nils-herrmann/clean_scirepeval huggingface dataset:
Formal Methods: State of the Art and Future Directions

ion Real systems are di cult to specify and verify without abstrac tions We need to identify di erent kinds of abstractions perhaps tailored for certain kinds of systems or problem domains and we need to develop ways to justify them formally perhaps using mechanical help Reusable models and theories Rather than de ning models and theories from scratch each time a new application is tackled it would be better to have reusable and parameterized models and theories Combinations of mathematical theories Many safety critical systems have both digital and analog components These hybrid systems require reasoning about both discrete and continuous mathematics System developers would like to be able to predict how well their system will operate in the eld Indeed they often care more about performance than cor rectness Performance modeling borrows strongly from probability statistics and queueing theory Data structures and algorithms To handle larger search spaces and larger systems new data structures and algorithms e g more concise data structures for representing boolean functions are needed
Fast Algorithms for Projected Clustering

The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Unfortunately, all known algorithms tend to break down in high dimensional spaces because of the inherent sparsity of the points. In such high dimensional spaces not all dimensions may be relevant to a given cluster. One way of handling this is to pick the closely correlated dimensions and find clusters in the corresponding subspace. Traditional feature selection algorithms attempt to achieve this. The weakness of this approach is that in typical high dimensional data mining applications different sets of points may cluster better for different subsets of dimensions. The number of dimensions in each such cluster-specific subspace may also vary. Hence, it may be impossible to find a single small subset of dimensions for all the clusters. We therefore discuss a generalization of the clustering problem, referred to as the projected clustering problem, in which the subsets of dimensions selected are specific to the clusters themselves. We develop an algorithmic framework for solving the projected clustering problem, and test its performance on synthetic data.
A new capacitive type MEMS microphone

A new capacitive type of MEMS microphone is presented. In contrast to existing technologies which are highly specialized for this particular type of application, our approach is based on a standard process and layer system which has been in use for more than a decade now for the manufacturing of inertial sensors. For signal conversion, a mixed-signal ASIC with digital sampling of the microphone capacitance is used. The MEMS microphone yields high signal-to-noise performance (58 dB) after mounting it in a standard LGA-type package. It is well-suited for a wide range of potential applications and demonstrates the universal scope of the used process technology.
Knowledge capital, growth, and the East Asian miracle

With per-capita gross domestic product (GDP) growing by an average of 4.5% annually since 1960, people in East Asia are about nine times as prosperous as two generations ago. By contrast, the average person in Latin America is only about two and a half times as prosperous. Over the past quarter-century, both theoretical and empirical analyses of possible drivers of the different growth rates seen around the world invariably assign an important role to human capital (1–4). This has led to development policies focused on increasing enrollment and retention in schools. We argue, however, that too much attention is paid to the time spent in school, and too little is paid to the quality of the schools and the types of skills developed there.
Halftone QR codes

QR code is a popular form of barcode pattern that is ubiquitously used to tag information to products or for linking advertisements. While, on one hand, it is essential to keep the patterns machine-readable; on the other hand, even small changes to the patterns can easily render them unreadable. Hence, in absence of any computational support, such QR codes appear as random collections of black/white modules, and are often visually unpleasant. We propose an approach to produce high quality visual QR codes, which we call halftone QR codes, that are still machine-readable. First, we build a pattern readability function wherein we learn a probability distribution of what modules can be replaced by which other modules. Then, given a text tag, we express the input image in terms of the learned dictionary to encode the source text. We demonstrate that our approach produces high quality results on a range of inputs and under different distortion effects.
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents

In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors characterize the intrinsic dynamics of the crowd. From the agent-based modeling, each pedestrian in the crowd is driven by a dynamic pedestrian-agent, which is a linear dynamic system with its initial and termination states reflecting a pedestrian's belief of the starting point and the destination. Then the whole crowd is modeled as a mixture of dynamic pedestrian-agents. Once the model is unsupervisedly learned from real data, MDA can simulate the crowd behaviors. Furthermore, MDA can well infer the past behaviors and predict the future behaviors of pedestrians given their trajectories only partially observed, and classify different pedestrian behaviors in the scene. The effectiveness of MDA and its applications are demonstrated by qualitative and quantitative experiments on the video surveillance dataset collected from the New York Grand Central Station.
Conceptual approaches for defining data, information, and knowledge

Therefore, information scientists are required to regularly review—and if necessary—redefine its fundamental building blocks. This article is one of a group of four articles, which resulted from a Critical Delphi study conducted in 2003–2005. The study, " Knowledge Map of Information Science, " was aimed at exploring the foundations of information science. The international panel was composed of 57 leading scholars from 16 countries, who represent (almost) all the major subfields and important aspects of the field. This particular article documents 130 definitions of data, information, and knowledge formulated by 45 scholars, and maps the major conceptual approaches for defining these three key concepts.
Sonographic Overview of Usual and Unusual Disorders of the Rectus Femoris Tendon Origins.

Rectus femoris muscle proximal injuries are not rare conditions. The proximal rectus femoris tendinous anatomy is complex and may be affected by traumatic, microtraumatic, or nontraumatic disorders. A good knowledge of the proximal rectus femoris anatomy allows a better understanding of injury and disorder patterns. A new sonographic lateral approach was recently described to assess the indirect head of the proximal rectus femoris, hence allowing for a complete sonographic assessment of the proximal rectus femoris tendons. This article will review sonographic features of direct, indirect, and conjoined rectus femoris tendon disorders.
Sepsis-induced cardiomyopathy: a review of pathophysiologic mechanisms

Cardiac dysfunction is a well-recognized complication of severe sepsis and septic shock. Cardiac dysfunction in sepsis is characterized by ventricular dilatation, reduction in ejection fraction and reduced contractility. Initially, cardiac dysfunction was considered to occur only during the “hypodynamic” phase of shock. But we now know that it occurs very early in sepsis even during the “hyperdynamic” phase of septic shock. Circulating blood-borne factors were suspected to be involved in the evolution of sepsis induced cardiomyopathy, but it is not until recently that the cellular and molecular events are being targeted by researchers in a quest to understand this enigmatic process. Septic cardiomyopathy has been the subject of investigation for nearly half a century now and yet controversies exist in understanding it’s pathophysiology. Here, we discuss our understanding of the pathogenesis of septic cardiomyopathy and the complex roles played by nitric oxide, mitochondrial dysfunction, complements and cytokines.
Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale screening. One of the largest challenges in the automatic analysis of cytological images is the segmentation of nuclei. In this study, four different clustering algorithms are tested and compared in the task of fast nuclei segmentation. K-means, fuzzy C-means, competitive learning neural networks and Gaussian mixture models were incorporated for clustering in the color space along with adaptive thresholding in grayscale. These methods were applied in a medical decision support system for breast cancer diagnosis, where the cases were classified as either benign or malignant. In the segmented nuclei, 42 morphological, topological and texture features were extracted. Then, these features were used in a classification procedure with three different classifiers. The system was tested for classification accuracy by means of microscopic images of fine needle breast biopsies. In cooperation with the Regional Hospital in Zielona Góra, 500 real case medical images from 50 patients were collected. The acquired classification accuracy was approximately 96-100%, which is very promising and shows that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis.
Optimizing oxide charge balanced devices for Unclamped Inductive Switching (UIS)

Power MOSFET designs have been moving to higher performance particularly in the medium voltage area. (60V to 300V) New designs require lower Specific On-resistance while not sacrificing Unclamped Inductive Switching (UIS) capability or increasing turn-off losses. Two charge balance technologies currently address these needs, the PN junction and the Shielded Gate Charge Balance device topologies. This paper will study the impact of drift region as well as other design parameters that influence the shielded gate class of charge balance devices. The optimum design for maximizing UIS capability and minimizing the impact on other design parameters such as RDSON and switching performance are addressed. It will be shown through TCAD simulation one can design devices to have a stable avalanche point that is not influenced by small variations within a die or die-to-die that result from normal processing. Finally, measured and simulated data will be presented showing a fabricated device with near theoretical UIS capability.
Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach.

Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with MIML classifiers. The proposed representation uses a 2D time-frequency segmentation of the audio signal, which can separate bird sounds that overlap in time. Experiments using audio data containing 13 species collected with unattended omnidirectional microphones in the H. J. Andrews Experimental Forest demonstrate that the proposed methods achieve high accuracy (96.1% true positives/negatives). Automated detection of bird species occurrence using MIML has many potential applications, particularly in long-term monitoring of remote sites, species distribution modeling, and conservation planning.
Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words

For aspect-level sentiment analysis, the important first step is to identify the aspects and their associated entities present in customer reviews. Aspects can be either explicit or implicit, where the identification of the latter is more difficult. For restaurant reviews, this difficulty is escalated due to the vast number of entities and aspects present in reviews. The problem of implicit aspect identification has been studied for customer reviews in different domains, including restaurant reviews. However, the existing work for implicit aspect identification in customer reviews has the limitation of choosing at most one implicit aspect for each sentence. Furthermore, they deal only with a limited set of aspects related to a particular domain, thus have not faced the problem of ambiguity that arises when an opinion word is used to describe different aspects. This paper presents a novel approach for implicit aspect detection, which overcomes these two limitations. Our approach yields an F1measure of 0.842 when applied for a set of restaurant reviews collected from Yelp.
Multi-domain dialogue success classifiers for policy training

We propose a method for constructing dialogue success classifiers that are capable of making accurate predictions in domains unseen during training. Pooling and adaptation are also investigated for constructing multi-domain models when data is available in the new domain. This is achieved by reformulating the features input to the recurrent neural network models introduced in . Importantly, on our task of main interest, this enables policy training in a new domain without the dialogue success classifier (which forms the reinforcement learning reward function) ever having seen data from that domain before. This occurs whilst incurring only a small reduction in performance relative to developing and using an in-domain dialogue success classifier. Finally, given the motivation with these dialogue success classifiers is to enable policy training with real users, we demonstrate that these initial policy training results obtained with a simulated user carry over to learning from paid human users.
Know your enemy: understanding the role of PfCRT in drug resistance could lead to new antimalarial tactics

The prevention and treatment of malaria is heavily dependent on antimalarial drugs. However, beginning with the emergence of chloroquine (CQ)-resistant Plasmodium falciparum parasites 50 years ago, efforts to control the disease have been thwarted by failed or failing drugs. Mutations in the parasite’s ‘chloroquine resistance transporter’ (PfCRT) are the primary cause of CQ resistance. Furthermore, changes in PfCRT (and in several other transport proteins) are associated with decreases or increases in the parasite’s susceptibility to a number of other antimalarial drugs. Here, we review recent advances in our understanding of CQ resistance and discuss these in the broader context of the parasite’s susceptibilities to other quinolines and related drugs. We suggest that PfCRT can be viewed both as a ‘multidrug-resistance carrier’ and as a drug target, and that the quinoline-resistance mechanism is a potential ‘Achilles’ heel’ of the parasite. We examine a number of the antimalarial strategies currently undergoing development that are designed to exploit the resistance mechanism, including relatively simple measures, such as alternative CQ dosages, as well as new drugs that either circumvent the resistance mechanism or target it directly.
The Temporal Singularity: time-accelerated simulated civilizations and their implications

Provided significant future progress in artificial intelligence and computing, it may ultimately be possible to create multiple Artificial General Intelligences (AGIs), and possibly entire societies living within simulated environments. In that case, it should be possible to improve the problem solving capabilities of the system by increasing the speed of the simulation. If a minimal simulation with sufficient capabilities is created, it might manage to increase its own speed by accelerating progress in science and technology, in a way similar to the Technological Singularity. This may ultimately lead to large simulated civilizations unfolding at extreme temporal speedups, achieving what from the outside would look like a Temporal Singularity. Here we discuss the feasibility of the minimal simulation and the potential advantages, dangers, and connection to the Fermi paradox of the Temporal Singularity. The medium-term importance of the topic derives from the amount of computational power required to start the process, which could be available within the next decades, making the Temporal Singularity theoretically possible before the end of the century.
Planning and Learning in Environments with Delayed Feedback

Deterministic transitions, continuous state Case III: “Mildly” stochastic trans., finite state: P(s,a,s’) ≥ 1 δ Case IV: Bounded-noise stochastic transitions, continuous state: st+1 = T(st, at) + wt , ||wt|| ≤ ∆ Planning and Learning in Environments with Delayed Feedback Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman Rutgers Laboratory for Real Life Reinforcement Learning Computer Science Department, Rutgers University, Piscataway NJ
Real-Time Simulation of Medical Ultrasound from CT Images

Medical ultrasound interpretation requires a great deal of experience. Real-time simulation of medical ultrasound provides a cost-effective tool for training and easy access to a variety of cases and exercises. However, fully synthetic and realistic simulation of ultrasound is complex and extremely time-consuming. In this paper, we present a novel method for simulation of ultrasound images from 3D CT scans by breaking down the computations into a preprocessing and a run-time phase. The preprocessing phase produces detailed fixed-view 3D scattering images and the run-time phase generates view-dependent ultrasonic artifacts for a given aperture geometry and position within a volume of interest. We develop a simple acoustic model of the ultrasound for the run-time phase, which produces realistic ultrasound images in real-time when combined with the previously computed scattering image.
Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization

This paper addresses the joint design of transmit and receive beamforming or linear processing (commonly termed linear precoding at the transmitter and equalization at the receiver) for multicarrier multi-input multi-output (MIMO) channels under a variety of design criteria. Instead of considering each design criterion in a separate way, we generalize the existing results by developing a unified framework based on considering two families of objective functions that embrace most reasonable criteria to design a communication system: Schur-concave and Schur-convex functions. Once the optimal structure of the transmit-receive processing is known, the design problem simplifies and can be formulated within the powerful framework of convex optimization theory, in which a great number of interesting design criteria can be easily accommodated and efficiently solved even though closed-form expressions may not exist. From this perspective, we analyze a variety of design criteria and, in particular, we derive optimal beamvectors in the sense of having minimum average bit error rate (BER). Additional constraints on the Peak-to-Average Ratio (PAR) or on the signal dynamic range are easily included in the design. We propose two multi-level water-filling practical solutions that perform very close to the optimal in terms of average BER with a low implementation complexity. If cooperation among the processing operating at different carriers is allowed, the performance improves significantly. Interestingly, with carrier cooperation, it turns out that the exact optimal solution in terms of average BER can be obtained in closed-form. Manuscript received February 25, 2002; revised December 20, 2002. Part of the work was presented at the 40th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, Oct. 2002  . This work was partially supported by the European Comission under Project IST-2000-30148 I-METRA; Samsung Advanced Institute of Technology; the Spanish Government (CICYT) TIC2000-1025, TIC2001-2356, TIC2002-04594, FIT-070000-2000-649 (MEDEA + A105 UniLAN); and the Catalan Government (DURSI) 1999FI 00588, 2001SGR 00268.
The Nature of Strategic Decision Making – Exploiting the role of managers ’ incremental and radical learning

The purpose of this paper is to examine the role of two basic types of learning in contemporary organizations – incremental (knowledge exploitation) and radical learning (knowledge exploration) – in making organization’s strategic decisions. In achieving this goal a conceptual model of influence of learning types on the nature of strategic decision making and their outcomes was formed, on the basis of which the empirical research was conducted, encompassing 54 top managers in large Croatian companies. The paper discusses the nature of organizational learning and decision making at strategic management level. The results obtained are suggesting that there is a relationship between managers' learning type and decision making approaches at strategic management level, as well as there is the interdependence between these two processes with strategic decision making outcomes. Within these results there are interesting insights, such as that the effect of radical learning on analytical decision making approach is significantly weaker and narrower when compared to the effect of incremental learning on the same approach, and that analytical decision making approach does not affect strategic decision making outcomes.
Facile and Scale Up Synthesis of Red Phosphorus-Graphitic Carbon Nitride Heterostructures for Energy and Environment Applications

The development of heterostructured materials for efficient solar energy conversion and energy storage devices are essential for practical applications. In this study, a simple and relatively inexpensive method was used to improve the visible light-driven photocatalytic activity and electrochemical supercapacitor behavior of the graphitic carbon nitride (g-C3N4) by elemental red phosphorus (RPh). The as-prepared RPh-g-C3N4 was characterized in detail using a range of spectroscopic techniques to understand the structure, morphology, chemical interaction, and chemical state of the materials. The visible light-driven photocatalytic activity and supercapacitive electrode performance were assessed by the photodegradation of model colored, non-colored organic pollutants, and electrochemical half-cell measurements, respectively. The RPh-g-C3N4 heterostructure with 30 weight percent of RPh exhibited remarkably high photocatalytic activity for the degradation of pollutants compared to the bare constituent materials, which was further confirmed by the photoelectrochemical study under similar visible photoirradiation conditions. The RPh-g-C3N4 heterostructure supercapacitor electrode displayed a high capacitance of 465 F/g and excellent cyclic stability with capacitance retention of 90% after 1000 cycles at a current of 10 A/g. The superior performance was attributed mainly to the narrow band gap, high surface area, capacitive nature of RPh, and nitrogen-rich skeleton of g-C3N4.