Miracle-dz/UniFAR-FTUs huggingface dataset:
Estonia: A Model for Inter-War Europe?

Abstract While Estonia's 1925 Law on Cultural Self-Government for National Minorities is often cited as a rare functioning example of Renner and Bauer's non-territorial autonomy scheme, there has until recently been comparatively little research on how the law operated in practice. This article analyses the institutions of German and Jewish minority self-government established in inter-war Estonia, arguing that (prior to the eclipse of democracy in 1934, at least) these possessed considerable depth and authoritative competence in the area of cultural and educational policy. Cultural autonomy did not resolve all outstanding points of division between state, majority and (in particular) Baltic German minority; nevertheless, it played a positive role in the regulation of ethnonational tensions. Even less well known is the international resonance of this unique law, which transnational minority activists argued should serve as a general model for the regulation of minority disputes. The League of Nations rightly questioned this claim, and the non-territorial autonomy model was scarcely applied beyond the Baltic region during the 1920s. However, it is still instructive to revisit the 1925 law today, at a time when several post-communist states have adopted minority rights legislation based on similar principles.
Spherical Methods for Spatial Interpolation: Review and Evaluation

Global change research has placed new demands on methods of spatial analysis. In particular, spherical methods for spatial interpolation are required when spatial analyses are performed over large areas of the Earth's surface. In this article, spherical spatial interpolation procedures are reviewed, compared, and evaluated. Three classes of spherical interpolants are evaluated in detail: distance weighting, functional minimization, and tesselation. The strengths and weaknesses of a method from each of these classes—inverse-distance weighting, thin-plate splines, and surfaces fit to triangulated patches—are evaluated using a hypothetical mathematical surface and a global scale representation of topography. For smooth functions, such as the hypothetical mathematical surface, thin-plate splines produce a visually pleasing surface and have low interpolation error. For non-smooth surfaces, such as global topography, inverse-distance weighting, interpolating thin-plate splines, and triangulated C0 patches appea.
Measuring red blood cell aggregation forces using double optical tweezers

Abstract Classic immunohematology approaches, based on agglutination techniques, have been used in manual and automated immunohematology laboratory routines. Red blood cell (RBC) agglutination depends on intermolecular attractive forces (hydrophobic bonds, Van der Walls, electrostatic forces and hydrogen bonds) and repulsive interactions (zeta potential). The aim of this study was to measure the force involved in RBC aggregation using double optical tweezers, in normal serum, in the presence of erythrocyte antibodies and associated to agglutination potentiator solutions (Dextran, low ionic strength solution [LISS] and enzymes). The optical tweezers consisted of a neodymium:yattrium aluminium garnet (Nd:YAG) laser beam focused through a microscope equipped with a minicam, which registered the trapped cell image in a computer where they could be analyzed using a software. For measuring RBC aggregation, a silica bead attached to RBCs was trapped and the force needed to slide one RBC over the other, as a function of the velocities, was determined. The median of the RBC aggregation force measured in normal serum (control) was 1 × 10−3 (0.1–2.5) poise.cm. The samples analyzed with anti-D showed 2 × 10−3 (1.0–4.0) poise.cm (p < 0.001). RBC diluted in potentiator solutions (Dextran 0.15%, Bromelain and LISS) in the absence of erythrocyte antibodies, did not present agglutination. High adherence was observed when RBCs were treated with papain. Results are in agreement with the imunohematological routine, in which non-specific results are not observed when using LISS, Dextran and Bromelain. Nevertheless, false positive results are frequently observed in manual and automated microplate analyzer using papain enzyme. The methodology proposed is simple and could provide specific information with the possibility of meansuration regarding RBC interaction.
Who Moved My Cheese: Towards Automatic and Fine-Grained Classification and Modeling Ad Network

The mobile advertisement (ad) network is gaining an increasing interest due to the high popularity of smart phones. Previous researches on the security issues of ad network primarily focus on the privacy, permission and malware detection while less attention has been paid to the traffic consumption issue incurred by ad network. Though it is well known that ad network plays an important role in network consumption, it represents a great challenge of giving a fine-grained classification of ad networks. Inspired by this, different from any previous researches, in this study, we take the initial step towards modeling the network consumption of Ad network in Android. We develop an automatic ad analysis platform to quantify the ad network traffic consumed by android applications (app). To achieve a fine-grained quantification, we combine two sources of network traffic. On one hand, we modify the android webview and log system in system level to capture network traffic accurately. On the other hand, we capture network traffic in router level to collect detailed information of traffic packets, such as packet size and URI. We have evaluated the developed system in terms of normal apps, repacked apps and malicious apps based on the real-world dataset, which is comprised of 93 Android apps. We find out that ad traffic takes major percentage of the whole network traffic caused by Android app. We have also studied the ad library mechanism for 10 popular ad libraries. We found ads from some ad libraries use much more network traffic because they have to be fetched from remote ad libraries each time they are shown to users while other ad libraries allow apps to store ads locally.
Flip-Chip Interconnection Effects on 60-GHz Microstrip Antenna Performance

In this letter, we investigate the effects of flip-chip interconnection on microstrip antenna performance. In particular, we explore the relationship of the flip-chip interconnection parameters to the bandwidth, radiation efficiency, and gain. It is shown that microstrip antenna bandwidths of 15% can be achieved with careful flip-chip interconnection design and fabrication. This study is of particular importance at 60 GHz for the integration of millimeter-wave antennas into the package.
Chronic GVHD in minor salivary glands and oral mucosa: histopathological and immunohistochemical evaluation of 25 patients.

BACKGROUND Graft-vs.-host disease (GVHD) is the major cause of morbidity and mortality in patients undergoing allogeneic Bone Marrow Transplantation (BMT). The aim of our study was to identify the most relevant histological features for diagnosis of chronic Graft-vs.-Host Disease (cGVHD) in oral mucosa and minor salivary glands of 25 patients, as well as to evaluate the immunophenotype of the inflammatory cells. METHODS Sixteen patients that were submitted to allogeneic BMT but did not present cGVHD were selected as a control group. The sections were studied on H & E and CD68, CD45, CD4, CD8, CD20 staining. RESULTS The most frequent histologic findings in oral mucosa at the day of diagnosis of cGVHD were: hydropic degeneration of the basal layer of the epithelium, apoptotic bodies, lymphocytic infiltration, and focal or total cleavage between the epithelial and connective tissue. In the labial salivary glands (LSG), lymphocytic infiltration, acinar loss and fibrosis were the main alterations. Cytotoxic CD8-T cells and macrophages were predominant both in the epithelium and connective tissue, as well as in minor salivary glands. CONCLUSIONS Histological features were useful in the diagnosis of oral cGVHD. It is suggested that CD8-T cells and macrophages play important role in the pathogenesis of the disease.
Robust Optimization for Tree-Structured Stochastic Network Design

Stochastic network design is a general framework for optimizing network connectivity. It has several applications in computational sustainability including spatial conservation planning, pre-disaster network preparation, and river network optimization. A common assumption in previous work has been made that network parameters (e.g., probability of species colonization) are precisely known, which is unrealistic in real- world settings. We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers. We assume that fish passability probabilities are known only imprecisely, but are within some interval bounds. We then develop a planning approach that computes the policies with either high robust ratio or low regret. Empirically, our approach scales well to large river networks. We also provide insights into the solutions generated by our robust approach, which has significantly higher robust ratio than the baseline solution with mean parameter estimates.
Measured performance of a memory-efficient segment-based packet-combining scheme

Packet-combining schemes are an attractive improvement to ARQ schemes, since they allow receivers to keep the good parts of an otherwise erroneous packet and use these with good parts from subsequent packets to allow for quicker decoding. In the previous paper [1] a memory-efficient hard-decision packet combining scheme has been described and analysed, which relies on intermediate checksums spread into the user data part of a packet. In this paper we provide experimental data on the performance of the proposed packet-combining scheme, showing that a significant percentage of the wireless links found in a large indoor wireless testbed (the TKN TWIST testbed [2]) indeed benefits from introducing such a packet-combining scheme.
Temporal awareness of changes in afflicted people's needs after East Japan Great Earthquake

This paper proposes a time series topic detection method to investigate changes in afflicted people's needs after the East Japan Great Earthquake using latent semantic analysis and singular vectors' pattern similarities. Our target data is a blog about afflicted people's needs provided by a non-profit organization in Tohoku, Japan. The method crawls blog messages, extracts terms, and forms document-term matrix over time. Then, it adopts the latent semantic analysis to extract people's needs as hidden topics from each snapshot matrix. We form time series hidden topic-term matrix as 3rd order tensor, so that changes in topics (people's needs) are detected by investigating time-series similarities between hidden topics. In this paper, to show the effectiveness of our proposed method, we also provide the experimental results.
Reversible multiresolution image coding based on adaptive lifting

We first calculate the optimal predictors of a lifting scheme in the general n-dimensional case. Then, we apply these optimal predictor filters with corresponding update filters for the lossless compression of still images using simple row-column sampling. The efficiency of the linear predictor is enhanced by nonlinear means, namely by adaptive-length postprocessing. The resulting filter bank in effect adapts to the features of the area of the image being processed and in this way it is shown to achieve superior performance than even the most efficient known lossless compression methods capable of progressive transmission.
Detection and segmentation of small renal masses in contrast-enhanced CT images using texture and context feature classification

Detection and segmentation of small renal mass (SRM) in renal CT images are important pre-processing for computer-aided diagnosis of renal cancer. However, the task is known to be challenging due to its variety of size, shape, and location. In this paper, we propose an automated method for detecting and segmenting SRM in contrast-enhanced CT images using texture and context feature classification. First, kidney ROIs are determined by intensity and location thresholding. Second, mass candidates are extracted by intensity and location thresholding. Third, false positive reduction is applied with patch-based texture and context feature classification. Finally, mass segmentation is performed, using the detection results as a seed, with region growing, active contours, and outlier removal with size and shape criteria. In experiments, our method detected SRM with specificity and PPV of 99.63% and 64.2%, respectively, and segmented them with sensitivity, specificity, and DSC of 89.91%, 98.96% and 88.94%, respectively.
Effects of Training Using Video Games on the Muscle Strength, Muscle Tone, and Activities of Daily Living of Chronic Stroke Patients

[Purpose] The purpose of this study was to investigate the effects of training using video games played on the Xbox Kinect on the muscle strength, muscle tone, and activities of daily living of post-stroke patients. [Subjects] Fourteen stroke patients were recruited. They were randomly allocated into two groups; the experimental group (n=7) and the control group (n=7). [Methods] The experimental group performed training using video games played on the Xbox Kinect together with conventional occupational therapy for 6 weeks (1 hour/day, 3 days/week), and the control group received conventional occupational therapy only for 6 weeks (30 min/day, 3 days/week). Before and after the intervention, the participants were measured for muscle strength, muscle tone, and performance of activities of daily living. [Results] There were significant differences pre- and post-test in muscle strength of the upper extremities, except the wrist, and performance of activities of daily living in the experimental group. There were no significant differences between the two groups at post-test. [Conclusion] The training using video games played on the Xbox Kinect had a positive effect on the motor function and performance of activities of daily living. This study showed that training using video games played on the Xbox Kinect may be an effective intervention for the rehabilitation of stroke patients.
Cleavage of preflagellins by an aspartic acid signal peptidase is essential for flagellation in the archaeon Methanococcus voltae

The differences between archaeal and bacterial flagella are becoming more apparent as research on the archaeal structure progresses. One crucial difference is the presence of a leader peptide on archaeal preflagellins, which is removed from the flagellin prior to its incorporation into the flagellar filament. The enzyme responsible for the removal of the flagellin leader peptide was identified as FlaK. FlaK of Methanococcus voltae retains its preflagellin peptidase activity when expressed in Escherichia coli and used in an in vitro assay. Homologous recombination of an integration vector into the chromosomal copy of flaK resulted in a non‐motile, non‐flagellated phenotype. The flagellins of the mutant had larger molecular weights than their wild‐type counterparts, as expected if they retained their 11‐ to 12‐amino‐acid leader peptide. Membranes of the flaK mutant were unable to process preflagellin in the in vitro assay. Site‐directed mutagenesis demonstrated that two aspartic acid residues conserved with ones in type IV prepilin peptidases were necessary for proper recognition or processing of the preflagellin. As bacterial flagellins lack a leader peptide and a peptidase is not required for export and assembly, the requirement for FlaK further emphasizes the similarity archaeal flagella have with type IV pili, rather than with bacterial flagella.
Model-Driven Development and Business Process Modeling Applied to Personal Productivity in the Consumer Mobile App Market

This paper discusses how the combination of model-driven development approaches and business process management (BPM) techniques and tools can be fruitfully applied to personal and social task management, in a consumer scenario where people are provided with user-friendly mobile applications that hide the complexity of modeling behind extremely simple interfaces and interaction paradigms. The main challenge addressed is related to finding the appropriate level of complexity to be exposed to the user, because the expressive power should be complete enough for describing basic processes but also simple enough to let people understand, accept and use them in their everyday life. This is then combined with social networking and content sharing capability. Once this simplification paradigm is devised, the findings can be transferred back to the business users too. The results of the work are implemented in a commercial mobile application called Fluxedo.
Term Structures of Credit Spreads with Incomplete Accounting Information

We study the implications of imperfect information for term structures of credit spreads on corporate bonds. We suppose that bond investors cannot observe the issuer's assets directly, and receive instead only periodic and imperfect accounting reports. For a setting in which the assets of the firm are a geometric Brownian motion until informed equityholders optimally liquidate, we derive the conditional distribution of the assets, given accounting data and survivorship. Contrary to the perfect-information case, there exists a default-arrival intensity process. That intensity is calculated in terms of the conditional distribution of assets. Credit yield spreads are characterized in terms of accounting information. Generalizations are provided.
Predictability in Real-time System Development (2) A Case Study

In a companion paper [HVV04], we have investigated the deficiency of existing design approaches in supporting predictability for real-time control system development. A design approach is then introduced with an adequate support for efficient and consistent design and code generation. In this paper, this design approach is illustrated by a case study of a rail-road crossing system. The development process shows that our approach is able to preserve properties not only between design descriptions at different abstraction levels, but also between the design model and the generated implementation.
Temporal and spatial effects of Sonic hedgehog signaling in chick eye morphogenesis.

Proper dorsal--ventral pattern formation of the optic cup is essential for vertebrate eye morphogenesis and retinotectal topographic mapping. Previous studies have suggested that midline tissue-derived Sonic hedgehog (Shh) molecules play critical roles in establishing the bilateral eye fields and in determining the proximal--distal axis of the eye primordium. Here, we have examined the temporal requirements for Shh during the optic vesicle to optic cup transition and after early optic cup formation in chick embryos. Both misexpressing Shh by virus and blocking Shh activity by antibodies resulted in disruption of ventral ocular tissues. Decreasing endogenous Shh signals unexpectedly revealed a sharp morphological boundary subdividing dorsal and ventral portions of the optic cup. In addition, Shh signals differentially influenced expression patterns of genes involved in ocular tissue specification (Pax6, Pax2, and Otx2) and dorsal--ventral patterning (cVax) within the ventral but not dorsal optic cup. Ectopic Shh suppressed expression of Bone Morphogenetic Protein 4 (BMP4) in the dorsal retina, whereas reducing endogenous Sonic hedgehog activity resulted in a ventral expansion of BMP4 territory. These results demonstrate that temporal requirements for Shh signals persist after the formation of the optic cup and suggest that the early vertebrate optic primordium may be subdivided into dorsal and ventral compartments. We propose a model in which ventrally derived Shh signals and dorsally restricted BMP4 signals act antagonistically to regulate the growth and specification of the optic primordium.
A Nonparametric Adaptive Cusum Method And Its Application In Network Anomaly Detection

Detecting anomalies that disrupt the symmetry in two-way communications is an important task for network defense systems. The subtlety and complexity of anomalous traffic challenge the existing detection methods, and the bottleneck is how to set thresholds to adapt to the variability in network traffic. In this paper, a nonparametric adaptive CUSUM (Cumulative Sum) method is presented to meet this challenge. It has three distinct features. (i) No assumption is made on the distribution of the observations. (ii) Its detection threshold is self-adjusted so that it can adapt itself to various traffic conditions. (iii) It can react to the end of an anomaly within a required delay time. Several practical expressions for evaluating this method on its probability of false alarms and detection delays are deduced. Experiments on source-end defense against SYN flooding attacks show the efficacy of this method in detecting low intensity anomalies.
Hardness vs. randomness

A simple construction for a pseudorandom bit generator is presented. It stretches a short string of truly random bits into a long string that looks random to any algorithm from a complexity class C (e.g. P, NC, PSPACE, etc.), using an arbitrary function that is hard for C. This generator reveals an equivalence between the problems of proving lower bounds and the problem of generating good pseudorandom sequences. Combining this construction with other arguments, a number of consequences are obtained. <<ETX>>
Marginal Majority at The Postcolonial News Agency

Using 74 archival interviews with former and current employees, this paper explores the sociology of journalistic hires in foreign bureaus at the Associated Press. It finds that locally hired staff play crucial roles in foreign bureaus by providing the often-inexperienced home staffers with local context and linguistic help. The locals thus become an integral part of the Associated Press’ foreign coverage, which became more in-depth and contextual after the 1960s, in tandem with the rest of American journalism. However, a discriminatory framework in compensation and status—which can be traced back to the colonial days—applies for the locals, a framework exacerbated by the paternalistic rhetoric in its tradition of the “Marine Corps of journalism. ” In recent decades, however, the organizational culture is changing, with the increased employment of multicultural and multilingual journalists, foreign and local.
Dynamic Reliability Assessment for Nonrepairable Multistate Systems by Aggregating Multilevel Imperfect Inspection Data

Traditional time-based reliability assessment methods compute reliability measures of a multistate system (MSS) purely based upon historical time-to-failure data collected from a large population of identical systems. Using these methods, one can only assess the reliability of a system from a population or statistical perspective. Moreover, these methods fail to characterize the stochastic behavior of a specific individual MSS over time. Accordingly, in this paper, a dynamic reliability assessment method that can aggregate inspection data across multiple levels (such as component level, subsystem level, and system level) of a nonrepairable MSS has been studied. In general, inspection data collected from multiple levels of a system can be imperfect, but they are stochastically correlated with the actual states of the inspected system and components. A set of two-stage recursive Bayesian formulations has been put forth to dynamically update the reliability function of a specific MSS over time by utilizing imperfect inspection data collected simultaneously or asynchronously from multiple levels of the system. The proposed method is exemplified via an illustrative example of an underground flow transmission system. The impact of the probability of detection on the accuracy of the remaining useful life prediction is also examined.