Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid algorithm for detecting changes in diagnostic signals received from technical devices

In this paper, a hybrid two-level algorithm of the original changes in diagnostic signals received from multiple technical devices is presented. Research are aimed at identification of the changes, deviations or patters (events), through concurrent diagnostic signals processing, which occur in one selected signal (proposed algorithm is adjusted to omit concurrent and time-lagged changes). In the first stage, detection is based on non-stationarity detection with the short-term prediction comparison. In the second stage, dedicated distance-like measure is employed. Detection results obtained for sample random signals including simulated large deviations are presented.
CityDrive: A map-generating and speed-optimizing driving system

There have been many traffic light control systems around the globe, but the high cost of infrastructure and maintenance hinders their wide deployment. However, speed-advisory systems enabled by on-vehicle devices are much cheaper and easier to deploy. The first challenge of such systems is to get the traffic signal schedule in complex intersections. The second challenge is to get map information and calculate the distance. Facing these challenges we devise and implement a speed-advisory driving system called CityDrive, which harnesses the sensor and GPS data from a wide participation of smartphones to suggest proper speed for drivers so that they arrive at intersections in green phase. CityDrive first generates a road map and then infers traffic signal schedules, using only smartphones and a server. CityDrive does not eliminate stops at intersections, but it tries to maximize the probability that vehicles cruise through intersections in green phase. Both simulation and real test show that this continuous speed advisory service effectively smoothes traffic flow and significantly reduces energy consumption.
Constructively learning a near-minimal neural network architecture

Rather than iteratively manually examining a variety of pre-specified architectures, a constructive learning algorithm dynamically creates a problem-specific neural network architecture. Here we present an revised version of our parallel constructive neural network learning algorithm which constructs such an architecture. The three steps of searching for points on separating hyperplanes, determining separating hyperplanes from separating points and selecting separating hyperplanes generate a near-minimal architecture. As expected, experimental results indicate improved network generalization. <<ETX>>
Naive, robust or fully-adaptive: An estimation problem for CES distributions

In this paper we deal with the estimation of the covariance matrix for Complex Elliptically Symmetric (CES) data. We follow three different approaches with different level of knowledge on the specific CES model and we compare the asymptotic performances under the three approaches in terms of Cramér-Rao Bounds and Huber limit.
Direct conversion of mouse fibroblasts to self-renewing, tripotent neural precursor cells

We recently showed that defined sets of transcription factors are sufficient to convert mouse and human fibroblasts directly into cells resembling functional neurons, referred to as “induced neuronal” (iN) cells. For some applications however, it would be desirable to convert fibroblasts into proliferative neural precursor cells (NPCs) instead of neurons. We hypothesized that NPC-like cells may be induced using the same principal approach used for generating iN cells. Toward this goal, we infected mouse embryonic fibroblasts derived from Sox2-EGFP mice with a set of 11 transcription factors highly expressed in NPCs. Twenty-four days after transgene induction, Sox2-EGFP+ colonies emerged that expressed NPC-specific genes and differentiated into neuronal and astrocytic cells. Using stepwise elimination, we found that Sox2 and FoxG1 are capable of generating clonal self-renewing, bipotent induced NPCs that gave rise to astrocytes and functional neurons. When we added the Pou and Homeobox domain-containing transcription factor Brn2 to Sox2 and FoxG1, we were able to induce tripotent NPCs that could be differentiated not only into neurons and astrocytes but also into oligodendrocytes. The transcription factors FoxG1 and Brn2 alone also were capable of inducing NPC-like cells; however, these cells generated less mature neurons, although they did produce astrocytes and even oligodendrocytes capable of integration into dysmyelinated Shiverer brain. Our data demonstrate that direct lineage reprogramming using target cell-type–specific transcription factors can be used to induce NPC-like cells that potentially could be used for autologous cell transplantation-based therapies in the brain or spinal cord.
Pannier: A Container-based Flash Cache for Compound Objects

Classic caching algorithms leverage recency, access count, and/or other properties of cached blocks at per-block granularity. However, for media such as flash which have performance and wear penalties for small overwrites, implementing cache policies at a larger granularity is beneficial. Recent research has focused on buffering small blocks and writing in large granularities, called containers, but it has not explored the ramifications and best strategies for caching compound blocks consisting of logically distinct, but physically co-located, blocks. Containers may have highly diverse blocks, with mixtures of frequently accessed, infrequently accessed, and invalidated blocks. We propose and evaluate Pannier, a flash cache middleware that provides high performance while extending flash lifespan. Pannier uses three main techniques: (1) leveraging block access counts to manage cache containers, (2) incorporating block liveness as a property to improve flash cache space efficiency, and (3) designing a multi-step feedback controller to ensure a flash cache does not wear out in its lifespan while maintaining performance. Our evaluation shows that Pannier improves flash cache performance and extends lifespan beyond previous per-block and container-aware caching policies. More fundamentally, our investigation highlights the importance of creating new policies for caching compound blocks in flash.
Automatic UPDRS Evaluation in the Sit-to-Stand Task of Parkinsonians: Kinematic Analysis and Comparative Outlook on the Leg Agility Task

In this study, we first characterize the sit-to-stand (S2S) task, which contributes to the evaluation of the degree of severity of the Parkinson's disease (PD), through kinematic features, which are then linked to the Unified Parkinson's disease rating scale (UPDRS) scores. We propose to use a single body-worn wireless inertial node placed on the chest of a patient. The experimental investigation is carried out considering 24 PD patients, comparing the obtained results directly with the kinematic characterization of the leg agility (LA) task performed by the same set of patients. We show that i) the S2S and LA tasks are rather unrelated and ii) the UPDRS distributions (for both S2S and LA tasks) across the patients have a direct impact on the observed system performance.
Solving Markov Random Fields using Second Order Cone Programming Relaxations

This paper presents a generic method for solvingMarkov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRFs. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.
Expressiva dansuppdrag : utmanande läruppgifter i ämnet idrott och hälsa

Syftet med foreliggande avhandling ar att belysa och diskutera larares och elevers meningsskapande processer nar expressiva dansuppdrag iscensatts i skolamnet idrott och halsa. Avhandlingen tar avstamp i pragmatismen och genom Deweys transaktionella perspektiv studeras larares undervisning, elevers larprocesser och expressiv dans som undervisningsinnehall i en sammanhallen helhet. I en pedagogisk intervention deltog fyra larare i amnet idrott och halsa samt 68 hogstadieelever pa en skola. Ett sammanhallet danstema med utgangspunkt i Rudolf Labans rorelseramverk skapades for att uppmarksamma rorelseupplevelser och estetiska uttryck. Multipla metoder i form av videoobservationer av undervisningen, intervjuer med lararna och elevers narrativ i loggbocker anvandes for att belysa fragestallningarna fran olika hall. Transaktionsbegreppet och praktisk epistemologisk analys (PEA) provades for analyser av rorelsehandlingar och elevers texter. Begreppet risk anvandes for analyser av pedagogiska konsekvenser i undervisningen. Resultaten visar att lararna forandrar sin undervisning och blir pedagoger som skapar forutsattningar for att utforska icke-forutbestamda rorelser i stallet for instruktorer av danssteg. Lararna utvecklar nya vanor som mojliggor nyanserade och differentierade samspel med miljon och formaga att ta och hantera risker i undervisningen. Ett forandrat undervisningsinnehall i form av expressiva dansuppdrag ger mojligheter att aterinfora estetiska aspekter pa rorelser och darigenom satta fokus pa rorelsers innebord och mening. Lararna upptacker nya grupper av elever, de som vanligen inte dominerar idrottsundervisningen. Resultaten visar vidare att normer kring flickor och pojkars rorelser i amnet idrott och halsa rubbas nar de deltar i expressiva dansuppdrag. Avhandlingen pekar pa att kroppsliggorande ar ett vardefullt kunnande och att Labans rorelseramverk ger larare och elever verktyg och sprak att tydliggora larandet i amnet idrott och halsa. Dartill ar estetiska larprocesser betydelsefulla i en kroppslig praktik for att battre lara kanna sig sjalv och andra. En utforskande undervisning i kombination med expressiva dansuppdrag kan utmana logiker av tavling och rangordning i amnet idrott och halsa.
In situ PCR for visualization of microscale distribution of specific genes and gene products in prokaryotic communities

Obtaining information on the genetic capabilities and phylogenetic affinities of individual prokaryotic cells within natural communities is a high priority in the fields of microbial ecology, microbial biogeochemistry, and applied microbiology, among others. A method for prokaryotic in situ PCR (PI-PCR), a technique which will allow single cells within complex mixtures to be identified and characterized genetically, is presented here. The method involves amplification of specific nuclei acid sequences inside intact prokaryotic cells followed by color or fluorescence detection of the localized PCR product via bright-field or epifluorescence microscopy. Prokaryotic DNA and mRNA were both used successfully as targets for PI-PCR. We demonstrate the use of PI-PCR to identify nahA-positive cells in mixtures of bacterial isolates and in model marine bacterial communities.
Theoretical Study on the Potential Energy Surface of the CH 4 – CO complex

Internet Electron. J. Mol. Des.2004 Abstract Motivation. Methane and carbon monoxide play important roles in atmospheric and combustion chemistry. In the atmosphere weakly bound complex will be a candidate for the green house effect due to the ability to accumulate the additional vibration energy. It is important to elucidate the intermolecular interaction between CH4 and CO. Method. The MP2 calculation are carried out for the geometry optimization and following frequency analysis of CH4 complexes with CO. Single point energies were calculated at the CCSD(T)/6-311++G(2d,2p) and MP2/augcc-pVXZ (X = D, T, and Q) levels to evaluate the basis set and electron correlation effects. Results. Interaction energies were calculated as a function of the intermolecular distance between CH4 and CO where the CO molecule approaches CH4 in the face, edge, or vertex direction. The HF potentials are calculated to be repulsive while after including the electron correlation the potentials become bound indicative of the importance of the dispersion energy for the attraction. The largest interaction energy was predicted for the face direction with oxygen-end approach of the CO molecule. On the basis of the equilibrium geometries, the structures were further optimized. Four weakly bound complexes have been found on the potential energy surface. Conclusions. The potential energy surface of the CH4-CO complex has been investigated. The Cs complex is calculated to be the most stable compared with the C3v complexes at the MP2/6-311++G(2d,2p) level. The structure of the Cs complex is in good agreement with that obtained by the microwave spectroscopy.
Isogeometric finite-elements methods and variational reconstruction tasks in vision — A perfect match

Inverse problems are abundant in vision. A common way to deal with their inherent ill-posedness is reformulating them within the framework of the calculus of variations. This always leads to partial differential equations as conditions of (local) optimality. In this paper, we propose solving such equations numerically by isogeometric analysis, a special kind of finite-elements method. We will expose its main advantages including superior computational performance, a natural ability to facilitate multi-scale reconstruction, and a high degree of compatibility with the spline geometries encountered in modern computer-aided design systems. To animate these fairly general arguments, their impact on the well-known depth-from-gradients problem is discussed, which amounts to solving a Poisson equation on the image plane. Experiments suggest that, by the isogeometry principle, reconstructions of unprecedented quality can be obtained without any prefiltering of the data.
Measuring Immigrants' Integration

This thesis seeks to deepen the understanding of immigrants’ integration. It consists of three interrelated studies. Each study assesses a distinct problem of quantitatively measuring immigrants’ integration and presents advanced measurement instruments and their implications for substantive results. Chapter 2 expounds the problems of quantitatively researching immigrants’ transnational mobility. The study applies factor mixture analyses (FMA) to the data of the Socio-economic panel study (GSOEP) and four indicators regarding frequency, length, and total duration of visits to the country of origin. The findings indicate that the commonly applied indicator of return visits largely fails to assess transnational mobility. Chapter 3 disentangles the interrelation between immigrants’ socioeconomic status and interethnic ties, while accounting for problems of measurement error by utilizing latent constructs. Social capital theory predicts a positive effect of interethnic ties on socioeconomic status, while due to social homophily a reverse causality might apply as well. The study applies fixed effects panel regressions and autoregressive cross-lagged panel models to GSOEP data of Italian, Turkish and former Yugoslavian immigrants in Germany. After accounting for simultaneity, reverse causality and unobserved heterogeneity, only a significant positive effect of interethnic ties on socioeconomic status remains. Hence, the results support social capital theory. Chapter 4 focuses on the deficiencies of existing quantitative measurements of immigrants’ national identification in a developmental mixed method design. The study applies exploratory factor analyses to GSOEP data and 54 qualitative interviews in order to ensure the reliability of the qualitative data. Subsequently it explores the predictors of immigrants’ identity formation by conducting a qualitative comparative analysis (QCA). The results indicate that an absence of discrimination is most important for identifying with Germany. Further, the predictors explaining the two dimensions of national identification differ considerably. Thus, as quantitative single-method studies commonly survey immigrants’ ethnic and national identities with reference to different dimensions, their results are most likely biased. Chapter 5 presents two general challenges for future research and embeds the substantive results into existing theoretical and empirical research. The last chapter presents this dissertation’s limitations and ends with an overall conclusion.
Analysis of TCP in Networks with Small Buffering Capacity and Large Bandwidth-Delay Product

It is well known that inefficiencies occur in the operation of TCP when the bandwidth delay product of the network is large compared to its buffering capacity. A central reason for that is a cyclic behavior of the protocol in which two consecutive slow-start phases appear in each cycle [5,1]. This results in low throughput and under-utilization of the available bandwidth. We show in this paper that, for an even larger ratio between the bandwidth-delay product and the buffer size, which is typical for satellite links, a new regime of three consecutive slow-start phases occurs. We need to refine previously studied models in order to explain this phenomeno- n. We analyze in this paper this phenomenon, predict when it occurs and evaluate the corresponding average throughput of the connection.
Low-rank Gramian applications in dynamics and control

This paper presents an efficient linear system reduction method that computes approximations to the controllability and observability Gramians of large sparse power system descriptor models. The method exploits the fact that a Lyapunov equation solution can be decomposed into low-rank Choleski factors, which are determined by the Alternating Direction Implicit (ADI) method. Advantages of the method are that it operates on the sparse descriptor matrices and does not require the computation of spectral projections onto deflating subspaces of finite eigenvalues, which are needed by other techniques applied to descriptor models. The Gramians, which are never explicitly formed, are used to compute reduced-order state-space models for large-scale systems. Extensions of the method's application to algebraic Riccati equation computation are also considered. Numerical results for small-signal stability power system descriptor models show that the method is efficient for large-scale systems reduced-order model (ROM) computation.
Image Content Clustering and Summarization for Photo Collections

Rapid growth of digital photography in recent years spurred the need of photo management tools. In this study, we propose an automatic organization framework for photo collections based on image content, so that a novel browsing experience is provided for users. For each photograph, human faces, together with corresponding clothes and nearby regions are located. We extract color histograms of these regions as the image content feature. Then a similarity matrix of a photo collection is generated according to temporal and content features of those photographs. We perform hierarchical clustering based on this matrix, and extract duplicate subjects of a cluster by introducing the contrast context histogram (CCH) technique. The experimental results show that the developed framework provides a promising result for photo management
ETDs ) 1-1-2009 Partial Order Reduction for Planning

OF THE THESIS Partial Order Reduction for Planning by You Xu Master of Science in Computer Science Washington University in St. Louis, 2009 Research Advisor: Professor Yixin Chen Partial Order Reduction (POR) is a technique that reduces the search space by recognizing interchangeable orders between actions and expanding only a subset of all possible orders during the search. It has been extensively studied in model checking and has proven to be an enabling technique for reducing the search space and costs. Several POR algorithms have been proposed in planning, including the Expansion Core (EC) and Stratified Planning (SP) algorithms. Being orthogonal to the development of accurate heuristic functions, these reduction methods show great potential to improve the planning efficiency from a new perspective. However, it is unclear how these POR methods relate to each other and whether there exist stronger reduction methods. In this thesis, we have proposed a unifying theory that provides a necessary and sufficient condition for two actions to be semi-commutative. We have also revealed that semi-commutativity is the central property that enables POR. We have also interpreted both EC and SP algorithms using this new theory. Further, we have ii proposed new, stronger POR algorithms based on the new theory. We have also applied these new algorithms to solve benchmark problems across various planning domains. Experimental results have shown significant search cost reduction.
Automatic , Efficient , and General Repair of Software Defects using Lightweight Program Analyses

Software errors are both pervasive and expensive. Mature software projects ship with both known and unknown bugs [49], and the number of outstanding defects typically exceeds the resources available to address them [5]. A 2008 FBI survey of over 500 large firms reported that security defects alone cost such firms $289,000 annually [66, p.16]; a 2002 NIST survey calculated that software errors in the US cost 59.5 billion (0.6% of GDP) annually [59]. Manual error repair remains the norm, but has become unsustainably time-consuming and expensive. A 2008 survey of 139 North American firms found that each spent an average of $22 million annually fixing software defects [10]. Many defects, including critical security defects [78], remain unaddressed for long periods of time [36] despite the fact that the cost of repairing a defect can increase dramatically as development progresses [85]. As a result, automatic error repair, in which an algorithm constructs a repair to an error without human intervention, has become urgent. However, previous approaches suffer from several key deficiencies. Most trade generality for correctness by considering only a predefined set of vulnerability and repair types; most repair memory overflow errors exclusively [71, 74]. Lack of generality reduces the utility of such techniques in the current landscape, where new types of vulnerabilities regularly gain prominence [35]. Additionally, existing repair techniques often prohibitively increase either code size [18] or run-time [70] or both [63], precluding applicability to the real-world, legacy systems where they are needed the most. We propose to automatically repair errors by using search algorithms, in conjunction with existing test cases and lightweight program analyses, to find a version of a program that does not exhibit a given error but that still maintains required functionality and correctness. An algorithm based on search admits a more general approach to automatic repair because it is not definitionally constrained to one type of runtime monitor or repair template. Unfortunately, previous efforts to apply search algorithms to automatic error repair have been stymied by scalability concerns [32], which must be overcome for such a technique to be successful. Our proposed approach relies on two key insights to achieve generality and scalability. First, test cases provide useful insight into program behavior. While they provide weaker guarantees than formal correctness proofs, test suites as measures of correctness scale more readily to realworld systems [48, 76]. They are also readily available in practice and easier to generate than formal behavioral specifications [28, 68]. Moreover, test cases can flexibly encode a broad set of behaviors, and program behavior on test inputs can provide considerable feedback about dynamic program