Miracle-dz/UniFAR-FTUs huggingface dataset:
The Blueprint for Retarding and Reversing Cardiovascular Aging

Cardiovascular Aging: Mechanisms and Pathways: The heart is made up of millions of myocardial cells that contract and relax to pump blood adequately to various body organs. In turn, it is itself dependent for oxygen and nutrients on vascular supply. There goes on the ongoing damage to the heart and vasculature with aging. Various adaptive and compensatory measures including lifestyle interventions and related treatment modalities contributing directly or indirectly to strengthening of heart and myocardial preservation. Modalities for Retarding CV Aging: Age is the most important determinant of cardiovascular health. There occurs an exponential rise in both the CV risk and the incidence of CV events in older adults parallel with the advancing age. With advancing age, there is an increase in incidence and prevalence of atherosclerosis, hypertension, coronary artery disease and heart failure; cerebrovascular and peripheral vascular disease. Simultaneously, important changes are taking place at micro level; at cellular, subcellular and molecular levels. The macro- and micro level changes have impact on quality of life and affect the individual survival. The most promising data comes from experimental biology highlightening calorie restriction as a tool for reduction in oxidative stress and increasing longevity. The metabolic manipulations enhance longevity by retarding aging comparable to CR, are not as effective as repair strategies which can reverse aging process. The Modalities for Reversing CV Aging: The genetic manipulations causing alterations work through a common pathway across many species endorsing that there is an evolutionary genetic program that controls aging. Further, it appears that the metabolic changes will not produce significant effects on longevity in humans as they do in mice for the simple reason that lifespan is much more plastic in short-lived species. There are important potential target genes for future gene therapies to retard and reverse CV aging or compensating for age-related damage. and impetus will come from bioengineering and stem cell technology. The 3-D bioprinting may provide the bulk supply of organs and blood vessls somatic repair and reversal of CV aging. These interventions will form the basis of regenerative medicine for aging and age-related CV aging and Cure for CVDs. Studies indicate that the flavonoids inhibit platelet aggregation, thrombus formation and coagulation. They reduce oxidative stress, atherosclerosis and arterial blood pressure. They may favorably modify vascular inflammatory and endothelial and capillary function. They may improve lipid profile values and regulate carbohydrate metabolism.
A Novel Aspect in Keloid Treatment : Short Term Effects of Kinesio ® Taping

Gul Oznur Karabıcak1, Zeynep Hazar Kanik 2 1IDepartment of Physiotherapy and Rehabilitation, Faculty of Health Science, Baskent University, Ankara, Turkey. 2Department of Physiotherapy and Rehabilitation, Faculty of Health Science, Gazi University, Ankara, Turkey. Corresponding Author: Gul Oznur Karabicak, Baskent University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Eskisehir Road 20 Km. 06810 Baglica, Ankara, Turkey. Tel: +90-312-2466666; Email: guloznur@gmail.com
A Hybrid Wavelet-Shearlet Approach to Robust Digital Image Watermarking

Watermarking systems are one of the most important techniques used to protect digital content. The main challenge facing most of these techniques is to hide and recover the message without losing much of information when a specific attack occurs. This paper proposes a novel method with a stable outcome under any type of attack. The proposed method is a hybrid approach of three different transforms, discrete wavelet transform (DWT), discrete shearlet transform (DST) and Arnold transform. We call this new hybrid method SWA (shearlet, wavelet, and Arnold). Initially, DWT applied to the cover image to get four sub-bands, we selected the HL (High-Low) sub band of DWT, since HL sub-band contains vertical features of the host image, where these features help maintain the embedded image with more stability. Next, we apply the DST with HL sub-band, at the same time applying Arnold transform to the message image. Finally, the output that obtained from Arnold transform will be stored within the Shearlet output. To evaluate the proposed method we used six performance evaluation measures, namely, peak signal to noise ratio (PSNR), mean squared error (MSE), root mean squared error (RMSE), signal to noise ratio (SNR), mean absolute error (MAE) and structural similarity (SSIM). We apply seven different types of attacks on test images, as well as apply combined multi-attacks on the same image. Extensive experimental results are undertaken to highlight the advantage of our approach with other transform based watermarking methods from the literature. Quantitative results indicate that the proposed SWA method performs significantly better than other transform based state-of-the-art watermarking approaches.
The Greeting Machine: An Abstract Robotic Object for Opening Encounters

Opening encounters are an integral element of everyday social interaction, and are essential for forming and maintaining social relationships between people. We present an abstract non-humanoid robotic object called the Greeting Machine, designed to communicate positive and negative social cues in the context of opening encounters. The design includes a small ball rolling on a larger dome, with a custom gear and lever mechanism that supports a variety of subtle movements. Gestures were designed with movement experts, and were evaluated using a physical first-person qualitative study. Our findings reveal that an abstract robot designed with no specific metaphor can effectively take part in opening encounters. Furthermore, a minimal brief movement, designed as an Approach or Avoid gesture, may be enough to evoke positive and negative experiences. The ability to create opening encounters with low Degree of Freedom abstract robots is promising, both due to the low complexity, low cost, and design flexibility of such devices, and due to the possible generalization of the Approach and Avoid gestures for a variety of morphologies.
CUDA-based GPU Implementation of Hierarchical Belief Propagation for Fast Stereo Matching

Stereo matching based on the Markov random field model has a global optimization problem. Solutions of the problem can be inferred by the belief propagation (BP) algorithm. The BP algorithm effectively estimates global solutions, but it takes a very long time to calculate messages. In this paper, we implement the hierarchical BP algorithm on a graphics processing unit (GPU) using compute unified device architecture. We perform memory uploads to the global memory of the GPU, and compute messages of all pixels in parallel. We analyze the performance according to block and image sizes. Experimental results show that our implementation is faster than the conventional loopy BP algorithm and the hierarchical BP algorithm implemented on CPU.
Trustworthy Audit Logs : Deterring Lies about a File System ’ s Past

Audit logs that trace the changes of file system data to prevent fraudulent manipulation of data play an important role in regulatory compliance. However, most existing approaches fail to provide the trustworthiness of such logs in the presence of a powerful and determined insider adversary with incentives to alter data covertly. The adversary can modify file system data without being logged by bypassing the logging mechanism of the file system. She can also modify the audit log itself to erase evidence of conducted modifications. This paper proposes an approach to providing trustworthy audit logs for file systems under insider attacks by leveraging tamperevident hardware devices such as secure coprocessors and HSMs. Covert modifications of data can be detected in the audit process by having each modify operation authenticated by the device, and the audit log is also protected from tampering by the device-generated MACs and counters. We have designed secure and efficient interaction protocols between the instrumented file system and the device for various file system modify operations and implemented our design with the ext3 file system. Experimental results show that adding the logging function to ext3 incurs a performance overhead of 5.7% under normal workloads.
Tiramisu : A Polyhedral Compiler with A Scheduling Language for Targeting High Performance Systems Riyadh

This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code formultiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel extensions to explicitly manage the complexities that arise when targeting these systems. The extensions include explicit communication, synchronization, and mapping buffers to different memory hierarchies. Tiramisu relies on a flexible representation based on the polyhedral model and explicitly uses a well-defined four-level IR that allows full separation between the algorithms, loop transformations, data-layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate Tiramisu by writing a set of image processing and stencil benchmarks and compare it with state-of-the-art compilers. We show that Tiramisu matches or outperforms existing compilers on different hardware architectures, including multicore CPUs, GPUs, and distributed machines.
Large Scale Traffic Surveillance :Vehicle Detection and Classification Using Cascade Classifier and Convolutional Neural Network

​ — ​ In this Paper, we are presenting a traffic surveillance system for detection and classification of vehicles in large scale videos. Vehicle detection is crucial part of Road safety. There are lots of different intelligent systems proposed for traffic surveillance. The system presented here is based on two steps, a descriptor of the image type haar-like, and a classifier type convolutional neural networks. A cascade classifier is used to extract objects rapidly and a ​ neural network is used for final classification of cars. In case of Haar Cascades, the learning of the system is performed on a set of positive images (vehicles) and negative images (non-vehicle), and the test is done on another set of scenes. For the second, we have used faster R-CNN architecture. The cascade classifier gives faster processing time and ​ Neural Network is used to increase the detection rate.
Discontinuous Shear Thickening in Cornstarch Suspensions

We study the emergence of discontinuous shear-thickening (DST) in cornstarch, the well know system for this phenomenon, by combining macroscopic rheometry with local Magnetic Resonance Imaging (MRI) measurements. We bring evidence that macroscopic DST is characterized in wide gap with a shear localization, part of the material close to the inner cylinder is flowing and the rest is not. The flow seperates into a low-density flowing and a high-density jammed region. Moreover, the local rheology of the flowing region does not directly reflect DST but, strikingly, is most often shear-thinning. Our data are not consistent with recent theoretical suggestions based on the presumed existence of s-shaped flow curves. Instead, they support that DST should be attributed to the existence of a shear jamming limit at volume fractions quite significantly below random close packing.
Design Mining for Minecraft Architecture

3D construction sandbox games such as Minecraft have provided new opportunities for people to express their creativity. However, individual players have few tools to help them learn about architectural style or how to improve the structure they are building. Ideally, players could utilize tools that capitalize on the large numbers of 3D models built by others to offer guidance for their particular project. We trained a neural network to classify a large collection of Minecraft models from various websites in terms of style (Ancient, Asian, Medieval, or Modern). We present experimental results demonstrating that our model can classify the user-indicated style of a structure with 55% accuracy. We further demonstrate use of this model to highlight nearest neighbors to a specific query structure. We have integrated these tools into a Minecraft Mod that allows players to classify their structure's style and view nearest neighbors in real-time.
Perceptually-motivated generation of electric guitar timbres using an interactive genetic algorithm

This paper presents a system for the interactive modification of electric guitar timbre. A genetic algorithm was used to explore the parameter space of a simplified re-amping circuit, which consisted of an initial high-pass filter, a soft-clipping circuit, equalisation and cabinet simulation. This allowed perceptually optimal solutions to be found in the parameter space, e.g. to find sounds that are “warm”, “bright”, “heavy” or any other perceptual quality, as perceived by the user. Such a system could be used to increase accessibility in music production. Additionally, it is hoped that this system can be used in future psychoacoustic experiments investigating the perception of electric guitar timbre, or that of similar instruments.
Attachment in syntax and discourse : Towards an explanation of the variable scope of non-restrictive relatives

Against trapping: It has been suggested e.g. by Martin (2016) that the contrast between (1-a) and (1-b) is due to the need to bind the anaphoric then in (1-a), which only finds an appropriate antecedent (the event of Max calling the dean) in the scope of the conditional—a solution parallel to van der Sandt’s (1992) trapping for presuppositions. In Zeevat’s (2016) view, the NRC would still be trapped in (1-a) even without an explicit then because of a weak causal relation between the events: the dean would not call the chair if Max didn’t call the dean. However, (2) shows that the NRC can receive an embedded interpretation in the conditional antecedent even in the absence of an explicit anaphor or a causal link. Conversely, the anaphoric pronoun him in (3), which can only be bound in the local context, does not seem to be able to license trapping and the sentence receives no coherent interpretation, although the corresponding conjoined conditional does.
1 Helical Actuators Inspired by Chiral Seedpod 2 Opening and Tendril Coiling 3

Actuators are essential components for intelligent machines that can fulfill certain tasks in 11 response to environmental stimuli. In recent years, actuators that can transform from a 2D ribbon 12 shape to a 3D helical configuration under certain external stimuli have attracted significant attention 13 due to the potential applications of the targeted helical structures in springs, propulsion generation, 14 and artificial muscles. Inspired by the chiral opening of Bauhinia variegate‘s seedpods and the coiling 15 of the Towel Gourd tendril with perversions, researchers have made significant breakthroughs in 16 synthesizing state-of-the-art actuators capable of mimicking helical transformations. In this review, 17 we give a brief overview of the shape evolution mechanisms of these two plant structures and then 18 review recent progress in the fabrication of biomimetic helical actuators. These structures are 19 categorized by the stimuli-responsive materials involved, including hydrogels, liquid crystal 20 networks/elastomers, shape memory polymers, and multiwall carbon nanotubes. By providing this 21 survey on important recent advances along with our perspectives, we hope to solicit new 22 inspirations and insights on the development and fabrication of smart actuators, as well as the future 23 development of interdisciplinary research at the interface of physics, engineering, and biology. 24
BUET BOSCH CONSORTIUM ( B 2 C ) ACOUSTIC SCENE CLASSIFICATION SYSTEMS FOR DCASE 2017 CHALLENGE

This technical report describes the systems jointly submitted by Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh, and Robert Bosch Research and Technology Center, Palo Alto, CA, USA, for the Acoustic scene classification (ASC) task of the DCASE 2017 challenge. Our sub-systems mainly consist of Convolutional Neural Network (CNN) based models trained on Spectrogram Image Features (SIF) using Mel and Logscaled filter-banks. We also used a novel multi-band approach that learns the CNN models from different frequency bands separately using a single spectrogram. In a variant of CNN sub-systems, large dimensional audio segment level feature vectors, termed as super vectors (SV), are extracted from the flattening layer of a trained CNN model. These features are later classified utilizing a Probabilistic Linear Discriminant Analysis (PLDA) model. This subsystem is termed as the CNN super vector (CNN-SV) system [1]. We also implemented an MFCC feature based GMM super vector (GSV) system with a PLDA classifier, and an acoustic feature ensemble based feed-forward Neural Network (NN) system. Finally, we utilized linear score-fusion to combine the class-wise scores obtained from the different sub-systems.
A Multi-Horizon Quantile Recurrent Forecaster

We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Recurrent Neural Networks, the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme for recurrent nets is designed to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future planned event spikes and cold-starts in real life large-scale forecasting. The performance of the framework is demonstrated in an application to predict the future demand of items sold on Amazon.com, and in a public probabilistic forecasting competition to predict electricity price and load.
Statement : The behavior of geometric complexes

As the demands for ways to collect, process, analyze, and understand complex datasets continue to grow, techniques summarizing the “shape” of a dataset are needed. Indeed, the shape of a dataset often reflects important patterns within, whose discovery allows one to better store, model, and make inferences from the data. One of the main algorithms in applied topology, persistent homology [11, 12], provides insight into the shape of nonlinear, high-dimensional, time-varying, and noisy datasets at multiple different scales. My work revolves around persistent homology, from both the theoretical and applied perspectives. Given a finite sample of data points near an unknown underlying shape, what information about the unknown shape can be recovered from the sample? When the ambient dimension is low, there are effective techniques for building a mesh on top of the sample. However, the computation required for meshing scales poorly as the ambient dimension grows; this is one example of the “curse of dimensionality. ” Even data from our 3D world can quickly become high-dimensional: a 100 × 100 pixel greyscale image can be thought of as a point in 10,000 Demo[verticesInitial, 0.82]
PetalVis-Floral Visualization for Communicating Set Operations

Communicating set operations like intersections, unions, or symmetric differences of several sets by a visual representation is a challenging task when the number of involved sets increases. Moreover, it is difficult to provide an intuitive, readable, and understandable visual metaphor with the goal to convince the spectators about the attractiveness and easy-to-use visual depiction in order to solve a certain kind of communication task. To address the communication and exploration of a larger number of sets and operations based on them, we introduce PetalVis making use of a floral visual metaphor. Moreover, we address the perceptual limitations of traditional Venn diagrams by exploiting the visual encoding of the set operations and selections by different color schemes focusing on making the diagrams even more informative as well as more aesthetically pleasing. However, although not each individual set operation result is uniquely represented in an area, our approach can show those by color codings. We illustrate the usefulness of the novel diagram type by varying several parameters like number of sets involved or selection of several set operation results.
CLASSIFICATION OF ACOUSTIC SCENES BASED ON THE MODULATION SPECTRUM

A system for the automatic classiﬁcation of acoustic scenes is proposed. This system calculates the spectral distribution of energy across auditory-relevant frequency bands and obtains some descriptors of the envelope modulation spectrum (EMS) by applying the discrete cosine transform to the logarithm of the EMS. This parametrisation scheme achieves good separation among scene classes, since it gets good classiﬁcation results with a simple clas-siﬁer consisting of a multilayer perceptron with only one hidden layer.
MLP-BASED FEATURE LEARNING FOR AUTOMATIC ACOUSTIC SCENE CLASSIFICATION

This paper presents an experimental setup for feature learning in the context of Automatic Acoustic Scene Classification. The setup presented in this paper has been successfully used for Automatic Music Genre Classification in [1]. First a MLP is trained with audio frames calculated from a 2048-sample STFT and one-shot encoding. Then, the activations of each hidden layer of the MLP are stored as learned features for the entire dataset. Such features are then used to train Random Forests in order to increase classification performance. Our results on the DCASE 2017 development dataset reaches 80% accuracy across supplied folds.
Microbial Hazards Associated With Feeding Colostrum

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