nils-herrmann/clean_scirepeval huggingface dataset:
Novel 3-dimensional Dual Control-gate with Surrounding Floating-gate (DC-SF) NAND flash cell for 1Tb file storage application

A novel 3-dimensional Dual Control-gate with Surrounding Floating-gate (DC-SF) NAND flash cell has been successfully developed, for the first time. The DC-SF cell consists of a surrounding floating gate with stacked dual control gate. With this structure, high coupling ratio, low voltage cell operation (program: 15V and erase: −11V), and wide P/E window (9.2V) can be obtained. Moreover, negligible FG-FG interference (12mV/V) is achieved due to the control gate shield effect. Then we propose 3D DC-SF NAND flash cell as the most promising candidate for 1Tb and beyond with stacked multi bit FG cell (2 ∼ 4bit/cell).
Big data: The future of biocuration

The exponential growth in the amount of biological data means that revolutionary measures are needed for data management, analysis and accessibility. Online databases have become important avenues for publishing biological data. Biocuration, the activity of organizing, representing and making biological information accessible to both humans and computers, has become an essential part of biological discovery and biomedical research. But curation increasingly lags behind data generation in funding, develop ment and recognition. We propose three urgent actions to advance this key field. First, authors, journals and curators should immediately begin to work together to facilitate the exchange of data between journal publications and databases. Second, in the next five years, curators, researchers and university administrations should develop an accepted recognition structure to facilitate community-based curation efforts. Third, curators, researchers, academic institutions and funding agencies should, in the next ten years, increase the visibility and support of scientific curation as a professional career. Failure to address these three issues will cause the available curated data to lag farther behind current biological knowledge. Researchers will observe an increasing occurrence of obvious gaps in knowledge. As these gaps expand, resources will become less effective for generating and testing hypotheses, and the usefulness of curated data will be seriously compromised. When all the data produced or published are curated to a high standard and made accessible as soon as they become available, biological research will be conducted in a manner that is quite unlike the way it is done now. Researchers will be able to process massive amounts of complex data much more quickly. They will garner insight about the areas of their interest rapidly with the help of inference programs. Digesting information and generating hypotheses at the computer screen will be so much faster that researchers will get back to the bench quickly for more experiments. Experiments will be designed with more insight; this increased specificity will cause an exponential growth in knowledge, much as we are experiencing exponential growth in data today.
Biomedical Applications of Biodegradable Polymers.

Utilization of polymers as biomaterials has greatly impacted the advancement of modern medicine. Specifically, polymeric biomaterials that are biodegradable provide the significant advantage of being able to be broken down and removed after they have served their function. Applications are wide ranging with degradable polymers being used clinically as surgical sutures and implants. In order to fit functional demand, materials with desired physical, chemical, biological, biomechanical and degradation properties must be selected. Fortunately, a wide range of natural and synthetic degradable polymers has been investigated for biomedical applications with novel materials constantly being developed to meet new challenges. This review summarizes the most recent advances in the field over the past 4 years, specifically highlighting new and interesting discoveries in tissue engineering and drug delivery applications.
Mininet as a Container Based Emulator for Software Defined Networks

Mininet is network emulation software that allows launching a virtual network with switches, hosts and an SDN controller all with a single command on a single Linux kernel. It is a great way to start learning about SDN and Open-Flow as well as test SDN controller and SDN applications. Mininet can be used to deploy large networks on a single computer or virtual machine provided with limited resources. It is freely available open source software that emulates Open-Flow device and SDN controllers.  SDN, Mininet, Open-Flow, Python, Wireshark
Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network

Taxonomic relation identification aims to recognize the ‘is-a’ relation between two terms. Previous works on identifying taxonomic relations are mostly based on statistical and linguistic approaches, but the accuracy of these approaches is far from satisfactory. In this paper, we propose a novel supervised learning approach for identifying taxonomic relations using term embeddings. For this purpose, we first design a dynamic weighting neural network to learn term embeddings based on not only the hypernym and hyponym terms, but also the contextual information between them. We then apply such embeddings as features to identify taxonomic relations using a supervised method. The experimental results show that our proposed approach significantly outperforms other state-of-the-art methods by 9% to 13% in terms of accuracy for both general and specific domain datasets.
Recursive Least Squares Dictionary Learning Algorithm

We present the recursive least squares dictionary learning algorithm, RLS-DLA, which can be used for learning overcomplete dictionaries for sparse signal representation. Most DLAs presented earlier, for example ILS-DLA and K-SVD, update the dictionary after a batch of training vectors has been processed, usually using the whole set of training vectors as one batch. The training set is used iteratively to gradually improve the dictionary. The approach in RLS-DLA is a continuous update of the dictionary as each training vector is being processed. The core of the algorithm is compact and can be effectively implemented. The algorithm is derived very much along the same path as the recursive least squares (RLS) algorithm for adaptive filtering. Thus, as in RLS, a forgetting factor ¿ can be introduced and easily implemented in the algorithm. Adjusting ¿ in an appropriate way makes the algorithm less dependent on the initial dictionary and it improves both convergence properties of RLS-DLA as well as the representation ability of the resulting dictionary. Two sets of experiments are done to test different methods for learning dictionaries. The goal of the first set is to explore some basic properties of the algorithm in a simple setup, and for the second set it is the reconstruction of a true underlying dictionary. The first experiment confirms the conjectural properties from the derivation part, while the second demonstrates excellent performance.
eSports as An Emerging Research Context at CHI: Diverse Perspectives on Definitions

This paper represents our first endeavor to explore how to better understand the complex nature, scope, and practices of eSports. Our goal is to explore diverse perspectives on what defines eSports as a starting point for further research. Specifically, we critically reviewed existing definitions/understandings of eSports in different disciplines. We then interviewed 26 eSports players and qualitatively analyzed their own perceptions of eSports. We contribute to further exploring definitions and theories of eSports for CHI researchers who have considered online gaming a serious and important area of research, and highlight opportunities for new avenues of inquiry for researchers who are interested in designing technologies for this unique genre.
A 2.4-GHz monolithic fractional-N frequency synthesizer with robust phase-switching prescaler and loop capacitance multiplier

The design of a 2.4-GHz fully integrated fractionalfrequency synthesizer in 0.35m CMOS process is presented. The design focuses on the prescaler and the loop filter, which are often the speed and the integration bottlenecks of the phase-locked loop (PLL), respectively. A 1.5-V 3-mW inherently glitch-free phase-switching prescaler is proposed. It is based on eight lower frequency 45-spaced phases and a reversed phase-switching sequence. The large integrating capacitor in the loop filter was integrated on chip via a simple capacitance multiplier that saves silicon area, consumes only 0.2 mW, and introduces negligible noise. The synthesizer has a 9.4% frequency tuning range from 2.23 to 2.45 GHz. It dissipates 16 mW and takes an active area of 0.35 mm excluding the 0.5-mm digital modulator.
Bilingual dictionaries for all EU languages

Bilingual dictionaries can be automatically generated using the GIZA++ tool. However, these dictionaries contain a lot of noise, because of which the qualities of outputs of tools relying on the dictionaries are negatively affected. In this work, we present three different methods for cleaning noise from automatically generated bilingual dictionaries: LLR, pivot and transliteration based approach. We have applied these approaches on the GIZA++ dictionaries – dictionaries covering official EU languages – in order to remove noise. Our evaluation showed that all methods help to reduce noise. However, the best performance is achieved using the transliteration based approach. We provide all bilingual dictionaries (the original GIZA++ dictionaries and the cleaned ones) free for download. We also provide the cleaning tools and scripts for free download.
Parallel WaveNet: Fast High-Fidelity Speech Synthesis

The recently-developed WaveNet architecture (van den Oord et al., 2016a) is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today’s massively parallel computers, and therefore hard to deploy in a real-time production setting. This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20 times faster than real-time, a 1000x speed up relative to the original WaveNet, and capable of serving multiple English and Japanese voices in a production setting.
0.13/spl mu/m CMOS SOI SP6T antenna switch for multi-standard handsets

This paper presents the design of a single pole 6 throw antenna switch able to manage all the four GSM standards, i.e. 850-900-1800-1900 MHz. The switch has been integrated in a 0.13mum CMOS SOI process with high resistivity substrate and a thick oxide (50Aring) option. The use of high resistivity substrate allows a good loss (IL)-isolation trade-off: IL is kept in the range of 0.55-0.8 dB for the RXs and at 0.7 dB for the TXs, while isolation varies from 40 dB at 900 MHz to 30 dB at 1900 MHz. Power handling capability is well compatible with GSM standards since an ICP0.1dB of 36 dBm has been measured and harmonics distortion is below -39 dBm for an input power of 34 dBm. Robustness to antenna mismatching condition has been successfully demonstrated up to a VSWR of 10:1. The chip size is of 1.23 mm2  and the power consumption is below 10muA and 0.5 mA respectively in stand by mode and during switching, under 2.5 voltage supply
Real-time Human Pose Estimation from Video with Convolutional Neural Networks

There is a growing need for real-time human pose estimation from monocular RGB images in applications such as human computer interaction, assisted living, video surveillance, people tracking, activity recognition and motion capture. For the task, depth sensors and multi-camera systems are usually more expensive and difficult to set up than conventional RGB video cameras. Recent advances in convolutional neural network research have allowed to replace of traditional methods with more efficient convolutional neural network based methods in many computer vision tasks. This thesis presents a method for real-time multi-person human pose estimation from video by utilizing convolutional neural networks. The method is aimed for use case specific applications, where good accuracy is essential and variation of the background and poses is limited. This enables to use a generic network architecture, which is both accurate and fast. The problem is divided into two phases: (1) pretraining and (2) fine-tuning. In pretraining, the network is learned with highly diverse input data from publicly available datasets, while in fine-tuning it is trained with application specific data recorded with Kinect. The method considers the whole system, including person detector, pose estimator and an automatic way to record application specific training material for fine-tuning. The method can be also thought of as a replacement for Kinect, and it can be used for higher level tasks such as gesture control, games, person tracking and action recognition.
Much ado about grit: A meta-analytic synthesis of the grit literature.

Grit has been presented as a higher order personality trait that is highly predictive of both success and performance and distinct from other traits such as conscientiousness. This paper provides a meta-analytic review of the grit literature with a particular focus on the structure of grit and the relation between grit and performance, retention, conscientiousness, cognitive ability, and demographic variables. Our results based on 584 effect sizes from 88 independent samples representing 66,807 individuals indicate that the higher order structure of grit is not confirmed, that grit is only moderately correlated with performance and retention, and that grit is very strongly correlated with conscientiousness. We also find that the perseverance of effort facet has significantly stronger criterion validities than the consistency of interest facet and that perseverance of effort explains variance in academic performance even after controlling for conscientiousness. In aggregate our results suggest that interventions designed to enhance grit may only have weak effects on performance and success, that the construct validity of grit is in question, and that the primary utility of the grit construct may lie in the perseverance facet.
Artificial intelligence for forest fire prediction

Forest fire prediction constitutes a significant component of forest fire management. It plays a major role in resource allocation, mitigation and recovery efforts. This paper presents a description and analysis of forest fire prediction methods based on artificial intelligence. A novel forest fire risk prediction algorithm, based on support vector machines, is presented. The algorithm depends on previous weather conditions in order to predict the fire hazard level of a day. The implementation of the algorithm using data from Lebanon demonstrated its ability to accurately predict the hazard of fire occurrence.
On the Necessity of a Prescribed Block Validity Consensus: Analyzing Bitcoin Unlimited Mining Protocol

Bitcoin has not only attracted many users but also been considered as a technical breakthrough by academia. However, the expanding potential of Bitcoin is largely untapped due to its limited throughput. The Bitcoin community is now facing its biggest crisis in history as the community splits on how to increase the throughput. Among various proposals, Bitcoin Unlimited recently became the most popular candidate, as it allows miners to collectively decide the block size limit according to the real network capacity. However, the security of BU is heatedly debated and no consensus has been reached as the issue is discussed in different miner incentive models. In this paper, we systematically evaluate BU's security with three incentive models via testing the two major arguments of BU supporters: the block validity consensus is not necessary for BU's security; such consensus would emerge in BU out of economic incentives. Our results invalidate both arguments and therefore disprove BU's security claims. Our paper further contributes to the field by addressing the necessity of a prescribed block validity consensus for cryptocurrencies.
Patch-based Terrain Synthesis

In this paper, we present an intuitive and controllable patch-based technique for terrain synthesis. Our method is based on classical patch-based texture synthesis approaches. It generates a new terrain model by combining patches extracted from a given set of exemplars, providing a control performed by a low frequency guide, a categorization of exemplars, and a map for distributing these categories. Furthermore, we propose criteria to validate the input, some structures to accelerate the patch choice, and a metric based on the process, these structures, and some specificities of the data.
Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.
A review of change point detection methods

In this work, methods to detect one or several change points in multivariate time series are reviewed. They include retrospective (o -line) procedure such as maximum likelihood estimation, regression, kernel methods, etc. In this large area of research, applications are numerous and diverse; many di erent models and operational constraints (on precision, complexity,...) exist. A formal framework for change point detection is introduced to give sens to this signi cant body of work. Precisely, all methods are described as a collection of three elements: a cost function, a search method and a constraint on the number of changes to detect. For a given method, we detail the assumed signal model, the associated algorithm, theoretical guarantees (if any) and the application domain. This approach is intended to facilitate prototyping of change point detection methods: for a given segmentation task, one can appropriately choose among the described elements to design an algorithm.
Exploiting an I/OMMU vulnerability

It is difficult to protect an operating system kernel in an efficient way. Attackers can corrupt or subvert it by two different means: (1) the CPU; (2) the Direct Memory Access (DMA) capability of I/O controllers. DMA-based attacks can be blocked using an I/OMMU. This component, embedded in most of current chipsets, enables the operating system to virtualize the main memory for I/O controllers and to restrict their access to only some memory regions. In this paper, we present different vulnerabilities we identified on Intel VT-d, which implements an I/OMMU. An example of exploitation of one of them is then detailed. Finally, we give some recommendations to prevent these vulnerabilities from being used for malicious purposes.
From enterprise models to dimensional models: a methodology for data warehouse and data mart design

This paper describes a method for developing dimensional models from traditional Entity Relationship models. This can be used to design data warehouses and data marts based on enterprise data models. The first step of the method involves classifying entities in the data model into a number of categories. The second step involves identifying hierarchies that exist in the model. The final step involves collapsing these hierarchies and aggregating transaction data to form dimensional models. A number of design alternatives are presented, including a flat schema, a terraced schema, a star schema and a snowflake schema. We also define a new type of schema called a star cluster schema . This is a restricted form of snowflake schema, which minimises the number of tables while avoiding overlap between different dimensional hierarchies. Individual schemas can be collected together to form constellations or galaxies. We illustrate the method using a simple example.
Convolutional Image Captioning

Image captioning is an important task, applicable to virtual assistants, editing tools, image indexing, and support of the disabled. In recent years significant progress has been made in image captioning, using Recurrent Neural Networks powered by long-short-term-memory (LSTM) units. Despite mitigating the vanishing gradient problem, and despite their compelling ability to memorize dependencies, LSTM units are complex and inherently sequential across time. To address this issue, recent work has shown benefits of convolutional networks for machine translation and conditional image generation [9, 34, 35]. Inspired by their success, in this paper, we develop a convolutional image captioning technique. We demonstrate its efficacy on the challenging MSCOCO dataset and demonstrate performance on par with the LSTM baseline [16], while having a faster training time per number of parameters. We also perform a detailed analysis, providing compelling reasons in favor of convolutional language generation approaches.
A low-power high-PSRR current-mode microphone preamplifier

Bionic implants for the deaf require wide-dynamicrange low-power microphone preamplifiers with good wide-band rejection of the supply noise. Widely used low-cost implementations of such preamplifiers typically use the buffered voltage output of an electret capacitor with a built-in JFET source follower. We describe a design in which the JFET microphone buffer’s output current, rather than its output voltage, is transduced via a sense-amplifier topology allowing good in-band power-supply rejection. The design employs a low-frequency feedback loop to subtract the dc bias current of the microphone and prevent it from causing saturation. Wide-band power-supply rejection is achieved by integrating a novel filter on all current-source biasing. Our design exhibits 80 dB of dynamic range with less than 5 Vrms of input noise while operating from a 2.8 V supply. The power consumption is 96 W which includes 60 W for the microphone built-in buffer. The in-band power-supply rejection ratio varies from 50 to 90 dB while out-of-band supply attenuation is greater than 60 dB until 25 MHz. Fabrication was done in a 1.5m CMOS process with gain programmability for both microphone and auxiliary channel inputs.