nils-herrmann/clean_scirepeval huggingface dataset:
Study of the General Kalman Filter for Echo Cancellation

The Kalman filter is a very interesting signal processing tool, which is widely used in many practical applications. In this paper, we study the Kalman filter in the context of echo cancellation. The contribution of this work is threefold. First, we derive a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach. Second, we show how this general Kalman filter (GKF) is connected with some of the most popular adaptive filters for echo cancellation, i.e., the normalized least-mean-square (NLMS) algorithm, the affine projection algorithm (APA) and its proportionate version (PAPA). Third, a simplified Kalman filter is developed in order to reduce the computational load of the GKF; this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the proposed algorithms, which can be attractive choices for echo cancellation.
Watermarking, Tamper-Proofing, and Obfuscation-Tools for Software Protection

We identify three types of attack on the intellectual property contained in software, and three corresponding technical defenses. A potent defense against reverse engineering is obfuscation, a process that renders software unintelligible but still functional. A defense against software piracy is watermarking, a process that makes it possible to determine the origin of software. A defense against tampering is tamper-proofing, so that unauthorized modifications to software (for example to remove a watermark) will result in non-functional code. We briefly survey the available technology for each type of defense.
A Rule Based Approach to Discourse Parsing

In this paper we present an overview of recent developments in discourse theory and parsing under the Linguistic Discourse Model (LDM) framework, a semantic theory of discourse structure. We give a novel approach to the problem of discourse segmentation based on discourse semantics and sketch a limited but robust approach to symbolic discourse parsing based on syntactic, semantic and lexical rules. To demonstrate the utility of the system in a real application, we briefly describe the architecture of the PALSUMM system, a symbolic summarization system being developed at FX Palo Alto Laboratory that uses discourse structures constructed using the theory outlined to summarize written English prose texts.
Cardiovascular simulation toolbox.

A toolbox for Matlab Simulink (trademark of Mathworks corp. etc.) was developed to simulate various models of flow in the cardiovascular system and study effects of different pathological conditions. The toolbox was based on well-known analog lumped models of blood flow in vessels, the varying elastance heart model, blood flow through vessels, shunts, and valves as well as models of oxygen exchange at lungs and tissue. The toolbox is modular providing the basic building blocks of the cardiovascular system. Parameters for the individual components may be set by the user to adapt the component to the simulated system. Several examples are shown. This modeling system is described and is also available for downloading as an open source for free use. The authors see this as the basis for wide collaboration and standardization in modeling. A web site will be available for accepting contributions from other researchers and to create a free exchange.
W-band AlGaN/GaN MMIC PA with 3.1W output power

A balanced four-stage W-band GaN MMIC PA with Lange couplers and micro-strips matching elements is reported. Electron-beam lithography has been used to produce a 100 nm T-shaped gate on the AlGaN/GaN HEMT structure with ultrahigh aluminum content. The MMIC PA offers a small signal gain of greater than 18 dB and an output power of greater than 2.5W from 88GHz to 93GHz with an associated power gain of greater than 15 dB. Moreover, it achieves a peak output power of 3.1W (34.9dBm) at 91GHz in continuous-wave mode, with an associated power density of 3.23W/mm. Most notably, it is the first time that the peak output power and power density exceed 3W and 3W/mm in AlGaN/GaN HEMT at W-band simultaneously.
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.
CAN FD performance analysis for ECU re-programming using the CANoe

This paper presents performance analysis of Controller Area Network with Flexible Data-Rate (CAN FD), a new automotive communication protocol that was officially proposed by Robert Bosch GmbH in March 2012, with consideration of the size of data field of the CAN FD message. Using the CANoe simulation tool by Vector GmbH, we measured the performance of CAN FD for ECU (Electronic Control Unit) re-programming, and compare it to that of CAN. The experimental result showed that when flashing large amounts of data to ECUs, CAN FD has significantly better performance than CAN. In addition, for identical amounts of data, the longer the size of the data field, the shorter the software download time.
Optimal Statistical and Computational Rates for One Bit Matrix Completion

We consider one bit matrix completion under rank constraint. We present an estimator based on rank constrained maximum likelihood estimation, and an e cient greedy algorithm to solve it approximately based on an extension of conditional gradient descent. The output of the proposed algorithm converges at a linear rate to the underlying true low-rank matrix up to the optimal statistical estimation error rate, i.e., O( p rn log(n)/|⌦|), where n is the dimension of the underlying matrix and |⌦| is the number of observed entries. Our work establishes the first computationally efficient approach with provable guarantee for optimal estimation in one bit matrix completion. Our theory is supported by thorough numerical results.
A Coarse-to-Fine Face Hallucination Method by Exploiting Facial Prior Knowledge

Face hallucination technique generates high-resolution (HR) face images from low-resolution (LR) ones. In this paper, we propose to use a coarse-to-fine method for face hallucination by constructing a two-branch network, which makes full use of the specific prior knowledge of face images and the advantages of generic image super-resolution (SR) methods. Specifically, we jointly build a deep neural network (DNN) with a face image SR branch and a semantic face parsing branch. The former branch implements the image upsampling and feature extraction using a cascade of convolutional layers. The latter branch extracts facial semantic parsing as prior knowledge. Then, we combine the image features and the prior know ledge to reconstruct HR face images. Finally, we optimize the DNN, by using adversarial training and a perceptual loss, in order to obtain high realism. Extensive experiments show that the proposed method outperforms the state-of-the-art alternatives in terms of accuracy and realism.
Classifying Photographic and Photorealistic Computer Graphic Images using Natural Image Statistics

As computer graphics (CG) is getting more photorealistic, for the purpose of image authentication, it becomes increasingly important to construct a detector for classifying photographic images (PIM) and photorealistic computer graphics (PRCG). To this end, we propose that photographic images contain natural-imaging quality (NIQ) and natural-scene quality (NSQ). NIQ is due to the imaging process, while NSQ is due to the subtle physical light transport in a real-world scene. We explicitly model NSQ of photographic images using natural image statistics (NIS). NIS has been used as an image prior in applications such as image compression and denoising. However, NIS has not been comprehensively and systematically employed for classifying PIM and PRCG. In this work, we study three types of NIS with different statistical order, i.e., NIS derived from the power spectrum, wavelet transform and local patch of images. The experiment shows that the classification is in line with the statistical order of the NIS. The local patch NIS achieves a classification accuracy of 83% which outperforms the features derived from modeling the characteristics of computer graphics.
Magnetic plethysmograph transducers for local blood pulse wave velocity measurement

We present the design of magnetic plethysmograph (MPG) transducers for detection of blood pulse waveform and evaluation of local pulse wave velocity (PWV), for potential use in cuffless blood pressure (BP) monitoring. The sensors utilize a Hall effect magnetic field sensor to capture the blood pulse waveform. A strap based design is performed to enable reliable capture of large number of cardiac cycles with relative ease. The ability of the transducer to consistently detect the blood pulse is verified by in-vivo trials on few volunteers. A duality of such transducers is utilized to capture the local PWV at the carotid artery. The pulse transit time (PTT) between the two detected pulse waveforms, measured along a small section of the carotid artery, was evaluated using automated algorithms to ensure consistency of measurements. The correlation between the measured values of local PWV and BP was also investigated. The developed transducers provide a reliable, easy modality for detecting pulse waveform on superficial arteries. Such transducers, used for measurement of local PWV, could potentially be utilized for cuffless, continuous evaluation of BP at various superficial arterial sites.
Offset calibration technique for capacitive transimpedance amplifier used in uncooled infrared detection

This paper presents a novel readout circuit of uncooled, bolometer-based, focal plane arrays (FPAs). The offset and flicker noise are the design challenges of microbolometer readout circuits (ROCs). The ROC is not only required to apply careful noise cancellation techniques, but also to be insensitive to process and supply voltage variations. The proposed circuit involves a new offset cancellation technique which overcomes process variations, noise, random, and systematic offset. & 2016
Mechanisms of antimicrobial peptide action and resistance.

Antimicrobial peptides have been isolated and characterized from tissues and organisms representing virtually every kingdom and phylum, ranging from prokaryotes to humans. Yet, recurrent structural and functional themes in mechanisms of action and resistance are observed among peptides of widely diverse source and composition. Biochemical distinctions among the peptides themselves, target versus host cells, and the microenvironments in which these counterparts convene, likely provide for varying degrees of selective toxicity among diverse antimicrobial peptide types. Moreover, many antimicrobial peptides employ sophisticated and dynamic mechanisms of action to effect rapid and potent activities consistent with their likely roles in antimicrobial host defense. In balance, successful microbial pathogens have evolved multifaceted and effective countermeasures to avoid exposure to and subvert mechanisms of antimicrobial peptides. A clearer recognition of these opposing themes will significantly advance our understanding of how antimicrobial peptides function in defense against infection. Furthermore, this understanding may provide new models and strategies for developing novel antimicrobial agents, that may also augment immunity, restore potency or amplify the mechanisms of conventional antibiotics, and minimize antimicrobial resistance mechanisms among pathogens. From these perspectives, the intention of this review is to illustrate the contemporary structural and functional themes among mechanisms of antimicrobial peptide action and resistance.
I-MOS: a novel semiconductor device with a subthreshold slope lower than kT/q

One of the "fundamental" problems in the continued scaling of MOSFETs is the 60 mV/decade room temperature limit in subthreshold slope. In this paper, we report initial studies on a new kind of transistor, the I-MOS. The I-MOS uses modulation of the breakdown voltage of a gated p-i-n structure in order to switch from the OFF to the ON state and vice versa. Since impact-ionization is an abrupt function of the electric field (or the carrier energy), simulations show that the device has a subthreshold slope much lower than kT/q. Simulations also show that it is indeed possible to make complementary circuits with switching speeds comparable to or exceeding CMOS. Experimental results on a silicon based prototype verify the basic concept and show very steep subthreshold slopes with high speed turn-on and turn-off. Lower bandgap materials are also being investigated to reduce the value of the breakdown voltage and permit lower voltage operation.
Effects of eicosapentaenoic acid on major coronary events in hypercholesterolaemic patients (JELIS): a randomised open-label, blinded endpoint analysis

Epidemiological and clinical evidence suggests that an increased intake of long-chain n-3 fatty acids protects against mortality from coronary artery disease. We aimed to test the hypothesis that long-term use of eicosapentaenoic acid (EPA) is effective for prevention of major coronary events in hypercholesterolaemic patients in Japan who consume a large amount of fish.



18 645 patients with a total cholesterol of 6.5 mmol/L or greater were recruited from local physicians throughout Japan between 1996 and 1999. Patients were randomly assigned to receive either 1800 mg of EPA daily with statin (EPA group; n=9326) or statin only (controls; n=9319) with a 5-year follow-up. The primary endpoint was any major coronary event, including sudden cardiac death, fatal and non-fatal myocardial infarction, and other non-fatal events including unstable angina pectoris, angioplasty, stenting, or coronary artery bypass grafting. Analysis was by intention-to-treat. The study was registered at ClinicalTrials.gov, number NCT00231738.



At mean follow-up of 4.6 years, we detected the primary endpoint in 262 (2.8%) patients in the EPA group and 324 (3.5%) in controls-a 19% relative reduction in major coronary events (p=0.011). Post-treatment LDL cholesterol concentrations decreased 25%, from 4.7 mmol/L in both groups. Serum LDL cholesterol was not a significant factor in a reduction of risk for major coronary events. Unstable angina and non-fatal coronary events were also significantly reduced in the EPA group. Sudden cardiac death and coronary death did not differ between groups. In patients with a history of coronary artery disease who were given EPA treatment, major coronary events were reduced by 19% (secondary prevention subgroup: 158 [8.7%] in the EPA group vs 197 [10.7%] in the control group; p=0.048). In patients with no history of coronary artery disease, EPA treatment reduced major coronary events by 18%, but this finding was not significant (104 [1.4%] in the EPA group vs 127 [1.7%] in the control group; p=0.132).



EPA is a promising treatment for prevention of major coronary events, and especially non-fatal coronary events, in Japanese hypercholesterolaemic patients.
Workload Matters: Why RDF Databases Need a New Design

The Resource Description Framework (RDF) is a standard for conceptually describing data on the Web, and SPARQL is the query language for RDF. As RDF is becoming widely utilized, RDF data management systems are being exposed to more diverse and dynamic workloads. Existing systems are workload-oblivious, and are therefore unable to provide consistently good performance. We propose a vision for a workload-aware and adaptive system. To realize this vision, we re-evaluate relevant existing physical design criteria for RDF and address the resulting set of new challenges.
Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking

Situation awareness is the key to security. Awareness requires information that spans multiple scales of space and time. Smart video surveillance systems are capable of enhancing situational awareness across multiple scales of space and time. However, at the present time, the component technologies are evolving in isolation. To provide comprehensive, nonintrusive situation awareness, it is imperative to address the challenge of multiscale, spatiotemporal tracking. This article explores the concepts of multiscale spatiotemporal tracking through the use of real-time video analysis, active cameras, multiple object models, and long-term pattern analysis to provide comprehensive situation awareness.
An MDP-based Recommender System

Typical Recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDP) provide a more appropriate model for Recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation, and they take into account the expected value of each recommendation. To succeed in practice, an MDP-based Recommender system must employ a strong initial model; and the bulk of this paper is concerned with the generation of such a model. In particular, we suggest the use of an n-gram predictive model for generating the initial MDP. Ourn-gram model induces a Markovchain model of user behavior whose predictive accuracy is greater than that of existing predictive models. We describe our predictive model in detail and evaluate its performance on real data. In addition, we show how the model can be used in an MDP-based Recommender system.
Coupled semi-supervised learning for information extraction

We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.
Secure Conjunctive Keyword Search over Encrypted Data

We study the setting in which a user stores encrypted documents (e.g. e-mails) on an untrusted server. In order to retrieve documents satisfying a certain search criterion, the user gives the server a capability that allows the server to identify exactly those documents. Work in this area has largely focused on search criteria consisting of a single keyword. If the user is actually interested in documents containing each of several keywords (conjunctive keyword search) the user must either give the server capabilities for each of the keywords individually and rely on an intersection calculation (by either the server or the user) to determine the correct set of documents, or alternatively, the user may store additional information on the server to facilitate such searches. Neither solution is desirable; the former enables the server to learn which documents match each individual keyword of the conjunctive search and the latter results in exponential storage if the user allows for searches on every set of keywords. We define a security model for conjunctive keyword search over encrypted data and present the first schemes for conducting such searches securely. We propose first a scheme for which the communication cost is linear in the number of documents, but that cost can be incurred “offline” before the conjunctive query is asked. The security of this scheme relies on the Decisional Diffie-Hellman (DDH) assumption. We propose a second scheme whose communication cost is on the order of the number of keyword fields and whose security relies on a new hardness assumption.
On-chip Spiral Inductors With Patterned Ground Shields For Si-based RF IC's

This paper presents a patterned ground shield inserted between an on-chip spiral inductor and silicon substrate. The patterned ground shield can be realized in standard silicon technologies without additional processing steps. The impacts of shield resistance and pattern on inductance, parasitic resistances and capacitances, and quality factor are studied extensively. Experimental results show that a polysilicon patterned ground shield achieves the most improvement. At 1–2 GHz, the addition of the shield increases the inductor quality factor up to 33% and reduces the substrate coupling between two adjacent inductors by as much as 25 dB. We also demonstrate that the quality factor of a 2-GHzLC tank can be nearly doubled with a shielded inductor.
The Quadtree and Related Hierarchical Data Structures

A tutorial survey is presented of the quadtree and related hierarchical data structures. They are based on the principle of recursive decomposition. The emphasis is on the representation of data used in applications in image processing, computer graphics, geographic information systems, and robotics. There is a greater emphasis on region data (i.e., two-dimensional shapes) and to a lesser extent on point, curvilinear, and threedimensional data. A number of operations in which such data structures find use are examined in greater detail.
Go with the Flow: When Listeners Use Music as Technology

Music has been shown to have a profound effect on listeners’ internal states as evidenced by neuroscience research. Listeners report selecting and listening to music with specific intent, thereby using music as a tool to achieve desired psychological effects within a given context. In light of these observations, we argue that music information retrieval research must revisit the dominant assumption that listening to music is only an end unto itself. Instead, researchers should embrace the idea that music is also a technology used by listeners to achieve a specific desired internal state, given a particular set of circumstances and a desired goal. This paper focuses on listening to music in isolation (i.e., when the user listens to music by themselves with headphones) and surveys research from the fields of social psychology and neuroscience to build a case for a new line of research in music information retrieval on the ability of music to produce flow states in listeners. We argue that interdisciplinary collaboration is necessary in order to develop the understanding and techniques necessary to allow listeners to exploit the full potential of music as psychological technology.