Miracle-dz/UniFAR-FTUs huggingface dataset:
Packetized Predictive Control of Stochastic Systems Over Bit-Rate Limited Channels With Packet Loss

We study a control architecture for linear time-invariant plants with random disturbances and where a network is placed between the controller output and the plant input. The network imposes a constraint on the expected bit-rate and is affected by random independent and identically distributed (i.i.d.) dropouts. Dropout-rates and acknowledgments of receipt are not available at the controller side. To achieve robustness with respect to i.i. d. dropouts, the controller transmits data packets containing quantized plant input predictions. These are provided by an appropriate optimal entropy coded dithered lattice vector quantizer. Within this context, we derive stochastic stability results and provide a noise-shaping model of the closed loop system. This model is employed for performance analysis by using rate-distortion theory.
New algorithms for $k$-degenerate graphs

A graph is $k$-degenerate if any induced subgraph has a vertex of degree at most $k$. In this paper we prove new algorithms for cliques and similar structures for these graphs. We design linear time Fixed-Parameter Tractable algorithms for induced and non induced bicliques. We prove an algorithm listing all maximal bicliques in time $\mathcal{O}(k^{3}(n-k)2^{k})$, improving the result of [D. Eppstein, Arboricity and bipartite subgraph listing algorithms, Information Processing Letters, (1994)]. We construct an algorithm listing all cliques of size $l$ in time $\mathcal{O}(l(n-k)k(k-1)^{l-2})$, improving a result of [N. Chiba and T. Nishizeki, Arboricity and subgraph listing algorithms, SIAM, (1985)]. As a consequence we can list all triangles in such graphs in time $\mathcal{O}((n-k)k^{2})$ improving the previous bound of $\mathcal{O}(nk^2)$. We show another optimal algorithm listing all maximal cliques in time $\mathcal{O}(k(n-k)3^{k/3})$, matching the best possible complexity proved in [D. Eppstein, M. L\"offler, and D. Strash, Listing all maximal cliques in large sparse real-world graphs, JEA, (2013)]. Finally we prove $(2-\frac{1}{k})$ and $\mathcal{O}(k(\log\log k)^{2}\slash (\log k)^{3})$-approximation algorithms for the minimum vertex cover and the maximum clique problems, respectively.
Ethanol Potentiation of GABAergic Synaptic Transmission May Be Self-Limiting: Role of Presynaptic GABAB Receptors

Ethanol enhances GABAergic synaptic inhibition, and this interaction contributes to many of the behavioral and cognitive effects of this drug. Most studies suggest that ethanol enhances GABAergic neurotransmission via an allosteric potentiation of the postsynaptic GABAA receptors that mediate fast synaptic inhibition in the mammalian CNS. Despite widespread acceptance of this hypothesis, direct support for such a mechanism has been difficult to obtain. Ethanol does not enhance GABAA receptor function in all brain regions or under all experimental conditions, and factors responsible for this variability remain mostly unknown. Notably, blockade of GABAB receptors dramatically enhances ethanol potentiation of hippocampal GABAA IPSPs and IPSCs, suggesting that some unknown GABAB receptor mechanism limits the overall potentiating effect of ethanol on GABAergic synapses. In this study, we demonstrate that, at perisomatic synapses in the rat hippocampus, ethanol enhances presynaptic GABAB autoreceptor function and that this interaction reduces the overall potentiating effect of ethanol at these synapses. We further show that ethanol significantly elevates basal presynaptic GABAB receptor tone, possibly via an increase in spontaneous GABA release, and that pretreatment with a subthreshold concentration of the GABAB receptor agonist baclofen blocks ethanol but not flunitrazepam or pentobarbital potentiation of GABAA IPSCs. These data suggest that an interaction between ethanol and presynaptic GABAB autoreceptor activity regulates the ethanol sensitivity of GABAergic synapses. Given that the in vitro ethanol sensitivity of these synapses correlates with in vivo ethanol responsiveness in a number of rodent lines, our data further suggest that presynaptic GABAB receptor activity may play a role in regulating behavioral sensitivity to ethanol.
Rockfalls and rockfall inventory data: Some observations from surprise valley, Jasper National Park, Canada

The major controls of rockfall activity are divided into two interacting groups. (a)Climatic factors which, through their control of temperatures and the availability and state of water, are primary controls of rockfall trigger mechanisms. (b) Geologic factors which, via cliff form (plan, profile, dissection etc.) and the character and availability of materials, influence the type, spatial distribution and intensity of rockfall activity. Detailed examination of these controls suggest that both seasonal and daily patterns of rockfall activity can vary markedly over a very small area. The implications of this variability for the design and interpretation of rockfall inventories are discussed using sample data from Surprise Valley, Jasper National Park, for the period from May-October 1969. On a seasonal basis rockfall activity showed a major spring peak with secondary maxima in the fall and associated with major summer storms. Two sites of differing aspect and morphology were studied in more detail revealing marked differences in the mean hourly frequency (0·94:0·20), mean hourly probability (0·40:0·16, hours with rockfall/hours observed) and daily pattern of rockfall activity. The west facing site showed equal hourly probability of rockfalls from 1000 to 2000 hr whereas the east facing slope had a greater hourly probability when it was in the sun (1100 1400hr) than in the shade (1400-1900hr). The differences in rockfall frequency and probabilities reflect the physical characteristics of the individual sites whereas the daily pattern of rockfalls is related to microclimate. These results suggest that if future inventory studies are to make a significant contribution, they must be carefully designed field experiments in which. (i) Data are restricted to the study or comparison of single continuously observed sites. (ii) Study sites and/or data periods are carefully selected to isolate and investigate specific controls e.g. the influence of cliff form, aspect, periods of rainfall etc. on rockfall patterns. (iii) Good on-site microclimate data are available. Also comparison of frequency measures based on the arithmetic mean should be avoided since the distribution of rockfalls per hour closely follows a Poisson distribution with occasional high values which unduly influence the value of the arithmetic mean. It is suggested that rockfall probabilities, based on the binary decision of whether or not a rockfall occurs in a given period, are a more useful measure for daily patterns of rockfall activity.
Safety analysis of patient transfers and handling tasks

Background: Low-back disorders are related to biomechanical demands, and nurses are among the professionals with the highest rates. Quantification of risk factors is important for safety assessment and reduction of low-back disorders. Objective: This study aimed to quantify physical demands of frequent nursing tasks and provide evidence-based recommendations to increase low-back safety. Methods: Thirty-six volunteer female nurses participated in a cross-sectional study of nine nursing tasks. Lumbar range of motion (ROM) and motion during nursing tasks were measured. Compression and shear forces at L5/S1, ligament strain and percentage of population without sufficient torso strength to perform 14 phases of nine nursing tasks were estimated. Results: Peak flexions during trolley-to-bed, bed-to-chair and chair-to-bed transfers reached the maximum flexion ROM of the nurses. Average lumbar flexion during trolley-to-bed transfers was >50% of flexion ROM, being higher than during all other tasks. Mean (SD) compression at L5/S1 (4754 N (437 N)) and population without sufficient torso strength (37% (9%)) were highest during the pushing phase of bed-to-trolley transfers. Shear force (487 N (40 N)) and ligament strain (14% (5%)) were highest during the pulling phase of trolley-to-bed transfers. Conclusions: Nursing tasks impose high biomechanical demands on the lumbar spine. Excessive lumbar flexion and forces are critical aspects of manual transfers requiring most of the nurses’ capabilities. Evidence-based recommendations to improve low-back safety in common nursing tasks were provided. Fitness to work, job modifications and training programs can now be designed and assessed based on the results.
Towards Static Analysis of Virtualization-Obfuscated Binaries

Virtualization-obfuscation protects a program from manual or automated analysis by compiling it into byte code for a randomized virtual architecture and attaching a corresponding interpreter. Static analysis appears to be helpless on such programs, where only the code of the interpreter is directly visible. In this paper, we explain the particular challenges for statically analyzing the combination of interpreter and byte code. Static analysis for computing possible variable values is commonly precise only to the program location. In the interpreter loop, however, this combines unrelated data flow information from different locations of the byte code program. To avoid this loss of information, we show how to lift an existing static analysis to an additional dimension of location, to become sensitive to the value of the virtual program counter. Thus, the static analysis merges data flow from equal byte code locations only. We lift an existing analysis implemented in the Jakstab static analyzer and present preliminary results for processing a virtualization-obfuscated binary.
Tourism segmentation by consumer-based variables

Preface. Part 1: Motivation and Need Recognition 1. Interpretive Consumer Research: Uncovering the 'Whys' Underlying Tourist Behavior 2. Antecedents and Consequences of Prestige Motivation in Tourism: An Expectancy-Value Motivation Part 2: Perception and Information Processing 3. Tourism Destination Image Formation 4. Tourist Information Search Part 3: Evaluation of Alternatives and Choice 5. Decision Strategies in Tourism Evaluation 6. Planning and Exploratory Buying Behavior Part 4: The Tourism Experience 7. Understanding Tourist Experience though Mindfulness Theory 8. Unlocking the Shared Experience: Challenges of Consumer Experience Research Part 5: Post-Choice Processes 9. Processes and Performances of Tourist (Dis)Satisfaction 10. Service Failure, Tourist Complaints and Service Recovery 11. What Determines Tourist Loyalty? In Search of a Theoretical Explanation Part 6: Individual Determinants of Tourist Behavior 12. Tourism Segmentation by Consumer-Based Variables 13. Emotions and Affective States in Tourism Behaviour Part 7: Environmental Determinants of Tourist Behavior 14. E-tourist Behavior: The Influence of IT on Consumers 15. The Role of Media Products on Consumer Behaviour in Tourism 16. Cross-Cultural Differences in Tourist Behavior
Annotating Causality in the TempEval-3 Corpus

While there is a wide consensus in the NLP community over the modeling of temporal relations between events, mainly based on Allen’s temporal logic, the question on how to annotate other types of event relations, in particular causal ones, is still open. In this work, we present some annotation guidelines to capture causality between event pairs, partly inspired by TimeML. We then implement a rule-based algorithm to automatically identify explicit causal relations in the TempEval-3 corpus. Based on this annotation, we report some statistics on the behavior of causal cues in text and perform a preliminary investigation on the interaction between causal and temporal relations.
SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis

This paper presents SentiSense, a concept-based affective lexicon. It is intended to be used in sentiment analysis-related tasks, specially in polarity and intensity classification and emotion identification. SentiSense attaches emotional meanings to concepts from the WordNet lexical database, instead of terms, thus allowing to address the word ambiguity problem using one of the many WordNet-based word sense disambiguation algorithms. SentiSense consists of 5,496 words and 2,190 synsets labeled with an emotion from a set of 14 emotional categories, which are related by an antonym relationship. SentiSense has been developed semi-automatically using several semantic relations between synsets in WordNet. SentiSense is endowed with a set of tools that allow users to visualize the lexicon and some statistics about the distribution of synsets and emotions in SentiSense, as well as to easily expand the lexicon. SentiSense is available for research purposes.
Adaptive Calibration of Imaging Array Detectors

We present two methods for nonuniformity correction of imaging array detectors based on neural networks; both exploit image properties to supply lack of calibrations and maximize the entropy of the output. The first method uses a self-organizing net that produces a linear correction of the raw data with coefficients that adapt continuously. The second method employs a kind of contrast equalization curve to match pixel distributions. Our work originates from silicon detectors, but the treatment is general enough to be applicable to many kinds of array detectors like those used in infrared imaging or in high-energy physics.
Sensitivity studies with a sea ice‐mixed layer‐pycnocline model in the Weddell Sea

The sensitivity of a dynamic-thermodynamic sea ice model coupled to a one-dimensional mixed layer-pycnocline model to variations of dynamic and thermodynamic model parameters is investigated. Furthermore, the modifications of the model results due to the inclusion of a prognostic snow cover and the implementation of simplified sea ice rheologies are investigated. In these comparisons special emphasis is placed upon the ice-ocean boundary conditions (buoyancy fluxes) and the mixed layer properties.
Modeling Fine-Grained Dynamics of Mood at Scale ⇤

Mental health affects all aspects of people’s lives, yet it remains difficult to obtain accurate data about influential factors. This work investigates quantifying, infering, and predicting—via social media data—the day-to-day mental state of individuals. We develop a statistical model of the affective state (mood) of specific individuals with up to hourly temporal resolution. This model enables us to quantify, in a unified way, aggregate mood trends, as well as patterns specific to individuals and groups of friends. It finds key features of mood variation over time and allows us to decompose a person’s emotional state into a weighted sum of contributing factors—shedding new light on how mood affects, and is affected by environment. We then show that individuals’ mood can be accurately predicted days into the future based on online behavior.
Application of Axiomatic Approaches to Crosslanguage Retrieval

Natural languages contain many ambiguous words. Detecting the correct sense of words within documents and queries could potentially improve the performance of an information retrieval system. This is the major motivation for the Robust WSD tasks of the Ad-Hoc Track of the CLEF 2009 campaign. For these tasks we have build a customizable and flexible retrieval system. The best performing configuration of this system is based on research in the area of axiomatic information retrieval approaches. Further, our experiments show that configurations that incorporate WSD information into the retrieval process did outperform those without. For the monolingual task the performance difference is more pronounced than for the bilingual task. Finally we are able to show that our query translation approach does work effectively, even if applied in the monolingual task.
Topic-Based Hierarchical Segmentation

Latent Dirichlet allocation (LDA) is a new paradigm of topic model which is powerful to capture the latent topic information from natural language. However, the topic information in text streams, e.g. meeting recording, lecture transcription and conversational dialogue, are inherently heterogeneous and nonstationary without explicit boundaries. It is difficult to train a precise topic model from the observed text streams. Furthermore, the usage of words in different paragraphs within a document is varied with different composition styles. In this paper, we present a new hierarchical segmentation model (HSM) where the heterogeneous topic information in stream level and the word variations in document level are characterized. We incorporate the contextual topic information in stream-level segmentation. The topic similarity between sentences is used to form a beta distribution reflecting the prior knowledge of document boundaries in a text stream. The distribution of segmentation variable is adaptively updated to achieve flexible segmentation and is used to group coherent sentences into a topic-specific document. For each pseudo-document, we further use a Markov chain to detect the stylistic segments within a document. The words in a segment are accordingly generated by the same composition style, which differs from the style of the next segment. Each segment is represented by a Markov state, and so the word variations within a document are compensated. The whole model is trained by a variational Bayesian EM procedure and is evaluated on using TDT2 corpus. Experimental results show benefits by using the proposed HSM in terms of perplexity, segmentation error, detection accuracy and F measure.
Machine Learning--Based Parametric Audiovisual Quality Prediction Models for Real-Time Communications

In order to mechanically predict audiovisual quality in interactive multimedia services, we have developed machine learning--based no-reference parametric models. We have compared Decision Trees--based ensemble methods, Genetic Programming and Deep Learning models that have one and more hidden layers. We have used the Institut national de la recherche scientifique (INRS) audiovisual quality dataset specifically designed to include ranges of parameters and degradations typically seen in real-time communications. Decision Trees--based ensemble methods have outperformed both Deep Learning-- and Genetic Programming--based models in terms of Root-Mean-Square Error (RMSE) and Pearson correlation values. We have also trained and developed models on various publicly available datasets and have compared our results with those of these original models. Our studies show that Random Forests--based prediction models achieve high accuracy for both the INRS audiovisual quality dataset and other publicly available comparable datasets.
From square to circular polymeric microchannels by spin coating technology: a low cost platform for endothelial cell culture

Square microchannels are easy to fabricate by means of micromachining or lithographic techniques. However, in vitro vascular microcapillaries—as well as plug production and microparticle alignment—require mainly circular microchannels that can be used also in applications based on open microchannels. Nowadays, a simple, low cost, and versatile method to fabricate circular microchannels is still missing. Here, we report on a fast, inexpensive, flexible and reproducible method to fabricate circular microchannels by coupling spin coating with micromilled square microchannels. The proposed method is based on the balance between the displacement of liquid PDMS induced by centrifugal forces and the surface tension that tends to keep the liquid accumulated especially in the corners, which become therefore rounded. To show the versatility of the described experimental study we prepared a variety of rounded microchannels, including branched and PMMA–PDMS hybrid configuration microchannels. Finally, an endothelial cell layer was formed by culturing brain endothelial bEnd.3 cells inside the proposed circular microchannels. Results demonstrated a more successful adhesion, growth, and homogeneous distribution of the cells along the circular microchannel than those observed in the square microchannel used as a control.
MULTI-DIMENSIONAL GEOSPATIAL DATA INTEGRATION FOR COASTAL CHANGE ANALYSIS

Coastal change analysis, particularly of the variation in shorelines and blufflines, is critical for coastal disaster mitigation, environmental protection, resource management, and coastal development decision making. As data from a variety of sources has become more easily available, it is highly desirable to investigate a strategy for the integration of multi-dimensional geospatial data for coastal mapping and change analysis. This paper summarizes an investigation of techniques for integrating satellite images, aerial images, and LiDAR data for high precision coastal mapping. The integration of IKONOS and QuickBird satellite stereo image pairs with aerial images for shoreline mapping and the integration of LiDAR data and aerial orthoimages for coastal bluffline extraction are both examined. Experiments using data collected at Tampa Bay, Florida, and Lake Erie, Ohio, have shown that sub-meter measurement accuracy can be achieved through these integration strategies. Using improved mapping products based on these new techniques, bluffline erosion analysis was conducted at Lake Erie near Painesville, Ohio, and correlations between bluffline recessions and various geological and meteorological factors were examined. * Corresponding author.
Testing recent chronological techniques for peat sites with contrasting anthropogenic influences

This thesis presents the results of a study of recent radionuclide chronological techniques applied to two contrasting locations in an ombrotrophic peat bog in West Central Scotland. The locations differed in that one was undisturbed but the other had been previously forested. The study demonstrated that with a limit of detection of 5 Bq kg-1 or better, rigorous sampling technique and high resolution (2 cm) sampling increments, 210Pb dating using either the CIC method or the CRS method and 241Am dating method all gave consistent chronologies. Use of a low resolution field sampling technique gave consistent CIC but not CRS chronologies. The study indicated that neither 137Cs nor 32Si could be used to derive reliable chronologies for peat. Implied temporal variations in deposition of anthropogenic species (ash, Pb and Pb isotopes) were consistent with known historical variations and other studies of archived materials, lake sediments and peat deposits. Metal inventories were observed to be consistently higher for the unforested site than for the previously forested site, but comparison with chronologies implied that this was a long term feature of the two sites rather than an influence of the relatively short term presence of the forest. This observation highlights the limitations of extrapolating from a single core to derive information on larger scale regional deposition of contaminants.
Selective Extraction of Entangled Textures via Adaptive PDE Transform

Texture and feature extraction is an important research area with a wide range of applications in science and technology. Selective extraction of entangled textures is a challenging task due to spatial entanglement, orientation mixing, and high-frequency overlapping. The partial differential equation (PDE) transform is an efficient method for functional mode decomposition. The present work introduces adaptive PDE transform algorithm to appropriately threshold the statistical variance of the local variation of functional modes. The proposed adaptive PDE transform is applied to the selective extraction of entangled textures. Successful separations of human face, clothes, background, natural landscape, text, forest, camouflaged sniper and neuron skeletons have validated the proposed method.