nils-herrmann/clean_scirepeval huggingface dataset:
A 249-Mpixel/s HEVC Video-Decoder Chip for 4K Ultra-HD Applications

High Efficiency Video Coding, the latest video standard, uses larger and variable-sized coding units and longer interpolation filters than H.264/AVC to better exploit redundancy in video signals. These algorithmic techniques enable a 50% decrease in bitrate at the cost of computational complexity, external memory bandwidth, and, for ASIC implementations, on-chip SRAM of the video codec. This paper describes architectural optimizations for an HEVC video decoder chip. The chip uses a two-stage subpipelining scheme to reduce on-chip SRAM by 56 kbytes-a 32% reduction. A high-throughput read-only cache combined with DRAM-latency-aware memory mapping reduces DRAM bandwidth by 67%. The chip is built for HEVC Working Draft 4 Low Complexity configuration and occupies 1.77 mm2 in 40-nm CMOS. It performs 4K Ultra HD 30-fps video decoding at 200 MHz while consuming 1.19 nJ/pixel of normalized system power.
Memory forensics using virtual machine introspection for Malware analysis

A security sandbox is a technology that is often used to detect advanced malware. However, current sandboxes are highly dependent on VM hypervisor types and versions. Thus, in this paper, we introduce a new sandbox design, using memory forensics techniques, to provide an agentless sandbox solution that is independent of the VM hypervisor. In particular, we leverage the VM introspection method to monitor malware running memory data outside the VM and analyze its system behaviors, such as process, file, registry, and network activities. We evaluate the feasibility of this method using 20 advanced and 8 script-based malware samples. We furthermore demonstrate how to analyze malware behavior from memory and verify the results with three different sandbox types. The results show that we can analyze suspicious malware activities, which is also helpful for cyber security defense.
Rhinoplasty: the nasal bones - anatomy and analysis.

The analysis of nasal anatomy, and especially the nasal bones including the osseocartilaginous vault, is significant for functional and aesthetic reasons.



The objective was to understand the anatomy of the nasal bones by establishing new descriptions, terms, and definitions because the existing parameters were insufficient. Adequate terminology was employed to harmonize the anthropometric and clinical measurements.



A two-part harvest technique consisting of resecting the specimen and then creating a replica of the skull was performed on 44 cadavers to obtain specific measurements.



The nasal bones have an irregular, variable shape, and three distinct angles can be found along the dorsal profile line beginning with the nasion angle (NA), the dorsal profile angulation (DPA) and the kyphion angulation (KA). In 12% of cases, the caudal portion of the nasal bones was straight and without angulation resulting in a "V-shape" configuration. In 88% of cases, the caudal portion of the bone was angulated, which resulted in an "S-shape" nasal bone configuration. The intervening cephalic bone, nasion to sellion (N-S), represents the radix while the caudal bone, sellion to r (S-R), represents the bony dorsum.



By standardizing and measuring existing nasal landmarks and understanding the different anatomic configurations of the nasal bones, rhinoplasty surgeons can better plan their operations within the radix and bony and osseocartilaginous vaults.
Malware Detection and Classification Based on N-Grams Attribute Similarity

Unknown malware has increased dramatically, but the existing security software cannot identify them effectively. In this paper, we propose a new malware detection and classification method based on n-grams attribute similarity. We extract all n-grams of byte codes from training samples and select the most relevant as attributes. After calculating the average value of attributes in malware and benign separately, we determine a test sample is malware or benign by attribute similarity between attributes of the test sample and the two average attributes of malware and benign. We compare our method with a variety of machine learning methods, including Naïve Bayes, Bayesian Networks, Support Vector Machine and C4.5 Decision Tree. Experimental results on public (Open Malware Benchmark) and private (self-collected) datasets both reveal that our method outperforms the other four methods.
Model-Based Design of Computer-Controlled Game Character Behavior

Recently, the complexity of modern, real-time computer games has increased drastically. The need for sophisticated game AI, in particular for Non-Player Characters, grows with the demand for realistic games. Writing consistent, re-useable and efficient AI code has become hard. We demonstrate how modeling game AI at an appropriate abstraction level using an appropriate modeling language has many advantages. A variant of Rhapsody Statecharts is proposed as an appropriate formalism. The Tank Wars game by Electronic Arts (EA) is used to demonstrate our concrete approach. We show how the use of the Statecharts formalism leads quite naturally to layered modeling of game AI and allows modelers to abstract away from choices between, for example, time-slicing and discrete-event time management. Finally, our custom tools are used to synthesize efficient C++ code to insert into the Tank Wars main game loop.
Developing an Enterprise Business Intelligence Capability: The Norfolk Southern Journey

Although many IT and business managers today may be lured into business intelligence (BI) investments by the promise of predictive analytics and emerging BI trends, creating an enterprise-wide BI capability is a journey that takes time. This article describes Norfolk Southern Railway’s BI journey, which began in the early 1990s with departmental reporting, evolved into data warehousing and analytic applications, and has resulted in a company that today uses BI to support corporate strategy. We describe how BI at Norfolk Southern evolved over several decades, with the company developing strong BI foundations and an effective enterprise-wide BI capability. We also identify the practices that kept the BI journey “on track.” These practices can be used by other IT and business leaders as they plan and develop BI capabilities in their own organizations.
State estimation and path following on curved and flat vertical surfaces with Omniclimber robots: Kinematics and control

Omnidirectional wheels used on Omniclimber inspection robot and in other robots enable a holonomic drive and a good maneuverability. On the other hand, they have a poor wheel traction and suffer from vertical and horizontal vibration, decreasing the trajectory following accuracy of the robot. In this study, we address this problem by integrating an orientation estimation and correction algorithm in the Omniclimber control by integration of an accelerometer. Moreover, since the Omniclimber chassis adapts to curved structures, the kinematics of the robot change when moving on a curved surface. We integrated an additional algorithm which corrects the robot's kinematics based on the curvature diameter and the current robot orientation. By integrating these two algorithms we could make remarkable improvements on the path following accuracy of the Omniclimber on flat and curved structures.
Active coevolutionary learning of requirements specifications from examples

Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user's needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson . The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification.
Automatic Speech Recognition for Mixed Dialect Utterances by Mixing Dialect Language Models

This paper presents an automatic speech recognition (ASR) system that accepts a mixture of various kinds of dialects. The system recognizes dialect utterances on the basis of the statistical simulation of vocabulary transformation and combinations of several dialect models. Previous dialect ASR systems were based on handcrafted dictionaries for several dialects, which involved costly processes. The proposed system statistically trains transformation rules between a common language and dialects, and simulates a dialect corpus for ASR on the basis of a machine translation technique. The rules are trained with small sets of parallel corpora to make up for the lack of linguistic resources on dialects. The proposed system also accepts mixed dialect utterances that contain a variety of vocabularies. In fact, spoken language is not a single dialect but a mixed dialect that is affected by the circumstances of speakers' backgrounds (e.g., native dialects of their parents or where they live). We addressed two methods to combine several dialects appropriately for each speaker. The first was recognition with language models of mixed dialects with automatically estimated weights that maximized the recognition likelihood. This method performed the best, but calculation was very expensive because it conducted grid searches of combinations of dialect mixing proportions that maximized the recognition likelihood. The second was integration of results of recognition from each single dialect language model. The improvements with this model were slightly smaller than those with the first method. Its calculation cost was, however, inexpensive and it worked in real-time on general workstations. Both methods achieved higher recognition accuracies for all speakers than those with the single dialect models and the common language model, and we could choose a suitable model for use in ASR that took into consideration the computational costs and recognition accuracies.
Co-Training for Demographic Classification Using Deep Learning from Label Proportions

Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we introduce a deep neural network for the Learning from Label Proportion (LLP) setting, in which the training data consist of bags of unlabeled instances with associated label distributions for each bag. We introduce a new regularization layer, Batch Averager, that can be appended to the last layer of any deep neural network to convert it from supervised learning to LLP. This layer can be implemented readily with existing deep learning packages. To further support domains in which the data consist of two conditionally independent feature views (e.g. image and text), we propose a co-training algorithm that iteratively generates pseudo bags and refits the deep LLP model to improve classification accuracy. We demonstrate our models on demographic attribute classification (gender and race/ethnicity), which has many applications in social media analysis, public health, and marketing. We conduct experiments to predict demographics of Twitter users based on their tweets and profile image, without requiring any user-level annotations for training. We find that the deep LLP approach outperforms baselines for both text and image features separately. Additionally, we find that co-training algorithm improves image and text classification by 4% and 8% absolute F1, respectively. Finally, an ensemble of text and image classifiers further improves the absolute F1 measure by 4% on average.
Dopamine release in the basal ganglia

Dopamine (DA) is a key transmitter in the basal ganglia, yet DA transmission does not conform to several aspects of the classic synaptic doctrine. Axonal DA release occurs through vesicular exocytosis and is action potential- and Ca²⁺-dependent. However, in addition to axonal release, DA neurons in midbrain exhibit somatodendritic release by an incompletely understood, but apparently exocytotic, mechanism. Even in striatum, axonal release sites are controversial, with evidence for DA varicosities that lack postsynaptic specialization, and largely extrasynaptic DA receptors and transporters. Moreover, DA release is often assumed to reflect a global response to a population of activities in midbrain DA neurons, whether tonic or phasic, with precise timing and specificity of action governed by other basal ganglia circuits. This view has been reinforced by anatomical evidence showing dense axonal DA arbors throughout striatum, and a lattice network formed by DA axons and glutamatergic input from cortex and thalamus. Nonetheless, localized DA transients are seen in vivo using voltammetric methods with high spatial and temporal resolution. Mechanistic studies using similar methods in vitro have revealed local regulation of DA release by other transmitters and modulators, as well as by proteins known to be disrupted in Parkinson's disease and other movement disorders. Notably, the actions of most other striatal transmitters on DA release also do not conform to the synaptic doctrine, with the absence of direct synaptic contacts for glutamate, GABA, and acetylcholine (ACh) on striatal DA axons. Overall, the findings reviewed here indicate that DA signaling in the basal ganglia is sculpted by cooperation between the timing and pattern of DA input and those of local regulatory factors.
Reviews , Reputation , and Revenue : The Case of Yelp . com

Do online consumer reviews affect restaurant demand? I investigate this question using a novel dataset combining reviews from the website Yelp.com and restaurant data from the Washington State Department of Revenue. Because Yelp prominently displays a restaurant's rounded average rating, I can identify the causal impact of Yelp ratings on demand with a regression discontinuity framework that exploits Yelp’s rounding thresholds. I present three findings about the impact of consumer reviews on the restaurant industry: (1) a one-star increase in Yelp rating leads to a 5-9 percent increase in revenue, (2) this effect is driven by independent restaurants; ratings do not affect restaurants with chain affiliation, and (3) chain restaurants have declined in market share as Yelp penetration has increased. This suggests that online consumer reviews substitute for more traditional forms of reputation. I then test whether consumers use these reviews in a way that is consistent with standard learning models. I present two additional findings: (4) consumers do not use all available information and are more responsive to quality changes that are more visible and (5) consumers respond more strongly when a rating contains more information. Consumer response to a restaurant’s average rating is affected by the number of reviews and whether the reviewers are certified as “elite” by Yelp, but is unaffected by the size of the reviewers’ Yelp friends network. † Harvard Business School,
Transcriptional integration of metabolism by the nuclear sterol-activated receptors LXR and FXR

Nuclear receptors are integrators of hormonal and nutritional signals, mediating changes to metabolic pathways within the body. Given that modulation of lipid and glucose metabolism has been linked to diseases including type 2 diabetes, obesity and atherosclerosis, a greater understanding of pathways that regulate metabolism in physiology and disease is crucial. The liver X receptors (LXRs) and the farnesoid X receptors (FXRs) are activated by oxysterols and bile acids, respectively. Mounting evidence indicates that these nuclear receptors have essential roles, not only in the regulation of cholesterol and bile acid metabolism but also in the integration of sterol, fatty acid and glucose metabolism.
Warpage Measurements and Characterizations of Fan-Out Wafer-Level Packaging with Large Chips and Multiple Redistributed Layers

In this study, the warpages of a chip-first and die face-up FOWLP (fan-out wafer-level packaging) with a very large silicon chip (10mmx10mmx0.15mm) and three RDLs (redistributed layers) are measured and characterized. Emphasis is placed on the measurement and 3D finite element simulation of the warpages during the FOWLP fabrication processes, especially for: (a) right after PMC (post mold cure), (b) right after backgrinding of the EMC (epoxy molding compound) to expose the Cu-contact pads, and (c) the individual package (right after the solder ball mounting and dicing) vs. SMT (surface mount technology) reflow temperatures. The simulation results are compared to the measurement results. Some recommendations on controlling the warpages are provided.
Anetoderma due to secondary syphilis: Report of two cases and discussion of the histopathological findings.

Anetoderma is a rare benign condition of diverse etiology whose characteristic is the diminution or absence of the dermal elastic fibers. Classified as primary and secondary, the latter associated with tumors, inflammatory, and infectious diseases. Although the etiology of the lesions is well described in literature, the pathogenesis is still poorly determined. Anetoderma in syphilis is rare, and occurs even in the most uncommon cutaneous manifestations of the disease, such as the nodular form. In order to better understand the changes that lead to elastolysis, we propose a better correlation with the histopathological findings of the lesions that precede it. We present two cases of anetoderma secondary to syphilis, whose clinical aspects resembled the pattern of their initial secondary syphilis rash.
The pillars of metrology

The more I study metrology, the more I get persuaded that the measuring activity is an implicit part of our lives, something we are not really aware of, though we do or rely on measurements several times a day. When we check time, put fuel in our cars, buy food, just to mention some everyday activity, either we measure something or we trust measurements done by somebody else. It is quite immediate to conclude that, nowadays, everything is measured and measurement results are the basis of many important decisions. Interestingly enough, measurement has always played an important role in mankind�s evolution and I fully agree with Bryan Kibble�s statement that the measuring stick came before the wheel, otherwise the wheel could not have been built . The measuring stick is also one of the most ancient instruments, and we find it together with time measuring instruments and weighs in almost every civilization of the past, proving that measurement is one of the most important branches of science, and there is no civilization without measurement. It proves also the intimate connection existing between instrumentation and measurement, being the two sides of a single medal: the measurement science, or metrology.
Radiation Performance Analysis of 28 GHz Antennas Integrated in 5G Mobile Terminal Housing

Radiation from printed millimeter-wave antennas integrated in mobile terminals is affected by surface currents on chassis, guided waves trapped in dielectric layers, superstrates, and the user’s hand, making mobile antenna design for 5G communication challenging. In this paper, four canonical types of printed 28-GHz antenna elements are integrated in a 5G mobile terminal mock-up. Different kinds of terminal housing effects are examined separately, and the terminal housing effects are also diagnosed through equivalent currents by using the inverse source technique. To account for the terminal housing effects on a beam-scanning antenna subarray, we propose the effective beam-scanning efficiency to evaluate its coverage performance. This paper presents the detailed analysis, results, and new concepts regarding the terminal housing effects, and thereby provides valuable insight into the practical 5G mobile antenna design and radiation performance characterization.
To be sure, to be sure: intolerance of uncertainty mediates symptoms of various anxiety disorders and depression.

The Intolerance of Uncertainty Model was initially developed as an explanation for worry within the context of generalized anxiety disorder. However, recent research has identified intolerance of uncertainty (IU) as a possible transdiagnostic maintaining factor across the anxiety disorders and depression. The aim of this study was to determine whether IU mediated the relationship between neuroticism and symptoms related to various anxiety disorders and depression in a treatment-seeking sample (N=328). Consistent with previous research, IU was significantly associated with neuroticism as well as with symptoms of social phobia, panic disorder and agoraphobia, obsessive-compulsive disorder, generalized anxiety disorder, and depression. Moreover, IU explained unique variance in these symptom measures when controlling for neuroticism. Mediational analyses showed that IU was a significant partial mediator between neuroticism and all symptom measures, even when controlling for symptoms of other disorders. More specifically, anxiety in anticipation of future uncertainty (prospective anxiety) partially mediated the relationship between neuroticism and symptoms of generalized anxiety disorder (i.e. worry) and obsessive-compulsive disorder, whereas inaction in the face of uncertainty (inhibitory anxiety) partially mediated the relationship between neuroticism and symptoms of social anxiety, panic disorder and agoraphobia, and depression. Sobel's test demonstrated that all hypothesized meditational pathways were associated with significant indirect effects, although the mediation effect was stronger for worry than other symptoms. Potential implications of these findings for the treatment of anxiety disorders and depression are discussed.