Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Interactive Dynamics of Pedestrians with Fictitious Play

We develop predictive models of pedestrian dynamics by encoding the coupled nature of multi-pedestrian interaction using game theory and deep learning-based visual analysis to estimate person-specific behavior parameters. We focus on predictive models since they are important for developing interactive autonomous systems (e.g., autonomous cars, home robots, smart homes) that can understand different human behavior and pre-emptively respond to future human actions. Building predictive models for multi-pedestrian interactions however, is very challenging due to two reasons: (1) the dynamics of interaction are complex interdependent processes, where the decision of one person can affect others, and (2) dynamics are variable, where each person may behave differently (e.g., an older person may walk slowly while the younger person may walk faster). To address these challenges, we utilize concepts from game theory to model the intertwined decision making process of multiple pedestrians and use visual classifiers to learn a mapping from pedestrian appearance to behavior parameters. We evaluate our proposed model on several public multiple pedestrian interaction video datasets. Results show that our strategic planning model predicts and explains human interactions 25% better when compared to a state-of-the-art activity forecasting method.
A Bayesian Ensemble Approach for Epidemiological Projections

Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, ensemble modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model ensembles based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the ensemble prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for ensembles with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for ensemble modeling of disease outbreaks.
Poduzorkovanje, samouzorkovanje, postupak 'univerzalnog noža' i njihova upotreba u postupcima za statističku analizu multivarijacionih podataka

In order to perform any data analysis procedure (analysis of variance, linear correlation, linear regression, etc.), a series of assumptions need to be true, most of which are concerned with the distribution of the variables. If one or more of these assumptions are violated, the obtained parameter estimates are inadequate. Resampling methods are offered as a means to overcome this issue, as they only require one assumption to be made - that the available data is reasonably representative of the population. Resampling methods use existing, available samples to create a large number of new subsamples. This produces an empirical distribution of the desired statistics, which forms a basis for an adequate parameter estimate. In this paper, we will discuss the methods of bootstrapping and jackknifing, which fall under the broader category of resampling. The most common form of the jackknifing procedure is sometimes labeled 'leave-one-out' procedure, because new subsamples are made by excluding one unit of the original sample at a time. Thus, the number of new subsamples is equal to the number of units in the original sample. In bootstrapping, subsamples are created by randomly picking units from the original subsample with replacement so that all subsamples are equal in size to the original sample. The number of bootstrapping subsamples is usually very large (over 1000). A canonical discriminant analysis will serve as an example to illustrate how the usage of resampling procedures can significantly alter the obtained parameter estimates and researcher’s conclusions. A detailed explanation on how to perform these methods in IBM SPSS Statistics package is also given.
Bantu Phonology *

This chapter surveys the main phonological phenom ena found in Bantu languages. One such phenomenon is vowel harmony, where suffixes typically alternate in height depending on the preceding vowel. Combina tio s of nasal plus consonant are also frequently subject to various modification s, and such clusters also play an important role in the debates over syllable structu re, since Bantu language seem to exhibit the peculiarity that the only onset cluster of consonantals is composed of nasal followed by obstruent, reversing the more gener al t ndency for sonority to rise in the syllable onset. The tone systems of Bantu langu ges are especially well-known for their complex systems of alternations in the fo rm of spreading, dissimilation and grammatically-conditioned melodic alternation. A ke y f ature of interest in the study of Bantu phonology is the considerable variability in the exact details of operation in a highly similar set of rules.
An improved LDA algorithm for text classification

Latent Dirichlet Allocation is a classic topic model which can extract latent topic from large data corpus. This model assumes that if a document is relevant to a topic, then all tokens in the document are relevant to that topic. In this paper, we present an algorithm called gLDA for topic text classification by adding topic-category distribution parameter to LDA, which can make the document generated from the most relevant category. Gibbs sampling is employed to conduct approximate inference, and experiment results in two datasets show the effectiveness of this method.
Optimizationof CPG-network for decentralized control of a snake-like robot

Periodic locomotion of animal bodies with large degree of freedom is known to be realized by decentralized control using network of central pattern generators (CPGs) that are distributed in spinal cord (in vertebrates) or nerve cords (in invertebrates). In this paper, optimization of a decentralized controller for a snake-like robot based on CPG-network is presented. The controller is composed of CPGs on every robot joints and connections between them. Oscillations of CPGs are coordinated by propagating them each other. For several types of network structure, genetic algorithm (GA) is used to optimize neural parameters, i.e. CPG parameters and synaptic weights. As a result, we derived a CPG-network that is optimal in terms of moving speed
Some problems in seam-carving algorithm

Efficient resizing about images should not only consider size constraints, but also regard the image's content. It is the fact that the image's content in the center is more attractive than content at the border necessarily, the image resizing algorithm in view of image content is becoming a new hotspot among the image resizing domain. Firstly, this algorithm treats the area of image that attracts the human eyes as more important content, but the region that does not attract the eyes as unimportant area. Secondly, to the greatest extent maintaining the important content but changing unimportant region in order to fit target displayer size. This algorithm is regarded as content-aware image retargeting. Among all the methods about content-aware image resizing, the seam carving method's status is beyond all doubt, its advancement and innovation are better than other ways. This article analyses the traditional seam carving method and shows two innovations. One innovation is the identification of the more important area, another is improving algorithm based on seam carving to dynamic retargeting. In our experiments, it is easy to see that the new way enhances the effect and achieves better effect.
Thermodynamic Analysis about Nucleation and Growth of Cubic Boron Nitride Crystals in the hBN-Li3N System under High Pressure and High Temperature

The nucleation of cubic boron nitride (cBN) single crystals synthesized with lithium nitride (Li3N) as a catalyst under high pressure and high temperature (HPHT) was analyzed. Many nanometer-sized cubic boron nitride nuclei formed in the near surface layer, as detected by high resolution transmission electron microscopy. Based on the experiment results, the transformation kinetics is described by a nucleation and growth process in the thermodynamic stability region of cBN. A theoretical description is developed based on the heterogeneous nucleation and layer growth mechanism, and the relevant parameters are estimated and discussed. The critical crystal radius, r*, increases with the temperature under constant pressure; the change with temperature more pronounced at lower pressure (such as 4.5 GPa). The crystal growth velocity increased with the temperature, and it is parabolic with temperature under certain pressure. These results are consistent with experimental data.
An Efficient Partitioning Strategy for Pseudo-Exhaustive Testing

Pseudo-exhaustive testing involves applying all possible input patterns to individual output cones of a circuit. Circuits with output cones driven by a large number of inputs often need to be partitioned to reduce the test application time. In this paper, we present an efficient heuristic partitioning procedure of polynomial complexity. Experiments on combinational benchmark circuits validate the efficiency and quality of our approach.
VLSI Reverse Converter for RNS Based on the Moduli Set

The {2n + 1, 2n - 1, 2(2n+1) - 3, 2(2n) - 2} moduli set and the respective reverse converter have been recently proposed for supporting Residue Number Systems (RNSs). The reverse converter originally proposed was based on the Chinese Remainder Theorems (CRTs), in particular the commonly called new CRTs, and requires at the end a 6nbit carry propagate adder to compute the binary value. In this paper we propose a more efficient Very-Large-Scale Integration (VLSI) reverse converter for the referred moduli set following a similar approach based on the Mixed-Radix Conversion (MRC). Experimental results show a reduction in the conversion delay of 22% and 17% without impact in the circuit area regarding the related art for a 90 nm ASIC and FPGA technology, respectively.
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval

Deep neural networks have been investigated in learning latent representations of medical images, yet most of the studies limit their approach in a single supervised convolutional neural network (CNN), which usually rely heavily on a large scale annotated dataset for training. To learn image representations with less supervision involved, we propose a deep Siamese CNN (SCNN) architecture that can be trained with only binary image pair information. We evaluated the learned image representations on a task of content-based medical image retrieval using a publicly available multiclass diabetic retinopathy fundus image dataset. The experimental results show that our proposed deep SCNN is comparable to the state-of-the-art single supervised CNN, and requires much less supervision for training.
A Set of XLispStat Subroutines for Detecting Outliers

A search for outliers using the grand tour method is performed. The grand tour enables to inspect the conguration of the data points imagined as a data cloud located in multivariate space. In the proposed method we rotate the data cloud and inspect the projections in a scatterplot located in a 2D plane. The algorithm works in two windows. In the rst one (exhibiting a plane)
A local constant factor approximation for the minimum dominating set problem on bounded genus graphs

The Minimum Dominating Set (MDS) problem is not only one of the most fundamental problems in distributed computing, it is also one of the most challenging ones. While it is well-known that minimum dominating sets cannot be approximated locally on general graphs, over the last years, several breakthroughs have been made on computing local approximations on sparse graphs. This paper presents a deterministic and local constant factor approximation for minimum dominating sets on bounded genus graphs, a very large family of sparse graphs. Our main technical contribution is a new analysis of a slightly modified, first-order definable variant of an existing algorithm by Lenzen et al. Interestingly, unlike existing proofs for planar graphs, our analysis does not rely on any topological arguments. We believe that our techniques can be useful for the study of local problems on sparse graphs beyond the scope of this paper.
How Interactions between RPL and Radio Duty Cycling Protocols Affect QoS in Wireless Sensor Networks

To guarantee that a Wireless Sensor Network succeeds in delivering its expected QoS, it is essential to understand and optimize cross-layer interactions. This paper presents a study on the interactions between the RPL routing protocol and Radio Duty Cycling (RDC) protocols. This study constitutes a first step towards a full understanding of their impact on the QoS of the network. By means of simulation and real world measurements, differences in the performance of RPL with different RDC protocols were observed and explained, and several properties of the RDC protocols impairing the performance of RPL were discovered.
Transgenic Mice Over-Expressing ET-1 in the Endothelial Cells Develop Systemic Hypertension with Altered Vascular Reactivity

Endothelin-1 (ET-1) is a potent vasoconstrictor involved in the regulation of vascular tone and implicated in hypertension. However, the role of small blood vessels endothelial ET-1 in hypertension remains unclear. The present study investigated the effect of chronic over-expression of endothelial ET-1 on arterial blood pressure and vascular reactivity using transgenic mice approach. Transgenic mice (TET-1) with endothelial ET-1 over-expression showed increased in ET-1 level in the endothelial cells of small pulmonary blood vessels. Although TET-1 mice appeared normal, they developed mild hypertension which was normalized by the ETA receptor (BQ123) but not by ETB receptor (BQ788) antagonist. Tail-cuff measurements showed a significant elevation of systolic and mean blood pressure in conscious TET-1 mice. The mice also exhibited left ventricular hypertrophy and left axis deviation in electrocardiogram, suggesting an increased peripheral resistance. The ionic concentrations in the urine and serum were normal in 8-week old TET-1 mice, indicating that the systemic hypertension was independent of renal function, although, higher serum urea levels suggested the occurrence of kidney dysfunction. The vascular reactivity of the aorta and the mesenteric artery was altered in the TET-1 mice indicating that chronic endothelial ET-1 up-regulation leads to vascular tone imbalance in both conduit and resistance arteries. These findings provide evidence for the role of spatial expression of ET-1 in the endothelium contributing to mild hypertension was mediated by ETA receptors. The results also suggest that chronic endothelial ET-1 over-expression affects both cardiac and vascular functions, which, at least in part, causes blood pressure elevation.
Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing

In order to meet the increasing demand for high performance in smartphones, recent studies suggested mobile cloud computing techniques that aim to connect the phones to adjacent powerful cloud servers to throw their computational burden to the servers. These techniques often employ execution offloading schemes that migrate a process between machines during its execution. In execution offloading, code regions to be executed on the server are decided statically or dynamically based on the complex analysis on execution time and process state transfer costs of every region. Expectedly, the transfer cost is a deciding factor for the success of execution offloading. According to our analysis, it is dominated by the total size of heap objects transferred over the network. But previous work did not try hard to minimize this size. Thus in this paper, we introduce novel techniques based on compiler code analysis that effectively reduce the transferred data size by transferring only the essential heap objects and the stack frames actually referenced in the server. The experiments exhibit that the reduced size positively influences not only the transfer time itself but also the overall effectiveness of execution offloading, and ultimately, improves the performance of our mobile cloud computing significantly in terms of execution time and energy consumption.
Adaptive Visualization for Interactive Geometric Modeling in Geoscience

Many engineering disciplines can proﬁtably use large high-resolution geometric models whose computational requirements exceed current computer hardware capacities. This paper presents an adaptive visualization solution for interactively building large geometric models in geoscience. We focus on model building using Boolean operations. Whereas adaptive visualization techniques have conventionally been applied to existing complete models, our work permits adaptive visualization of models while under construction. To achieve this, we use the same multiresolution surface representation for both Boolean operations and visualization. The paper develops techniques that dynamically and adaptively decimate models, adjusting to changing camera positions. The decimation algorithm preserves intersection curves between surfaces, and applies to models whose surface triangulation is either globally coherent or incoherent. We have embedded the technology presented in this paper into a 3D geoscience geometric modeling application framework that supports many applications.
Learning-based Detection and Tracking in Medical Imaging : A Robust Information-Fusion Approach

Medical image processing tools are playing an increasingly important role in assisting the clinicians in diagnosis, therapy planning and image-guided interventions. Accurate, robust and fast tracking of deformable anatomical objects such as the heart, is a crucial task in medical image analysis. One of the main challenges is to maintain an anatomically consistent representation of target appearance that is robust enough to cope with inherent changes due to target movement, imaging device movement, varying imaging conditions and is consistent with the domain expert clinical knowledge. To address these challenges this chapter presents a robust learning-based fusion framework that relies on anatomically indexed componentbased object models that integrate several sources of information to determine the temporal trajectory of the deformable target. Large annotated imaging databases are exploited to encode the domain knowledge in shape models and motion models and to learn discriminative image classifiers for the target appearance. The framework robustly fuses the prior information with traditional tracking approaches based on template matching and registration. We demonstrate various medical image analysis applications with focus on cardiology such as 2D auto left heart, catheter detection and tracking, 3D cardiac chambers surface tracking, and 4D complex cardiac structure tracking, in multiple modalities including Ultrasound (US), Cardiac Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and X-ray fluoroscopy.
The Study of White Matter Hyperintensity (WMH) and Factors Related to Geriatric Late-Onset Depression

Background Geriatric depression is one of the most common and harmful mental illnesses seen in the elderly. However, there are few studies focusing on the relationship between late-onset depression (LOD) and social and psychological factors, as well as brain structure. Aims To explore factors related to late-onset depression (LOD) in elderly patients. Methods 24 first onset LOD patients over 60 years old (meeting ICD-10 diagnostic criteria for depression) and 23 non-depressed elders were selected for inclusion into this study. Scale assessments, including Fazelasscale for white matter hyperintensity (WMH) high signal level and the MTA-scale for medial temporal lobe atrophy levels, were combined with general demography and sociology data to find factors related to LOD. Results There was no significant difference in age (t=0.419, p=0.678), gender (X2=1.705, p=0.244), or years of education (t=1.478, p=0.146) between the two groups. However, statistical differences were shown on scores on the WMH, (X2=7.817, p=0.008), periventricular white matter hyperintensity (PWMH)(Fisher exact test: p=0.031), having or not having religious beliefs (Fisher exact test: p=0.265) and family harmony (yes or no) (Fisher exact test: p=0.253) between the LOD group and control group. The results of linear regression analysis showed that the total score for WMH, religious beliefs (with or without) and family harmony (yes or no) were associated with depressive symptomology. Conclusion Scores on the WMH, religious beliefs and family harmony are all potentially related to LOD in elderly patients.
Onset Detection Exploiting Wavelet Transform with Bidirectional Long Short-Term Memory Neural Networks

A plethora of different onset detection methods have been proposed in the recent years. However few attempts have been made with regard to widely-applicable approaches in order to achieve superior performances over different types of music and with considerable temporal precision. This paper concerns the usage of Wavelet Packet Transform in order to exploits multi-resolution time-frequency features. We apply early fusion in the feature space by combining Wavelet Packet Energy Coefficients and auditory spectral features. The features are then processed by a bidirectional Long Short-Term Memory recurrent neural network, acting as reduction function. The network is trained with a large database of onset data covering various genres and onset types. Due to the data driven nature, our approach does not require the onset detection method and its parameters to be tuned to a particular type of music. 1. ALGORITHM DESCRIPTION The algorithm can be seen divided in three parts. First, the audio data is transformed into the frequency domain via a Discrete Wavelet Packet Transform (DWPT) with 22 bands (cf. Table 1) and via two parallel STFTs with two different window sizes. Energy-based information and its evolution over time are used as the final feature set. Second, the features are used as inputs to the BLSTM network, which produces an onset activation function as output. Finally, the network output is post-processed by a thresholding and peak picking methods in order to obtain the correct position of the onsets. Figure 1 shows this procedure. The individual blocks are described in more detail in the following sections. 1.1 Feature Extraction Discrete input audio files, sampled at Fs = 44.1kHz, have been used for our experiments. This document is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. http://creativecommons.org/licenses/by-nc-sa/3.0/ c © 2013 The Authors. Auditory Spectral Feat. WPEC BLSTM Network Peak detection Signal Onsets Figure 1. General block scheme. A new features set is obtained exploiting wavelet transformation (cf. Figure 2) by obtaining Wavelet Packet Energy Coefficients (WPEC). The discrete input audio signal is segmented into overlapping frames ofW46 = 2048 samples, which are sampled at a rate of 100 fps, log-energy of each frame is calculated before applying the Hamming window following: