Miracle-dz/UniFAR-FTUs huggingface dataset:
How the meaning a person gives to tranquility could affect the appraisal of the urban park soundscape

It has previously been established that different people attach a different meaning to the concept of “tranquil public space”. A majority of persons associate tranquility with social interaction, but others associate tranquility with hearing sounds from nature or even with pure silence. Having these different beliefs and views in mind, hypotheses could be formulated on their effect on the perception and appraisal of soundscapes in urban parks. Firstly, persons associating tranquility with sounds from nature may focus more on hearing these sounds while visiting a park, and might notice them more often as a consequence. Secondly, the meaning given to tranquility also influences one’s frame of reference and expectations. Thus, persons associating silence or sounds from nature to tranquility might state more easily that they hear less natural sounds and more mechanical sounds when visiting a park. These hypotheses were tested on survey and measurement data from 8 parks in Antwerp and on computational models of auditory perception. Results show that attention focusing is dominated by the change in frame of reference or expectation. In addition, a weak relationship was found between the park where persons were encountered and their view on the concept of tranquility.
An Approach for Automated Registration of Hyperspectral Images for Boresight Calibration

Parametric geocoding is a standard procedure for orthorectification of hyperspectral airborne scanner data. Boresight parameters are estimated by using the image coordinates of precisely known ground reference points. To fully automate the processing of the recorded hyperspectral data ground reference points should be selected and matched by an algorithm without user interaction. In this article a two-step procedure is presented. First, direct geocoding of the data is done without ground reference points. This step is solely based on the data of the inertial measurement unit with differential GPS. Next, the hyperspectral data is normalized and pixels are clustered with an unsupervised method. Image blocks of an edge image of the resulting regions are matched with a high resolution edge image of a digital orthophoto of the calibration site. The found correspondences are used as ground control points in the second geocoding step.
Experimental analysis of thermal conductivity of semi-rigid base asphalt pavement

Thermal conductivity is an important feature of asphalt pavement. However, because of the lacking of a proper experiment device, the experimental analysis of the thermal conductivity of semi-rigid base asphalt pavement is difficult. According to the slab method and Fourier’s heat conduction law, an experiment device is developed to examine the coefficient of thermal conductivity of granular mixtures stacked in large volume and help understanding the thermal conductivity of asphalt pavement. This device is used to examine the thermal conductivity of both asphalt mixtures and cement-stabilised aggregates based on Guang-ba motorway construction. The experimental results show that the coefficient of thermal conductivity of both asphalt mixtures and cement-stabilised aggregates linearly increases with the temperature growth. In general, the asphalt mixtures have better thermal conductivity than the cement-stabilised aggregates. In terms of asphalt mixtures, the void ratio heavily affects their thermal conductivity and the aggregates have larger contribution on the thermal conductivity than the binder. In terms of cement-stabilised aggregates, the most important factor of thermal conductivity is the void ratio, followed by gravel size Also the heat conduction of the asphalt pavement is often instantaneous rather than stable.
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings

Recently, neural twitter sentiment classification has become one of state-of-thearts, which relies less feature engineering work compared with traditional methods. In this paper, we propose a simple and effective ensemble method to further boost the performances of neural models. We collect several word embedding sets which are publicly released (often are learned on different corpus) or constructed by running Skip-gram on released large-scale corpus. We make an assumption that different word embeddings cover different words and encode different semantic knowledge, thus using them together can improve the generalizations and performances of neural models. In the SemEval 2017, our method ranks 1st in Accuracy, 5th in AverageR. Meanwhile, the additional comparisons demonstrate the superiority of our model over these ones based on only one word embedding set. We release our code for the method duplicability.
The Effect of Olive Leaf Extract on Hepatic Fat Accumulation in Sprague-Dawley Rats Fed a High-fat Diet

Oleuropein, the active constituent of olive leaf extract, possesses anti-oxidant, hypoglycemic, and hypolipidemic activities. We aimed to assess whether the effect of olive leaf extract on hepatic fat accumulation is preventive or therapeutic. Sprague-Dawley (SD) rats were fed a high-fat diet with (ODOD group) or without (HDHD group) olive leaf extract (1,000 mg/kg diet) for 38 weeks. Another group of rats were fed a high-fat diet for 23 weeks, followed by a high-fat diet with olive leaf extract (1,000 mg/kg diet) for 15 weeks (HDOD group). Serology, histopathology, anti-oxidative activity, and liver fatty acid synthesis were compared to those fed a standard diet (LDLD group) at 26 and 41 weeks of age. The serum levels of total cholesterol, triglyceride and aspartate aminotransferase tended to be lower in the ODOD group as compared to the HDHD and HDOD groups, although there were no significant differences. Histopathologically, hepatic steatosis tended to be less evident in the HDOD and ODOD groups as compared to the HDHD group, and lobular inflammation was not observed in the ODOD group at 26 weeks of age. Hepatic thioredoxin-1 staining tended to be less evident in the ODOD group than in the HDHD and HDOD groups at 41 weeks of age. There were no significant differences in hepatic lipogenic enzyme activities between the ODOD group and HDHD/HDOD groups. Our data suggest that olive leaf extract had a preventive, rather than therapeutic, effect on hepatic steatohepatitis in SD rats fed a high-fat diet.
Block coding of graphics: A tutorial review

This paper gives a review of block coding for picture data compression. Block coding has been devised primarily for coding of graphics, but it has subsequently been extended to multilevel pictures. All the proposed codes are simple suboptimum prefix codes. Their simplicity make them suitable for real-time applications. Although blocks can be of any shape, higher efficiencies are obtained with two-dimensional blocks, thus exploiting the inherent two-dimensional correlation of pictures. According to the value of a preset parameter, block coding can be either information lossless or information lossy. In the former case, the original digitized picture can be exactly reconstructed from its coded version. In the latter case, where the compression is much higher, distortions possess easily identified features. An appropriate filtering can restore the decoded picture satisfactorily. With a slight increase in complexity, block coding can be made adaptive in a number of ways, leading to much higher compressions. For each case, comprehensive theoretical models are developed to predict the performances and to optimize the parameters. The dependence of the compression ratio on image resolution for each specific code is also examined.
HaVOC: A hybrid memory-aware virtualization layer for on-chip distributed ScratchPad and Non-Volatile Memories

Hybrid on-chip memories that combine Non-Volatile Memories (NVMs) with SRAMs promise to mitigate the increasing leakage power of traditional on-chip SRAMs. We present HaVOC: a run-time memory manager that virtualizes the hybrid on-chip memory space and supports efficient sharing of distributed ScratchPad Memories (SPMs) and NVMs. HaVOC allows programmers and the compiler to partition the application's address space and generate data/instruction block layouts considering virtualized hybrid address spaces. We define a data volatility metric used by our hybrid memory-aware compilation flow to generate memory allocation policies that are enforced at run-time by a filter-inspired dynamic memory algorithm. Our experimental results with a set of embedded benchmarks executing simultaneously on a Chip-Multiprocessor with hybrid NVM/SPMs show that HaVOC is able to reduce execution time and energy by 60.8% and 74.7% respectively with respect to traditional multitasking based SPM allocation policies.
COGNITIVE TASK ANALYSIS: ANALYZING THE COGNITION OF GAMEPLAY & GAME DESIGN

A prior study performed by ADL, which measured the effects of certain video game design features thought to increase cognitive adaptability, brought to light how little is understood of the cognitive elements of video games, even by those who design them, let alone those who wish to study them or utilize them for learning or improving cognitive functioning. It has long been standard practice by instructional designers and those in industrial/organizational psychology to utilize a process known as cognitive task analysis (CTA) in order to analyze the cognitive and behavioral requirements of an expert-level performance of a certain task. They are broadly recognized as an effective tool for cognitive mapping. ADL researchers scoured the existing base of research in industrial/organizational psychology, cognitive psychology, and gaming, as well as consulted with experts in the video game industry, and found that no efforts to apply CTA methods to or cognitively map a video game— identifying and enumerating on features such as implicit and explicit rules and reinforcement, rule and environment shifts, audio and visual cues, behavioral and cognitive requirements of players, and goals, sub-goals, and micropuzzles—in order to analyze the cognitive effects of its design could be found. It was towards the end of filling these gaps in knowledge about the cognitive makeup of Portal 2, as well as developing a methodology for applying CTA techniques to video games for future research and utilization for learning, that the researchers at the ADL Initiative are undertaking an effort to perform a cognitive task analysis on Portal 2 play. This paper details the novel methods the team has developed, both by adapting traditional CTA methods to analyze the cognitive requirements of gameplay as well as creating techniques to capture the cognitive effects of unique video game design principles, as well as initial findings.
Flow-Aware Congestion Control to Improve Throughput under TCP Incast in Datacenter Networks

Data enter networks (DCNs) host cloud computing and various applications. In this environment, there are mice and elephant flows and the partition/aggregate communication pattern is common, so TCP in cast may often occur. Massive mice flows directly affect the elephant flows under in cast, and the standard TCP mechanism or SDN approach with centralized controller is insufficient to solve this problem. In this paper, we focused on how a flow-aware congestion control method that takes into account flow characteristics can improve throughput under in cast. It is required to establish our long-term goal that provides different network level services based on flow characteristics. We propose a switch-based method that identifies target flows and provides different congestion control using explicit congestion notification (ECN). Although our method uses ECN, it does not require any change to TCP stacks on end nodes or a dedicated queue for the targets. In our experiments, we focused on the throughput of flows, and selected elephant flows as targets because their throughput is more sensitive than mice flows. We compared the experimental results from our method to those of standard TCP, ECN, and a dedicated queue for elephants. Our method improved the aggregate throughput of elephant flows by 1.7× to 5.1× and the good put of elephant flows by 1.9× to 5.4× compared to those of the above methods under our in cast scenario.
Learning Compact Visual Representation with Canonical Views for Robust Mobile Landmark Search

Mobile Landmark Search (MLS) recently receives increasing attention. However, it still remains unsolved due to two important issues. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images. This paper proposes a Canonical View based Compact Visual Representation (2CVR) to handle these problems via novel three-stage learning. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multimodal sparse coding is applied to transform multiple visual features into an intermediate representation which can robustly characterize visual contents of varied landmark images with only fixed canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored binary embedding model which preserves visual relations of images measured with canonical views and removes noises. With 2CVR, robust visual query processing, low-cost of query transmission, and fast search process are simultaneously supported. Experiments demonstrate the superior performance of 2CVR over several state-of-the-art methods.
The Natural Drift: What Happened to Operations Research?

“Crisis? What crisis? ” could also have been an appropriate title for this paper. The OR/MS literature contains more than enough papers addressing the crisis in OR/MS to take the matter seriously, but it is not always clear exactly what is meant by crisis. The complaints usually concern the perceived gap between theory and practice, pointing out that there are too many theoretical and too few practice-oriented papers. This may well be true, but we suggest a slightly different view of the crisis, by hypothesizing that a ‘natural drift’ has occurred, i.e., that old-style OR has remained underdeveloped relative to its more purely theoretical and practical counterparts. To explain how this hypothesis arose, we provide an overview of the debate on professional concerns in OR/MS, and contrast it with Harvard Business Review papers providing a managerial perspective. We also explore the extent to which such a natural drift would be truly natural, by comparing the development of OR/MS to that of other professions.
Booms and Systemic Banking Crises

The empirical literature on systemic banking crises (SBCs) has shown that SBCs are rare events that break out in the midst of credit intensive booms and bring about particularly deep and long-lasting recessions. We attempt to explain these phenomena within a dynamic general equilibrium model featuring a non-trivial banking sector. In the model, banks are heterogeneous with respect to their intermediation skills, which gives rise to an interbank market. Moral hazard and asymmetric information on this market may generate sudden interbank market freezes, SBCs, credit crunches and, ultimately, severe recessions. Simulations of a calibrated version of the model indicate that typical SBCs break out in the midst of a credit boom generated by a sequence of positive supply shocks rather than being the outcome of a big negative wealth shock. We also show that the model can account for the relative severity of recessions with SBCs and their longer duration. JEL Classification: E32, E44, G01, G21
Less is More: Compact Matrix Decomposition for Large Sparse Graphs

Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, research citation network analysis, social network analysis, and regulatory networks in genes. Low rank decompositions, such as SVD and CUR, are powerful techniques for revealing latent/hidden variables and associated patterns from high dimensional data. However, those methods often ignore the sparsity property of the graph, and hence usually incur too high memory and computational cost to be practical. We propose a novel method, the Compact Matrix Decomposition (CMD), to compute sparse low rank approximations. CMD dramatically reduces both the computation cost and the space requirements over existing decomposition methods (SVD, CUR). Using CMD as the key building block, we further propose procedures to efficiently construct and analyze dynamic graphs from real-time application data. We provide theoretical guarantee for our methods, and present results on two real, large datasets, one on network flow data (100GB trace of 22K hosts over one month) and one on DBLP (200MB over 25 years). We show that CMD is often an order of magnitude more efficient than the state of the art (SVD and CUR): it is over 10X faster, but requires less than 1/10 of the space, for the same reconstruction accuracy. Finally, we demonstrate how CMD is used for detecting anomalies and monitoring timeevolving graphs, in which it successfully detects worm-like hierarchical scanning patterns in real network data.
Multi-Antenna Capacities of Waveguide and Cavity

—MIMO capacity of waveguide and cavity channels is analyzed in details in this paper. The rationale for this is threefold: (i) waveguide / cavity models can be used to model corridors, tunnels and other confined space channels, (ii) this is a canonical problem; its analysis allows to develop appropriate techniques, which can be further used for more complex problems, (iii) it allows to shed light on the relation between information theory and electromagnetism and, in particular, to establish the limits imposed by the laws of electromagnetism on achievable channel capacity. It is demonstrated that the number of degrees of freedom of the electromagnetic field inside of waveguides, which can be used for MIMO communication, is determined by the waveguide cross-sectional a rea expressed in terms of the wavelength. A system architecture is proposed, which allows using these degrees of freedom.
Mathematical study of a petroleum-engineering scheme

Models of two phase flows in porous media, used in petroleum engineering, lead to a system of two coupled equations with elliptic and parabolic degenerate terms, and two unknowns, the saturation and the pressure. For the purpose of their approximation, a coupled scheme, consisting in a finite volume method together with a phase-by-phase upstream weighting scheme, is used in the industrial setting. This paper presents a mathematical analysis of this coupled scheme, first showing that it satisfies some a priori estimates: the saturation is shown to remain in a fixed interval, and a discrete L2 (0,T;H1 (Ω)) estimate is proved for both the pressure and a function of the saturation. Thanks to these properties, a subsequence of the sequence of approximate solutions is shown to converge to a weak solution of the continuous equations as the size of the discretization tends to zero.
Analysis of cognitive fatigue using EEG parameters

Continuous and repetitive performance of a task is likely to induce a drop in alertness levels of an individual. Changes in Electroencephalogram (EEG) have been proposed in literature as a marker of alertness during repeated performance of a cognitive task set. The present paper investigates the increase in fatigue levels and resultant drop in alertness of subjects during continuous performance of cognitive tasks by analyzing changes in energy of EEG frequency bands. The trends reflected in the EEG parameters correspond to a gradual increase in fatigue levels of subjects with increase in cognitive loading.
Analyzing the Actual Execution of a Large Software-Intensive System for Determining Dependencies

In this paper, we present a dynamic analysis approach to increase the understandability of a large software-intensive system, more particularly to enable the identification of dependencies between its execution entities. This approach analyzes the execution of a software system in a top-down fashion to cope with complexity and uses execution entities such as scenarios, components, and processes rather than code artifacts such as modules, classes, or objects. The approach synchronizes and analyzes two sources of execution information (logging and process activity), and builds architectural views of the system execution, according to a specific metamodel. We have validated this approach on an MRI scanner, a representative large software-intensive system, enabling the identification of dependencies in the execution of its software subsystem.
Transient B-Cell Depletion with Anti-CD20 in Combination with Proinsulin DNA Vaccine or Oral Insulin: Immunologic Effects and Efficacy in NOD Mice

A recent type 1 diabetes (T1D) clinical trial of rituximab (a B cell-depleting anti-CD20 antibody) achieved some therapeutic benefit in preserving C-peptide for a period of approximately nine months in patients with recently diagnosed diabetes. Our previous data in the NOD mouse demonstrated that co-administration of antigen (insulin) with anti-CD3 antibody (a T cell-directed immunomodulator) offers better protection than either entity alone, indicating that novel combination therapies that include a T1D-related autoantigen are possible. To accelerate the identification and development of novel combination therapies that can be advanced into the clinic, we have evaluated the combination of a mouse anti-CD20 antibody with either oral insulin or a proinsulin-expressing DNA vaccine. Anti-CD20 alone, given once or on 4 consecutive days, produced transient B cell depletion but did not prevent or reverse T1D in the NOD mouse. Oral insulin alone (twice weekly for 6 weeks) was also ineffective, while proinsulin DNA (weekly for up to 12 weeks) showed a trend toward modest efficacy. Combination of anti-CD20 with oral insulin was ineffective in reversing diabetes in NOD mice whose glycemia was controlled with SC insulin pellets; these experiments were performed in three independent labs. Combination of anti-CD20 with proinsulin DNA was also ineffective in diabetes reversal, but did show modest efficacy in diabetes prevention (p = 0.04). In the prevention studies, anti-CD20 plus proinsulin resulted in modest increases in Tregs in pancreatic lymph nodes and elevated levels of proinsulin-specific CD4+ T-cells that produced IL-4. Thus, combination therapy with anti-CD20 and either oral insulin or proinsulin does not protect hyperglycemic NOD mice, but the combination with proinsulin offers limited efficacy in T1D prevention, potentially by augmentation of proinsulin-specific IL-4 production.