nils-herrmann/clean_scirepeval huggingface dataset:
Fast Image Inpainting Based on Coherence Transport

High-quality image inpainting methods based on nonlinear higher-order partial differential equations have been developed in the last few years. These methods are iterative by nature, with a time variable serving as iteration parameter. For reasons of stability a large number of iterations can be needed which results in a computational complexity that is often too large for interactive image manipulation. Based on a detailed analysis of stationary first order transport equations the current paper develops a fast noniterative method for image inpainting. It traverses the inpainting domain by the fast marching method just once while transporting, along the way, image values in a coherence direction robustly estimated by means of the structure tensor. Depending on a measure of coherence strength the method switches continuously between diffusion and directional transport. It satisfies a comparison principle. Experiments with the inpainting of gray tone and color images show that the novel algorithm meets the high level of quality of the methods of Bertalmio et al. (SIG-GRAPH ’00: Proc. 27th Conf. on Computer Graphics and Interactive Techniques, New Orleans, ACM Press/Addison-Wesley, New York, pp. 417–424, 2000), Masnou ( 2002), and Tschumperlé ( 2006), while being faster by at least an order of magnitude.
A Bottom-Source Single-Gate Vertical Channel (BS-SGVC) 3D NAND Flash Architecture and Studies of Bottom Source Engineering

Vertical channel (VC) 3D NAND Flash may be categorized into two types of channel formation: (1) "U-turn" string, where both BL and source are connected at top thus channel current flows in a U-turn way; (2) "Bottom source", where source is connected at the bottom thus channel current flows only in one way. For the single-gate vertical channel (SGVC) 3D NAND architecture , it is also possible to develop a bottom source structure. The detailed array decoding method is illustrated. In this work, the challenges of bottom source processing and thin poly channel formation are extensively studied. It is found that the two-step poly formation and the bottom recess control are two key factors governing the device initial performance. In general, the two-step poly formation with additional poly spacer etching technique seems to cause degradation of both the poly mobility and device subthreshold slope. Sufficient thermal annealing is needed to recover the damage. Moreover, the bottom connection needs an elegant recess control for better read current as well as bottom ground-select transistor (GSL) device optimizations.
Computational reconstruction of ancient artifacts

In this article, we discuss the development of automatic artifact reconstruction systems capable of coping with the realities of real-world geometric puzzles that anthropologists and archaeologists face on a daily basis. Such systems must do more than find matching fragments and subsequently align these matched fragments; these systems must be capable of simultaneously solving an unknown number of multiple puzzles where all of the puzzle pieces are mixed together in an unorganized pile and each puzzle may be missing an unknown number of its pieces. Discussion has cast the puzzle reconstruction problem into a generic terminology that is formalized appropriately for the 2-D and 3-D artifact reconstruction problems. Two leading approaches for 2-D tablet reconstruction and four leading approaches for 3-D object reconstruction have been discussed in detail, including partial or complete descriptions for the numerous algorithms upon which these systems rely. Several extensions to the geometric matching problem that use patterns apparent on the fragment outer surface were also discussed that generalize the problem beyond that of matching strictly geometry. The models needed for solving these problems are new and challenging, and most involve 3-D that is largely unexplored by the signal processing community. This work is highly relevant to the new 3-D signal processing that is looming on the horizon for tele-immersion.
Local positioning for wireless sensors based on Bluetooth/spl trade/

Self-organizing sensor networks are one of the systems that would benefit from the new local positioning features offered by the new generation of wireless technologies. Location dependent sensor data transfers could be optimized by means of local positioning services. Many start-up companies have available proprietary positioning systems meeting the unique requirements of each application. Therefore, the use of a standard like Bluetooth/spl trade/ is a step towards a universal solution. Our system provides location services for mobile industrial and biomedical Bluetooth/spl trade/ -enabled sensors. The RSSI distance estimation, together with a GPS-like triangulation algorithm, lead to a 3 m error positioning system with remote and self positioning topologies. A real time positioning tracking system for one mobile sensor is provided in this article.
The Aladdin Approach to Accelerator Design and Modeling

Hardware specialization, in the form of datapath and control circuitry customized to particular algorithms or applications, promises impressive performance and energy advantages compared to traditional architectures. Current research in accelerators relies on RTL-based synthesis flows to produce accurate timing, power, and area estimates. Such techniques not only require significant effort and expertise but also are slow and tedious to use, making large design space exploration infeasible. To overcome this problem, the authors developed Aladdin, a pre-RTL, power-performance accelerator modeling framework and demonstrated its application to system-on-chip (SoC) simulation. Aladdin estimates performance, power, and area of accelerators within 0.9, 4.9, and 6.6 percent with respect to RTL implementations. Integrated with architecture-level general-purpose core and memory hierarchy simulators, Aladdin provides researchers with a fast but accurate way to model the power and performance of accelerators in an SoC environment.
On Orchestrating Virtual Network Functions in NFV

Middleboxes or network appliances like firewalls, proxies and WAN optimizers have become an integral part of today’s ISP and enterprise networks. Middlebox functionalities are usually deployed on expensive and proprietary hardware that require trained personnel for deployment and maintenance. Middleboxes contribute significantly to a network’s capital and operational costs. In addition, organizations often require their traffic to pass through a specific sequence of middleboxes for compliance with security and performance policies. This makes the middlebox deployment and maintenance tasks even more complicated. Network Function Virtualization (NFV) is an emerging and promising technology that is envisioned to overcome these challenges. It proposes to move packet processing from dedicated hardware middleboxes to software running on commodity servers. In NFV terminology, software middleboxes are referred to as Virtualized Network Functions (VNFs). It is a challenging problem to determine the required number and placement of VNFs that optimizes network operational costs and utilization, without violating service level agreements. We call this the VNF Orchestration Problem (VNF-OP) and provide an Integer Linear Programming (ILP) formulation with implementation in CPLEX. We also provide a dynamic programming based heuristic to solve larger instances of VNF-OP. Trace driven simulations on real-world network topologies demonstrate that the heuristic can provide solutions that are within 1.3 times of the optimal solution. Our experiments suggest that a VNF based approach can provide more than 4× reduction in the operational cost of a network.
Inductive Semi-supervised Multi-Label Learning with Co-Training

In multi-label learning, each training example is associated with multiple class labels and the task is to learn a mapping from the feature space to the power set of label space. It is generally demanding and time-consuming to obtain labels for training examples, especially for multi-label learning task where a number of class labels need to be annotated for the instance. To circumvent this difficulty, semi-supervised multi-label learning aims to exploit the readily-available unlabeled data to help build multi-label predictive model. Nonetheless, most semi-supervised solutions to multi-label learning work under transductive setting, which only focus on making predictions on existing unlabeled data and cannot generalize to unseen instances. In this paper, a novel approach named COINS is proposed to learning from labeled and unlabeled data by adapting the well-known co-training strategy which naturally works under inductive setting. In each co-training round, a dichotomy over the feature space is learned by maximizing the diversity between the two classifiers induced on either dichotomized feature subset. After that, pairwise ranking predictions on unlabeled data are communicated between either classifier for model refinement. Extensive experiments on a number of benchmark data sets show that COINS performs favorably against state-of-the-art multi-label learning approaches.
XNOR-POP: A processing-in-memory architecture for binary Convolutional Neural Networks in Wide-IO2 DRAMs

It is challenging to adopt computing-intensive and parameter-rich Convolutional Neural Networks (CNNs) in mobile devices due to limited hardware resources and low power budgets. To support multiple concurrently running applications, one mobile device needs to perform multiple CNN tests simultaneously in real-time. Previous solutions cannot guarantee a high enough frame rate when serving multiple applications with reasonable hardware and power cost. In this paper, we present a novel process-in-memory architecture to process emerging binary CNN tests in Wide-IO2 DRAMs. Compared to state-of-the-art accelerators, our design improves CNN test performance by 4× ∼ 11× with small hardware and power overhead.
A Regularized Framework for Sparse and Structured Neural Attention

Modern neural networks are often augmented with an attention mechanism, which tells the network where to focus within the input. We propose in this paper a new framework for sparse and structured attention, building upon a smoothed max operator. We show that the gradient of this operator defines a mapping from real values to probabilities, suitable as an attention mechanism. Our framework includes softmax and a slight generalization of the recently-proposed sparsemax as special cases. However, we also show how our framework can incorporate modern structured penalties, resulting in more interpretable attention mechanisms, that focus on entire segments or groups of an input. We derive efficient algorithms to compute the forward and backward passes of our attention mechanisms, enabling their use in a neural network trained with backpropagation. To showcase their potential as a drop-in replacement for existing ones, we evaluate our attention mechanisms on three large-scale tasks: textual entailment, machine translation, and sentence summarization. Our attention mechanisms improve interpretability without sacrificing performance; notably, on textual entailment and summarization, we outperform the standard attention mechanisms based on softmax and sparsemax.
Electronically Tunable Doherty Power Amplifier for Multi-Mode Multi-Band Base Stations

This paper proposes an electronically reconfigurable Doherty amplifier capable of efficiently amplifying multi-standard multi-band wireless signals centered at widely spaced frequencies. The paper outlines closed form equations for an effective design methodology of frequency agile Doherty amplifiers driven with multi-mode signals using a small number of electronically tunable devices. As a proof of concept, a reconfigurable Doherty prototype is designed and fabricated to operate at 1.9, 2.14, and 2.6 GHz meant to efficiently amplify signals with peak-to-average power ratio equal to 6, 9 and 12 dB. The measurement results obtained using continuous wave signals reveal drain efficiencies of about 67% and 42% at the peak power and 12 dB output back off power respectively for the three operating frequencies. In addition, the reconfigurable Doherty amplifier is successfully linearized when driven with 20 MHz wideband code-division multiple access and 20 MHz long term evolution signals, using a Volterra based digital predistrtion algorithm which exploits a pruned Volterra series.
Sensorfusion Using Spatio-Temporal Aligned Video and Lidar for Improved Vehicle Detection

This contribution introduces a novel approach to cross-calibrate automotive vision and ranging sensors. The resulting sensor alignment allows the incorporation of multiple sensor data into a detection and tracking framework. Exemplarily, we show how a realtime vehicle detection system, intended for emergency breaking or ACC applications, benefits from the low level fusion of multibeam lidar and vision sensor measurements in discrimination performance and computational complexity
Real functions for representation of rigid solids

A range of values of a real function f : Ed + Iw can be used to implicitly define a subset of Euclidean space Ed. Such “implicit functions” have many uses in geometric and solid modeling. This paper focuses on the properties and construction of real functions for the representation of rigid solids (compact, semi-analytic, and regular subsets of Ed). We review some known facts about real functions defining compact semi-analytic sets, and their applications. The theory of R-functions developed in (Rvachev, 1982) provides means for constructing real function representations of solids described by the standard (non-regularized) set operations. But solids are not closed under the standard set operations, and such real function representations are rarely available in modem solid modeling systems. More generally, assuring that a real function f represents a regular set may be difficult. Until now, the regularity has either been assumed, or treated in an ad hoc fashion. We show that topological and extremal properties of real functions can be used to test for regularity, and discuss procedures for constructing real functions with desired properties for arbitrary solids.
Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMSRC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMSRC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.
The Philosophy of Information

and streamlined conditions of possibility of the available narratives, with a view not only to their explanation, but also their modification and innovation. How has the regress developed? The vulgata suggests that the scientific revolution made seventeenth-century philosophers redirect their attention from the nature of the knowable object to the epistemic relation between it and the knowing subject, and hence from metaphysics to epistemology. Th e subsequent growth of the information society and the appearance of the infosphere (the semantic environment which millions of people inhabit nowadays) led contemporary philosophy to privilege critical reflection on the domain represented by the memory and languages of organized knowledge, the instruments whereby the infosphere is modeled and managed – thus moving from epistemology to philosophy of language and logic (Dummett 1993) – and then on the nature of its very fabric and essence, information itself. Information has thus arisen as a concept as fundamental and important as “being,” “knowledge,” “life,” “intelligence,” “meaning,” or “good and evil” – all pivotal concepts with which it is interdependent – and so equally worthy of autonomous investigation. It is also a more basic concept, in terms of which the others can be expressed and interrelated, when not defined. In this sense, Evans was right: Evans had the idea that there is a much cruder and more fundamental concept than that of knowledge on which philosophers have concentrated so much, namely the concept of information. Information is conveyed by perception, and retained by memory, though also transmitted by means of language. One needs to concentrate on that concept before one approaches that of knowledge in the proper sense. Information is acquired, for example, without one’s necessarily having a grasp of the proposition which embodies it; the flow of information operates at a much more basic level than the acquisition and transmission of knowledge. I think that this conception deserves to be explored. It’s not one that ever occurred to me before I read Evans, but it is probably fruitful. That also distinguishes this work very sharply from traditional epistemology.
A Knowledge-Grounded Neural Conversation Model

Neural network models are capable of generating extremely natural sounding conversational interactions. However, these models have been mostly applied to casual scenarios (e.g., as “chatbots”) and have yet to demonstrate they can serve in more useful conversational applications. This paper presents a novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses. We generalize the widely-used Sequence-toSequence (SEQ2SEQ) approach by conditioning responses on both conversation history and external “facts”, allowing the model to be versatile and applicable in an open-domain setting. Our approach yields significant improvements over a competitive SEQ2SEQ baseline. Human judges found that our outputs are significantly more informative.
NEW DIMENSIONS IN THE ASSESSMENT OF VIRTUAL REALITY INDUCED SIDE EFFECTS EXPLORING NEW DIMENSIONS IN THE ASSESSMENT OF VIRTUAL REALITY INDUCED SIDE EFFECTS

Two issues are increasingly of interests in the scientific literature regarding virtual reality induced unwanted side effects: (a) the latent structure of the Simulator Sickness Questionnaire (SSQ), and (b) the overlap between anxiety symptoms and unwanted side effects. Study 1 was conducted with a sample of 517 participants. A confirmatory factor analysis clearly supported a two-factor model composed of nausea and oculomotor symptoms. To tease-out symptoms of anxiety from negative side effects of immersion, Study 2 was conducted with 47 participants who were stressed without being immersed in virtual reality. Five of the 16 side effects correlated significantly with anxiety. In a third study conducted with 72 participants, the post-immersion scores of the SSQ and the State Anxiety scale were subjected to a factor analysis to detect items side effects that would load on the anxiety factor. The results converged with those of Study 2 and revealed that general discomfort and difficulty concentrating loaded significantly on the anxiety factor. The overall results support the notion that side effects consists mostly of a nausea and an oculomotor latent structure and that (only) a few items may reflect anxiety more than unwanted negative side effects.
From taxi GPS traces to social and community dynamics: A survey

Vehicles equipped with GPS localizers are an important sensory device for examining people’s movements and activities. Taxis equipped with GPS localizers serve the transportation needs of a large number of people driven by diverse needs; their traces can tell us where passengers were picked up and dropped off, which route was taken, and what steps the driver took to find a new passenger. In this article, we provide an exhaustive survey of the work on mining these traces. We first provide a formalization of the data sets, along with an overview of different mechanisms for preprocessing the data. We then classify the existing work into three main categories: social dynamics, traffic dynamics and operational dynamics. Social dynamics refers to the study of the collective behaviour of a city’s population, based on their observed movements; Traffic dynamics studies the resulting flow of the movement through the road network; Operational dynamics refers to the study and analysis of taxi driver’s modus operandi. We discuss the different problems currently being researched, the various approaches proposed, and suggest new avenues of research. Finally, we present a historical overview of the research work in this field and discuss which areas hold most promise for future research.
Customer satisfaction factors of mobile commerce in Korea

The purpose of the paper is to distinguish features of m-commerce from those of e-commerce and identify factors to influence customer satisfaction (m-satisfaction) and loyalty (m-loyalty) in m-commerce by empirically-based case study.  First, based on previous literature, the paper builds sets of customer satisfaction factors for both e-commerce and m-commerce. Second, features of m-commerce are identified by comparing it with current e-commerce through decision tree (DT). Third, with the derived factors from DT, significant factors and relationships among the factors, m-satisfaction and m-loyalty are examined by m-satisfaction model employing structural equation model.  The paper finds that m-commerce is partially similar in factors like “transaction process” and “customization” which lead customer satisfaction after connecting an m-commerce site, but it has unique aspects of “content reliability”, “availability”, and “perceived price level of mobile Internet (m-Internet)” which build customer’s intention to the m-commerce site. Through the m-satisfaction model, “content reliability”, and “transaction process” are proven to be significantly influential factors to m-satisfaction and m-loyalty.  The paper can be a meaningful step to provide empirical analysis and evaluation based on questionnaire survey targeting actual users. The research is based on a case study on digital music transaction, which is indicative, rather than general.  The paper meets the needs to focus on customer under the fiercer competition in Korean m-commerce market. It can guide those who want to initiate, move or broaden their business to m-commerce from e-commerce.  The paper develops a revised ACSI model to identify individual critical factors and the degree of effect.
Sequential PAttern mining using a bitmap representation

We introduce a new algorithm for mining sequential patterns. Our algorithm is especially efficient when the sequential patterns in the database are very long. We introduce a novel depth-first search strategy that integrates a depth-first traversal of the search space with effective pruning mechanisms.Our implementation of the search strategy combines a vertical bitmap representation of the database with efficient support counting. A salient feature of our algorithm is that it incrementally outputs new frequent itemsets in an online fashion.In a thorough experimental evaluation of our algorithm on standard benchmark data from the literature, our algorithm outperforms previous work up to an order of magnitude.