Miracle-dz/UniFAR-FTUs huggingface dataset:
An adaptive compression technique based on real-time RTT feedback

The dynamic nature of traffic over Internet protocol (IP) networks often induce high end-to-end latency and packet loss rate. These problems hamper the Quality of Service (QoS) of various conventional and emerging applications over Internet. In order to mitigate these challenges and improve the network efficiency, an adaptive compression technique (ACT) is proposed. ACT exploits lossless data compression algorithms where compression is applied seamlessly to a packet's payload. Our adaptive compression is based on the situational awareness of a given network derived from gathered network statistical data, such as the varying Round Trip Time (RTT) as well as the packet loss rate during a transmission session. The real-time observation of the varying RTT and packet loss rate triggers the ACT compression when a defined threshold, which is compared to the observed values, is crossed. Using Network Simulator 3 (NS3), two different real-time latency reduction schemes using ACT were compared with an uncompressed transmission. The results show ACT improvement in network conditions such as reducing the number of dropped packets by approximately 30%, as well as, reducing delayed packet transmissions by 26.5% which results in fundamentally increasing the TCP efficiency by approximately 3%.
Massively parallel implementation of character recognition systems

A massively parallel character recognition system has been implemented. The system is designed to study the feasibility of the recognition of handprinted text in a loosely constrained environment. The NIST handprint database, NIST Special Database 1, is used to provide test data for the recognition system. The system consists of eight functional components. The loading of the image into the system and storing the recognition results from the system are I/O components. In between are components responsible for image processing and recognition. The first image processing component is responsible for image correction for scale and rotation, data field isolation, and character data location within each field; the second performs character segmentation; and the third does character normalization. Three recognition components are responsible for feature extraction and character reconstruction, neural network-based character recognition, and low-confidence classification rejection. The image processing to load and isolate 34 fields on a scientific workstation takes 900 seconds. The same processing takes only 11 seconds using a massively parallel array processor. The image processing components, including the time to load the image data, use 94 of the system time. The segmentation time is 15 ms/character and segmentation accuracy is 89 for handprinted digits and alphas. Character recognition accuracy for medium quality machine print is 99.8. On handprinted digits, the recognition accuracy is 96 and recognition speeds of 10,100 characters/second can be realized. The limiting factor in the recognition portion of the system is feature extraction, which occurs at 806 characters/second. Through the use of a massively parallel machine and neural recognition algorithms, significant improvements in both accuracy and speed have been achieved, making this technology effective as a replacement for key data entry in existing data capture systems.
Bimodal occupancy-frequency distributions uncover the importance of regional dynamics in shaping marine microbial biogeography

Metapopulation theory developed in terrestrial ecology provides applicable frameworks for interpreting the role of local and regional processes in shaping species distribution patterns. Yet, empirical testing of metapopulation models on microbial communities is essentially lacking. Here we determined regional bacterioplankton dynamics from monthly transect sampling in the Baltic Sea Proper (16 sites, 11 occasions, 2010-2011) using 16S rRNA gene pyrosequencing. A strong positive correlation was found between local relative abundance and occupancy of populations. Notably, the occupancy-frequency distributions (the number of populations occupying different number of sites) were significantly bimodal with a satellite mode of mostly rare endemic populations and a core mode of abundant cosmopolitan populations (e.g. Synechococcus, SAR11 and SAR86 clade members). Observed temporal changes in population distributions supported theoretical predictions that stochastic variation in local extinction and colonization rates accounted for observed bimodality. Moreover, bimodality was found for bacterioplankton across the entire Baltic Sea, and was also frequent in globally distributed datasets where average Bray-Curtis distances were significantly different between bimodal and non-bimodal datasets. Still, datasets spanning waters with distinct physicochemical characteristics or environmental gradients, e.g. brackish and marine or surface to deep waters, typically lacked significant bimodal patterns. When such datasets were divided into subsets with coherent environmental conditions, bimodal patterns emerged, highlighting the importance of positive feedbacks between local abundance and occupancy within specific biomes. Thus, metapopulation theory applied to microbial biogeography can provide novel insights into the mechanisms governing shifts in biodiversity resulting from natural or anthropogenically induced changes in the environment. Significance statement Marine bacteria regulate global cycles of elements essential to life and respond rapidly to environmental change. Yet, the ecological factors that determine distribution and activity patterns of microbial populations across different spatial scales and environmental gradients remain basically unconstrained. Our metapopulation model-based analyses show that dispersal-driven processes contribute to structuring the biogeography of marine microorganisms from small to large geographical areas. Discovery of bimodal distribution patterns pinpointed satellite microbial populations with highly restricted ranges and defined abundant core populations widely distributed in coherence with environmental conditions. Thus, application of metapopulation models on microbial community structure may allow the definition of biogeographic regions critical for interpreting the outcome of future ocean changes. Classification Biological Sciences, Environmental Sciences
Fair and Efficient TCP Access in the IEEE 802.11 Infrastructure Basic Service Set

When the stations in an IEEE 802.11 infrastructure Basic Service Set (BSS) employ Transmission Control Protocol (TCP) in the transport layer, this exacerbates per-flow unfair access which is a direct result of uplink/downlink bandwidth asymmetry in the BSS. We propose a novel and simple analytical model to approximately calculate the per-flow TCP congestion window limit that provides fair and efficient TCP access in a heterogeneous wired-wireless scenario. The proposed analysis is unique in that it considers the effects of varying number of uplink and downlink TCP flows, differing Round Trip Times (RTTs) among TCP connections, and the use of delayed TCP Acknowledgment (ACK) mechanism. Motivated by the findings of this analysis, we design a link layer access control block to be employed only at the Access Point (AP) in order to resolve the unfair access problem. The novel and simple idea of the proposed link layer access control block is employing a congestion control and filtering algorithm on TCP ACK packets of uplink flows, thereby prioritizing the access of TCP data packets of downlink flows at the AP. Via simulations, we show that short- and long-term fair access can be provisioned with the introduction of the proposed link layer access control block to the protocol stack of the AP while improving channel utilization and access delay.
The initiative experiments for utilizing real cards in online Trading Card Game

Current virtual game interfaces can't comprehend our metaphor, can't reflect on our natural behavior aspect, can't make us immerse into a game, and makes a barrier between virtual game space and our real behavior. It is very challenging issue to use real objects tightly related to human-being's behaviors or reactions for interacting with game applications. Interactive AR interfaces may augment users' perception of the real world by adding virtual information to it. In addition, users' interaction with the system may be augmented, making the computer as seamlessly as possible. We attempted an experiment on camera-based interface for online TCG game application which emits excellence of AR. This experiment uses real TCG cards which are recognized by texture based AR algorithm. These initiative experiments not only guarantee the consistency between the game-player's behaviors and the gaming environment, but also make real and virtual world seamless. This consistency increases friendliness and immersion to a game as well as the ability of bodily sensation for a game. This new interface takes the results of human-being's behaviors as input event, and will open the new field of digital interfaces for forthcoming ubiquitous computing.
Concentration of measure on product spaces with applications to Markov processes

For a stochastic process with state space some Polish space, this paper gives sufficient conditions on the initial and conditional distributions for the joint law to satisfy Gaussian concentration and transportation inequalities. In the case of Euclidean space, there are sufficient conditions for the joint law to satisfy a logarithmic Sobolev inequality. In several cases, the constants obtained are of optimal growth with respect to the number of random variables, or are independent of this number. These results extend results known for mutually independent random variables and weakly dependent random variabels under Dobrushkin--Shlosman type conditions. The paper also contains applications to Markov processes including the ARMA process.
A Comparison of PBDHMM and CHMM for Isolated Word Recognition

Using phoneme-based Gaussian mixture as a VQ codebook in DHMM speech recognition system (PBDHMM) is an efficient way to improve the system performance. This paper compares the performances of PBDHMM system with that of the well known continuous HMM system for isolated word recognition task. The results shown that PBDHMM system obtained better results than CHMM system, especially for phoneme-distinct data.
The Relationship between Corporate Social Performance and Financial Performance : Modified Models and Their Application — — Evidence from Listed Companies in China

The study employs modified interactive models and lead-lag models to investigate the correlation between corporate social performace (CSP) and financial performance (CFP) of listed companies in China. The samples are 1228 in both 2005 year and 2006 year, 1251 in the period of 20052006, and 1577 in both 2009 year and 2010 year, 1603 in the period of 2009-2010 year. The serious empirical research finds the relationship between CSP and CFP measured by Tobin‟s Q is negative; and further research reveals social performance based on various stakeholders has different impact upon market value of listed companies in China. Moreover, more factors, such as special ownership structure, governace structure, culture background and wage rigidity, influence the CSP-CFP relationship in China. JEL Classifications: M14; G32
RUSH: Routing and scheduling for hybrid data center networks

The recent development of 60GHz technology has made hybrid Data Center Networks (hybrid DCNs) possible, i.e., augmenting wired DCNs with highly directional 60GHz wireless links to provide flexible network connectivity. Although a few recent proposals have demonstrated the feasibility of this hybrid design, it still remains an open problem how to route DCN traffics with guaranteed performance under a hybrid DCN environment. In this paper, we make the first attempt to tackle this challenge, and propose the RUSH framework to minimize the network congestion in hybrid DCNs, by jointly routing flows and scheduling wireless (directional) antennas. Though the problem is shown to be NP-hard, the RUSH algorithms offer guaranteed performance bounds. Our algorithms are able to handle both batched arrivals and sequential arrivals of flow demands, and the theoretical analysis shows that they achieve competitive ratios of O(log n), where n is the number of switches in the network. We also conduct extensive simulations using ns-3 to verify the effectiveness of RUSH. The results demonstrate that RUSH produces nearly optimal performance and significantly outperforms the current practice and a simple greedy heuristics.
Multi-grain remote access cache in NUMA system

We suggest the use of the multi-grain remote access cache (RAC) to adoptively control the RAC line size for each application behavior. We simulate NUMA systems with multi-grain RAC using MINT, event-driven memory hierarchy simulator, and analyze the performance results. Firstly, with a profile-based determination method, we verify the optimal RAC line size for each application and then, we compare and analyze the performance differences among NUMA systems with normal RAC, with optimal line size RAC, and with multi-grain RAC. The simulation shows that the worst case can be always avoided and in addition, the results are very close to the optimal case with any combination of application and RAC format.
Intelligent Data Analysis

We believe the problem of automating the process of building models from empirical data is a critical issue for both Artificial Intelligence and other scientific computing researchers. Although both fields require models of the behavior of complex systems, as AI researchers we may more directly address our particular needs. AI researchers require models that let us determine the influence of design decisions and environmental factors on the performance of AI programs so as to inform the design of the next generation of intelligent agents. Our research includes the complementary projects of building a blackboard-based automated modelbuilding assistant and analyzing the efficacy of heuristics used in function finding programs.
Structural Elucidation of Small Organic Molecules by 1D, 2D and Multi Dimensional-Solution NMR Spectroscopy

For the last fifty years nuclear magnetic resonance spectroscopy, generally referred as NMR, is one of the most versatile techniques for elucidation of structure of organic compounds. Among all available spectrometric methods, NMR is the only technique which offers a complete analysis and interpretation of the entire spectrum. Due to improved experimental technology and novel approaches, over the last decade nuclear magnetic resonance (NMR) has shown a tremendous progress. Generally, NMR spectroscopy makes use of three approaches; those are one dimension (1D), two dimensions (2D) and three dimensions (3D). Usually, the first approach of 1D-NMR ( 1H DEPT, 13 C, 15 N, 19 F, 31 P, etc.) generates good information about the structure of simple organic compounds, but in case of larger molecules the 1D-NMR spectra are generally overcrowded. Hence, the second approach of 2D-NMR (COSY, DQFCOSY, MQFCOSY, HETCOR, HSQC, HMQC, HMBC, TOCSY, NOESY, EXSY, etc.) is used for the further larger molecules, but 2D-NMR spectra also becomes complex and overlapping when used for further very large molecules like proteins. Hence, so as to achieve high resolution and reduced overlapping in spectra of very large molecules, Multi Dimensional-NMR (Homonuclear and Heteronuclear) are generally used. This paper supports interpretation of structure of different organic compounds by different NMR techniques.
Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization

This paper addresses the joint design of transmit and receive beamforming or linear processing (commonly termed linear precoding at the transmitter and equalization at the receiver) for multicarrier multiple-input multiple-output (MIMO) channels under a variety of design criteria. Instead of considering each design criterion in a separate way, we generalize the existing results by developing a unified framework based on considering two families of objective functions that embrace most reasonable criteria to design a communication system: Schur-concave and Schur-convex functions. Once the optimal structure of the transmit-receive processing is known, the design problem simplifies and can be formulated within the powerful framework of convex optimization theory, in which a great number of interesting design criteria can be easily accommodated and efficiently solved, even though closed-form expressions may not exist. From this perspective, we analyze a variety of design criteria, and in particular, we derive optimal beamvectors in the sense of having minimum average bit error rate (BER). Additional constraints on the peak-to-average ratio (PAR) or on the signal dynamic range are easily included in the design. We propose two multilevel water-filling practical solutions that perform very close to the optimal in terms of average BER with a low implementation complexity. If cooperation among the processing operating at different carriers is allowed, the performance improves significantly. Interestingly, with carrier cooperation, it turns out that the exact optimal solution in terms of average BER can be obtained in closed form.
Snake Robot Obstacle-Aided Locomotion: Modeling, Simulations, and Experiments

Snakes utilize irregularities in the terrain, such as rocks and vegetation, for faster and more efficient locomotion. This motivates the development of snake robots that actively use the terrain for locomotion, i.e., obstacle-aided locomotion. In order to accurately model and understand this phenomenon, this paper presents a novel nonsmooth (hybrid) mathematical model for wheel-less snake robots, which allows the snake robot to push against external obstacles apart from a flat ground. The framework of nonsmooth dynamics and convex analysis allows us to systematically and accurately incorporate both unilateral contact forces (from the obstacles) and isotropic friction forces based on Coulomb's law using set-valued force laws. The mathematical model is verified through experiments. In particular, a back-to-back comparison between numerical simulations and experimental results is presented. It is, furthermore, shown that the snake robot is able to move forward faster and more robustly by exploiting obstacles.
Unpacking Attitudes on Immigrants and Refugees: A Focus on Household Composition and News Media Consumption

This study examines how household composition and news media consumption and trust are related to attitudes towards two minority groups—immigrants and refugees—in a representative sample of the adult population (n = 6000) in Belgium, Sweden, France, and the Netherlands. We find that Swedes hold the most positive attitudes towards both groups, while the French are found to be most negative. The Belgians and Dutch hold moderate attitudes. There is also evidence that attitudes on refugees are more negative than attitudes on immigrants in Sweden and France, but not in Belgium and the Netherlands. Using structural equation modeling, we find that household composition is not directly related to attitudes, but indirect effects through socio-economic status and media consumption indicate that singles hold more negative attitudes than couples. Public television consumption, popular online news consumption, and trust in media are positively related to attitudes, whereas commercial television consumption is negatively associated with them.
Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

Abstract. DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1–200 GHz similar to those acquired routinely by space-based microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large ice-sheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.
Reflections: eLearning Colloquia enhance a Community of Practice

In this context specific reflection on an annual eLearning Colloquia hosted by the Centre for Innovative Education and Communication Technologies (CIECT) at the University of the Western Cape (UWC), in Cape Town, South Africa; the researchers explore how the colloquia contributed to the institutional Community of Practice (CoP). Furthermore, it highlights the shifts in the institutional discourse on blended teaching and learning practices over an eight year period. The paper showcases CIECT’s efforts in driving the effective use of eTools to increase awareness and understanding of concepts such as agency, culture and structure. This awareness is critical in a post-modern Higher Education (HE) setting where the need to create a knowledge-intensive institutional culture exists. The human agents are central to the formation of a sustainable CoP which contributes to effective teaching and learning practices. Secondary data was used for the study and non-probability, purposive sampling resulted in a sample group of 78 individuals comprising of the Instructional Design team as well as the academic staff members at UWC who utilize the institutional Learning Management System or a range of Personal Learning Environments (PLEs) in their teaching and learning practices.
Tracking and detection of lane and vehicle integrating lane and vehicle information using PDAF tracking model

We propose a robust system for multi-vehicle and multi-lane detection with integrating lane and vehicle information. Most research work only can detect the lanes or vehicles separately. However, the dependency between lane information and vehicle information are able to support each other achieving more reliable results. We use probabilistic data association filter to integrate the information of lane and vehicle. In probabilistic data association filter, cumulate history of target is kept in the data association probability. Target tracking can improve the detection results through region of interests. At the same time, a high-level traffic model combines the lane and vehicle information. The tracking and detection can benefit each other through iterations. Experimental results show that our approach can detect multi-vehicle and multi-lane reliably.
Vector space weightless neural networks

By embedding the boolean space Z2 as an orthonormal basis in a vec- tor space we can treat the RAM based neuron as a matrix (operator) acting on the vector space. We show how this model (inspired by our research on quantum neu- ral networks) is of sufficient generality as to have classical weighted (perceptron- like), classical weightless (RAM-based, PLN, etc), quantum weighted and quantum weightless neural models as particular cases. It is also indicated how one could use it to polynomially solve 3-SAT and briefly mention how could one train this novel model.