Miracle-dz/UniFAR-FTUs huggingface dataset:
How Deployment Realism withstands Doppelt's Criticisms

Gerald Doppelt claims that Deployment Realism cannot withstand the antirealist objections based on the “pessimistic meta-induction” and Laudan’s historical counterexamples. Moreover it is incomplete, as it purports to explain the predictive success of theories, but overlooks the necessity to explain also their explanatory success. Accordingly, he proposes a new version of realism, presented as the best explanation of both predictive and explanatory success, and committed only to the truth of best current theories, not of the discarded ones (Doppelt (2007, 2011, 2013, 2014). Elsewhere I criticized his new brand of realism. Here instead I argue that (a) Doppelt has not shown that Deployment Realism cannot solve the problems raised by the history of science, (b) explaining explanatory success does not add much to explaining novel predictive success, and (c) Doppelt is right that truth is not a sufficient explanans, but for different reasons, and this does not refute Deployment Realism, but helps to detail it better. In a more explicit formulation, the realist IBE concludes not only to the truth of theories, but also to the reliability of scientists and scientific method, the order and simplicity of nature, and the approximate truth of background theories.
Topologically Characterized Movement Running head: TOPOLOGICALLY CHARACTERIZED MOVEMENT PATTERNS Topologically Characterized Movement Patterns: A Cognitive Assessment

In this paper, we discuss the role of topology as a predictor for the conceptualization of dynamically changing spatial configurations (referred to as movement patterns). We define meaningful units of movement patterns as paths through a topologically defined conceptual neighborhood graph. Topology plays a central role in formal approaches to human cognition and in predicting cognitive similarity ratings—although primarily for static spatial configurations. Formal specifications of the role of topology for characterizing movement patterns do exist, yet there is paucity of behavioral validation. To bridge this gap, we conducted an experiment based on the grouping paradigm to assess factors that underlie conceptualizations of movement patterns. The experiment was designed such that paths through the conceptual neighborhood graph were distinguished by topologically differentiated ending relations. We believe topology can make an important contribution in explaining movement conceptualizations. One recently formulated topology-based contribution is the endpoint hypothesis, asserting that a cognitive focus is placed on event ending relations. We discuss the results of our experiment in relation to previous experiments targeted toward a framework for modeling the cognitive conceptualization of dynamically changing spatial relations.
Questionnaires for evaluation in information visualization

The position taken in this paper is that the availability of standardized questionnaires specifically developed for measuring users' perception of usability in evaluation studies in information visualization would provide the community with an excellent additional instrument. The need for such an instrument is evident for several important reasons. Pursuing the development, validation and use of questionnaires will add significantly to the evidence base necessary for the community to guide the production of high-quality visualization techniques, facilitate adoption by users, promote successful commercialization and guide future research tasks.
Biological Crystallography Crystallization of Foot-and-mouth Disease Virus 3c Protease: Surface Mutagenesis and a Novel Crystal-optimization Strategy

Foot-and-mouth disease virus (FMDV) 3C protease (3C pro) plays a vital role in virus replication by performing most of the cleavages required to divide the viral polyprotein precursor into its functional component proteins. To date, no structural information has been available for FMDV 3C pro , which is an attractive target for antiviral drugs. Targeted mutagenesis of surface amino acids identified two Cys residues that were detrimental to solubility and contributed to the time-dependent formation of a proteinaceous skin in samples of purified wild-type protein. Substitution of these amino acids, combined with trimming of the N-and C-termini, yielded a 3C pro construct that was amenable to crystallization. High-resolution diffraction (1.9 A ˚) was only obtained following 'iterative screening' in which commercial crystal screening solutions were used as additives once initial crystallization conditions had been obtained.
Enlightening the I/O Path: A Holistic Approach for Application Performance

In data-intensive applications, such as databases and key-value stores, reducing the request handling latency is important for providing better data services. In such applications, I/O-intensive background tasks, such as checkpointing, are the major culprit in worsening the latency due to the contention in shared I/O stack and storage. To minimize the contention, properly prioritizing I/Os is crucial but the effectiveness of existing approaches is limited for two reasons. First, statically deciding the priority of an I/O is insufficient since high-priority tasks can wait for low-priority I/Os due to I/O priority inversion. Second, multiple independent layers in modern storage stacks are not holistically considered by existing approacheswhich thereby fail to effectively prioritize I/Os throughout the I/O path. In this paper, we propose a request-centric I/O prioritization that dynamically detects and prioritizes I/Os delaying request handling at all layers in the I/O path. The proposed scheme is implemented on Linux and is evaluated with three applications, PostgreSQL, MongoDB, and Redis. The evaluation results show that our scheme achieves up to 53% better request throughput and 42× better 99th percentile request latency (84 ms vs. 3581 ms), compared to the default configuration in Linux.
A Two-Step Approach for Analytical Optimal Hedging with Two Triggers

Hedging is widely used to mitigate severe water shortages in the operation of reservoirs during droughts. Rationing is usually instituted with one hedging policy, which is based only on one trigger, i.e., initial storage level or current water availability. It may perform poorly in balancing the benefits of a release during the current period versus those of carryover storage during future droughts. This study proposes a novel hedging rule to improve the efficiency of a reservoir operated to supply water, in which, based on two triggers, hedging is initiated with three different hedging sub-rules through a two-step approach. In the first step, the sub-rule is triggered based on the relationship between the initial reservoir storage level and the level of the target rule curve or the firm rule curve at the end of the current period. This step is mainly concerned with increasing the water level or not in the current period. Hedging is then triggered under the sub-rule based on current water availability in the second step, in which the trigger implicitly considers both initial and ending reservoir storage levels in the current period. Moreover, the amount of hedging is analytically derived based on the Karush–Kuhn–Tucker (KKT) conditions. In addition, the hedging parameters are optimized using the improved particle swarm optimization (IPSO) algorithm coupled with a rule-based simulation. A single water-supply reservoir located in Hubei Province in central China is selected as a case study. The operation results show that the proposed rule is reasonable and significantly improves the reservoir operation performance for both long-term and critical periods relative to other operation policies, such as the standard operating policy (SOP) and the most commonly used hedging rules.
Bayesian calibration of the Schwartz-Smith Model adapted to the energy market

We consider an application of Bayesian signal processing to the energy trading problem. In particular, we address the problem of calibrating the Schwartz-Smith Model using the observed electricity futures prices traded on the markets. As compared with the other financial markets, basic electricity derivatives such as futures are more complicated, as these products are based not on the spot prices themselves but on the arithmetic averages of the spot prices during the delivery period. As a result, the (log) futures prices are no longer affine function of the model factors and as such, an approach based on Kalman filtering, to estimate the latent model factors and the parameters seems meaningless. Here, we envisage a Bayesian approach using the particle marginal Metropolis Hastings (PMMH) algorithm for this challenging estimation task. We demonstrate the efficacy of our approach on simulated data.
Combining gray-level co-occurrence matrix and statistics features for rotation invariant texture classification in wavelet domain

In order to improve the accuracy and efficiency of rotation invariant texture classification, we develop a novel classification method based on gray-level co-occurrence matrix and discrete wavelet transform in the paper. The method combines the probability of specific neighboring resolution cell pairs occurred at the same time in the whole image and statistics features in wavelet domain. Discrete wavelet transform is firstly adopted to decompose images into several sub-bands. Then probability and statistics features are extracted from these different sub-bands. The probability is calculated from the approximation sub-band and statistics features are calculated from both approximation sub-band and detail sub-bands. Finally, the method combines the probability and statistics together as features for rotation invariant texture classification. Experiments are conducted on two texture image sets and the results of experiments show the good performance of our method.
Changing Perceptions About Changes in the Teen Brain: An Overview

Education changes the brain. The purpose of this meta-review is to afford teachers access to the most upto- date research regarding principal neuro-processes of adolescent development and behavior to improve students’ well-being and motivation in the classroom. This includes facets such as emotional, social, risk and reward mechanisms, stressors, technology, and learning. Understanding the scientific basis behind processes, such as learning and memory, empowers teachers with the knowledge to quantify the effects of their teaching on the brain, to facilitate successful learning, and to contribute to the present as well as future health and well-being of their adolescent students.
A Double Interval Centroid-Based Watermark for network flow traceback

Network flow watermarks exploiting active traffic analysis approaches have been proposed for tracing attacks implemented through stepping stones or anonymized channels. Existing watermarking schemes either require longer observation duration leading to low efficiency of traceback or introduce large delays to the target flows, making themselves vulnerable to attacks. Especially, they are unsuitable for tracing multiple flows simultaneously due to their interference with each other. We herein propose a novel efficient and secret Double Interval Centroid-Based Watermark (DICBW) for network flow traceback by modulating the packet timing within each pair of adjacent intervals collaboratively and efficiently. By combining DICBW with spread spectrum (SS) coding techniques, we further present a general hybrid watermarking framework for efficient multi-flow traceback. Comparing with existing Interval Centroid-Based Watermark (ICBW), both statistical analysis of DICBW and the experiments using a series of synthetically generated SSH traffic flows demonstrate that DICBW can trace network flows more efficiently and the DICBW based hybrid watermarking framework can trace multiple flows simultaneously by exploiting the low cross-correlation of SS coding techniques.
Estimation of occupant distribution by detecting the entrance and leaving events of zones in building

For energy saving and security in building, the information of occupant number of each zone is very important. This paper works on the estimation of the occupant number of zones in building by detecting the entrance and leaving events. In this paper, we first formulate the problem under an assumption of Markov Chain, and basing on the theoretical analysis of the model, we propose a method of occupant distribution estimation, which can be implemented distributively. The method counts occupant by detecting the entrance and leaving events of zones in real time and uses the prior information of the occupant's entrance and leaving events in each zone to reduce the estimation error, which increases the accuracy of the estimation of occupant distribution in building. Numerical experiments including simulation and field test demonstrate the performance of the method.
Temporal connectivity of vehicular networks: The power of store-carry-and-forward

Store-carry-and-forward is extensively used in vehicular environments for many and varied purposes, including routing, disseminating, downloading, uploading, or offloading delay-tolerant content. The performance gain of store-carry-and-forward over traditional connected forwarding is primarily determined by the fact that it grants a much improved network connectivity. Indeed, by letting vehicles physically carry data, store-carry-and-forward adds a temporal dimension to the (typically fragmented) instantaneous network topology that is employed by connected forwarding. Temporal connectivity has thus a important role in the operation of a wide range of vehicular network protocols. Still, our understanding of the dynamics of the temporal connectivity of vehicular networks is extremely limited. In this paper, we shed light on this underrated aspect of vehicular networking, by exploring a vast space of scenarios through an evolving graph-theoretical approach. Our results show that using store-carry-and-forward greatly increases connectivity, especially in very sparse networks. Moreover, using store-carry-and-forward mechanisms to share content within a geographically-bounded area can be very efficient, i.e., new entering vehicles can be reached rapidly.
Digesting News Reader Comments via Fine-Grained Associations with Event Facets and News Contents

News articles from different sources reporting the same event are often associated with an enormous amount of reader comments resulting in difficulty in digesting the comments manually. Some of these comments, despite coming from different sources, discuss about a certain facet of the event. On the other hand, some comments discuss on the specific topic of the corresponding news article. We propose a framework that can digest reader comments automatically via fine-grained associations with event facets and news. We propose an unsupervised model called DRC, based on collective matrix factorization and develop a multiplicative-update method to infer the parameters. Experimental results show that our proposed DRC model can provide an effective way to digest news reader comments.
The design of binary shaping filter of binary code

In information theory, in order to maximize the total throughput, it is required that the codebook has an empirical distribution that maximizes the mutual information over the channel. In coding theory, however, most codes we can generate have Bernoulli (1 over 2) distribution. In this paper, we present a new coding scheme to efficiently generate binary codes with different distributions. Our main approach is to first encode the information bits by a linear code C, and then quantized the codeword to the closest codeword in another linear code Cs. The quantization error is then treated as the encoded codeword in our coding scheme. We discuss the practicality and advantage of such codes and give a design example.
Evaluation of semantic role labeling based on lexical features using conditional random fields and support vector machine

The main objective of this paper is to identify the semantic roles of arguments in a sentence based on lexicalized features even if less semantic information is available. The semantic role labeling task (SRL) involves identifying which groups of words act as arguments to a given predicate. These arguments must be labeled with their role with respect to the predicate, indicating how the proposition should be semantically interpreted. The approach mainly focuses on improving the task of SRL by adding the similar words and selectional preferences to the existing lexical features, thereby avoiding data sparsity problem. Addition of richer lexical information can improve SRL task even when very little syntactic knowledge is available in the input sentence. We analyze the performance of SRL which use a probabilistic graphical model (Conditional Random Field) and a machine learning model (Support Vector Machines). The statistical modelling is trained by CONLL-2004 Shared Task training data.
A robust linear receiver for uplink multi-user MIMO systems based on probability-constrained optimization and second-order cone programming

Traditional receiver algorithms developed for multiple-input multiple-output (MIMO) wireless communication systems are based on the assumption that the channel state information (CSI) is perfectly known at the receiver. However, in real environments the exact CSI is unavailable. In this paper, we address the problem of robustness of multiuser space-time block coded MIMO systems against imperfect CSI and develop a simple linear receiver which guarantees robustness against CSI errors with a certain selected probability. The proposed receiver is formulated via a probability-constrained optimization problem which is further relaxed using Chebyshev inequality to a much simpler second-order cone programming (SOCP) problem. Simulations demonstrate that the proposed receiver has a moderate performance degradation as compared to the exact solution to the original probability-constrained optimization problem, while offering much simpler implementation.
Both introns and long 3′-UTRs operate as cis-acting elements to trigger nonsense-mediated decay in plants

Nonsense-mediated mRNA decay (NMD) is a eukaryotic quality control mechanism that identifies and eliminates aberrant mRNAs containing a premature termination codon (PTC). Although, key trans-acting NMD factors, UPF1, UPF2 and UPF3 are conserved in yeast and mammals, the cis-acting NMD elements are different. In yeast, short specific sequences or long 3′-untranslated regions (3′-UTRs) render an mRNA subject to NMD, while in mammals' 3′-UTR located introns trigger NMD. Plants also possess an NMD system, although little is known about how it functions. We have elaborated an agroinfiltration-based transient NMD assay system and defined the cis-acting elements that mediate plant NMD. We show that unusually long 3′-UTRs or the presence of introns in the 3′-UTR can subject mRNAs to NMD. These data suggest that both long 3′-UTR-based and intron-based PTC definition operated in the common ancestors of extant eukaryotes (stem eukaryotes) and support the theory that intron-based NMD facilitated the spreading of introns in stem eukaryotes. We have also identified plant UPF1 and showed that tethering of UPF1 to either the 5′- or 3′-UTR of an mRNA results in reduced transcript accumulation. Thus, plant UPF1 might bind to mRNA in a late, irreversible phase of NMD.
An Application Demand aware Scheduling Algorithm in Heterogeneous Environment

Due to the emergence of grid computing, which has linked up all the various resources widely distributed (eg: computing resources, storage resources, software resources, information resources, etc.) through the Internet, grid scheduling has become a new focus on grid research. However, a suitable and efficient scheduling algorithm is also badly needed during scheduling process to serve as allocating distributed resources to jobs of different users reasonably. In this paper, we present a new scheduling algorithm based on application demand, through which, can we make most users satisfied by guaranteeing their submitted jobs to be completed within their expected completion times without sacrificing system utilization more. This is the novelty of our algorithm, and it is beneficial for job scheduling and resource sharing in heterogeneous environment.
Implementing new ways of working: interventions and their effect on the use of an electronic medication record

Successful deployment of information technology (IT) involves implementation of new ways of working. Under-recognition of this organizational element of implementation entails considerable risk of not attaining the benefits that motivated deployment, yet knowledge of how to work systematically with organizational implementation is sparse. This study investigates a set of interventions undertaken to implement one mandated procedure associated with an electronic medication record, namely that all information about medication is recorded in the system. Medical record audits show that the interventions, which were devised and performed as part of the study, significantly lowered the number of records that violated the procedure. This positive effect was, however, not achieved until multiple interventions had been employed, and there is some indication that the effect may be wearing off after the interventions have ended. We discuss the implications of these results for efforts to work systematically with the organizational implementation of IT systems.
Alien vs. Quine

Is it possible to prove that a computer is malware-free without pulling out its hard disk? This article introduces a novel hardware inspection technique based on the injection of carefully crafted code and the analysis of its output and execution time. In theory, the easiest way to exterminate malware is to reformat the disk and then reinstall the operating system (OS) from a trusted distribution GD. This procedure assumes we can force computers to boot from trusted media, but most modern PCs have a flash BIOS, which means that the code component in charge of booting is recorded on a rewritable memory chip. Specific programs called flashers - or even malware such as the CIH (Chernobyl) virus - have the ability to update this chip. This article addresses this concern, namely, ascertaining that malware doesn't re-flash the BIOS to derail disk-reformatting attempts or simulate their successful completion
IODINE: a tool to automatically infer dynamic invariants for hardware designs

We describe IODINE, a tool to automatically extract likely design properties using dynamic analysis. A practical bottleneck in the formal verification of hardware designs is the need to manually specify design-specific properties. IODINE presents a way to automatically extract properties such as state machine protocols, request-acknowledge pairs, and mutual exclusion between signals from design simulations. We show that dynamic invariant detection for hardware designs can infer relevant and accurate properties.
Applied Nonlinear Optimization in the DFG-Center

Modelling and simulation of complex processes in key technologies are the main issues of the DFG Research Center. Being able to efficiently deal with the complex models established and having the corresponding mathematical basis available, engineers and practitioners are interested in optimizing these models according to some objective. This is reflected by several scientific projects in the Center. They cover various fields of optimization ranging from discrete, nonlinear, or stochastic optimization to optimal control of ordinary or partial differential equations. In some of the projects, different fields interact and grow together.