiple layers of flux barriers in order to reduce torque ripple. The effectiveness of using stators with odd number of slots per pole pair in reducing torque ripple is validated by applying a finite-element-based Monte Carlo optimization method to four IPM machine topologies, which are combinations of two stator topologies (even or odd number of slots per pole pair) and two IPM rotor topologies (one- or two-layer). It is demonstrated that the torque ripple can be reduced to less than 5% by selecting a stator with an odd number of slots per pole pair and the IPM rotor with optimized barrier configurations, without using stator/rotor skewing or rotor pole shaping.
Current evidence and future directions for research with omega-3 fatty acids and attention deficit hyperactivity disorder.

Nutritional insufficiencies of nutrients such as omega-3 highly unsaturated fatty acids (HUFAs), vitamins and minerals have been linked to suboptimal developmental outcomes including attention deficit hyperactivity disorder (ADHD). Although the predominant treatment is currently psychostimulant medications, randomized clinical trials with omega-3 HUFAs have reported small-to-modest effects in reducing symptoms of ADHD in children despite arguable individual methodological and design misgivings.



This review presents, discusses and critically evaluates data and findings from meta-analytic and systematic reviews and clinical trials published within the last 12 months. Recent trajectories of this research are discussed, such as comparing eicosapentaenoic acid and docosahexaenoic acid and testing the efficacy of omega-3 HUFAs as an adjunct to methylphenidate. Discussion includes highlighting limitations and potential future directions such as addressing variable findings by accounting for other nutritional deficiencies and behavioural food intolerances.



The authors conclude that given the current economic burden of ADHD, estimated in the region of $77 billion in the USA alone, in addition to the fact that a proportion of patients with ADHD are either treatment resistant, nonresponders or withdraw from medication because of adverse side-effects, the investigation of nonpharmacological interventions including omega-3 HUFAs in clinical practice warrants extrapolating.
Chitchat: Navigating tradeoffs in device-to-device context sharing

Acquiring local context information and sharing it among co-located devices is critical for emerging pervasive computing applications. The devices belonging to a group of co-located people may need to detect a shared activity (e.g., a meeting) to adapt their devices to support the activity. Today's devices are almost universally equipped with device-to-device communication that easily enables direct context sharing. While existing context sharing models tend not to consider devices' resource limitations or users' constraints, enabling devices to directly share context has significant benefits for efficiency, cost, and privacy. However, as we demonstrate quantitatively, when devices share context via device-to-device communication, it needs to be represented in a size-efficient way that does not sacrifice its expressiveness or accuracy. We present CHITCHAT, a suite of context representations that allows application developers to tune tradeoffs between the size of the representation, the flexibility of the application to update context information, the energy required to create and share context, and the quality of the information shared. We can substantially reduce the size of context representation (thereby reducing applications' overheads when they share their contexts with one another) with only a minimal reduction in the quality of shared contexts.
An Efficient Hardware-Based Multi-hash Scheme for High Speed IP Lookup

The increasingly more stringent performance and power requirements of Internet routers call for scalable IP lookup strategies that go beyond the currently viable TCAM- and trie-based solutions. This paper describes a new hash-based IP lookup scheme that is both storage efficient and high performance. In order to achieve high storage efficiency, we take a multi-hashing approach and employ an advanced hashing technique that effectively resolves hashing collisions by dynamically migrating IP prefixes that are already in the lookup table as new prefixes are inserted. To obtain high lookup throughput, the multiple hash tables are accessed in parallel (using different hash functions) or in a pipelined manner. We evaluate the proposed scheme using up-to-date core routing tables and discuss how its key design parameters can be determined. When compared with state-of-the-art TCAM designs, our scheme reduces area and power requirements by 60% and 80% respectively, while achieving competitive lookup rates. We expect that the proposed scheme scales well with the anticipated routing table sizes and technologies in the future.
User-perspective augmented reality magic lens from gradients

In this paper we present a new approach to creating a geometrically-correct user-perspective magic lens and a prototype device implementing the approach. Our prototype uses just standard color cameras, with no active depth sensing. We achieve this by pairing a recent gradient domain image-based rendering method with a novel semi-dense stereo matching algorithm inspired by PatchMatch. Our stereo algorithm is simple but fast and accurate within its search area. The resulting system is a real-time magic lens that displays the correct user perspective with a high-quality rendering, despite the lack of a dense disparity map.
sCompile: Critical Path Identification and Analysis for Smart Contracts

Smart contracts are an innovation built on top of the blockchain technology. It provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way, which has the potential to revolutionize many industries. The most popular programming language for creating smart contracts is called Solidity, which is supported by Ethereum. Like ordinary programs, Solidity programs may contain vulnerabilities, which potentially lead to attacks. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is thus important that smart contracts are checked against potential vulnerabilities. Existing approaches tackle the problem by developing methods which aim to automatically analyze or verify smart contracts. Such approaches often results in false alarms or poor scalability, fundamentally because Solidity is Turing-complete. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise present them with user friendly warnings for user inspection. We identify paths which involve monetary transaction as critical paths, and prioritize those which potentially violate important properties. For scalability, symbolic execution techniques are only applied to top ranked critical paths. Our approach has been implemented in a tool called sCompile, which has been applied to 36,099 smart contracts. The experiment results show that sCompile is efficient, i.e., 5 seconds on average for one smart contract. Furthermore, we show that many known vulnerability can be captured if the user inspects as few as 10 program paths generated by sCompile. Lastly, sCompile discovered 224 unknown vulnerabilities with a false positive rate of 15.4% before user inspection.
Symmetry in 3D Geometry: Extraction and Applications

The concept of symmetry has received significant attention in computer graphics and computer vision research in recent years. Numerous methods have been proposed to find and extract geometric symmetries and exploit such high-level structural information for a wide variety of geometry processing tasks. This report surveys and classifies recent developments in symmetry detection. We focus on elucidating the similarities and differences between existing methods to gain a better understanding of a fundamental problem in digital geometry processing and shape understanding in general. We discuss a variety of applications in computer graphics and geometry that benefit from symmetry information for more effective processing. An analysis of the strengths and limitations of existing algorithms highlights the plenitude of opportunities for future research both in terms of theory and applications.