Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of visual navigation algorithms on the Eye-RIS 1.3 system

Tracking multiple stationary environmental points from a moving platform vision system enables the calculation of the rotation and relative displacement components of the ego-motion. This is very useful for small autonomously moving robotic platforms, like a UAV, where this data can be either the primary or the auxiliary navigation information. In case of a high speed, light moving platform (like a quadcopter), the speed the power consumption, and the accuracy of these calculations is critical. This paper analyzes the implementation methods of different point tracking approaches to find the most suitable for the Eye-RIS vision system from speed and accuracy point of views.
Molecular Mechanisms of Cardiotoxicity Induced by ErbB Receptor Inhibitor Cancer Therapeutics

The introduction of the so-called “targeted therapies”, particularly those drugs that inhibit the activity of tyrosine kinases, has represented a remarkable progress in the treatment of cancer. Although these drugs improve survival rates in cancer, significant cardiotoxicity, manifesting as left vertricular dysfunction and/or heart failure, has emerged. The ErbB receptor tyrosine kinases are being pursued as therapeutic targets because of their important roles in normal physiology and in cancer. Besides the fact that the ErbB receptors are indispensable during development and in normal adult physiology, epidermal growth factor (EGFR) and ErbB2 in particular have been implicated in the development of many human cancers. This review focuses on the rationale for targeting members of ErbB receptor family and numerous agents that are in use for inhibiting the pathway. We summarize the current knowledge on the physiological role of ErbB signaling in the ventricle and on structural aspects of ErbB receptor activation in cancer and cardiac cells. We examine the underlying mechanisms that result in on-target or off-target cardiotoxicities of ErbB inhibitors, which can influence the design of future anticancer therapies.
An Adaptive Primary Path Switching Scheme for Seamless mSCTP Handover

In this paper, a primary path switching scheme is proposed to provide a seamless handover for dual-homed mobile terminals. This scheme is proposed as an enhancement to the mSCTP protocol. With this scheme, a mobile terminal performs primary path switching before it becomes unavailable due to its primary path drop. The improvement achieved by the scheme is that it considers the temporal velocity of the mobile terminal with relative RTT variances of all available paths when it performs the handover process in the overlapped area between two different networks. Our simulation results show that the proposed scheme achieves a better overall performance than other schemes, and anticipatory switching is more important for faster moving terminals.
Detection of ectopic heart beats using ECG and blood pressure signals

In this paper, we propose using a combination of ECG and blood pressure signals to detect ectopic heartbeats, specifically premature supraventricular and ventricular contractions (PSC and PVC). Detection of these beats are important are they could be pre-cursor for serious arrhythmias. Common detection methods of these beats use only ECG signals. However, the stroke volume changes after the occurrence of these beats, which results in blood pressure variations. Following this fact, we combined features extracted from QRS complex of ECG signal Lead I with systolic and diastolic arterial blood pressure values to classify normal, PSC and PVC beats. Data from 5 subjects totaling 750 beats (250 normal, 250 PSC and 250 PVC) from Massachusetts General Hospital/Marquette Foundation (MGH/MF) database were used. The data were split equally for multilayer perceptron-backpropagation (MLP-BP) neural network training and testing. The combined features were classified by the MLP-BP neural network into the 3 classes. The features were normalized using some parameter inherent in the signals. This was to normalize the features across different subjects. The results gave classification performance up to 92.00%. It is concluded that ECG and blood pressure features could detect PSC and PVC.
Treatment of Anemia in Inflammatory Bowel Disease– Systematic Review and Meta-Analysis

Background Anemia is considered the most common systemic complication of inflammatory bowel disease (IBD). We aimed to provide all available evidence regarding the safety and efficacy of therapy existing today to correct anemia in IBD. Methods Systematic review and meta-analysis of randomized controlled trials that compared any treatment for anemia in patients with IBD. We searched electronic databases, conference proceedings and clinical trials registries. Two reviewers independently extracted data from included trials. The primary outcome was the effect of treatment for anemia in IBD on the hemoglobin (Hb) response, defined as rate of patients who achieved an increase of 2 g/dl in Hb concentration at the end of the follow-up. Secondary outcomes included disease severity scores, iron indices, Hb levels, inflammatory markers, adverse effects, and mortality. Dichotomous data were analysed by calculating the relative risk (RR) for each trial with the uncertainty in each result being expressed using 95% confidence intervals (CI). A fixed effect model was used, except in the event of significant heterogeneity between the trials (P<0.10, I2>40%), in which we used a random effects model. Results Nine trials fulfilled the inclusion criteria, to a total of 973 patients. We were able to perform meta-analysis for intravenous (IV) versus oral iron and for ESAs versus placebo. IV iron was associated with a higher rate of achieving Hb response in comparison to oral iron; RR 1.25 (95% CI 1.04–1.51, I2 = 2%, 4 trials), CRP levels and disease activity indexes were not significantly affected by IV iron. IV iron was associated with a decrease in adverse events that required discontinuation of intervention and without an increase in serious adverse. Discussion Treatment for anemia in IBD should include IV iron and not oral iron replacement, due to improved Hb response, no added toxicity and no negative effect on disease activity.
Coverage-based Approach for Model-based Testing in Software Product Line

Rapid Quality assurance is an important element in software testing in order to produce high quality products in Software Product Line (SPL). One of the testing techniques that can enhance product quality is Model-Based Testing (MBT). Due to MBT effectiveness in terms of reuse and potential to be adapted, this technique has become an efficient approach that is capable to handle SPL requirements. In this paper, the authors present an approach to manage variability and requirements by using Feature Model (FM) and MBT. This paper focuses on modelling the integration towards enhancing product quality and reducing testing effort. Further, the authors considered coverage criteria, including pairwise coverage, all-state coverage, and all-transition coverage, in order to improve the quality of products. For modelling purposes, the authors constructed a mapping model based on variability in FM and behaviour from statecharts. The proposed approach was validated using mobile phone SPL case study.
K-best Iterative Viterbi Parsing

This paper presents an efficient and optimal parsing algorithm for probabilistic context-free grammars (PCFGs). To achieve faster parsing, our proposal employs a pruning technique to reduce unnecessary edges in the search space. The key is to conduct repetitively Viterbi inside and outside parsing, while gradually expanding the search space to efficiently compute heuristic bounds used for pruning. Our experimental results using the English Penn Treebank corpus show that the proposed algorithm is faster than the standard CKY parsing algorithm. In addition, we also show how to extend this algorithm to extract k-best Viterbi parse trees.
THE BODHIDHARMA SYSTEM AND THE RESULTS OF THE MIREX 2005 SYMBOLIC GENRE CLASSIFICATION CONTEST

This paper discusses the results of the MIREX 2005 symbolic genre classification contest and describes the Bodhidharma system, which attained the highest classification success rates in all four of the evaluated categories. Five systems were submitted to this contest, which was conducted independently at the University of Illinois at Urbana-Champagne (UIUC). Each system was evaluated in two different experiments, one involving thirty-eight genre classes and one involving nine classes. Success rates were measured in two ways: one based only on how well each system was able to find the single correct genre of each recording, and the other giving partial scores to incorrect classifications that were relatively close to the correct genre. Evaluations were performed using stratified cross-validation. Bodhidharma is a sophisticated system that utilizes a combination of flat, hierarchical and round-robin classification strategies based on classifier ensembles consisting of feedforward neural networks and k-nearest neighbour classifiers. Bodhidharma bases its classifications on 111 high-level features that it extracts from MIDI recordings. Each classifier ensemble uses genetic algorithms to evolve a weighted sub-set of the features that are appropriate for that particular ensemble.
Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization

We show that the average stability notion introduced by \cite{kearns1999algorithmic, bousquet2002stability} is invariant to data preconditioning, for a wide class of generalized linear models that includes most of the known exp-concave losses. In other words, when analyzing the stability rate of a given algorithm, we may assume the optimal preconditioning of the data. This implies that, at least from a statistical perspective, explicit regularization is not required in order to compensate for ill-conditioned data, which stands in contrast to a widely common approach that includes a regularization for analyzing the sample complexity of generalized linear models. Several important implications of our findings include: a) We demonstrate that the excess risk of empirical risk minimization (ERM) is controlled by the preconditioned stability rate. This immediately yields a relatively short and elegant proof for the fast rates attained by ERM in our context. b) We strengthen the recent bounds of \cite{hardt2015train} on the stability rate of the Stochastic Gradient Descent algorithm.
SAR image change detection using regularized dictionary learning and fuzzy clustering

In this paper, we propose and present a novel unsupervised change detection(CD) algorithm for synthetic aperture radar(SAR) images based on regularized dictionary learning and fuzzy clustering. The regularized sparse reconstruction technique is introduced to generate a de-noised, low time consuming reconstructed image by using K-SVD dictionary learning. In order to obtain proper difference image, minus and ratio maps are discussed with the comparison of the other state-of-the-art approaches. Finally, to transfer the difference map into change map, we employ the optimized FCM called FLICM algorithm to undertake the task which aims to segment the difference map into two classes: changed and unchanged. Experimental results clearly show that the proposed approach consistently yields superior performance (accuracy, efficiency and robustness) compared to several well-known change detection techniques on both noise-free and noisy satellite images, further optimization methods are discusses in the end.
Psilocybin produces substantial and sustained decreases in depression and anxiety in patients with life-threatening cancer: A randomized double-blind trial

Cancer patients often develop chronic, clinically significant symptoms of depression and anxiety. Previous studies suggest that psilocybin may decrease depression and anxiety in cancer patients. The effects of psilocybin were studied in 51 cancer patients with life-threatening diagnoses and symptoms of depression and/or anxiety. This randomized, double-blind, cross-over trial investigated the effects of a very low (placebo-like) dose (1 or 3 mg/70 kg) vs. a high dose (22 or 30 mg/70 kg) of psilocybin administered in counterbalanced sequence with 5 weeks between sessions and a 6-month follow-up. Instructions to participants and staff minimized expectancy effects. Participants, staff, and community observers rated participant moods, attitudes, and behaviors throughout the study. High-dose psilocybin produced large decreases in clinician- and self-rated measures of depressed mood and anxiety, along with increases in quality of life, life meaning, and optimism, and decreases in death anxiety. At 6-month follow-up, these changes were sustained, with about 80% of participants continuing to show clinically significant decreases in depressed mood and anxiety. Participants attributed improvements in attitudes about life/self, mood, relationships, and spirituality to the high-dose experience, with >80% endorsing moderately or greater increased well-being/life satisfaction. Community observer ratings showed corresponding changes. Mystical-type psilocybin experience on session day mediated the effect of psilocybin dose on therapeutic outcomes. Trial Registration ClinicalTrials.gov identifier: NCT00465595
Target tracking for mobile robot based on Spatio-Temporal Context model

Object tracking is essential as a basis in robot field. In this paper, a tracking system based on the Spatio-Temporal Context (STC) model is proposed to make most use of effective information between the appearances of the target and the changes of its surroundings over space and time. The platform is by necessity equipped with a Microsoft Kinect which is used to capture the images of the scene and obtain the position of target. Then, the depth information is used for the robot localization and target tracking. Based on the information of the target, fuzzy-based intelligent control strategy is applied to adjust the movement of the mobile robot according to the target's speed and the distance from the robot. It contains two fuzzy controllers for adjusting the linear velocity and the turning-gain. Finally, the experimental results conducted on the Pioeer3-DX show that it can follow the target robustly and have a good real-time performance.
Beyond the one-locus-one-miRNA paradigm: microRNA isoforms enable deeper insights into breast cancer heterogeneity

Here we describe our study of miRNA isoforms (isomiRs) in breast cancer (BRCA) and normal breast data sets from the Cancer Genome Atlas (TCGA) repository. We report that the full isomiR profiles, from both known and novel human-specific miRNA loci, are particularly rich in information and can distinguish tumor from normal tissue much better than the archetype miRNAs. IsomiR expression is also dependent on the patient's race, exemplified by miR-183-5p, several isomiRs of which are upregulated in triple negative BRCA in white but not black women. Additionally, we find that an isomiR's 5′ endpoint and length, but not the genomic origin, are key determinants of the regulation of its expression. Overexpression of distinct miR-183-5p isomiRs in MDA-MB-231 cells followed by microarray analysis revealed that each isomiR has a distinct impact on the cellular transcriptome. Parallel integrative analysis of mRNA expression from BRCA data sets of the TCGA repository demonstrated that isomiRs can distinguish between the luminal A and luminal B subtypes and explain in more depth the molecular differences between them than the archetype molecules. In conclusion, our findings provide evidence that post-transcriptional studies of BRCA will benefit from transcending the one-locus-one-miRNA paradigm and taking into account all isoforms from each miRNA locus as well as the patient's race.
Application of SQRD Algorithm for Efficient MIMO-OFDM Systems

High data rate techniques in wireless communication systems have gained considerable significance in recent years. A strong candidate for broadband wireless communication systems for achieving high data rate and combating mutipath fading is OFDM (Orthogonal Frequency Division Multiplexing). Combining OFDM with multiple antenna systems has been shown to provide significant increase in the capacity in rich-scattering environments. In this paper, we combine a MIMO (Multiple Input Multiple Output) system based on the V-BLAST architecture with OFDM multicarrier transmission. The receiver is implemented using previously proposed sorted QR decomposition (SQRD) for the successive interference cancellation (SIC) between the different transmitted data streams. We will show a significant performance improvement of this technique over linear detection algorithms. In addition, we evaluate the influence of the incorporation of a soft output detector on the bit error rate (BER) performance.
Modulation of Dilator Responses of Cerebral Arterioles by Extracellular Superoxide Dismutase

Background and Purpose— Extracellular superoxide dismutase (ECSOD) is highly expressed in the wall of blood vessels and plays an important role in modulation of vascular function in extracranial arteries. Expression of ECSOD appears to affect cerebral vascular responses during disease states. Effects of ECSOD on dilator function in cerebral arterioles, however, have not been fully elucidated. In the present study, we examined effects of ECSOD deficiency on cerebrovascular reactivity under control conditions and during oxidative stress. Methods— Dilator responses of cerebral arterioles were examined in cranial windows in vivo in anesthetized ECSOD-deficient (−/−) and wild-type (+/+) mice under normal conditions and during oxidative stress induced by angiotensin II. Results— Total SOD activity in the aorta in ECSOD−/− mice (176±24 [mean±SEM] U/mg) was approximately 30% lower than in ECSOD+/+ mice (270±38, P=0.051). Dilator responses to acetylcholine (10 &mgr;mol/L) in cerebral arterioles were similar under control conditions in ECSOD+/+ (34±5% changes in diameter) and −/− mice (32±4%). Angiotensin II (500 nmol/L for 30 minutes) tended to reduce responses to acetylcholine in ECSOD+/+ mice (not significant) and significantly impaired responses in ECSOD−/− mice (42% reduction, P<0.05). Tempol (1 mmol/L), a scavenger of superoxide, restored the impaired dilator responses in ECSOD−/− mice. Responses to nitroprusside in cerebral arterioles were similar in ECSOD+/+ and −/− mice and were not affected by angiotensin II nor by tempol. Conclusions— ECSOD deficiency has little effect on cerebrovascular reactivity in control conditions but plays an important role in the regulation of vascular tone during oxidative stress produced by angiotensin II.
A practical guide to biometric security technology

As organizations search for more secure authentication methods for user access, e-commerce. and other security applications, biometrics is gaining increasing attention. But should your company use biometrics? And, if so, which ones should you use and how do you choose them? There is no one best biometric technology. Different applications require different biometrics. To select the right biometric for your situation, you will need to navigate through some complex vendor products and keep an eye on future developments in technology and standards. Your options have never been more diverse. After years of research and development, vendors now have several products to offer. Some are relatively immature, having only recently become commercially available, but even these can substantially improve your company's information security posture. We briefly describe some emerging biometric technologies to help guide your decision making.
Episode-Constrained Cross-Validation in Video Concept Retrieval

Whereas video tells a narrative by a composition of shots, current video retrieval methods focus mainly on single shots. In retrieval performance estimation, similar shots in a narrative may result in performance overestimation. We propose an episode-based version of cross-validation leading up to 14% classification improvement over shot-based cross-validation.
Space-efficient multiversion index scheme for PCM-based embedded database systems

Embedded database systems are widely adopted in various control and motoring systems, e.g., cyber-physical systems (CPSes). To support the functionality to access the historical data, a multiversion index is adopted to maintain multiple versions of data items and their index information. However, CPSes are usually battery-powered embedded systems that have limited energy, computing power, and storage space. In this work, we consider the systems with phase-change memory (PCM) as their storage due to its non-volatility and low energy consumption. In order to resolve the problem of the limited storage space and the fact that existing multiversion index designs are lack of space efficiency, we propose a space-efficient multiversion index scheme to enhance the space utilization and access performance of embedded multiversion database systems on PCM by utilizing the byte-addressability and write asymmetry of PCM. A series of experiments was conducted to evaluate the efficacy of the proposed scheme. The results show that the proposed scheme achieves very high space utilization and has good performance on serving update transactions and range queries.
Absorbing-State Phase Transition for Stochastic Sandpiles and Activated Random Walks

We study the long-time behavior of conservative interacting particle systems in Z: The Activated Random Walk Model for reaction-diffusion systems and the Stochastic Sandpile. Our main result states that both systems locally fixate when the initial density of particles is small enough, establishing the existence of a non-trivial phase transition in the density parameter. This fact is predicted by theoretical physics arguments and supported by numerical analysis. AMS Subject Classifications: 60K35, 82C20, 82C22, 82C26.
Stieltjes like functions and inverse problems for systems with Schr

A class of scalar Stieltjes like functions is realized as linear-fractional transformations of transfer functions of conservative systems based on a Schr\"odinger operator T_h in $L_2[a,+\infty)$ with a non-selfadjoint boundary condition. In particular it is shown that any Stieltjes function of this class can be realized in the unique way so that the main operator $\bA$ of a system is an accretive (*)-extension of a Schr\"odinger operator T_h. We derive formulas that restore the system uniquely and allow to find the exact value of a non-real parameter h in the definition of T_h as well as a real parameter $\mu$ that appears in the construction of the elements of the realizing system. An elaborate investigation of these formulas shows the dynamics of the restored parameters h and $\mu$ in terms of the changing free term $\gamma$ from the integral representation of the realizable function. It turns our that the parametric equations for the restored parameter h represent different circles whose centers and radii are determined by the realizable function. Similarly, the behavior of the restored parameter $\mu$ are described by hyperbolas.
A Neural Network based cognitive engine for IEEE 802.11 WLAN Access Point selection

Nowadays IEEE 802.11 WLANs are widely deployed; in spite of this, the issue of designing an efficient and practical Access Point selection schemes that can provide the best throughput performance in a variety of link conditions is still open. In this paper we present a Cognitive AP selection scheme that allows the mobile station to learn from its past experience how to select the best AP. In our proposal the mobile station collects measurements regarding the past link conditions and throughput performance, and a cognitive engine based on a Neural Network trained on this data drives the AP selection process. Our performance evaluation shows that the proposed scheme has very good performance in a variety of scenarios, as opposed to other algorithms previously proposed in the literature which perform well only in specific cases and cannot address the non-idealities typical under real conditions.