Miracle-dz/UniFAR-FTUs huggingface dataset:
A 2-D numerical simulation methodology for noise figure optimization in UHV/CVD SiGe HBT's

This paper explores the feasibility of combining calibrated AC 2-D numerical simulation and two of the latest Y-parameter-based noise models for predictive noise figure optimization in advanced UHV/CVD SiGe HBT's. At the operating current where the NE/sub min/ is the lowest, a close agreement between simulation using both noise models and measurement is achieved from 2 GHz to 18 GHz. The impact of collector doping level on noise figure is also discussed.
Optimal representation of sparse matrices

This paper introduces a novel data structure used to store sparse matrices optimally – minimizing the space of the matrix representation and the time complexity of an access to the matrix element. The size of our data structure is close to the information theoretic minimum – it differs in the second order term – and permits constant access to the matrix elements and a constant amortized time with a high probability for the insertion of a new element in the matrix and a deletion of an existing element. We tested our solution in a setup of mixed model equations that is used for genetic evaluation of breeding values and estimation of dispersion parameters. The coefficient matrices are large (over 1 mio. of equations) and sparse. The new algorithm reduced the number of accesses to the data structure (at most two accesses) and reduced the time for building the equation system, especially in theworst cases.
Vibrotactile haptic feedback for intuitive control of robotic extra fingers

Wearable robots have been mostly designed as exoskeletons, with segments and joints corresponding to those of the person they are coupled with. Exoskeletons are mainly employed to augment human body force and precision capabilities, or for rehabilitation purposes. More recently, new wearable robots resembling additional robotic limbs have been developed thanks to the progress in miniaturization and efficiency of mechanical and sensing components. However, wearable robotic extra limbs presented in the literature lack of effective haptic feedback systems. In this paper, we present a robotic extra finger coupled with a vibrotactile ring interface. The human user is able to control the motion of the robotic finger through a switch placed on the ring, while being provided with vibrotactile feedback about the forces exerted by the robotic finger on the environment. To understand how to control the vibrotactile interface to evoke the most effective cutaneous sensations, we executed perceptual experiments to evaluate its absolute and differential thresholds. We also carried out a pick-and-place experiment with ten subjects. Haptic feedback significantly improved the performance in task execution in terms of completion time, exerted force, and perceived effectiveness. All subjects preferred experimental conditions employing haptic feedback with respect to those not providing any force feedback.
Constraint Integration for Multiview Pose Estimation of Humans with Self-Occlusions

Detection of articulated objects such as humans is an important task in computer vision. We present a system that incorporates a variety of constraints in a unified multi- view framework to automatically detect humans in possibly crowded scenes. These constraints include the kinematic constraints, the occlusion of one part by another and the high correlation between the appearance of parts such as the two arms. The graphical structure (non-tree) obtained is optimized in a nonparametric belief propagation framework using prior based search.
Identification and Characterization of ErbB-3-Binding Protein-1 as a Target for Immunotherapy1

Based on immune reactivity in response to a whole-cell colon tumor vaccine and using serological identification of Ags by recombinant cDNA expression cloning, we here describe the molecular and functional identification of a novel human tumor Ag. By screening a cDNA expression library derived from the coloncarcinoma cell line HT-29 with pooled colorectal cancer patients’ sera, 26 clones reactive with IgG Abs could be identified. Characterization of these cDNA clones by sequence analysis and alignment, and detailed serological analysis revealed cancer-related immunoreactivity for the ErbB-3-binding protein-1 (Ebp1). Immunohistochemical staining of colorectal tumors and neighboring normal colon tissue indicated the observed cancer-related immunogenicity of Ebp1 to be related to overexpression. Via reverse immunology, five potential HLA-A2-restricted T cell epitopes were identified, of which two (Ebp145–54 and Ebp159–67) bound HLA-A2 with intermediate and high affinity, respectively. Analysis of their immunogenicity in vitro indicated that only the high-affinity Ebp159 epitope gave rise to CD8+ T cells capable of recognizing both exogenously loaded Ebp1 peptide and endogenously expressed Ebp1 on target cells. In addition, in vivo CD8+ T cell responsiveness against the Ebp159 epitope could be detected in two of nine and three of six cancer patients PBMC and tumor draining lymph nodes, respectively, but not in nine of nine healthy donors tested. These data confirm that Ebp1 is an immunogenic protein, capable of eliciting CD8-mediated responses in vivo and in vitro, providing a rationale for further exploration of Ebp1 as a possible target for anticancer immunotherapy.
2 The Curse of Dimensionality for Classical Non-Parametric Models

We present a series of theoretical arguments supporting the claim that a large class of modern learning algorithms based on local kernels are sensitive to the curse of dimensionality. These include local manifold learning algorithms such as Isomap and LLE, support vector classifiers with Gaussian or other local kernels, and graph-based semisupervised learning algorithms using a local similarity function. These algorithms are shown to be local in the sense that crucial properties of the learned function at x depend mostly on the neighbors of x in the training set. This makes them sensitive to the curse of dimensionality, well studied for classical non-parametric statistical learning. There is a large class of data distributions for which non-local solutions could be expressed compactly and potentially be learned with few examples, but which will require a large number of local bases and therefore a large number of training examples when using a local learning algorithm.
Interactive and Automated Segmentation and Generalisation of Raster Data

This paper review past and current research activity in the area of generalisation of spatial data and presents a new methodological framework for segmentation and generalisation of raster data. In order to overcome drawbacks associated with supervised classification and generalisation of raster data, an Interactive Automated Segmentation and Raster Generalisation Framework (IASRGF) was developed and tested. Test results of the IASGRF shows that all objects derived from the generalisation of landuse data over Canberra, Australia, were well classified and mapped. The error assessment indicates that the percentile classification accuracy is 85.5%, whereas the commission error is relatively high (38.5%). More importantly, the maximum likelihood classifier using training sites and associated ground truth data suggests that the Kappa index is 0.798, which can be interpreted as a reliable and satisfactory classification result. In order to further enhance supervised classification, a post-classification was carried out. As a result, this extra process improved the overall classification accuracy slightly, however its commission error also increased by 6%.
Improving students’ reading comprehension by implementing TOEFL extensive reading activities

The competence of TOEFL has now been demanded by not only English students but also non-English students. In the University of Palangka Raya, the undergraduate English students are required to have TOEFL score of 450 to propose their thesis and to register for graduation. Surprisingly, the average TOEFL score of undergraduate English students is 429 which is below the standard. Reading seemed to be a problem for the students since they wrongly maintained the time and strategies when facing the TOEFL Reading Comprehension texts. Based on the regulation and the data, the research was conducted as an action research focusing only on reading comprehension section of TOEFL and aiming at improving 41 students’ TOEFL Reading Comprehension. The participants joined extensive reading activities on the reading comprehension texts of TOEFL for three weeks. The activities were carried out by blended-learning, combining face-to-face reading activities and online activities via a group of WhatsApp and LINE. The results of the research showed that 51% subjects (21 students) made 5 or more correct answer improvements and 32% subjects (13 students) below 5 correct answer improvements on their TOEFL Reading Comprehension post-test registered after the activities were completed. Otherwise, there were 7 (17%) subjects experiencing no improvement at all.
Inhibition of Neuraminidase Inhibitor-Resistant Influenza Virus by DAS181, a Novel Sialidase Fusion Protein

Antiviral drug resistance for influenza therapies remains a concern due to the high prevalence of H1N1 2009 seasonal influenza isolates which display H274Y associated oseltamivir-resistance. Furthermore, the emergence of novel H1N1 raises the potential that additional reassortments can occur, resulting in drug resistant virus. Thus, additional antiviral approaches are urgently needed. DAS181 (Fludase), a sialidase fusion protein, has been shown to have inhibitory activity against a large number of seasonal influenza strains and a highly pathogenic avian influenza (HPAI) strain (H5N1). Here, we examine the in vitro activity of DAS181 against a panel of 2009 oseltamivir-resistant seasonal H1N1 clinical isolates. The activity of DAS181 against nine 2009, two 2007, and two 2004 clinical isolates of seasonal IFV H1N1 was examined using plaque number reduction assay on MDCK cells. DAS181 strongly inhibited all tested isolates. EC50 values remained constant against isolates from 2004, 2007, and 2009, suggesting that there was no change in DAS181 sensitivity over time. As expected, all 2007 and 2009 isolates were resistant to oseltamivir, consistent with the identification of the H274Y mutation in the NA gene of all these isolates. Interestingly, several of the 2007 and 2009 isolates also exhibited reduced sensitivity to zanamivir, and accompanying HA mutations near the sialic acid binding site were observed. DAS181 inhibits IFV that is resistant to NAIs. Thus, DAS181 may offer an alternative therapeutic option for seasonal or pandemic IFVs that become resistant to currently available antiviral drugs.
Shape Recognition by Clustering and Matching of Skeletons

We perform the task of shape recognition using a skeleton based method. Skeleton of the shape is considered as a free tree and is represented by a connectivity graph. Geometric features of the shape are captured using Radius function along the skeletal curve segments. Matching of the connectivity graphs based on their topologies and geometric features gives a distance measure for determining similarity or dissimilarity of the shapes. Then the distance measure is used for clustering and classification of the shapes by employing hierarchical clustering methods. Moreover, for each class, a median skeleton is computed and is located as the indicator of its related class. The resulted hierarchy of the shapes classes and their indicators are used for the task of shape recognition. This is performed for any given shape by a top-down traversing of the resulted hierarchy and matching with the indicators. We evaluate the proposed method by different shapes of silhouette datasets and we show how the method efficiently recognizes and classifies shapes.
Mobile Crowdsourcing Task Allocation with Differential-and-Distortion Geo-Obfuscation

In mobile crowdsourcing, organizers usually need participants’ precise locations for optimal task allocation, e.g., minimizing selected workers’ travel distance to task locations. However, the exposure of users’ locations raises privacy concerns. In this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users’ locations during task assignments. More specifically, we make participants obfuscate their reported locations under the guarantee of two rigorous privacy-preserving schemes, differential and distortion privacy, without the need to involve any third-party trusted entity. In order to achieve optimal task allocation with the differential-and-distortion geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraints of differential and distortion privacy. Moreover, a worker may be willing to accept multiple tasks, and a task organizer may be concerned with multiple utility objectives such as task acceptance ratio in addition to travel distance. Against this background, we also extend our solution to the multi-task allocation and multi-objective optimization cases. Evaluation results on both simulation and real-world user mobility traces verify the effectiveness of our framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art geo-obfuscation mechanism, by achieving up to 47 percent shorter average travel distance on real-world data under the same level of privacy protection.
Single-Gaussian messages and noise thresholds for decoding low-density lattice codes

A new method for decoding low-density lattice codes is given, wherein the belief-propagation decoder messages are single Gaussian functions. Since the message can be represented by two numbers, a mean and a variance, the complexity of this decoder is lower than previous decoders, which either quantized the mixture, or used a mixture of Gaussians. The computational complexity at the check node is also lower. The performance of various code designs, under single-Gaussian decoding, is evaluated by noise thresholds. In particular, the proposed decoding algorithm has noise threshold within 0.1 dB of the quantized-message decoder, which is considerably more complex.
Generalized instruction selection using SSA-graphs

Instruction selection is a well-studied compiler phase that translates the compiler's intermediate representation of programs to a sequence of target-dependent machine instructions optimizing for various compiler objectives (e.g. speed and space). Most existing instruction selection techniques are limited to the scope of a single statement or a basic block and cannot cope with irregular instruction sets that are frequently found in embedded systems. We consider an optimal technique for instruction selection that uses Static Single Assignment (SSA) graphs as an intermediate representation of programs and employs the Partitioned Boolean Quadratic Problem (PBQP) for finding an optimal instruction selection. While existing approaches are limited to instruction patterns that can be expressed in a simple tree structure, we consider complex patterns producing multiple results at the same time including pre/post increment addressing modes, div-mod instructions, and SIMD extensions frequently found in embedded systems. Although both instruction selection on SSA-graphs and PBQP are known to be NP-complete, the problem can be solved efficiently - even for very large instances. Our approach has been implemented in LLVM for an embedded ARMv5 architecture. Extensive experiments show speedups of up to 57% on typical DSP kernels and up to 10% on SPECINT 2000 and MiBench benchmarks. All of the test programs could be compiled within less than half a minute using a heuristic PBQP solver that solves 99.83% of all instances optimally.
Optimum projection angle for attaining maximum distance in a soccer punt kick.

To produce the greatest horizontal distance in a punt kick the ball must be projected at an appropriate angle. Here, we investigated the optimum projection angle that maximises the distance attained in a punt kick by a soccer goalkeeper. Two male players performed many maximum-effort kicks using projection angles of between 10° and 90°. The kicks were recorded by a video camera at 100 Hz and a 2 D biomechanical analysis was conducted to obtain measures of the projection velocity, projection angle, projection height, ball spin rate, and foot velocity at impact. The player's optimum projection angle was calculated by substituting mathematical equations for the relationships between the projection variables into the equations for the aerodynamic flight of a soccer ball. The calculated optimum projection angles were in agreement with the player's preferred projection angles (40° and 44°). In projectile sports even a small dependence of projection velocity on projection angle is sufficient to produce a substantial shift in the optimum projection angle away from 45°. In the punt kicks studied here, the optimum projection angle was close to 45° because the projection velocity of the ball remained almost constant across all projection angles. This result is in contrast to throwing and jumping for maximum distance, where the projection velocity the athlete is able to achieve decreases substantially with increasing projection angle and so the optimum projection angle is well below 45°. Key pointsThe optimum projection angle that maximizes the distance of a punt kick by a soccer goalkeeper is about 45°. The optimum projection angle is close to 45° because the projection velocity of the ball is almost the same at all projection angles.This result is in contrast to throwing and jumping for maximum distance, where the optimum projection angle is well below 45° because the projection velocity the athlete is able to achieve decreases substantially with increasing projection angle.
Integrated dynamic scheduling of hard and QoS degradable real-time tasks in multiprocessor systems

Many time critical applications require predictable performance and tasks in these applications have deadlines to be met. For tasks with hard deadlines, a deadline miss can be catastrophic, while for QoS degradable tasks (soft real time tasks) timely approximate results of poorer quality or occasional deadline misses are acceptable. Imprecise computation and (m,k) firm guarantee are two workload models that quantify the trade off between schedulability and result quality. We propose dynamic scheduling algorithms for integrated scheduling of real time tasks, represented by these workload models, in multiprocessor systems. The algorithms aim at improving the schedulability of tasks by exploiting the properties of these models in QoS degradation. We also show how the proposed algorithms can be adapted for integrated scheduling of multimedia streams and hard real time tasks, and demonstrate their effectiveness in quantifying QoS degradation. Through simulation, we evaluate the performance of these algorithms using the metrics-success ratio (measure of schedulability) and quality. Our simulation results show that one of the proposed algorithms, multilevel degradation algorithm, outperforms the others in terms of both the performance metrics.
Feature Point Correspondence of Stereo Images by Monogenic Phase

The concept of the monogenic phase was applied to feature point matching for stereo pair images. This paper evaluated the process of feature point matching from finding feature point by the Harris corner detector, forming a correlation matrix in terms of the monogenic signal elements {p(x),q1(x). q2(x)}, then to the stage of the RANSAC inlier selection based on the fundamental matrix of epipolar geometry. The use of the monogenic phase has lead to nearly perfect matching confirmed by visual inspections.
Altered Ca2+ Kinetics Associated with α-Actinin-3 Deficiency May Explain Positive Selection for ACTN3 Null Allele in Human Evolution

Over 1.5 billion people lack the skeletal muscle fast-twitch fibre protein α-actinin-3 due to homozygosity for a common null polymorphism (R577X) in the ACTN3 gene. α-Actinin-3 deficiency is detrimental to sprint performance in elite athletes and beneficial to endurance activities. In the human genome, it is very difficult to find single-gene loss-of-function variants that bear signatures of positive selection, yet intriguingly, the ACTN3 null variant has undergone strong positive selection during recent evolution, appearing to provide a survival advantage where food resources are scarce and climate is cold. We have previously demonstrated that α-actinin-3 deficiency in the Actn3 KO mouse results in a shift in fast-twitch fibres towards oxidative metabolism, which would be more “energy efficient” in famine, and beneficial to endurance performance. Prolonged exposure to cold can also induce changes in skeletal muscle similar to those observed with endurance training, and changes in Ca2+ handling by the sarcoplasmic reticulum (SR) are a key factor underlying these adaptations. On this basis, we explored the effects of α-actinin-3 deficiency on Ca2+ kinetics in single flexor digitorum brevis muscle fibres from Actn3 KO mice, using the Ca2+-sensitive dye fura-2. Compared to wild-type, fibres of Actn3 KO mice showed: (i) an increased rate of decay of the twitch transient; (ii) a fourfold increase in the rate of SR Ca2+ leak; (iii) a threefold increase in the rate of SR Ca2+ pumping; and (iv) enhanced maintenance of tetanic Ca2+ during fatigue. The SR Ca2+ pump, SERCA1, and the Ca2+-binding proteins, calsequestrin and sarcalumenin, showed markedly increased expression in muscles of KO mice. Together, these changes in Ca2+ handling in the absence of α-actinin-3 are consistent with cold acclimatisation and thermogenesis, and offer an additional explanation for the positive selection of the ACTN3 577X null allele in populations living in cold environments during recent evolution.
Con gurable, Mixed-Initiative Systems for Planning and Scheduling

This paper discusses work aimed at accelerating the construction and introduction of planning and scheduling systems in complex application domains. We begin by outlining our basic perspective on planning and scheduling, and our assumptions about the requirements and characteristics of practical planning and scheduling problems. This leads to a particular constraint-based solution framework, and a speci c architecture for con guring application systems. We describe OZONE, a planning and scheduling toolkit that implements these ideas, and give examples from di erent application building e orts that illustrate the e cacy of the approach.
Applying Space Syntax to Online Mapping Tools

To walk around the city, individuals use mobile mapping services, and such services mostly suggest shortest routes. To go beyond recommending such walkable routes, we propose a new framework for automatic wayfinding for pedestrians. This framework tackles two main drawbacks from which past work suffers, namely coarse-grained representation of space and absence of contextual dynamics. We model the human tendency to regularize space by borrowing a spatial representation, Space Syntax, from the discipline of Architecture. Moreover, the proposed framework accounts for contextual dynamics of individual streets by predicting the popularity of each street under different contexts (e.g., at a given time, with a certain weather condition). Using Foursquare check-ins (i.e., whereabouts of the users of the popular location-based service) and publicly available weather data, we validate our framework in the entire city of Barcelona. We find that, with paths slightly longer than the shortest ones, our framework is able to accommodate our mental topography and effectively capture contextual changes.