nils-herrmann/clean_scirepeval huggingface dataset:
Pilot wave quantum model for the stock market

We use methods of classical and quantum mechanics for mathematical modeling of price dynamics at the financial market. The Hamiltonian formalism on the price/price-change phase space is used to describe the classical-like evolution of prices. This classical dynamics of prices is determined by ”hard” conditions (natural resources, industrial production, services and so on). These conditions as well as ”hard” relations between traders at the financial market are mathematically described by the classical financial potential. At the real financial market ”hard” conditions are not the only source of price changes. The information exchange and market psychology play important (and sometimes determining) role in price dynamics. We propose to describe this ”soft” financial factors by using the pilot wave (Bohmian) model of quantum mechanics. The theory of financial mental (or psychological) waves is used to take into account market psychology. The real trajectories of prices are determined (by the financial analogue of the second Newton law) by two financial potentials: classical-like (”hard” market conditions) and quantum-like (”soft” market conditions).
BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation

Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048×1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.
Human Conceptions of Spaces : Implications for Geographic Information Systems 1

The way people conceptualize space is an important consideration for the design of geographic information systems, because a better match with peopleÕs thinking is expected to lead to easier-touse information systems. Everyday space, the basis to geographic information systems (GISs), has been characterized in the literature as being either small-scale (from table-top to room-size spaces) or large-scale (inside-of-building spaces to city-size space). While this dichotomy of space is grounded in the view from psychology that peopleÕs perception of space, spatial cognition, and spatial behavior are experience-based, it is in contrast to current GISs, which enable us to interact with large-scale spaces as though they were small-scale or manipulable. We analyze different approaches to characterizing spaces and propose a unified view in which space is based on the physical properties of manipulability, locomotion, and size of space. Within the structure of our framework, we distinguish six types of spaces: manipulable object space (smaller than the human body), non-manipulable object space (greater than the human body, but less than the size of a building), environmental space (from inside building spaces to city-size spaces), geographic space (state, country, and continent-size spaces), panoramic space (spaces perceived via scanning the landscape), and map space. Such a categorization is an important part of Naive Geography, a set of theories of how people intuitively or spontaneously conceptualize geographic space and time, because it has implications for various theoretical and methodological questions concerning the design and use of spatial information tools. Of particular concern is the design of effective spatial information tools that lead to better communication.
Energy-Efficient Caching for Mobile Edge Computing in 5 G Networks

Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing studies focus on cache allocation, mechanism design and coding design for caching. However, grid power supply with fixed power uninterruptedly in support of a MEC server (MECS) is costly and even infeasible, especially when the load changes dynamically over time. In this paper, we investigate the energy consumption of the MECS problem in cellular networks. Given the average download latency constraints, we take the MECS’s energy consumption, backhaul capacities and content popularity distributions into account and formulate a joint optimization framework to minimize the energy consumption of the system. As a complicated joint optimization problem, we apply a genetic algorithm to solve it. Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies. In particular, it is shown that the proposed scheme can significantly reduce the joint cost when backhaul capacity is low.
Practicing a Science of Security: A Philosophy of Science Perspective

Our goal is to refocus the question about cybersecurity research from 'is this process scientific' to 'why is this scientific process producing unsatisfactory results'. We focus on five common complaints that claim cybersecurity is not or cannot be scientific. Many of these complaints presume views associated with the philosophical school known as Logical Empiricism that more recent scholarship has largely modified or rejected. Modern philosophy of science, supported by mathematical modeling methods, provides constructive resources to mitigate all purported challenges to a science of security. Therefore, we argue the community currently practices a science of cybersecurity. A philosophy of science perspective suggests the following form of practice: structured observation to seek intelligible explanations of phenomena, evaluating explanations in many ways, with specialized fields (including engineering and forensics) constraining explanations within their own expertise, inter-translating where necessary. A natural question to pursue in future work is how collecting, evaluating, and analyzing evidence for such explanations is different in security than other sciences.
Depth-based 3D gesture multi-level radial menu for virtual object manipulation

In this work, we present a depth-based solution to multi-level menus for selection and manipulation of virtual objects using freehand gestures. Navigation between and through menus is performed using three gesture states that utilize X, Y translations of the finger with boundary crossing. Although presented in a single context, this menu structure can be applied to a myriad of domains requiring several levels of menu data, and serves to supplement existing and emerging menu design for augmented, virtual, and mixed-reality applications.
Extracting Appraisal Expressions

Sentiment analysis seeks to characterize opinionated or evaluative aspects of natural language text. We suggest here that appraisal expression extraction should be viewed as a fundamental task in sentiment analysis. An appraisal expression is a textual unit expressing an evaluative stance towards some target. The task is to find and characterize the evaluative attributes of such elements. This paper describes a system for effectively extracting and disambiguating adjectival appraisal expressions in English outputting a generic representation in terms of their evaluative function in the text. Data mining on appraisal expressions gives meaningful and non-obvious insights.
E-government evaluation: a framework and case study

The importance of measuring the performance of e-government cannot be overemphasized. In this paper, a flexible framework is suggested to choose an appropriate strategy to measure the tangible and intangible benefits of e-government. An Indian case study of NDMC (New Delhi Municipal Corporation) has been taken up for analysis and placement into the framework. The results obtained suggest that to have a proper evaluation of tangible and intangible benefits of e-government, the projects should be in a mature stage with proper information systems in place. All of the e-government projects in India are still in a nascent stage; hence, proper information flow for calculating 'return on e-government' considering tangible and intangible benefits cannot be fully ascertained.
Tutorial for the WGCNA package for R : I . Network analysis of liver expression data in female mice 3 . Relating modules to external information and identifying important genes

3 Relating modules to external clinical traits 2 3.a Quantifying module–trait associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3.b Gene relationship to trait and important modules: Gene Significance and Module Membership . . . . 2 3.c Intramodular analysis: identifying genes with high GS and MM . . . . . . . . . . . . . . . . . . . . . . 3 3.d Summary output of network analysis results .
From Orthogonal to Non-orthogonal Multiple Access : Energy- and Spectrum-Efficient Resource Allocation

The rapid pace of innovations in information and communication technology (ICT) industry over the past decade has greatly improved people’s mobile communication experience. This, in turn, has escalated exponential growth in the number of connected mobile devices and data traffic volume in wireless networks. Researchers and network service providers have faced many challenges in providing seamless, ubiquitous, reliable, and high-speed data service to mobile users. Mathematical optimization, as a powerful tool, plays an important role in addressing such challenging issues. This dissertation addresses several radio resource allocation problems in 4G and 5G mobile communication systems, in order to improve network performance in terms of throughput, energy, or fairness. Mathematical optimization is applied as the main approach to analyze and solve the problems. Theoretical analysis and algorithmic solutions are derived. Numerical results are obtained to validate our theoretical findings and demonstrate the algorithms’ ability of attaining optimal or near-optimal solutions. Five research papers are included in the dissertation. In Paper I, we study a set of optimization problems of consecutive-channel allocation in single carrier-frequency division multiple access (SC-FDMA) systems. We provide a unified algorithmic framework to optimize the channel allocation and improve system performance. The next three papers are devoted to studying energy-saving problems in orthogonal frequency division multiple access (OFDMA) systems. In Paper II, we investigate a problem of jointly minimizing energy consumption at both transmitter and receiver sides. An energy-efficient scheduling algorithm is developed to provide optimality bounds and near-optimal solutions. Next in Paper III, we derive fundamental properties for energy minimization in load-coupled OFDMA networks. Our analytical results
Demographic Dialectal Variation in Social Media: A Case Study of African-American English

Though dialectal language is increasingly abundant on social media, few resources exist for developing NLP tools to handle such language. We conduct a case study of dialectal language in online conversational text by investigating African-American English (AAE) on Twitter. We propose a distantly supervised model to identify AAE-like language from demographics associated with geo-located messages, and we verify that this language follows well-known AAE linguistic phenomena. In addition, we analyze the quality of existing language identification and dependency parsing tools on AAE-like text, demonstrating that they perform poorly on such text compared to text associated with white speakers. We also provide an ensemble classifier for language identification which eliminates this disparity and release a new corpus of tweets containing AAE-like language. Data and software resources are available at: http://slanglab.cs.umass.edu/TwitterAAE ( paper, including the appendix at end.
Control of a Quadrotor Helicopter Using Dual Camera Visual Feedback

In this paper we propose a vision-based stabilization and output tracking control method for a model helicopter. A novel two-camera method is introduced for estimating the full six-degrees-of-freedom pose of the helicopter. One of these cameras is located on-board the helicopter, and the other camera is located on the ground. Unlike previous work, these two cameras are set to see each other. The pose estimation algorithm is compared in simulation to other methods and is shown to be less sensitive to errors on feature detection. In order to build an autonomous helicopter, two methods of control are studied: one using a series of mode-based, feedback linearizing controllers and the other using a backstepping-like control law. Various simulations demonstrate the implementation of these controllers. Finally, we present flight experiments where the proposed pose estimation algorithm and non-linear control techniques have been implemented on a remote-controlled helicopter. helicopter control, pose estimation, unmanned aerial vehicle, vision-based control
A taper optimization for pattern synthesis of microstrip series-fed patch array antennas

An EM based straight forward design and pattern synthesis technique for series fed microstrip patch array antennas is proposed. An optimization of each antenna element (λ/4-transmission line, λ/2-patch, λ/4-transmission line) of the array is performed separately. By introducing an equivalent circuit along with an EM parameter extraction method, each antenna element can be optimized for its resonance frequency and taper amplitude, so to shape the aperture distribution for the cascaded elements. It will be shown that the array design based on the multiplication of element factor and array factor fails in case of patch width tapering, due to the inconsistency of the element patterns. To overcome this problem a line width tapering is suggested which keeps the element patterns nearly constant while still providing a broad amplitude taper range. A symmetric 10 element antenna array with a Chebyshev tapering (-20dB side lobe level) operating at 5.8 GHz has been designed, compared for the two tapering methods and validated with measurement.
Cancer immunotherapy: the beginning of the end of cancer?

These are exciting times for cancer immunotherapy. After many years of disappointing results, the tide has finally changed and immunotherapy has become a clinically validated treatment for many cancers. Immunotherapeutic strategies include cancer vaccines, oncolytic viruses, adoptive transfer of ex vivo activated T and natural killer cells, and administration of antibodies or recombinant proteins that either costimulate cells or block the so-called immune checkpoint pathways. The recent success of several immunotherapeutic regimes, such as monoclonal antibody blocking of cytotoxic T lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD1), has boosted the development of this treatment modality, with the consequence that new therapeutic targets and schemes which combine various immunological agents are now being described at a breathtaking pace. In this review, we outline some of the main strategies in cancer immunotherapy (cancer vaccines, adoptive cellular immunotherapy, immune checkpoint blockade, and oncolytic viruses) and discuss the progress in the synergistic design of immune-targeting combination therapies.
Faith in Vehicles: A Set of Evaluation Criteria for Trust Management in Vehicular Ad-Hoc Network

Vehicular Ad-hoc Network (VANET) is a key technology in the domain of transportation which serves as a platform for vehicles to communicate with each other and intelligently exchange critical information, such as collision avoidance messages. Given that potentially life critical information could be exchanged in real time among network entities, it is paramount that this information is authentic (from a legitimate source), reliable and accurate. Moreover, mobility of vehicles creates different contexts in VANET resulting in a diversity of requirements for trust management. This work focuses on the effective modelling and management of trust both as a prerequisite to inter-vehicle communication and as a security measure. To this end, we propose a comprehensive set of criteria which work towards the effective modelling and management of trust in VANET to ensure that the unique characteristics of the network (such as high mobility, dispersion of vehicles, and lack of central architecture) are considered. The contribution of this paper is twofold. (1) We propose 16 criteria for effective trust management in VANET, and (2) evaluate various available trust models based on these proposed criteria.
Top 10 Replicated Findings From Behavioral Genetics.

In the context of current concerns about replication in psychological science, we describe 10 findings from behavioral genetic research that have replicated robustly. These are "big" findings, both in terms of effect size and potential impact on psychological science, such as linearly increasing heritability of intelligence from infancy (20%) through adulthood (60%). Four of our top 10 findings involve the environment, discoveries that could have been found only with genetically sensitive research designs. We also consider reasons specific to behavioral genetics that might explain why these findings replicate.
Dynamic Video Segmentation Network

In this paper, we present a detailed design of dynamic video segmentation network (DVSNet) for fast and efficient semantic video segmentation. DVSNet consists of two convolutional neural networks: a segmentation network and a flow network. The former generates highly accurate semantic segmentations, but is deeper and slower. The latter is much faster than the former, but its output requires further processing to generate less accurate semantic segmentations. We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score. Frame regions with a higher expected confidence score traverse the flow network. Frame regions with a lower expected confidence score have to pass through the segmentation network. We have extensively performed experiments on various configurations of DVSNet, and investigated a number of variants for the proposed decision network. The experimental results show that our DVSNet is able to achieve up to 70.4% mIoU at 19.8 fps on the Cityscape dataset. A high speed version of DVSNet is able to deliver an fps of 30.4 with 63.2% mIoU on the same dataset. DVSNet is also able to reduce up to 95% of the computational workloads.
ATL: A model transformation tool

In the context of Model Driven Engineering, models are the main development artifacts and model transformations are among the most important operations applied to models. A number of specialized languages have been proposed, aimed at specifying model transformations. Apart from the software engineering properties of transformation languages, the availability of high quality tool support is also of key importance for the industrial adoption and ultimate success of MDE. In this paper we present ATL: a model transformation language and its execution environment based on the Eclipse framework. ATL tools provide support for the major tasks involved in using a language: editing, compiling, executing, and debugging.
Design and Construction Planning of the Burj Khalifa , Dubai , UAE

The Burj Khalifa Project is the tallest structure ever built by man; the tower is 828 meters tall and compromise of 162 floors above grade and 3 basement levels. Early integration of aerodynamic shaping and wind engineering played a major role in the architectural massing and design of this multi-use tower, where mitigating and taming the dynamic wind effects was one of the most important design criteria set forth at the onset of the project design. This paper provides brief description of the tower structural systems, focuses on the key issues considered in construction planning of the key structural components, and briefly outlines the execution of one of the most comprehensive structural health monitoring program in tall buildings.
A Survey of Security Services and Techniques in Distributed Storage Systems

The rapid growth of data and data sharing have been driven an evolution in distributed storage infrastructure. The need for sensitive data protection and the capacity to handle massive data sets have encouraged the research and development of secure and scalable storage systems. This paper identifies major security issues and requirements of data protection related to distributed data storage systems. We classify the security services and techniques in existing or proposed storage systems. We then discuss potential research topics and future trends.
Network of breathing. Multifunctional role of the diaphragm: a review.

The diaphragm is the primary muscle involved in active inspiration and serves also as an important anatomical landmark that separates the thoracic and abdominal cavity. However, the diaphragm muscle like other structures and organs in the human body has more than one function, and displays many anatomic links throughout the body, thereby forming a 'network of breathing'. Besides respiratory function, it is important for postural control as it stabilises the lumbar spine during loading tasks. It also plays a vital role in the vascular and lymphatic systems, as well as, is greatly involved in gastroesophageal functions such as swallowing, vomiting, and contributing to the gastroesophageal reflux barrier. In this paper we set out in detail the anatomy and embryology of the diaphragm and attempt to show it serves as both: an important exchange point of information, originating in different areas of the body, and a source of information in itself. The study also discusses all of its functions related to breathing.
Saliency Detection with Recurrent Fully Convolutional Networks

Employs three kind of low-level contrast features, including color, intensity and orientation, and the center prior knowledge to introduce saliency prior maps. • Train the RFCN with two stage training strategy, pre-training on the segmentation data set and fine-tuning on the saliency data set. The recurrent structure can incorporate the saliency prior maps into the CNNs with an end-to-end training method. • Refine the saliency maps with a postprocessing method which can improve the performance of RFCN. Edge-preserving maps are produced with the computation of color confidence and spatial confidence. 3. Algorithm 4.