Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact Solution Using Matrix Geometric Techniques

We consider a router that uses a shared input queue and distributes traffic over multiple output ports. Though such an architecture is subject to the Head-of-the-Line (HOL) blocking problem, it is very attractive when the router supports both load balancing over alternate paths and QoS. Therefore, in this work, we consider per-flow (or destination) queueing which (1) eliminates the need to further consider per-flow queueing at the individual destination ports therefore avoids the HOL blocking problem and (2) achieves QoS. We model a single traffic class at a routing node as it passes through a single-shared-input and then assigned to two-output ports that lead to alternate paths to the same destination. We assume that the remainder of the network transit delay beyond the router are different for both paths. Therefore our routing policy reduces the end-to-end delay by favoring the faster path by assigning packets to the higher-delay path only if the input queue length exceeds a given threshold . We generalize the model by permitting the output port that leads to the slower path to represent a multichannel link group (e.g., multiple parallel wavelengths on the same optical fiber). We model our system as a two-dimensional Markov chain and use matrix geometric techniques to solve for its stationary probabilities.
Participant Reactivity in an Exhibition: The Effect of Overt Observation on Engagement Times

Abstract Participant reactivity was examined in a section of the National Park Center Multimar Wattforum in Tönning, Germany, which combines aquariums with exhibits typical of museums and science centers. Engagement times with exhibits were compared between cued and uncued visitors. Cued visitors were overtly observed. Some were also prompted before the observation period to think aloud. Data were collected by paper-and-pencil timing and tracking. Participants spent approximately 15 min in the observation section. Reactivity was not significant for overtly observed cued visitors unless they were asked to think aloud. In this case, reactivity depended on the type of exhibit. Reactivity was high for information stations, moderate for interactive stations, and low for aquariums. The results indicate that reactivity effects are negligible for overt observation if after the cueing no further interaction between participants and observers takes place but increase considerably even with minimal interaction.
Sensory Stream Data Mining on Chip

Mining physical properties from real-time sensor stream data is important to the atmospheric studies, ecology and oceanography. An FPGA-based reconfigurable sensory stream data mining processor is presented in this paper. The processor is based on Generalized Non-Linear Regression algorithm and trained with radiative transfer simulations and observations for autonomous detection of satellite measurement signatures and retrievals of atmospheric physical properties. The results show that the embedded computing approach is faster than traditional computing methods in orders of magnitude. The data mining processor is able to automatically adapt to multi-platform sensors, eliminate redundant algorithm development and provide a vehicle for autonomous onboard image analysis and physics-based data compressions.
Energy Implications of Filtration in Residential and Light-Commercial Buildings

Higher-efficiency HVAC filters generally have a higher pressure drop and are widely assumed to increase energy consumption in smaller air conditioning systems. To explore the effects of filters in real buildings, we monitored 17 residential and light-commercial forced air cooling systems in Austin, TX. Measurements were made once per month for one year at each site with filters from three different MERV range categories. Measured parameters included system airflow, fan power draw, outdoor unit power draw, cooling capacity, pressure drops across filters and coils, and duct leakage. Higher-efficiency (MERV 11-12) filters generally had a small impact on parameters related to cooling energy consumption in the residential and light-commercial test systems when compared to lower-efficiency (MERV 2) filters. The median energy consequence of higher-efficiency filtration in the test systems was estimated as a decrease of approximately 16 kWh per ton of nominal capacity (4.6 kWh per kW) per month of cooling season operation, albeit with large variation, with most of these small savings coming from fan energy reductions. These results suggest a weak link between higher-efficiency filters and energy use in residential and lightcommercial systems and that other factors should govern filter selection.
Text S3.dvi

Supervised learning requires a training set on which the classifier’s parameters are learnt. An independent test set is used to evaluate its performance. For each test sample the classifier returns a vector that estimates the probability that the sample belongs to any of the classes under consideration, in our case edges and non-edges. We employ the random forest classifier [1], a state-of-the-art supervised learning approach which was recently successfully used for link prediction [2]. A random forest consists of a set of decision trees. Each node of a decision tree contains a simple test of the form Is the value of the ith dimension di lower than threshold ti? , and all leave nodes contain a deterministic class label (in our case: edge, non-edge). Given a test sample, all trees of the forest are traversed from their roots to one of their leaves. Finally, the sample arrives in one of the leaves that, in our case, classifies this sample as an edge or a non-edge sample. The number of trees voting for a certain class are counted and the percentage of trees classifying the test sample as an edge defines its prediction value. The random forest is built (learnt) on a given set of training samples. For each tree, the following procedure is repeated: We start with the root node that contains all samples. At each node, the feature that best splits the samples in the node is selected among a randomly chosen subset of size mtry of the P available features and an adequate threshold is found. Subsequently, the node is split and the process is recursively repeated for each of the resulting subsets until a pure subset with samples from only one class is produced. Here we build ntree = 500 decision trees. We learn based on P = 15 features and at each node of the decision trees, the number of features sampled for splitting is mtry = √ P .
An Evaluation of a Sensor Fusion System to Improve Drivers' Nighttime Detection of Road Hazards

Objective: To determine whether combination of two spectral bands within a single sensor-fused image can improve drivers' detection of road hazards. Methods: Images were collected with visible and short wave infrared sensors, and were combined by an image fusion algorithm derived from a computational model of human retinal processing (Werblin et al., 1997). Test stimuli were single-band and fused images of a nighttime scene, collected with sensors mounted atop a vehicle and facing down a stretch of road into an opposing vehicle's headlights. The intensity of the opposing headlights was varied to produce images of low and of high glare. The subjects' task was to detect the presence of a pedestrian within each image. Results: Sensor-fused imagery reliably produced performance better than or equivalent to that produced by either format of single-band imagery. Conclusions: Sensor fusion may provide an effective method of facilitating the detection of road hazards under low visibility conditions.
Combining the power of Internal and External denoising

Image denoising methods can broadly be classified into two types: “Internal Denoising” (denoising an image patch using other noisy patches within the noisy image), and “External Denoising” (denoising a patch using external clean natural image patches). Any such method, whether Internal or External, is typically applied to all image patches. In this paper we show that different image patches inherently have different preferences for Internal or External de-noising. Moreover, and surprisingly, the higher the noise in the image, the stronger the preference for Internal De-noising. We identify and explain the source of this behavior, and show that Internal/External preference of a patch is directly related to its individual Signal-to-Noise-Ratio (“PatchSNR”). Patches with high PatchSNR (e.g., patches on strong edges) benefit much from External Denoising, whereas patches with low PatchSNR (e.g., patches in noisy uniform regions) benefit much more from Internal Denoising. Combining the power of Internal or External denoising selectively for each patch based on its estimated PatchSNR leads to improvement in denoising performance.
System Combination by Driven Decoding

The combination of automatic speech recognition (ASR) systems generally relies on a posteriori merge of system outputs or on a cross-adaptation. In this paper, we propose an integrated approach where the search of a primary system is driven by the outputs of a secondary one. This method allows to drive the primary system search by using the one-best hypotheses and the word posteriors gathered from the secondary system. Experiments are carried out within the experimental framework of the ESTER evaluation campaign (S. Galliano et al. 2005). Results show that the driven decoding algorithm significantly outperforms the two single ASR systems (-8% of relative WER, -1.7% absolute). Finally, we investigate the interactions between driven decoding and cross-adaptations. The best cross-adaptation strategy in combination with the driven decoding process brings to a final absolute gain of about 1.9% WER.
Dictyostelium myosin-IE is a fast molecular motor involved in phagocytosis

Class I myosins are single-headed motor proteins, implicated in various motile processes including organelle translocation, ion-channel gating, and cytoskeleton reorganization. Here we describe the cellular localization of myosin-IE and its role in the phagocytic uptake of solid particles and cells. A complete analysis of the kinetic and motor properties of Dictyostelium discoideum myosin-IE was achieved by the use of motor domain constructs with artificial lever arms. Class I myosins belonging to subclass IC like myosin-IE are thought to be tuned for tension maintenance or stress sensing. In contrast to this prediction, our results show myosin-IE to be a fast motor. Myosin-IE motor activity is regulated by myosin heavy chain phosphorylation, which increases the coupling efficiency between the actin and nucleotide binding sites tenfold and the motile activity more than fivefold. Changes in the level of free Mg2+ ions, which are within the physiological range, are shown to modulate the motor activity of myosin-IE by inhibiting the release of adenosine diphosphate.
Co-segmentation of aircrafts from high-resolution satellite images

Purely bottom-up, unsupervised target segmentation is one of the most challenging problems in satellite image interpretation within the computer vision community. In this paper, we focus on the problem of automatically segmenting aircrafts from high-resolution satellite images based on the idea of co-segmentation. First, we selectively segment out the regions of interest (ROIs) from the satellite images. Then, we apply a region based shadow detection and removal approach to remove shadows. Finally, the anisotropic heat diffusion model is employed to fulfill our co-segmentation task. Experimental results on a given dataset have demonstrated the effectiveness of our method.
Pursuing the second best: The history, momentum, and remaining issues of China's anti-monopoly law

After thirteen years of debates, China at long last promulgated its Anti-Monopoly Law (AML) on August 30, 2007.1 Few would doubt that anti-monopoly, or competition, law is fundamental to guarding the mar ket economy in the western countries.2 However, China which has a cul ture thousands of years old, with an ideology that is extremely conservative and even feudalistic, is a country undergoing a painful tran sition from command economy to market orientation. Under these cir cumstances, it is essential to examine the answers to these questions: What is the backstory to the creation of China's AML? What is the driv ing force behind developing China's AML? What were the controversies that held up the legislative process, and to what extent were they resolved?
Meta-programming and Multi-stage Programming for GPGPUs

GPGPUs and other accelerators are becoming a mainstream asset for high-performance computing. Raising the programmability of such hardware is essential to enable users to discover, master and subsequently use accelerators in day-to-day simulations. Furthermore, tools for high-level programming of parallel architectures are becoming a great way to simplify the exploitation of such systems. For this reason, we have extended NT2 - the Numerical Template Toolbox - a C++ scientific computing library which can generate code for SIMD and multithreading systems in a transparent way using a MATLAB like syntax. In this paper, we study how to introduce an accelerator based programming model into this library to allow developers to reap the benefits of such an architecture. After a brief description of the NT2 framework, we explain how our accelerator programming model has been designed and integrated in a pure C++ library. We conclude by showing the applicability and performance of this tool on some practical applications.
A Robust Dynamic Operating Model for a Supply Chain under Uncertain Markets

A dynamic operating model is proposed in this paper to deal with a multi-period, multi-product supply chain consisting of a supplier and a producer. The amount of raw materials supply and their prices, ultimate products demand and their prices are uncertain. The uncertainties of prices are described by the uncertain intervals; the supplies and demands are described as a scenario set with certain probability. The model is constructed as a dynamic programming problem and satisfying several conflict objectives, such as the operating coordination of supply chain, making the maximum profit of all participants as much as possible, and robustness of decision to uncertain markets. The result of a numerical example shows that the model we proposed is robust
Effective Clustering Technique for Selecting Cluster Heads and Super Cluster Head in MANET

Mobile ad Hoc Network consists of a group of mobile nodes which can communicate with each other without any infrastructure. This feature makes them self organized and rapidly deployable. Clustering is a technique used in these networks for best utilization of available bandwidth and for prolonging the network life time also. The entire group of nodes participate in election of the Cluster Head which is most of the times used for network administration but sometimes can also be used for the task of intrusion detection i.e. network security. Super Cluster Head is a node which can be a part of network but not itself a cluster Head and is used to detect a misbehaving Cluster Head not doing the task of intrusion detection properly. In this paper an effective clustering technique has been proposed which is being used for the election of Cluster Heads and Super Cluster Head. It uses five parameters i.e. Communication range, Hop Count, Battery Power, Relative Velocity, Fairness at a time to select a Cluster Head. Always an efficient node which passes all of the criteria will become the Cluster Head. After the selection of Cluster Heads, the election is performed to select a Super Cluster Head. A node is selected as a Super Cluster Head only if it is having maximum battery power and it is not a Cluster Head. The technique used chooses the best of the nodes to become Cluster Head and Super Cluster Head. This technique seems to increase network life time also because of the slow dissipation of the energy.
Straightforward Feature Selection for Scalable Latent Semantic Indexing

Latent Semantic Indexing (LSI) has been validated to be effective on many small scale text collections. However, little evidence has shown its effectiveness on unsampled large scale text corpus due to its high computational complexity. In this paper, we propose a straightforward feature selection strategy, which is named as Feature Selection for Latent Semantic Indexing (FSLSI), as a preprocessing step such that LSI can be efficiently approximated on large scale text corpus. We formulate LSI as a continuous optimization problem and propose to optimize its objective function in terms of discrete optimization, which leads to the FSLSI algorithm. We show that the closed form solution of this optimization is as simple as scoring each feature by Frobenius norm and filter out the ones with small scores. Theoretical analysis guarantees the loss of the features filtered out by FSLSI algorithm is minimized for approximating LSI. Thus we offer a general way for studying and applying LSI on large scale corpus. The large scale study on more than 1 million TREC documents shows the effectiveness of FSLSI in Information Retrieval (IR) tasks.
Blow-up of solutions to a periodic nonlinear dispersive rod equation

In this paper, firstly we find an optimal constant for a convolution problem on the unit circle via the variational method. Then by using the optimal constant, we give a new and improved sufficient condition on the initial data to guarantee the corresponding strong solution blows up in finite time. We also analyze the corresponding ordinary difference equation associate to the convolution problem and give numerical simulation for the optimal constant. 2010 Mathematics Subject Classification: Primary 30C70; Secondary 37L05, 35Q58, 58E35.
SCATTERING THEORY APPROACH TO RANDOM SCHRÖDINGER OPERATORS IN ONE DIMENSION

Methods from scattering theory are introduced to analyze random Schrodinger operators in one dimension by applying a volume cutoff to the potential. The key ingredient is the Lifshitz–Krein spectral shift function, which is related to the scattering phase by the theorem of Birman and Krein. The spectral shift density is defined as the "thermodynamic limit" of the spectral shift function per unit length of the interaction region. This density is shown to be equal to the difference of the densities of states for the free and the interacting Hamiltonians. Based on this construction, we give a new proof of the Thouless formula. We provide a prescription how to obtain the Lyapunov exponent from the scattering matrix, which suggest a way how to extend this notion to the higher dimensional case. This prescription also allows a characterization of those energies which have vanishing Lyapunov exponent.
Temporal attention control system for multiple objects tracking

An important ability for mobile robots is to process multiple tasks in complex environments. Since the sensor resources on a robot are limited, it is necessary to distribute the sensors attention to different tasks along the time scale. This paper proposes a temporal attention control method which aims at detecting multiple objects and estimating their poses with a single actuated camera. The proposed method is based on three criteria which are partially inspired by human behavior: (i) minimization of the overall object poses perception uncertainty and minimization of the variance of the perception uncertainty of different objects; (ii) minimization of the camera movements for completing the tasks; (iii) maximization of the number of objects in the cameras field of view. The proposed approach use Kalman filters to estimate object poses and to determine the perception uncertainty. The method was evaluated with both simulation and experiment on actual robot. The results show that the proposed approach is able to switch the camera's attention according to the objects poses and movements efficiently with a low frequency of the camera movements.
A Fast and High Quality Approach for Overlapping Community Detection through Minimizing Conductance

Communities play an important role in the field of graph structure, especially in domains of networks analysis. A community (also referred to as a cluster) is a dense subgraph of the whole graph with more links between its members than between its members to the outside nodes. Communities overlap when nodes in graph belongs to multiple communities. Overlapping community detection is developing in the years, and seed set expansion is one successful technique in the area. In this paper, we propose a fast and high quality method in overlapping community detection using seeds expansion approach. In particular, we use personalized PageRank scheme that optimized the conductance of each community around all the seeds previously found, and after the phase we further minimize the conductance community score by searching for better position for each node in the graph with a novel and efficient algorithm. Experimental results in real graphs with ground truth communities show that our approach outperforms other state-of-art overlapping community detection methods, in particular, it is about two orders of magnitude faster than most of the existing methods with better or similar quality.
SINGLE CHANNEL CONDUCTANCE OF THE CaV 2 . 2 CALCIUM CHANNEL by

Calcium ions (Ca) are admitted into presynaptic nerve terminals through voltage gated calcium channels and diffuse to bind and activate the secretory vesicle discharge mechanism. Current research favors a highly ‘modal’ organization where the release sites are activated by one or a few closely apposed channels (Stanley, 1997). To fully understand the nanophysiology of transmitter release site activation, it is necessary to determine the rate of Ca flux through individual channels at normal physiological external concentrations. OBJECTIVE: To explore the relationship between CaV2.2 channel conductance and external Ca across the physiological range. CONCLUSION: The conductance of the CaV2.2 channel was determined across the range of 1-100 mM [Ca]EXT . With 2 mM [Ca]EXT, the conductance was determined to be 2.76 ± 0.24 pS.
GPU based detection and mapping of collisions for haptic rendering in Immersive Virtual Reality

We present a method that maps collisions on a dynamic deformable virtual character designed to be used for tactile haptic rendering in Immersive Virtual Reality (IVR). Our method computes exact intersections by relying on the use of programmable graphics hardware. Based on interference tests between deformable meshes (an avatar controlled by a human participant) and a few hundred collider objects, our method gives coherent haptic feedback to the participant. We use GPU textures to map surface regions of the avatar to haptic actuators. We illustrate our approach by using a vest composed of vibrators for haptic rendering and we show that our method achieves collision detection at rates well over 1kHz on good quality deformable avatar meshes which makes our method suitable for video games and virtual training applications
VolumeFlies: Illustrative Volume Visualization using Particles

Non-photorealistic (NPR) techniques are usually applied to produce stylistic renderings. However, they can also be useful to visualize scientiﬁc datasets. NPR techniques are often able to simplify data, producing clearer images than traditional photorealistic methods. We propose a framework for visualizing volume datasets using non-photorealistic techniques. Our framework is based on particle systems, with user-selectable rules affecting properties of the particles such as position and appearance. The techniques presented do not require the generation of explicit intermediary surfaces. Furthermore, the framework is versatile enough to produce a variety of illustrative techniques within the same framework.