Miracle-dz/UniFAR-FTUs huggingface dataset:
pySPACE—a signal processing and classification environment in Python

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.
Notice of Violation of IEEE Publication PrinciplesWhich models should be applied to measure computer security and information assurance?

The paper attempts to investigate a feasibility of developing new models and methodologies integrating probabilistic and soft computing techniques and applying them to measurement of computer system security. It reviews the methods, which have been developed and applied up to now for this purpose, and analyses their applicability. It concludes that neither of methodologies being applied for measurement of computer security and/or reliability may be considered as comprehensive and good. Summarizing the required features of the measurement models, the paper concludes that new synergetic models and approaches need to be developed.
Comparative Evaluation of Three Z-Source/Quasi-Z-Source Indirect Matrix Converters

Conventional matrix converters (MCs) have limited voltage gain that is less than 0.866, whether for direct MC (DMC) or indirect MC (IMC). Existing Z-source (ZS)/discontinuous quasi-Z-source (qZS) DMCs and ZS IMCs achieved larger than 0.866 voltage gain; however, the former inherited the complex commutation from DMC, and the latter involved a ZS network in dc link and led to non-all-silicon solution. In this paper, ZS and discontinuous qZS networks of existing ZS/qZS DMCs are used for an IMC topology for simplifying commutation and having high voltage gain, but they require extra input filter to reduce current harmonics. A new continuous qZS IMC is proposed, where a qZS network integrates the filtering function of the grid side; hence, additional input filter is avoided in the proposed continuous qZS IMC. Three new IMCs are compared in detail, in terms of voltage gain, current ripple, voltage ripple, inductor current and capacitor voltage stresses, ZS/qZS switch current and voltage stresses, filtering function, input current total harmonic distortion (THD), output voltage THD, and efficiency. Their control and modulation methods are presented to achieve the desired performances. Experimental comparisons verify the theoretical analysis and that the proposed continuous qZS IMC is a promising topology.
Detrended Fluctuation Analysis: A Suitable Long-term Measure of HRV Signals in Children with Sleep Disordered Breathing

On the body surface the electric field generated by the cardiac muscles consists of electric potential maxima and minima that increase and decrease during each cardiac cycle. The recording of these electric potentials as a function of time is called electrocardiography, and the resulting signal is called the electrocardiogram (ECG). The ECG signal is used extensively as a low cost diagnostic tool to provide information concerning the heart's state of health. Reliable and accurate detection of the QRS complex and R wave peak in ECG signals is essential in computer-based ECG analysis. In this paper we evaluate the significance of detrended fluctuation analysis (DFA) for studying heart rate variability in children with sleep disordered breathing. An enhanced Hilbert transform (EHT) algorithm was used to derive the heart rate variability (HRV) signal. We compare the DFA values with approximate entropy and Poincare plots of HRV signals as these are very useful in characterization and visualization of HRV data. Our data demonstrated differences in DFA parameters between periods of normal and abnormal breathing and also between sleep stages. These results suggest that DFA is suitable for the long-term analysis of non-stationary time series such as HRV signals and may also be applied in the detection of sleep disordered breathing
Protein kinase inhibitors selectively block phorbol ester- or forskolin- induced changes in excitability of Aplysia neurons

Exposure of the bag cell neurons of Aplysia to activators of protein kinase C, such as phorbol esters, enhances electrically evoked action potentials by increasing the voltage-dependent calcium current. We have hypothesized that this effect is mediated by the activation of protein kinase C (PKC). An important prediction of this hypothesis is that inhibitors of PKC should inhibit these phorbol ester-induced changes in bag cell neuronal excitability. We have now found that treatment of bag cell neurons with the protein kinase inhibitor 1-[5- isoquinolinesulfonyl]-2-methyl piperazine (H-7) inhibits the phorbol ester-induced enhancement of bag cell action potentials and prevents the enhancement of calcium current by phorbol esters. The height and width of electrically evoked action potentials in bag cell neurons can also be enhanced by cAMP analogs or agents that elevate cAMP. These agents do not influence the major voltage-dependent calcium current in the bag cell neurons but may act by modulating potassium currents and other voltage-dependent currents. We have found that microinjection of a protein inhibitor of cAMP-PK (PKA-I) into isolated bag cell neurons prevents and reverses the effect of the adenylate cyclase activator forskolin on action potentials of these cells. In contrast, H-7 does not inhibit the effects of forskolin on a variety of responses in these cells, including its effects on action potentials, granule movement, and 32P incorporation into phosphoproteins. This suggests that H-7 is selective for PKC relative to cAMP-PK in intact bag cell neurons.
Section based program analysis to reduce overhead of detecting unsynchronized thread communication

We propose Section Based Program Analysis (SBPA), a novel way to decompose programs into disjoint sections to identify non-communicating loads and stores during program compilation. We implemented SBPA for a deterministic execution runtime environment and reduced 63% of dynamic memory access instrumentations. We also integrated SBPA with ThreadSanitizer, and achieved a speed-up of 2.74 on a geometric mean basis.
Bootstrapping security associations for routing in mobile ad-hoc networks

To date, most solutions proposed for secure routing in mobile ad-hoc networks (MANETs), assume that secure associations between pairs of nodes can be established on-line; e.g., by a trusted third party, by distributed trust establishment. However, establishing such security associations, with or without trusted third parties, requires reliance on routing layer security. In this paper, we eliminate this apparent cyclic dependency between security services and secure routing in MANETs and show how to bootstrap security for the routing layer. We use the notion of statistically unique and cryptographically verifiable (SUCV) identifiers to implement a secure binding between IP addresses and keys that is independent of any trusted security service. We illustrate our solution with the dynamic source routing (DSR) protocol and compare it with other solutions for secure routing.
A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems.
A benchmark study on the planning and control of industrial robots with elastic joints

In this paper, we perform a comparative study of methods for improving motion performance of industrial robots. The compared methods address trajectory planning, trajectory preshaping, and feedforward control. Simulations are performed using a realistic 2DOF dynamic model with elastic joints. The simulation results demonstrate that the trajectory improvements may not be sufficient to obtain the required motion accuracy for industrial robots operating at high speeds due to the high nonlinearity of the system. We show that an improved performance may be achieved through nonlinear feedforward control even if the reference trajectory is planned using a trapezoidal velocity profile generator.
A framework for Compiler Level statistical analysis over customized VLIW architecture

Very Long Instruction Word (VLIW) application specific processors represent an attractive solution for embedded computing, offering significant computational power with reduced hardware complexity. However, they impose higher compiler complexity since the instructions are executed in parallel based on the static compiler schedule. Therefore, finding a promising set of compiler transformations and defining their effects have a significant impact on the overall system performance. The proposed methodology provides the designer with an integrated framework to automatically (i) generate optimized application-specific VLIW architectural configurations and (ii) analyze compiler level transformations, enabling application-specific compiler tuning over customized VLIW system architectures. We based the aforementioned analysis on a Design of Experiments (DoEs) procedure that captures in a statistical manner the higher order effects among different sets of activated compiler transformations. Applying the proposed methodology onto real-case embedded application scenarios, we show that (i) only a limited set of compiler transformations exposes high confidence level (over 95%) in affecting the performance and (ii) using them we could be able to achieve gains between (16-23)% in comparison to the default optimization levels.
RGBN Image Editing

We propose a method to edit RGBNs (images with a color and a normal channel). High resolution RGBNs are easy to obtain using photometric stereo. Free editing will result in normals which do not correspond to any realizable surface. Our normal operators guarantee the integrability of the results.Our method can filter normals with any linear kernel allowing high-pass and edge-enhancement filters. We have designed a normal linear combination method for adding details with frequency control. New geometric features can be created with a custom brush that warps deformations along its path. A nonlinear operator is also proposed. Its integrability is controlled with a two-band separation of frequencies: smooth shape and details.
Automated Metadata Hierarchy Derivation

This paper presents an automated approach for building a metadata hierarchy of a set of Web sites without the use of any predefined external hierarchies, and then merging and comparing them. The nodes of the hierarchy are the keywords of the specified Web sites, and the links between these keywords are the weak subsumption relationships. We apply this method in the RTGI project (Ghitalla et al., 2004) on clusters of Web sites already defined. The hierarchies can show how homogeneous each cluster is and permit to outline the contents of each corresponding cluster effectively. Moreover, we construct the common hierarchy of multiple clusters so that we check if their individual hierarchies are well distinguished and separated in the common one, which in turn indicates the correctness of clustering. At the end, we build the semantic-hypertextual graph of the sites which explains the semantic contents along with the topological structure of the sites
Glaucoma Elevated Intraocular Pressure Induces Amyloid-b Deposition and Tauopathy in the Lateral Geniculate Nucleus in a Monkey Model of Glaucoma

Zhichao Yan, Huanquan Liao, Hongrui Chen, Shuifeng Deng, Yu Jia, Caibin Deng, Jianxian Lin, Jian Ge, and Yehong Zhuo State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People’s Republic of China Department of Ophthalmology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, People’s Republic of China Department of Neurology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, People’s Republic of China Department of Ophthalmology, The Third People’s Hospital of Huizhou, Huizhou, People’s Republic of China
Optimal drugs for second-line treatment of patients with small-cell lung cancer

ABSTRACT Introduction: Despite the high response rate to chemotherapy, there have been few advances in the treatment of small-cell lung cancer (SCLC) in the last decades. The state-of-the-art second-line therapy and future research developments in relapsed SCLC are reviewed. Areas covered: There are no optimal drugs for second-line treatment of recurrent SCLC but only agents registered for this use. Topotecan remains the standard-of-care for the treatment of second-line platinum-sensitive SCLC patients worldwide, while amrubicin is another option, but registered only in Japan. To date, no targeted agents reporting interesting results in second-line SCLC treatment are available. The next-generation DNA sequencing should discover somatic gene alterations and their roles in SCLC to help in selecting patients who could benefit from a targeted agent. Two immunotherapeutics, ipilimumab and nivolumab, have shown promising preliminary results and are being investigated in ongoing trials. Expert opinion: Second-line treatment is not an option for most SCLC patients. Given the evidence up to now, the potentials for immuno-oncology in SCLC are high. The hope is that these expectations are met, and that all drugs being developed will offer new and improved treatment options for SCLC patients.
A weighted distance approach to relevance feedback

Content-based image retrieval systems use low-level features like color and texture for image representation. Given these representations as feature vectors, similarity between images is measured by computing distances in the feature space. Unfortunately, these low-level features cannot always capture the high-level concept of similarity in human perception. Relevance feedback tries to improve the performance by allowing iterative retrievals where the feedback information from the user is incorporated into the database search. We present a weighted distance approach where the weights are the ratios of standard deviations of the feature values both for the whole database and also among the images selected as relevant by the user. The feedback is used for both independent and incremental updating of the weights and these weights are used to iteratively refine the effects of different features in the database search. Retrieval performance is evaluated using average precision and progress that are computed on a database of approximately 10,000 images and an average performance improvement of 19% is obtained after the first iteration.
Backward Beam Search Algorithm for Dependency Analysis of Japanese

Backward beam search for dependency analysis of Japanese is proposed. As dependencies normally go from left to right in Japanese, it is effective to analyze sentences backwards (from right to left). The analysis is based on a statistical method and employs a beam search strategy. Based on experiments varying the beam search width, we found that the accuracy is not sensitive to the beam width and even the analysis with a beam width of 1 gets almost the same dependency accuracy as the best accuracy using a wider beam width. This suggested a deterministic algorithm for backwards Japanese dependency analysis, although still the beam search is effective as the N-best sentence accuracy is quite high. The time of analysis is observed to be quadratic in the sentence length.
Evolving , Probabilistic Spiking Neural Networks and Neurogenetic Systems for Spatio-and Spectro-Temporal Data Modelling and Pattern Recognition

Spatioand spectro-temporal data (SSTD) are the most common types of data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, there is lack of methods for the efficient analysis of such data and for spatiotemporal pattern recognition (STPR). The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Its organisation and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. The brain-inspired spiking neural networks (SNN) are considered the third generation of neural networks and are a promising paradigm for the creation of new intelligent ICT for SSTD. This new generation of computational models and systems are potentially capable of modelling complex information processes due to their ability to represent and integrate different information dimensions, such as time, space, frequency, and phase, and to deal with large volumes of data in an adaptive and self-organising manner. The paper reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN) and computational neuro-genetic models (CNGM). Software and hardware implementations and some pilot applications for audio-visual pattern recognition, EEG data analysis, cognitive robotic systems, BCI, neurodegenerative diseases, and others are discussed.
Steady State Stress in a Coated Infinite Half-Space Subjected to a Moving Load

Abstract : This research investigates the use of coatings to mitigate the stress distribution into an infinite half-space. High energy impact phenomenon at velocities exceeding the speed of sound is an important area of interest to the Air Force Research Laboratory. Holloman Air Force Base's High Speed Test Track sustains significant damage due to this phenomenon. In this thesis, the track system and coating are modeled analytically with equations of motion in terms of linear displacements. Coating thickness and material properties of epoxy or polymer laminates are investigated to understand their affect of stress distribution in the rail. An analytic solution is used to verify a finite difference method. It is found that due to limitations in coating thickness of the track system, this property has no significant affect on the stress distribution. However, the shear modulus of the material is found to have a significant affect representing the possible onset of material failure through the consideration of the combined stress field at the coating-rail interface.
Homogeneity of Inorganic Glasses: Quantification and Ranking

Homogeneity of glasses is a key factor determining their physical and chemical properties and overall quality. However, quantification of the homogeneity of a variety of glasses is still a challenge for glass scientists and technologists. Here, we show a simple approach by which the homogeneity of different glass products can be quantified and ranked. This approach is based on determination of both the optical intensity and dimension of the striations in glasses. These two characteristic values are obtained using the image processing method established recently. The logarithmic ratio between the dimension and the intensity is used to quantify and rank the homogeneity of glass products. Compared with the refractive index method, the image processing method has a wider detection range and a lower statistical uncertainty.
Generation and Fracturing of Thick Shells

In this paper we present methods to generate and animate shells with a pre-defined thickness. Given a polygonal surface mesh, a thick shell is constructed by computing a second, extruded polygonal surface. We introduce methods to simulate deformation and fracture of thick shells in realtime. Mass-spring models are used in this context. A novel simulation framework to generate, animate and interact with shells in real-time has been created. The entire pipeline of shell animation may be handled, starting at the generation of shell, continuing on to deformation and fracture and ending with rigid body simulation of fractured shell fragments.
Long-term survival results of a randomized trial comparing gemcitabine/cisplatin and methotrexate/vinblastine/doxorubicin/cisplatin in patients with locally advanced and metastatic bladder cancer.

PURPOSE To compare long-term survival in patients with locally advanced and metastatic transitional cell carcinoma (TCC) of the urothelium treated with gemcitabine plus cisplatin (GC) or methotrexate/vinblastine/doxorubicin/cisplatin (MVAC). PATIENTS AND METHODS Efficacy data from a large randomized phase III study of GC versus MVAC were updated. Time-to-event analyses were performed on the observed distributions of overall survival time and progression-free survival. RESULTS Four hundred and five patients were randomized, 203 to the GC arm and 202 to the MVAC arm. At the time of this analysis, 347 patients have died (GC 176, MVAC 171). Overall survival was similar in both arms (HR 1.09; 95% confidence interval [CI] 0.88-1.34, P = 0.66) with a median survival of 14.0 months (95% CI 12.3-15.5 months) in the GC, and 15.2 months (95% CI 13.2-17.3 months) in the MVAC arm. The median progression-free survival was 7.7 months with GC (95% CI 6.8-8.8) and 8.3 months with MVAC (95% CI 7.3-9.7) with a HR of 1.09 (95% CI 0.89-1.34). Significant prognostic factors favoring overall survival included performance status (>70), TNM staging (M0 vs. M1), low/normal alkaline phosphatase expression, number of sites of disease <3, and the absence of visceral metastasis. By adjusting for these prognostic factors, the HR was 0.99 for overall survival and 1.01 for progression-free survival. CONCLUSIONS Long-term overall and progression-free survival following treatment with GC or MVAC are similar. These results strengthen the role of GC as a standard of care in patients with locally advanced and metastatic transitional-cell carcinoma (TCC).