Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous Medium Access Protocol for Multi-User MIMO Based Uplink WLANs

Multi-user multiple-input multiple-output (MIMO) technology makes it possible for wireless nodes to successfully receive multiple packets from simultaneous transmitters in wireless networks. As it can provide more transmission opportunities without causing collisions, the network throughput performance can be dramatically improved. In this paper, we propose an asynchronous medium access control (MAC) protocol, which enables senders to independently start their transmissions if the access point (AP) can receive more simultaneous packets up to its multi-packet reception capability. This asynchronous protocol makes the multi-user MIMO channel more efficiently used, especially in wireless networks where transmission durations are dynamically varying due to different packet sizes and transmission rates. Through our performance analysis and extensive simulations, we show that the proposed asynchronous MAC protocol achieves significantly higher uplink throughput performance in multi-user MIMO wireless networks.
Novel approach for segmentation of brain magnetic resonance imaging using intensity based thresholding

Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. The goal of brain MRI segmentation is to accurately identify the principal tissue structures in these image volumes. There are many methods that exist to segment the brain. One of these, conventional methods that use pure image processing techniques are not preferred because they need human interaction for accurate and reliable segmentation. Unsupervised methods, on the other hand, do not require any human interference and can segment the brain with high precision. In the light to this fact, A novel approach is developed. Our approach is based on intensity based thresholding to get boundaries between cerebrospinal fluid (CSF), Gray Matter (GM), White Matter (WM) and others. After the brain extraction by mathematical morphology algorithm, we proceed to brain segmentation. The CSF is segmented by using orthogonal polynomial transform. Finally the gray matter and white matter regions in the MRI are segmented based up on the intensity values. Experimental results show that the proposed method achieves reasonable good segmentation.
WebProfiler: Cooperative diagnosis of Web failures

Despite tremendous growth in the importance and reach of the Web, users have little recourse when a Web page fails to load. Web browsers provide little feedback on such failures, typically offering only generic suggestions such as re-checking the URL or the machine's network settings. Hence, users are often unable to diagnose Web access problems, and resort to haphazardly modifying their settings or simply trying again later. We advocate a client-based collaborative approach for diagnosing Web browsing failures. Our system, WebProfiler, leverages end-host cooperation to pool together observations on the success or failure of Web accesses from multiple vantage points. These are fed into a simple, collaborative blame attribution algorithm. Our evaluation on a controlled testbed shows WebProfiler can accurately diagnose 3.6 times as many failures than possible from a single client's perspective. We present the design and prototype implementation of WebProfiler for an enterprise network.
Cortisol, Dehydroepiandrosterone, and Estradiol Measured Over 24 Hours in Women With Childhood Sexual Abuse-Related Posttraumatic Stress Disorder

Preclinical studies have shown long-term alterations in several hormonal systems including cortisol, dehydroepiandrosterone (DHEA) and DHEA-Sulfate, and estradiol. The purpose of this study was to assess cortisol, DHEA, and estradiol over a 24-hour period in women with early childhood sexual abuse and posttraumatic stress disorder (PTSD); with early abuse and without PTSD; and women without early abuse or PTSD. Forty-three women with early childhood sexual abuse and PTSD, early abuse without PTSD, and without abuse or PTSD, underwent a comprehensive assessment of hormones in plasma at multiple time points over a 24-hour period. Abused women with PTSD had lower concentrations of cortisol during the afternoon hours (12–8 p.m.) compared with women with abuse without PTSD and women without abuse or PTSD. DHEA-Sulfate was elevated throughout the 24-hour period in PTSD women, although this was of marginal statistical significance. There were no differences between groups in DHEA or estradiol. PTSD women also had increased cortisol pulsatility compared with the other groups. These findings suggest that a resting hypocortisolemia in the afternoon hours with increased cortisol pulsatility is associated with childhood abuse-related PTSD in women.
A Manual Annotation Method for Melodic Similarity and the Study of Melody Feature Sets

This paper describes both a newly developed method for manual annotation for aspects of melodic similarity and its use for evaluating melody features concerning their contribution to perceived similarity. The second issue is also addressed with a computational evaluation method. These approaches are applied to a corpus of folk song melodies. We show that classification of melodies could not be based on single features and that the feature sets from the literature are not sufficient to classify melodies into groups of related melodies. The manual annotations enable us to evaluate various models for melodic similarity.
Modelling natural and artificial hands with synergies

We report on recent work in modelling the process of grasping and active touch by natural and artificial hands. Starting from observations made in human hands about the correlation of degrees of freedom in patterns of more frequent use (postural synergies), we consider the implications of a geometrical model accounting for such data, which is applicable to the pre-grasping phase occurring when shaping the hand before actual contact with the grasped object. To extend applicability of the synergy model to study force distribution in the actual grasp, we introduce a modified model including the mechanical compliance of the hand's musculotendinous system. Numerical results obtained by this model indicate that the same principal synergies observed from pre-grasp postural data are also fundamental in achieving proper grasp force distribution. To illustrate the concept of synergies in the dual domain of haptic sensing, we provide a review of models of how the complexity and heterogeneity of sensory information from touch can be harnessed in simplified, tractable abstractions. These abstractions are amenable to fast processing to enable quick reflexes as well as elaboration of high-level percepts. Applications of the synergy model to the design and control of artificial hands and tactile sensors are illustrated.
A mathematical model for calculating river hydrographs using high resolution digital elevation models

Prediction of floods is important to prevent damage to human lives, buildings or infrastructure, and we have developed a simulation model to address this. The model is a so-called distributed rainfall-runoff model, which accounts for spatial variations within the watershed; it is based on a distributed version of the time-area method (Clark, 1945). Travel times are calculated by assuming that the flow can be modelled as creeping flow, and the resulting velocity field is used to solve the so-called time-of-flight/Eikonal equation. Both topographical and heterogeneous properties can be accounted for by the model, which ideally can be used for both gauged and ungauged watersheds after calibration. An automatic watershed delineation algorithm has been implemented to delineate the river’s watershed using the D8 Algorithm (O’Callaghan and Mark, 1984). To test our model, we create artificial rainfalls, and calculate the hydrograph response at the watershed outlet. The model works well given the simplifications, and is a framework which can be expanded upon and made more complex.
SaarCOR: a hardware architecture for ray tracing

The ray tracing algorithmis well-known for its ability to generate high-quality images and its flexibility to support advanced rendering and lighting effects. Interactive ray tracing has been shown to work well on clusters of PCs and supercomputers but direct hardware support for ray tracing has been difficult to implement.In this paper, we present a new, scalable, modular, and highly efficient hardware architecture for real-time ray tracing. It achieves high performance with extremely low memory bandwidth requirements by efficiently tracing bundles of rays. The architecture is easily configurable to support a variety of workloads. For OpenGL-like scenes our architecture offers performance comparable to state-of-the-artrasterization chips. In addition, it supports all the usual ray tracing features including exact shadows, reflections, and refraction and is capable of efficiently handling complex scenes with millions of triangles. The architecture and its performance in different configurations is analyzed based on cycle-accurate simulations.
Zero-shot Image Categorization by Image Correlation Exploration

The problem of image categorization from zero or only a few training examples, called zero-shot learning, occurs frequently, but it has hardly been studied in computer vision research. To tackle this problem, mid-level semantic attributes are introduced to identify image categories. For example, one can construct a classifier for the giant panda category by enumerating its attributes (e.g., black, white and four-footed) even without providing giant panda training images. Recently, several studies have investigated to learn attribute classifiers, based on which new classes can be detected. However, an often-encountered problem is the limited number of training data due to the time-consuming manual annotation of the attributes. Also, using single feature is hard to detect some attributes, e.g., the HSV feature is not robust enough to predict 'tusk' or 'flies' attributes. In this paper, we propose a unified semi-supervised learning (SSL) framework that learns the attribute classifiers by utilizing multiple feature and exploring the correlations between images. Specifically, we learn an optimal graph which embeds the relationships among the data points more accurately. Then, this graph is used to generate a geometrical regularizers for a semi-supervised learning model to learn the attribute classifier by utilizing both labeled and unlabeled images. Afterward, new classes can be detected based on their attribute representation. The use of SSL can boost the performances of attribute classifiers with very few training examples, and the adoption of multiple features makes the attribute prediction more robust. Experimental results on a series of real benchmark data sets suggest that semi-supervised learning do enhance the performances of attribute prediction and zero-shot categorization, compared with state-of-the-art methods.
Validating MRI Field Homogeneity Correction Using Image Information Measures

For image analysis techniques to be of utility in medical diagnosis systems it is necessary to be able to perform quality control over the results they produce. Input data must conform to the assumptions within the algorithm if useful results are to be achieved. Automation of this process is essential if vision algorithms are to form components in analysis systems. In this paper we present a technique to validate the correction of field inhomogeneity in MR images. The initial intention was to use information measures to check the improvement due to correction. However, it will be shown that the standard log entropy calculation for information measurement does not have the required properties, specifically grey-level scale invariance. We present an alternative, scale-invariant information measure derived using conventional likelihood approaches, that can be applied as an absolute measure of information content. We show this technique in use for the validation of our existing coil correction method.
Deformable Model Segmentation of Abdominal Aortic Aneurysm

In this paper we propose a technique for 3-D segmentation of abdominal aortic aneurysm (AAA) from computed tomography angiography (CTA) images. Output data (3-D model) form the proposed method can be used for measurement of aortic shape and dimensions. Knowledge of aortic shape and size is very important in planning of minimally invasive procedure that is for selection of appropriate stent graft device for treatment of AAA. The technique is based on a 3-D deformable model and utilizes the level-set algorithm for implementation of the method. The method performs 3-D segmentation of CTA images and extracts a 3-D model of aortic wall. Once the 3-D model of aortic wall is available it is easy to perform all required measurements for appropriate stent graft selection. The method proposed in this paper uses the level-set algorithm for deformable models, instead of the classical snake algorithm. The main advantage of the level set algorithm is that it enables easy segmentation of complex structures, surpassing most of the drawbacks of the classical approach. We have extended the deformable model to incorporate the a priori knowledge about the shape of the AAA. This helps direct the evolution of the deformable model to correctly segment the aorta. The algorithm has been implemented in IDL and C languages. Experiments have been performed using real patient CTA images and have shown good results.
Representing Perceptual Product Features in Databases

Many modern goods have both factual and perceptual features. While factual features such as technical specifications can easily be handled by existing database technology, perceptual features such as design or usage experience are very hard to deal with. However, with the huge success and growing market share of online shopping, retailers face the need to provided detailed and structured information about perceptual product features to their customers. In this paper, we analyze why dealing with perceptual product features in databases is difficult and summarize our current efforts on tackling this problem.
Situated Representation Situated Representation

Preface During my M.Sc. research I studied the behaviour of a model of a rat learning to reach a goal position in a maze. The virtual rat exhibited several intelligent strategies to reach the goal position. Being a na¨ıve cognitive psychology student I did what most researchers in psychology do; analyse the rat's artificial brain for clues on the origins of its intelligent behaviour. After months of performing analyses, the results obtained consisted of some mere correlations between the recorded activation and the rat's position in the maze. However, the analyses did not provide any answers on how and where the rat represented knowledge about the maze and the goal position. I slowly started to realise that the rat's brain only held part of the answer, while the environment held the remainder. One of the first things I (re)discovered in my Ph.D. research is that the current notion of knowledge representation is conceptually underdeveloped; this holds in particular for artificial intelligent systems situated in the real world. While the established view on cognition (the computational theory of mind) regards knowledge representation as a purely system-internal affair, the more recent view on cognition (the theory of situated cognition) explains knowledge representation in terms of the dynamic interaction between a system and its environment, i.e., in terms of a system's situatedness. So far, in artificial intelligence, the notion of knowledge representation has not grown with this development, but is still deeply rooted in the established view. The thesis conceptually improves the notion of knowledge representation in artificial intelligence and accomodates adequately the situated view on cognition. The improvement is formulated in the operationalisation of situated representation and is supported by several experiments with simulated robot models of cognitive behaviour. The operationalisation is a first step towards the conceptual improvement of the notion of knowledge representation, and paves the way for the general acceptance of the theory of situated cognition in artificial intelligence and the cognitive sciences. This scientific contribution was only possible with the help of many friends and colleagues. During my four years as a Ph.D. researcher I worked at the Institute of Knowledge and Agent Technology (IKAT) in the Department of Computer Science of the Universiteit Maastricht. During this period I was fortunate to benefit from the guidance by Eric Postma and Jaap van den Herik. I am very grateful to both. At IKAT, I enjoyed many inspiring conversations with …
Nano Scale Low Power Chopper Amplifier using Cascode and Miller Compensation Nutrilization in 45 nm CMOS

paper, reduced power consumption, low-noise CMOS amplifier using chopper technique is designed, chopper- equalized amplifier had been used as a front end of a voltage- to-frequency converter instrument which performs pretty well, and they were meeting just about every specification, but they had a problem with undesired and unpleasant little offset shifts and jumps. Trying to solve this problem of the jumpy offset, Chopping technique proved to be an efficient approach to decrease the low-frequency offset and 1/f noise of amplifiers. The chopper amplifier consist of combination of two-stage amplifier . The first stage is by the introduction of cascade amplifier produces high output impedance to the amplifier and the equivalent Miller capacitance of the second stage amplifier constitute to reduce the modulating noise by introduction of low pass filter in a attainable objective to gain the required Performa, so the chopper amplifier need low- pass filter, which can decrease the power consumption. The circuit of the proposed chopper amplifier is designed and simulated with cadence 45nm CMOS process and at a supply voltage 0.7V. Simulation results shows that the equivalent input noise is 23nV/ Hz at 100 Hz and the power consumption is 102 Watt. Keywordsamplifier, low-noise, equivalent input noise, low-
The Two-Fold Singularity of Discontinuous Vector Fields

When a vector field in $\mathbb{R}^3$ is discontinuous on a smooth codimension one surface, it may be simultaneously tangent to both sides of the surface at generic isolated points (singularities). For a piecewise-smooth dynamical system governed by the vector field, we show that the local dynamics depends on a single quantity—the jump in direction of the vector field through the singularity. This quantity controls a bifurcation, in which the initially repelling singularity becomes the apex of a pair of parabolic invariant surfaces. The surfaces are smooth except where they intersect the discontinuity surface, and they divide local space into regions of attraction to, and repulsion from, the singularity.
Rab5 modulates aggregation and toxicity of mutant huntingtin through macroautophagy in cell and fly models of Huntington disease

Huntington disease (HD) is caused by a polyglutamine-expansion mutation in huntingtin (HTT) that makes the protein toxic and aggregate-prone. The subcellular localisation of huntingtin and many of its interactors suggest a role in endocytosis, and recently it has been shown that huntingtin interacts indirectly with the early endosomal protein Rab5 through HAP40. Here we show that Rab5 inhibition enhanced polyglutamine toxicity, whereas Rab5 overexpression attenuated toxicity in our cell and fly models of HD. We tried to identify a mechanism for the Rab5 effects in our HD model systems, and our data suggest that Rab5 acts at an early stage of autophagosome formation in a macromolecular complex that contains beclin 1 (BECN1) and Vps34. Interestingly chemical or genetic inhibition of endocytosis also impeded macroautophagy, and enhanced aggregation and toxicity of mutant huntingtin. However, in contrast to Rab5, inhibition of endocytosis by various means suppressed autophagosome-lysosome fusion (the final step in the macroautophagy pathway) similar to bafilomycin A1. Thus, Rab5, which has previously been thought to be exclusively involved in endocytosis, has a new role in macroautophagy. We have previously shown that macroautophagy is an important clearance route for several aggregate-prone proteins including mutant huntingtin. Thus, better understanding of Rab5-regulated autophagy might lead to rational therapeutic targets for HD and other protein-conformation diseases.
Quality in Use of DSLs: Current Evaluation Methods

Domain Specific Languages (DSLs) are claimed to contribute to increment productivity, while reducing the required maintenance and programming expertise. In this context, the usability of these languages becomes a major issue: if the language is not easy to learn and use, it is unlikely to be successfully adopted. It is important to foster high quality DSLs during its engineering process. We argue that a systematic approach based on User Interface Experimental validation techniques should be used to assess the impact of the introduction of DSLs in the productivity of DSL user. Productivity can be fostered by assessing important usability attributes early in the language construction . This work’s contribution, besides highlighting the problem of the absence of systematic approaches for experimental validation of DSLs in general, is to identify existing evaluation approaches that can be adapted from the field of User Interfaces.
Hybrid analytical-statistical modeling for efficiently exploring architecture and workload design spaces

Microprocessor design time and effort are getting impractical due to the huge number of simulations that need to be done to evaluate various processor configurations for various workloads. An early design stage methodology could be useful to efficiently cull huge design spaces to identify regions of interest to be further explored using more accurate simulations. The authors present an early design stage method that bridges the gap between analytical and statistical modeling. The hybrid analytical-statistical method presented is based on the observation that register traffic characteristics exhibit power law properties which allows its to fully characterize a workload with just a few parameters which is much more efficient than the collection of distributions that need to be specified in classical statistical modeling. We evaluate the applicability and the usefulness of this hybrid analytical-statistical modeling technique to efficiently and accurately cull huge architectural design spaces. In addition, we demonstrate that this hybrid analytical-statistical modeling technique can be used to explore the entire workload space by varying just a few workload parameters.
Vertical accuracy assessment of ALOS World 3D - 30M Digital Elevation Model over northeastern Mindanao, Philippines

A quantitative assessment of vertical accuracy of the recently released ALOS World 3D - 30m (AW3D30) Digital Elevation Model (DEM) over northeastern Mindanao, Philippines is presented. A comparison of DEM-derived and field measured Mean Sea Level (MSL) elevations of 274 ground control points revealed a Root Mean Square Error (RMSE) of 5.68 m, average difference of +4.36 m, and a standard deviation of 3.66 m. Under different landcover types, the AW3D30's RMSEs ranges from 4.29 m (built-up) to 6.67 m (grassland). This study hopes to contribute to a number of studies assessing AW3D, and in establishing AW3D30 as a viable alternative to existing global DEMs such as SRTM-30m and ASTER GDEM Version 2 for certain applications that requires highly accurate DEMs such as in flood modelling and hazard mapping.