Miracle-dz/UniFAR-FTUs huggingface dataset:
Formale Methoden der Künstlichen Intelligenz

Aufgrund der zunehmenden Komplexität von Hardund Softwaresystemen werden computergestützte formale Methoden in der Zukunft immer weiter an Bedeutung zunehmen. Aufgabe dieser Methoden ist es, menschlichen Benutzern bei der Verifikation und Synthese von Computersystemen auf intelligente Weise zu assistieren. In diesem Artikel werden Ziele, Herausforderungen und Entwicklungen im Bereich formaler Methoden der Künstlichen Intelligenz angesprochen, die aller Voraussicht nach lohnenswerte Aufgabenstellungen für die Forschung der kommenden Jahre darstellen.
An Analysis of Interface Quality for Decision Support

This paper examines the quality of user interfaces in an application to choose products. A high-quality interface should enable users to perform prescribed tasks quickly and without errors. The interface should also enable users to find products which match their personal values. A high-quality interface can especially empower those diverse users who might otherwise be overwhelmed by technology but who need information to ground their actions. By examining user performance in such regards, insight into the quality of interface may be acheived. For this purpose, we examined user performance while utilizing different interfaces for product selections. An analysis and discussion of the examination is presented, along with opportunities for future work.
14-3-3 Proteins Participate in Light Signaling through Association with PHYTOCHROME INTERACTING FACTORs

14-3-3 proteins are regulatory proteins found in all eukaryotes and are known to selectively interact with phosphorylated proteins to regulate physiological processes. Through an affinity purification screening, many light-related proteins were recovered as 14-3-3 candidate binding partners. Yeast two-hybrid analysis revealed that the 14-3-3 kappa isoform (14-3-3κ) could bind to PHYTOCHROME INTERACTING FACTOR3 (PIF3) and CONSTITUTIVE PHOTOMORPHOGENIC1 (COP1). Further analysis by in vitro pull-down assay confirmed the interaction between 14-3-3κ and PIF3. Interruption of putative phosphorylation sites on the 14-3-3 binding motifs of PIF3 was not sufficient to inhibit 14-3-3κ from binding or to disturb nuclear localization of PIF3. It was also indicated that 14-3-3κ could bind to other members of the PIF family, such as PIF1 and PIF6, but not to LONG HYPOCOTYL IN FAR-RED1 (HFR1). 14-3-3 mutants, as well as the PIF3 overexpressor, displayed longer hypocotyls, and a pif3 mutant displayed shorter hypocotyls than the wild-type in red light, suggesting that 14-3-3 proteins are positive regulators of photomorphogenesis and function antagonistically with PIF3. Consequently, our results indicate that 14-3-3 proteins bind to PIFs and initiate photomorphogenesis in response to a light signal.
Oscillatory Survival Probability: Analytical, Numerical Study for oscillatory narrow escape and applications to neural network dynamics

We study the escape of Brownian motion from the domain of attraction $\Omega$ of a stable focus with a strong drift. The boundary $\partial \Omega$ of $\Omega$ is an unstable limit cycle of the drift and the focus is very close to the limit cycle. We find a new phenomenon of oscillatory decay of the peaks of the survival probability of the Brownian motion in $\Omega$. We compute explicitly the complex-valued second eigenvalue $\lambda_2(\Omega$) of the Fokker-Planck operator with Dirichlet boundary conditions and show that it is responsible for the peaks. Specifically, we demonstrate that the dominant oscillation frequency equals $1/{\mathfrak{I}}m\{\lambda_2(\Omega)\}$ and is independent of the relative noise strength. We apply the analysis to a canonical system and compare the density of exit points on $\partial \Omega$ to that obtained from stochastic simulations. We find that this density is concentrated in a small portion of the boundary, thus rendering the exit a narrow escape problem. Unlike the case in the classical activated escape problem, the principal eigenvalue does not necessarily decay exponentially as the relative noise strength decays. The oscillatory narrow escape problem arises in a mathematical model of neural networks with synaptic depression. We identify oscillation peaks in the density of the time the network spends in a specific state. This observation explains the oscillations of stochastic trajectories around a focus prior to escape and also the non-Poissonian escape times. This phenomenon has been observed and reported in the neural network literature.
Classification of individual finger motions hybridizing electromyogram in transient and converged states

To classify the five individual finger motions from an electromyogram (EMG) signal, a classification system that hybridizes EMG signals in both the transient and converged states of a motion is proposed. The classifications of finger motions are executed individually in each state by a well-established artificial neural network (ANN). Then, the outputs of the two classifiers are combined. The efficacy of the result is evaluated via a piano-tapping task, in which the subjects are instructed to tap a keyboard with each of their five fingers. We use this task to compare the proposed hybrid system and a conventional converged system that uses an EMG signal only in the converged state. For five of the six subjects, the accuracy ratio of finger motions was better in the proposed method: approximately 85% for each finger except the second. Further analysis suggests two remarkable advantages of the hybrid method: 1) the output of the ANN is more credible, and 2) finger motion in the transient state (i.e., the early phase) is more predictable.
Parametric Inference for Discretely Sampled Stochastic Differential Equations

A review is given of parametric estimation methods for discretely sampled multivariate diffusion processes. The main focus is on estimating functions and asymptotic results. Maximum likelihood estimation is briefly considered, but the emphasis is on computationally less demanding martingale estimating functions. Particular attention is given to explicit estimating functions. Results on both fixed frequency and high frequency asymptotics are given. When choosing among the many estimators available, guidance is provided by simple criteria for high frequency efficiency and rate optimality that are presented in the framework of approximate martingale estimating functions.
The Resource Theory of Steering

We present an operational framework for Einstein-Podolsky-Rosen steering as a physical resource. To begin with, we characterize the set of steering non-increasing operations (SNIOs) – i.e., those that do not create steering– on arbitrary-dimensional bipartite systems composed of a quantum subsystem and a black-box device. Next, we introduce the notion of convex steering monotones as the fundamental axiomatic quantifiers of steering. As a convenient example thereof, we present the relative entropy of steering. In addition, we prove that two previously proposed quantifiers, the steerable weight and the robustness of steering, are also convex steering monotones. To end up with, for minimal-dimensional systems, we establish, on the one hand, necessary and sufficient conditions for pure-state steering conversions under stochastic SNIOs and prove, on the other hand, the non-existence of steering bits, i.e., measure-independent maximally steerable states from which all states can be obtained by means of the free operations. Our findings reveal unexpected aspects of steering and lay foundations for further resource-theory approaches, with potential implications in Bell non-locality.
Nicotine binding and nicotinic receptor subunit RNA after chronic nicotine treatment

DBA mice were chronically treated with nicotine by continuous intravenous infusion of 4.0 mg/kg/hr for 10 d. Drug-treated mice were tolerant to the acute effects of nicotine on locomotor activity and body temperature. The effect of chronic treatment on the amount of L-3H- nicotine binding and RNA encoding for alpha 4, the most widely expressed nicotinic alpha-subunit, was measured in three brain regions. Chronic treatment increased L-3H-nicotine binding in cortex and midbrain but had no effect in cerebellum. In contrast, chronic treatment had no effect on the levels of mRNA encoding for alpha 4 in any of the three brain regions. Subsequently brains were sectioned and L-3H-nicotine binding was measured using quantitative autoradiographic methods. In addition, the relative amounts of mRNA for the major nicotinic receptor subunits (alpha 4 and beta 2), as well as for three additional minor subunits (alpha 2, alpha 3, and alpha 5), were determined by in situ hybridization histochemistry followed by quantitation of image intensity. Chronic nicotine treatment resulted in increases in the amount of L-3H-nicotine binding in many but not all brain areas measured. In contrast, chronic treatment had little effect on the intensity of the hybridization signal for the nicotinic subunit mRNA. The results suggest that chronic treatment with nicotine under conditions resulting in maximal steady-state increases in L-3H-nicotine binding has little effect on RNA levels encoding any of four nicotinic alpha-subunits and the beta 2-subunit.
A Riemannian gossip approach to decentralized matrix completion

In this paper, we propose novel gossip algorithms for the low-rank decentralized matrix completion problem. The proposed approach is on the Riemannian Grassmann manifold that allows local matrix completion by different agents while achieving asymptotic consensus on the global low-rank factors. The resulting approach is scalable and parallelizable. Our numerical experiments show the good performance of the proposed algorithms on various benchmarks.
Hippocampal theta sequences: from phenomenology to circuit mechanisms

The hippocampus is a brain structure involved in episodic memory and spatial cognition. Neuronal activity within the hippocampus exhibits intricate temporal patterning, including oscillatory and sequential dynamics, which are believed to underlie these cognitive processes. In individual cells, a temporal activity pattern called phase precession occurs which leads to the organisation of neuronal populations into sequences. These sequences are hypothesised to form a substrate for episodic memory and the representation of spatial trajectories during navigation. In this thesis, I present a novel theory of the phenomenological properties of these neuronal activity sequences. In particular, I propose that the sequential organisation of population activity is governed by the independent phase precession of each cell. By comparison of models in which cells are independent and models in which cells exhibit coordinated activity against experimental data, I provide empirical evidence to support this hypothesis. Further, I show how independent coding affords a vast capacity for the generation of sequential activity patterns across distinct environments, allowing the representation of episodes and spatial experiences across a large number of contexts. This theory is then extended to account for grid cells, whose activity patterns form a hexagonal lattice over external space. By analysing simple forms of phase coding in populations of grid cells, I show how previously undocumented constraints on phase coding in two dimensional environments are imposed by the symmetries of grid cell firing fields. To overcome these constraints, I propose a more complex phenomenological model which can account for phase precession in both place cells and grid cells in two dimensional environments. Using insights from this theory, I then propose a biophysical circuit mechanism for hippocampal sequences. I show that this biophysical circuit model can account for the proposed phenomenological coding properties and provide experimentally testable predictions which can distinguish this model from existing models of phase precession. Finally, I outline a scheme by which this biophysical mechanism can implement supervised learning using spike time dependent plasticity in order to learn associations between events occurring on behavioural timescales. The models presented in this thesis challenge previous theories of hippocampal circuit function and suggest a much higher degree of flexibility and capacity for the generation of sequences than previously believed. This flexibility may underlie our ability to represent spatial experiences and store episodic memories across a seemingly unlimited number of distinct contexts.
DPlace2.0: A stable and efficient analytical placement based on diffusion

Nowadays a placement problem often involves multi-million objects and excessive fixed blockages. We present a new global placement algorithm that scales well to the modern large-scale circuit placement problems. We simulate the natural diffusion process to spread cells smoothly over the placement region, and use both analytical and discrete techniques to improve the wire length. Although any analytical wire length technique can be used in our new framework, by using the quadratic wire length model, the hessian of our formulation is extremely sparse compared with conventional formulations, which brings 24x speed up on quadratic solver. We also propose a wire linearization technique that transform quadratic star model into HPWL exactly. The overall runtime of our tool is close to the fastest placement tool in existing literature and significantly better than others. And meanwhile, we obtain competitive wire length results to the best known ones. The average total wire length is 2.2% higher than mPL6, 0.2%, 3.1%, and 9.1% better than FastPlace3.0, APlace2.0, and Capol0.2 respectively.
Robust Routing Under BGP Reroutes

Configuration of the routing is critical for the quality and reliability of the communication in a large IP backbone. Large traffic shifts can occur due to changes in the inter-domain routing that are hard to control by the network operator. This paper describes a framework for modeling potential traffic shifts due to BGP reroutes, calculating worst-case traffic scenarios, and finding a single routing configuration that is robust against all possible traffic shifts due to BGP reroutes. The benefit of our approach is illustrated using BGP routing updates and network topology from an operational IP network. Experiments demonstrate that the robust routing is able to obtain a consistently strong performance under large inter-domain routing changes.
Effects on Balance and Walking with the CoDuSe Balance Exercise Program in People with Multiple Sclerosis: A Multicenter Randomized Controlled Trial

Background. Balance and walking impairments are frequent in people with multiple sclerosis (MS). Objective. The aim was to investigate the effects of a group-based balance exercise program targeting core stability, dual tasking, and sensory strategies (CoDuSe) on balance, postural sway, walking, perceived walking limitations, and balance confidence. Design. A single-blinded randomized multicenter trial. No intervention was given to controls. Participants. People with MS able to walk 100 meters but unable to maintain tandem stance ≥30 seconds. Eighty-seven participants were randomized to intervention or control. Intervention. The 60-minute CoDuSe group program, twice weekly for seven weeks, supervised by physical therapists. Measurements. Primary outcome was dynamic balance (Berg Balance Scale (BBS)). Secondary outcomes were postural sway, walking (Timed-Up and Go test; Functional Gait Assessment (FGA)), MS Walking Scale, and Activities-specific Balance Confidence (ABC) Scale. Assessments were performed before and after (week 8) the intervention. Results. 73 participants fulfilled the study. There were significant differences between the intervention and the control groups in change in the BBS and in the secondary measures: postural sway with eyes open, FGA, MS Walking Scale, and ABC scale in favor of the intervention. Conclusions. The seven-week CoDuSe program improved dynamic balance more than no intervention.
Error Recovery Mechanism using Dynamic Partial Reconfiguration

In this paper an error recovery mechanism for SRAM based FPGA systems is presented. Previous recovery methods employ processor cores as a reconfiguration controller consuming notable amount of device resources and introducing additional error detection and recovery latency. The described mechanism is controlled by a finite state machine architecture providing small hardware overhead and short recovery latency. The mechanism has been originally designed for mission critical applications with a required level of fault tolerance. However, it can also be used as a countermeasure to fault attack of faulttolerant secure devices. Keywords— dynamic partial reconfiguration; FPGA; error mitigation; error recovery
Actions in practice: On details in collections

Several of the contributions to the Lynch et al. Special issue make the claim that conversation-analytic research into epistemics is ‘routinely crafted at the expense of actual, produced and constitutive detail, and what that detail may show us’. Here, we seek to address the inappositeness of this critique by tracing precisely how it is that recognizable actions emerge from distinct practices of interaction. We begin by reviewing some of the foundational tenets of conversation-analytic theory and method – including the relationship between position and composition, and the making of collections – as these appear to be primary sources of confusion for many of the contributors to the Lynch et al. Special Issue. We then target some of the specific arguments presented in the Special Issue, including the alleged ‘over-hearer’s’ writing of metrics, the provision of so-called ‘alternative’ analyses and the supposed ‘crafting’ of generalizations in epistemics research. In addition, in light of Lynch’s more general assertion that conversation analysis (CA) has recently been experiencing a ‘rapprochement’ with what he disparagingly refers to as the ‘juggernaut’ of linguistics, we discuss the specific expertise that linguists have to offer in analyzing particular sorts of interactional detail. The article as a whole thus illustrates that, rather than being produced ‘at the expense of actual, produced and constitutive detail’, conversation-analytic findings – including its work in epistemics – are unambiguously anchored in such detail. We conclude by offering our comments as to the link between CA and linguistics more generally, arguing that this relationship has long proven to be – and indeed continues to be – a mutually beneficial one.
A Smart Camera Network with SVM Classifiers for Crowd Event Recognition

—It is now popular to develop video surveillance systems to enhance the security of our daily life. At present, the surveillance systems have turned to automatically identify the continuous human behaviors to detect various events from video streams. It means that more efficient approaches are in demand to deal with the largely increasing amount of data. This study presents a low-cost distributed smart cameras system, together with a machine learning technique to detect abnormal events by analyzing the sequential behaviors of a crowd of people. Moreover, this system employs a collaboration strategy to perform collective decision making for event recognition. Experiments have been conducted to evaluate the proposed approach and the results confirm its reliability and stability in event recognition.
AGING AND FUNCTIONAL COMPLEX ACTIVITIES: CLINICAL AND SOCIAL PERSPECTIVES

Background: Several papers describe functional changes in the aging population, and its relevance in early detection.Objectives: In order to count on a tool to evaluate the performance in the use of new technologies we have designed a new protocol - Complex Functional Study (CFS)-that quantifies performance and functional changes related to previous states.Our objectives are to study the aging population with this lately developed tool.Methods: We evaluated 350 healthy subjects grouped according to age and instruction levels. We administered a General Cognitive Screening and the CFS. We used ANOVA Test, and Regression Analysis and Principal Components.Results: Our finding verifies that the 15.75% registered functional loss and the 2.8%, registered changes in performance. The differences in the averages were higher at the age range 80-90. The measure of change proved to have less impact in groups with higher instruction level. A greater functional loss has been registered in older participants with less instruction level.Conclusions: This study enables the use of the CFS as a tool of clinical interest for the detection of incipient impairment in daily living complex functional skills.The results obtained verify a good performance in those skills in most of the studied population. On the other hand, this study has also a psycho-social perspective insofar as it fosters significant learning processes. In this way it will be possible to implement strategies to develop social awareness about the learning capacity in the elderly to impact in the social net and on their wellbeing.
An OSGi-based Model-driven Data Management Module for Robust Open Data Harvesting

The German governmental open data platform, GovData.DE, launched in February 2013, has served as the single point to access open governmental data. The outcome of this was a single metadata format, called the Open Governmental Metadata (OGD) that servers as the common standardized metadata structure for open government data in German speaking countries. The simplicity and extensibility of OGD allows it to be used for sharing open data from other domains, such as the cultural or educational domains. However, this requires that big data facets be addressed by the current Open Data Platform (ODP) implementation. Presently, CKAN is the de facto system for open data harvesting, storage and management. The open data platform in its current form is built on top of CKAN. The current implementation of harvesters and data management operations are highly CKAN dependent, which is a problem. Thus, in this paper, we present a novel ODP architecture based on OSGi and modeldriven engineering that provides decoupling between the data management and CKAN. We showcase how our architecture addresses the required scalability and modularity concerns and provides a pathway to address the big data requirements.