nils-herrmann/clean_scirepeval huggingface dataset:
From Molecule to Metaphor - A Neural Theory of Language

Over the last decade or so, it has become increasingly clear to many cognitive scientists that research into human language (and cognition in general, for that matter) has largely neglected how language and thought are embedded in the body and the world. As argued by, for instance, Clark (1997), cognition is fundamentally embodied, that is, it can only be studied in relation to human action, perception, thought, and experience. As Feldman puts it: " Human language and thought are crucially shaped by the properties of our bodies and the structure of our physical and social environment. Language and thought are not best studied as formal mathematics and logic, but as adaptations that enable creatures like us to thrive in a wide range of situations " (p. 7). Although it may seem paradoxical to try formalizing this view in a computational theory of language comprehension, this is exactly what From Molecule to Metaphor does. Starting from the assumption that human thought is neural computation, Feldman develops a computational theory that takes the embodied nature of language into account: the neural theory of language. The book comprises 27 short chapters, distributed over nine parts. Part I presents the basic ideas behind embodied language and cognition and explains how the embodiment of language is apparent in the brain: The neural circuits involved in a particular experience or action are, for a large part, the same circuits involved in processing language about this experience or action. Part II discusses neural computation, starting from the molecules that take part in information processing by neurons. This detailed exposition is followed by a description of neuronal networks in the human body, in particular in the brain. The description of the neural theory of language begins in Part III, where it is explained how localist neural networks, often used as psycholinguistic models, can represent the meaning of concepts. This is done by introducing triangle nodes into the network. Each triangle node connects the nodes representing a concept, a role, and a filler—for example, " pea, " " has-color, " and " green. " Such networks are trained by a process called recruitment learning, which is described only very informally. This is certainly an interesting idea for combining propositional and connectionist models, but it does leave the reader with a number of questions. For instance, how is the concept distinguished from the filler when they can be interchanged, as …
Max-SINR ISI/ICI-Shaping Multicarrier Communication Over the Doubly Dispersive Channel

For communication over doubly dispersive channels, we consider the design of multicarrier modulation (MCM) schemes based on time-frequency shifts of prototype pulses. We consider the case where the receiver knows the channel state and the transmitter knows the channel statistics (e.g., delay spread and Doppler spread) but not the channel state. Previous work has examined MCM pulses designed for suppression of inter-symbol/inter-carrier interference (ISI/ICI) subject to orthogonal or biorthogonal constraints. In doubly dispersive channels, however, complete suppression of ISI/ICI is impossible, and the ISI/ICI pattern generated by these (bi)orthogonal schemes can be difficult to equalize, especially when operating at high bandwidth efficiency. We propose a different approach to MCM pulse design, whereby a limited expanse of ISI/ICI is tolerated in modulation/demodulation and treated near-optimally by a downstream equalizer. Specifically, we propose MCM pulse designs that maximize a signal-to-interference-plus-noise ratio (SINR) which suppresses ISI/ICI outside a target pattern. In addition, we propose two low-complexity turbo equalizers, based on minimum mean-squared error and maximum likelihood criteria, respectively, that leverage the structure of the target ISI/ICI pattern. The resulting system exhibits an excellent combination of low complexity, low bit-error rate, and high spectral efficiency.
Sequential Optimization for Efficient High-Quality Object Proposal Generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING  but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.
Comparing the Visual Representations and Performance of Humans and Deep Neural Networks

Although deep neural networks (DNNs) are state-of-the-art artificial intelligence systems, it is unclear what insights, if any, they provide about human intelligence. We address this issue in the domain of visual perception. After briefly describing DNNs, we provide an overview of recent results comparing human visual representations and performance with those of DNNs. In many cases, DNNs acquire visual representations and processing strategies that are very different from those used by people. We conjecture that there are at least two factors preventing them from serving as better psychological models. First, DNNs are currently trained with impoverished data, such as data lacking important visual cues to three-dimensional structure, data lacking multisensory statistical regularities, and data in which stimuli are unconnected to an observer’s actions and goals. Second, DNNs typically lack adaptations to capacity limits, such as attentional mechanisms, visual working memory, and compressed mental representations biased toward preserving task-relevant abstractions.
Electronic-Paper : The Electronic Display of the Future .

Made of flexible material, requiring ultra-low power consumption, cheap to manufacture, and most importantly, easy and convenient to read, Epapers of the future are just around the corner, with the promise to hold libraries on a chip and replace most printed newspapers before the end of the next decade. This paper discusses the history, features, and technology of the electronic paper revolution. It also highlights the challenges facing E-paper and its various applications. The paper concludes that E-paper, which can be termed as the second paper revolution, is closer to changing the way we read, write and study; a revolution so phenomenal that some researchers see it as second only to the invention of the printing press by Gutenberg in the 15th century.
Optimal sizing of stand-alone hybrid wind/PV system with battery storage

In this paper, a new methodology developed to design a hybrid wind/photovoltaic (wind/PV) system, is presented. Based on an optimization process using a deterministic algorithm, the developed methodology helps the authors to obtain the optimal number and type of PV panels, wind turbines and storage units ensuring that the system total cost is minimized while guaranteeing the permanent availability of energy to cover the load energy requirements. The hybrid system configuration and modeling are presented and the used deterministic algorithm (DIRECT algorithm) is described and implemented in MATLAB environment to optimize the hybrid wind/PV system.
Text joins in an RDBMS for web data integration

The integration of data produced and collected across autonomous, heterogeneous web services is an increasingly important and challenging problem. Due to the lack of global identifiers, the same entity (e.g., a product) might have different textual representations across databases. Textual data is also often noisy because of transcription errors, incomplete information, and lack of standard formats. A fundamental task during data integration is matching of strings that refer to the same entity. In this paper, we adopt the widely used and established cosine similarity metric from the information retrieval field in order to identify potential string matches across web sources. We then use this similarity metric to characterize this key aspect of data integration as a join between relations on textual attributes, where the similarity of matches exceeds a specified threshold. Computing an exact answer to the text join can be expensive. For query processing efficiency, we propose a sampling-based join approximation strategy for execution in a standard, unmodified relational database management system (RDBMS), since more and more web sites are powered by RDBMSs with a web-based front end. We implement the join inside an RDBMS, using SQL queries, for scalability and robustness reasons. Finally, we present a detailed performance evaluation of an implementation of our algorithm within a commercial RDBMS, using real-life data sets. Our experimental results demonstrate the efficiency and accuracy of our techniques.
Surreptitious Deployment and Execution of Kernel Agents in Windows Guests

As more and more virtual machines (VM) are packed into a physical machine, refactoring common kernel components shared by the virtual machines running on the same physical machine significantly reduces the overall resource consumption. A refactored kernel component typically runs on a special VM called a virtual appliance. Because of the semantics gap in Hardware Abstraction Layer (HAL)-based virtualization, a physical machine's virtual appliance requires the support of per-VM in-guest agents to perform VM-specific operations such as kernel data structure access and modification. To simplify deployment, these agents must be injected into guest virtual machines without requiring any manual installation. Moreover, it is essential to protect the integrity of in-guest agents at run time, especially when the underlying refactored kernel service is security-related. This paper describes the design, implementation and evaluation of a surreptitious kernel agent deployment and execution mechanism called SADE that requires zero installation effort and effectively hides the execution of agent code. To demonstrate the efficacy of SADE, we describe a signature-based memory scanning virtual appliance that uses SADE to inject its in-guest kernel agents without any support from the injected virtual machine, and show that both the start-up overhead and the run-time performance penalty of SADE are quite modest in practice.
Time-Resolved Magnetic Resonance Angiography in the Evaluation of Intracranial Vascular Lesions and Tumors: A Pictorial Essay of Our Experience.

Time-resolved magnetic resonance angiography (TR MRA) is a promising less invasive technique for the diagnosis of intracranial vascular lesions and hypervascular tumors. Similar to 4-dimensional computed tomographic angiography obtaining high frame rate images, TR MRA utilizes acceleration techniques to acquire sequential arterial and venous phase images for identifying, localizing, and classifying vascular lesions. Because of the good agreement with digital subtraction angiography for grading brain arteriovenous malformations with the Spetzler-Martin classification and the good sensitivity for visualizing arteriovenous fistulas, studies have suggested that TR MRA could serve as a screening or routine follow-up tool for diagnosing intracranial vascular disorders. In this pictorial essay, we report on the use of TR MRA at 3.0 T to diagnose intracranial vascular lesions and hypervascular tumors, employing DSA as the reference technique.
Designing the Business Models for Circular Economy — Towards the Conceptual Framework

Switching from the current linear model of economy to a circular one has recently attracted increased attention from major global companies e.g., Google, Unilever, Renault, and policymakers attending the World Economic Forum. The reasons for this are the huge financial, social and environmental benefits. However, the global shift from one model of economy to another also concerns smaller companies on a micro-level. Thus, comprehensive knowledge on designing circular business models is needed to stimulate and foster implementation of the circular economy. Existing business models for the circular economy have limited transferability and there is no comprehensive framework supporting every kind of company in designing a circular business model. This study employs a literature review to identify and classify the circular economy characteristics according to a business model structure. The investigation in the eight sub-domains of research on circular business models was used to redefine the components of the business model canvas in the context of the circular economy. Two new components—the take-back system and adoption factors—have been identified, thereby leading to the conceptualization of an extended framework for the circular business model canvas. Additionally, the triple fit challenge has been recognized as an enabler of the transition towards a circular business model. Some directions for further research have been outlined, as well.
Seamless and Secure Bluetooth LE Connection Migration

At present, Bluetooth Low Energy (BLE) is dominantly used in commercially available Internet of Things (IoT) devices -- such as smart watches, fitness trackers, and smart appliances. Compared to classic Bluetooth, BLE has been simplified in many ways that include its connection establishment, data exchange, and encryption processes. Unfortunately, this simplification comes at a cost. For example, only a star topology is supported in BLE environments and a peripheral (an IoT device) can communicate with only one gateway (e.g. a smartphone, or a BLE hub) at a set time. When a peripheral goes out of range, it loses connectivity to a gateway, and cannot connect and seamlessly communicate with another gateway without user interventions. In other words, BLE connections do not get automatically migrated or handed-off to another gateway. In this paper, we propose a system which brings seamless connectivity to BLE-capable mobile IoT devices in an environment that consists of a network of gateways. Our framework ensures that unmodified, commercial off-the-shelf BLE devices seamlessly and securely connect to a nearby gateway without any user intervention.
Extraction and Recognition of the Vehicle License Plate for Passing under Outside Environment

Persian License Plate Detection and Recognition System is an image-processing technique used to identify a vehicle by its license plate. In fact this system is one kind of automatic inspection of transport, traffic and security systems and is of considerable interest because of its potential applications to areas such as automatic toll collection, traffic law enforcement and security control of restricted areas. License plate location is an important stage in vehicle license plate recognition for automated transport system. This paper presents a real time and robust method of license plate detection and recognition from cluttered images based on the morphology and template matching. In this system main stage is the isolation of the license plate from the digital image of the car obtained by a digital camera under different circumstances such as illumination, slop, distance, and angle. The algorithm starts with preprocessing and signal conditioning. Next license plate is localized using morphological operators. Then a template matching scheme will be used to recognize the digits and characters within the plate. This system implemented with help of Isfahan Control Traffic organization and the performance was 98.2% of correct plates identification and localization and 92% of correct recognized characters. The results regarding the complexity of the problem and diversity of the test cases show the high accuracy and robustness of the proposed method. The method could also be applicable for other applications in the transport information systems, where automatic recognition of registration plates, shields, signs, and so on is often necessary. This paper presents a morphology-based method.
Dual-band antenna system for SDARS and GPS applications

This paper presents a dual frequency band circularly polarized antenna system for Satellite Digital Audio Radio Service (SDARS) and Global Positioning System (GPS) applications. The proposed dual band antenna system consists of two circular patches each having four perpendicular slots for operating in two different frequency bands with left-handed circular polarization (LHCP) for SDARS and a right-handed circular polarization (RHCP) for GPS. The circular polarization characteristics for both antennas are obtained by choosing appropriate lengths of four perpendicular slots on the circular patches. The LHCP or RHCP radiation is readily obtained by selecting the proper feeding location. Results are presented in terms of the |S11|, radiation pattern and axial ratio.
Recognition of normal-abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients.

Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals.



The goal of the 2016 PhysioNet/CinC Challenge was to encourage the creation of an intelligent system that fused information from different phonocardiographic signals to create a robust set of normal/abnormal signal detections.



Deep convolutional neural networks and mel-frequency spectral coefficients were used for recognition of normal-abnormal phonocardiographic signals of the human heart. This technique was developed using the PhysioNet.org Heart Sound database and was submitted for scoring on the challenge test set.



The current entry for the proposed approach obtained an overall score of 84.15% in the last phase of the challenge, which provided the sixth official score and differs from the best score of 86.02% by just 1.87%.
Stem cells of the human cornea.

Blindness affects 50 million individuals worldwide; a significant proportion of them require a cell or tissue-based repair or replacement strategy to mend their damaged or diseased cornea. This review will focus on the epithelial stem cell (ESC) population of the cornea, where they reside, how they are identified and what alternative cells can be used as functional substitutes.



Data for this review were collated after performing literature searches by inserting key words (cornea, limbal, stem cells (SCs), epithelium, stroma, and endothelium) into the search engine PubMed.


AREAS OF AGREEMENT
The prevailing notion is that corneal ESCs reside in an exclusive niche and their activation is dictated by niche-specific signals.


AREAS OF CONTROVERSY
Recent studies refute this hypothesis, as the central cornea of many animal species also possesses similar proliferative and clonogenic activity. The other area of controversy is in relation to the use of autologous and/or allogeneic cell therapies which are mostly contaminated with xenogeneic factors, potentially exposing the recipient to potentially harmful foreign infection.



Due to the shortage of donor corneal biomaterial, alternative cellular sources are being sought, discovered and trialed.


AREAS TIMELY FOR DEVELOPING RESEARCH
With the exception of conjunctival and oral mucosal epithelium, no other cell type has been successfully used to treat patients with severe corneal epithelial defects. Embryonic and foetal SCs may have the greatest potential of all; however there are moral, legal, religious and scientific hurdles to overcome before they become routinely used in the clinic.
On the use of visualization to support awareness of human activities in software development: a survey and a framework

This paper proposes a framework for describing, comparing and understanding visualization tools that provide awareness of human activities in software development. The framework has several purposes -- it can act as a formative evaluation mechanism for tool designers; as an assessment tool for potential tool users; and as a comparison tool so that tool researchers can compare and understand the differences between various tools and identify potential new research areas. We use this framework to structure a survey of visualization tools for activity awareness in software development. Based on this survey we suggest directions for future research.
Adverse Childhood Experiences (ACEs), Stress and Mental Health in College Students.

The goal of this short-term longitudinal study was to examine whether adverse childhood experiences (ACEs) could be used to identify college students at risk for mental health problems and whether current level of stress mediates the relationship between ACEs and mental health. Data on ACEs and mental health (depression, anxiety and suicidality) were collected at the beginning of the semester, and data on current stressors and mental health were collected toward the end of the semester (n = 239). Findings indicated that ACEs predicted worsening of mental health over the course of a semester and suggested current number of stressors as a mediator of the relationship between ACEs and mental health. Results suggest that screening for ACEs might be useful to identify students at high risk for deterioration in mental health. Results further suggest that stress-related interventions would be beneficial for students with high levels of ACEs and point to the need for more research and strategies to increase help-seeking in college students.
Forcefield_NCAA: Ab Initio Charge
Parameters to Aid in the Discovery and Design of Therapeutic Proteins
and Peptides with Unnatural Amino Acids and Their Application to Complement
Inhibitors of the Compstatin Family

We describe the development and testing of ab initio derived, AMBER ff03 compatible charge parameters for a large library of 147 noncanonical amino acids including β- and N-methylated amino acids for use in applications such as protein structure prediction and de novo protein design. The charge parameter derivation was performed using the RESP fitting approach. Studies were performed assessing the suitability of the derived charge parameters in discriminating the activity/inactivity between 63 analogs of the complement inhibitor Compstatin on the basis of previously published experimental IC50 data and a screening procedure involving short simulations and binding free energy calculations. We found that both the approximate binding affinity (K*) and the binding free energy calculated through MM-GBSA are capable of discriminating between active and inactive Compstatin analogs, with MM-GBSA performing significantly better. Key interactions between the most potent Compstatin analog that contains a noncanonical amino acid are presented and compared to the most potent analog containing only natural amino acids and native Compstatin. We make the derived parameters and an associated web interface that is capable of performing modifications on proteins using Forcefield_NCAA and outputting AMBER-ready topology and parameter files freely available for academic use at http://selene.princeton.edu/FFNCAA . The forcefield allows one to incorporate these customized amino acids into design applications with control over size, van der Waals, and electrostatic interactions.