Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of deformation-induced structures in a Zr-based bulk metallic glass via high energy x-ray diffraction

3 Kuzfassung . 6 1 Motivation and objectives . 9 2 Theoretical background . 12 2.1 Structure of metallic glasses . 12 2.2 Mechanical properties and deformation behavior of metallic glasses. 14 2.2.1 Deformation mechanisms . 14 2.2.2 Shear banding . 18 2.2.3 Temperature rise in shear bands . 23 2.2.4 Strength and yield criterion . 28 2.2. 5 Parameters affecting plastic deformation in BMGs . 31 2.3 Improving of plasticity of BMGs . 36 2.3.1 Bulk metallic glass composites . 36 2.3.2 Mechanical treatment . 37 2.4 High energy x-ray characterization of deformed BMGs. 39 2.4. 1 Advantage of synchrotron over x-ray machines . 39 2.4. 2 Real and reciprocal space analysis of metallic glasses . 40 2.4. 3 Determination of strain from synchrotron data . 41 2.4. 4 Characterization of elastic response of BMGs . 42 2.4. 5 Length scale dependence of strain in metallic glasses . 46 2.4. 6 Short range order characterization.50 2.4. 7 Characterization of medium range order and plastic response of BMGs . 55 3 Experimental procedures . 60 3.1 Sample preparation . 60 3.1. 1 Pre-alloy preparation . 60 3.1. 2 Arc melting.60 3.1. 3 Centrifugal casting . 60 3.2 Characterization . 61 3.2.1 Scanning electron microscopy (SEM). 61 3.2.2 Nanoindentation . 61 3.2.3 Tensile test . 62
Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks

Heterogeneous information network (HIN) is a general representation of many real world data. The difference between HIN and traditional homogeneous network is that the nodes and edges in HIN are with types. In many applications, we need to consider the types to make the decision more semantically meaningful. For annotationexpensive applications, a natural way is to consider semi-supervised learning over HIN. In this paper, we present a semi-supervised learning algorithm constrained by the types of HINs. We first decompose the original HIN schema into several semantically meaningful meta-graphs consisting of entity and relation types. Then random walk is performed to propagate the labels from labeled data to unlabeled data guided by the meta-graphs. After receiving labels from the results of random walk guided by meta-graphs, we carefully compare different ensemble algorithms to generate the final label with respect to all the clues from each metagraphs. Experimental results on two knowledge based text classification datasets show that our algorithm outperforms traditional semi-supervised learning algorithms for HINs.
Mixing Time of Markov chain of the Knapsack Problem

To find the number of assignments of zeros and ones satisfying a specific Knapsack Problem is $\#P$ hard, so only approximations are envisageable. A Markov chain allowing uniform sampling of all possible solutions is given by Luby, Randall and Sinclair. In 2005, Morris and Sinclair, by using a flow argument, have shown that the mixing time of this Markov chain is $\mathcal{O}(n^{9/2+\epsilon})$, for any $\epsilon > 0$. By using a canonical path argument on the distributive lattice structure of the set of solutions, we obtain an improved bound, the mixing time is given as $\tau_{_{x}}(\epsilon) \leq n^{3} \ln (16 \epsilon^{-1})$.
Clustering Approach Based on Mini Batch Kmeans for Intrusion Detection System Over Big Data

Intrusion detection system (IDS) provides an important basis for the network defense. Due to the development of the cloud computing and social network, massive amounts of data are generated, which inevitably brings much pressure to IDS. And therefore, it becomes crucial to efficiently divide the data into different classes over big data according to data features. Moreover, we can further determine whether one is normal behavior or not based on the classes information. Although the clustering approach based on <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means for IDS has been well studied, unfortunately directly using it in big data environment may suffer from inappropriateness. On the one hand, the efficiency of data clustering needs to be improved. On the other hand, differ from the classification, there is no unified evaluation indicator for clustering issue, and thus, it is necessary to study which indicator is more suitable for evaluating the clustering results of IDS. In this paper, we propose a clustering method for IDS based on Mini Batch <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means combined with principal component analysis. First, a preprocessing method is proposed to digitize the strings and then the data set is normalized so as to improve the clustering efficiency. Second, the principal component analysis method is used to reduce the dimension of the processed data set aiming to further improve the clustering efficiency, and then mini batch <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means method is used for data clustering. More specifically, we use <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means++ to initialize the centers of cluster in order to avoid the algorithm getting into the local optimum, in addition, we choose the Calsski Harabasz indicator so that the clustering result is more easily determined. Compared with the other methods, the experimental results and the time complexity analysis show that our proposed method is effective and efficient. Above all, our proposed clustering method can be used for IDS over big data environment.
Fabrication of 2D and 3D inductors for DC-DC converters integrated on glass interposer

Inductors are a key component for voltage regulators and its on-chip integration could significantly improve their performance and scalability capabilities. However, innovative microfabrication processes have yet to be developed in order to miniaturise the inductors while maintaining their overall performance. In that regards, thin-film magnetic materials on glass substrates, which amplifies inductance values, should ease on-chip inductor integration. Using Ni45Fe55 and Co80P20 magnetic materials, we successfully fabricated 2D core and coreless copper inductors, on both glass and silicon substrates for comparison purposes. Inductors on glass exhibit better performance than the ones fabricated on silicon. An inductance of 10.3 nH (14.7 nH/mm2) at 100 MHz and a quality factor of 5 were reached. We also propose a process flow and morphological characterizations of a 3D inductor architecture on glass substrate featuring through glass vias.
Thermal Stability and Degradation of Poly N-(4- Methoxy-2-methylphenyl)acrylamide Homopolymer and Copolymer of N-(4-Methoxy-2-methylphenyl)acrylamide with Methyl Methacrylate

Different concentrations of copolymer of N-(4-methoxy-2-methylphenyl)acrylamide(MA) with methyl methacrylate (MMA) were prepared and the reactivity ratio values of copolymerization were calculated using Microanalysis technique. Thermal analysis of the copolymers showed that the thermal stability are intermediate between poly(N-(4-methoxy-2-methylphenyl)acrylamide) (PMA) and poly(methyl methacrylate) (PMMA) homopolymers. Thermal degradation products of the PMA were identified by GC-MS techniques. It seems that the mechanism of degradation of PMA homopolymer is characterized by free radical formation followed by recombination along the backbone chain. The activation energies of the thermal degradation of the copolymers were calculated using Arrhenius relationship.
Line Artist: A Multiple Style Sketch to Painting Synthesis Scheme

Drawing a beautiful painting is a dream of many people since childhood. In this paper, we propose a novel scheme, Line Artist, to synthesize artistic style paintings with freehand sketch images, leveraging the power of deep learning and advanced algorithms. Our scheme includes three models. The Sketch Image Extraction (SIE) model is applied to generate the training data. It includes smoothing reality images and pencil sketch extraction. The Detailed Image Synthesis (DIS) model trains a conditional generative adversarial network to generate detailed real-world information. The Adaptively Weighted Artistic Style Transfer (AWAST) model is capable to combine multiple style images with a content with the VGG19 network and PageRank algorithm. The appealing artistic images are then generated by optimization iterations. Experiments are operated on the Kaggle Cats dataset and The Oxford Buildings Dataset. Our synthesis results are proved to be artistic, beautiful and robust.
Protein-protein interactions: detection, reliability assessment and applications

Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.
Enactment-Based Direct-Drive Test of a Novel Radial-Gap Helical RotLin Machine

The future trend of mechanical systems is to substitute most of them by permanent magnet (PM) and/or electromagnetic based ones. This current research highlights a paradigm for future studies of the advanced direct-drivable safe and precise motion actuators, and headlines design, modeling, simulation, and implementation-based control of a novel RotLin (rotary–linear) radial gap helical machine. The machine is comprised of stator, midlayer rotor, and the inner layer translator. This is a hybrid of magnetic screw with a synchronous motor by modifying the design concept. Structurally, it is made using smartly piecewise helical-shaped PM that are radially magnetized, and helically layered on both rotor and translator. This machine has a wide range of applications, it can be used for linear actuation or for wave energy harvesting. The three-dimensional finite element analysis simulation results and calculations confirmed the realization of rotary and linear motions. To confirm this model, a prototype was enacted and experimental verifications of its performance in terms of direct-drive position and speed control were conducted. This compact machine can realize high thrust-force densities when used as a linear actuator.
Long time series of Landsat images to reconstruct River surface temperature and turbidity regimes of Guadalquivir Estuary

In the last century the Guadalquivir River in Andalusia has been dramatically transformed affecting its ecological and hydrological functioning. In view of a new dredging plan to increase ship size and number arriving to Seville, a research consortium has started to study the state of the Guadalquivir Estuary. The Remote Sensing and GIS laboratory of Doñana Biological Station participates in the project with the aim to reconstruct the last 35 years regime of turbidity and river surface temperature (RST), both parameters revealing the estuarine response to natural and human-driven dynamics. Based on a long time series of Landsat images (TM and ETM+), we have developed accurate methods to systematically map RST and turbidity gradients across the last 100 km stretch of the River. We present the results of applying an empirical model for river turbidity mapping and a physical model to map RST by using optical and thermal bands of TM and ETM + sensors. Both models were validated with ground-truth data and are used to reconstruct the historical dynamics of the Guadalquivir Estuary.
Statistics of natural reverberation enable perceptual separation of sound and space

Significance Sounds produced in the world reflect off surrounding surfaces on their way to our ears. Known as reverberation, these reflections distort sound but provide information about the world around us. We asked whether reverberation exhibits statistical regularities that listeners use to separate its effects from those of a sound’s source. We conducted a large-scale statistical analysis of real-world acoustics, revealing strong regularities of reverberation in natural scenes. We found that human listeners can estimate the contributions of the source and the environment from reverberant sound, but that they depend critically on whether environmental acoustics conform to the observed statistical regularities. The results suggest a separation process constrained by knowledge of environmental acoustics that is internalized over development or evolution. In everyday listening, sound reaches our ears directly from a source as well as indirectly via reflections known as reverberation. Reverberation profoundly distorts the sound from a source, yet humans can both identify sound sources and distinguish environments from the resulting sound, via mechanisms that remain unclear. The core computational challenge is that the acoustic signatures of the source and environment are combined in a single signal received by the ear. Here we ask whether our recognition of sound sources and spaces reflects an ability to separate their effects and whether any such separation is enabled by statistical regularities of real-world reverberation. To first determine whether such statistical regularities exist, we measured impulse responses (IRs) of 271 spaces sampled from the distribution encountered by humans during daily life. The sampled spaces were diverse, but their IRs were tightly constrained, exhibiting exponential decay at frequency-dependent rates: Mid frequencies reverberated longest whereas higher and lower frequencies decayed more rapidly, presumably due to absorptive properties of materials and air. To test whether humans leverage these regularities, we manipulated IR decay characteristics in simulated reverberant audio. Listeners could discriminate sound sources and environments from these signals, but their abilities degraded when reverberation characteristics deviated from those of real-world environments. Subjectively, atypical IRs were mistaken for sound sources. The results suggest the brain separates sound into contributions from the source and the environment, constrained by a prior on natural reverberation. This separation process may contribute to robust recognition while providing information about spaces around us.
Topic-based playlist to improve video lecture accessibility

Learning is considered one of the most important activities for the human being, and many educational institutes are trying to improve the engagement with students through the availability of video lectures. However, the access to video material is not easy as one may think. For millions of people with some form of disabilities the simple act of browsing a video lecture archive represents an insuperable burden. Motivated by the need to improve accessibility to video lecture materials, in this paper we present VLP, which stands for Video Lecture Playlist. The idea is to use low-level audio/video features, video segmentation and OCR analysis to “understand” the content of the video lectures. In this way, students can search for specific topic through keywords and the system browses the entire video lecture archive to find all the pieces of video lectures that cover the searched topic. These pieces are then provided through a playlist, so that a single search and a single playout return a complete view of how the searched topic is covered within the entire archive. A developed prototype shows the feasibility of the approach and results obtained from a survey highlight that students would like to browse video lecture archives with keywords and playlists.
Application of remote thermal imaging and night vision technology to improve endangered wildlife resource management with minimal animal distress and hazard to humans

Advanced electromagnetic sensor systems more commonly associated with the hightech military battlefield may be applied to remote surveillance of wildlife. The first comprehensive study of a wide global variety of Near Infra Red (NIR) and thermal wildlife portraits are presented with this technology: for mammals, birds and other animals. The paper illustrates the safety aspects afforded to zoo staff and personnel in the wild during the day and night from potentially lethal and aggressive animals, and those difficult to approach normally. Such remote sensing systems are non-invasive and provide minimal disruption and distress to animals both in captivity and in the wild. We present some of the veterinarian advantages of such all weather day and night systems to identify sickness and injuries at an early diagnostic stage, as well as age related effects and mammalian cancer. Animals have very different textured surfaces, reflective and emissive properties in the NIR and thermal bands than when compared with the visible spectrum. Some surface features may offer biomimetic materials design advantages.
Visual tracking of bare fingers for interactive surfaces

Visual tracking of bare fingers allows more direct manipulation of digital objects, multiple simultaneous users interacting with their two hands, and permits the interaction on large surfaces, using only commodity hardware. After presenting related work, we detail our implementation. Its design is based on our modeling of two classes of algorithms that are key to the tracker: Image Differencing Segmentation (IDS) and Fast Rejection Filters (FRF). We introduce a new chromatic distance for IDS and a FRF that is independent to finger rotation. The system runs at full frame rate (25 Hz) with an average total system latency of 80 ms, independently of the number of tracked fingers. When used in a controlled environment such as a meeting room, its robustness is satisfying for everyday use.
The use of cloud point extraction and hydride generation for improving the Sb and Se limits of detection in ICP OES

This study deals with the application of cloud point extraction (CPE) and hydride generation (HG) for determination of Sb and Se using inductively coupled plasma optical emission spectrometry (ICP OES). The complexing agent was ammonium diethylditiophosphate (DDTP), whereas Triton X-114 was used as surfactant for the formation of micelles. Conditions for Sb(III) and Se(IV) preconcentration, reduction of Se(VI) and Sb(V), hydrides generation and interferences were discussed. The performance of HG was compared with pneumatic nebulization (PN) for introducing the surfactant-rich phase into the plasma. Upon application of HG, the limits of detection (LODs) for Se(IV) and Sb(III) were 0.05 and 0.08 µg L-1, respectively. Limits of detection using PN were 0.90 and 2.90 ug L-1 for Se(IV) and Sb(III), respectively. Samples of nuts, water and white wine were analyzed. Selenium(IV) was quantified in Brazil nut whereas Sb(III) was in estuarine water, seawater and mineral water stored in polyethylene terephthalate (PET) bottle.
Immunological Approaches Towards Cancer and Inflammation: A Cross Talk

The inflammation is the protective response of the body against various harmful stimuli; however, the aberrant and inappropriate activation tends to become harmful. The acute inflammatory response tends to resolved once the offending agent is subside but this acute response becomes chronic in nature when the body is unable to successfully neutralized the noxious stimuli. This chronic inflammatory microenvironment is associated with the release of various pro-inflammatory and oncogenic mediators such as nitric oxide (NO), cytokines [IL-1β, IL-2, interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)], growth factor, and chemokines. These mediators make the inflammatory microenvironment more vulnerable toward tumorigenesis. The pro-inflammatory mediators released during the chronic inflammation tends to induce several molecular signaling cascades such as nuclear factor kappa B, MAPKinase, nuclear factor erythroid 2-related factor 2, phosphoinositide-3-kinase, Janus kinases/STAT, Wnt/B-catenin, and cyclic AMP response element binding protein. The immune system and its components have a pleiotropic effect on inflammation and cancer progression. Immune components such as T cells, natural killer cells, macrophages, and neutrophils either inhibit or enhance tumor initiation depending on the type of tumor and immune cells involved. Tumor-associated macrophages and tumor-associated neutrophils are pro-tumorigenic cells highly prevalent in inflammation-mediated tumors. Similarly, presence of T regulatory (Treg) cells in an inflammatory and tumor setting suppresses the immune system, thus paving the way for oncogenesis. However, Treg cells also inhibit autoimmune inflammation. By contrast, cytotoxic T cells and T helper cells confer antitumor immunity and are associated with better prognosis in patients with cancer. Cytotoxic T cells inflict a direct cytotoxic effect on cells expressing oncogenic markers. Currently, several anti-inflammatory and antitumor therapies are under trials in which these immune cells are exploited. Adoptive cell transfer composed of tumor-infiltrating lymphocytes has been tried for the treatment of tumors after their ex vivo expansion. Mediators released by cells in a tumorigenic and inflammatory microenvironment cross talk with nearby cells, either promoting or inhibiting inflammation and cancer. Recently, several cytokine-based therapies are either being developed or are under trial to treat such types of manifestations. Monoclonal antibodies directed against TNF-α, VEGF, and IL-6 has shown promising results to ameliorate inflammation and cancer, while direct administration of IL-2 has been shown to cause tumor regression.