Miracle-dz/UniFAR-FTUs huggingface dataset:
High performance energy conversion of Fe-Pd thin film

pressure. The sputtering was performed under 6.0 x 10-2 Pa of Ar gas partial pressure with 200 W of sputtering power and 3600 s of sputtering time. The film composition was analyzed by energy disperse X-ray spectroscopy (EDS; JSM-6301F, JEOL) and electron prove microanalysis (EPMA; EPMA-8705, SHIMAZU). The contaminant concentrations were measured by auger electron spectroscopy (AES; PHI-155-15, ALVAC PHI inc.). The film crystal structure was analyzed by X-ray diffraction (XRD; X'Part-MRD, PHII.,IPS) and by transmission electron microscope (TEM; H-9000 NAR, HITACHI). The magnetic property was measured by a vibration sample magnetometer (VSM; Model BHV-55, RIKEN). The film texture was observed by means of field emission scanning electron microscopy (FE-SEM; JEM-6000 series WDS/EDS system, JEOL). The magnetostriction (ƒÉ//) of film was measured by a bending method generally used for bi-metal and was estimated by a following equation.4-5
Polar Codes for Quantum Reading

Quantum readout provides a general framework for formulating statistical discrimination of quantum channels. Several paths have been taken for such this problem. However, there is much to be done in the avenue of optimizing channel discrimination using classical codes. At least two open questions can be pointed out: how to construct low complexity encoding schemes that are interesting for channel discrimination and, more importantly, how to develop capacity-achieving protocols. This paper aims at presenting a solution to these questions using polar codes. Firstly, we characterize the information rate and reliability parameter of the channels under polar encoding. We also show that the error probability of the scheme proposed decays exponentially with the square root of the code length. Secondly, an analysis of the optimal quantum states to be used as probes is given.
Preferential attachment with fitness dependent choice

We study the asymptotic behavior of the maximum degree in the evolving tree model with a choice based on both degree and fitness of a vertex. The tree is constructed in the following recursive way. Each vertex is assigned a parameter to it that is called a fitness of a vertex. We start from two vertices and an edge between them. On each step we consider a sample with repetition of $d$ vertices, chosen with probabilities proportional to their degrees plus some parameter $\beta>-1$. Then we add a new vertex and draw an edge from it to the vertex from the sample with the highest product of fitness and degree. We prove that dependent on parameters of the model, the maximum degree could exhibit three types of asymptotic behavior: sublinear, linear and of $n/\ln n$ order, where $n$ is the number of edges in the graph.
Towards Neurohaptics: Brain-Computer Interfaces for Decoding Intuitive Sense of Touch

Noninvasive brain–computer interface (BCI) is widely used to recognize users' intentions. Especially, BCI related to tactile and sensation decoding could provide various effects on many industrial fields such as manufacturing advanced touch displays, controlling robotic devices, and more immersive virtual reality or augmented reality. In this paper, we introduce haptic and sensory perception-based BCI systems called neurohaptics. It is a preliminary study for a variety of scenarios using actual touch and touch imagery paradigms. We designed a novel experimental environment and a device that could acquire brain signals under touching designated materials to generate natural touch and texture sensations. Through the experiment, we collected the electroencephalogram (EEG) signals with respect to four different texture objects. Seven subjects were recruited for the experiment and evaluated classification performances using machine learning and deep learning approaches. Hence, we could confirm the feasibility of decoding actual touch and touch imagery on EEG signals to develop practical neurohaptics.
High Order Local Directional Pattern Based Pyramidal Multi-structure for Robust Face Recognition

Derived from a general definition of texture in a local neighborhood, local directional pattern (LDP) encodes the directional information in the small local 3x3 neighborhood of a pixel, which may fail to extract detailed information especially during changes in the input image due to illumination variations. Therefore, in this paper we introduce a novel feature extraction technique that calculates the nth order direction variation patterns, named high order local directional pattern (HOLDP). The proposed HOLDP can capture more detailed discriminative information than the conventional LDP. Unlike the LDP operator, our proposed technique extracts nth order local information by encoding various distinctive spatial relationships from each neighborhood layer of a pixel in the pyramidal multi-structure way. Then we concatenate the feature vector of each neighborhood layer to form the final HOLDP feature vector. The performance evaluation of the proposed HOLDP algorithm is conducted on several publicly available face databases and observed the superiority of HOLDP under extreme illumination conditions.
Back in control - An extensible middle-box on your phone

The closed design of mobile devices -- with the increased security and consistent user interfaces -- is in large part responsible for their becoming the dominant platform for accessing the Internet. These benefits, however, are not without a cost. Their operation of mobile devices and their apps is not easy to understand by either users or operators. We argue for recovering transparency and control on mobile devices through an extensible platform that can intercept and modify traffic before leaving the device or, on arrival, before it reaches the operating system. Conceptually, this is the same view of the traffic that a traditional middlebox would have at the far end of the first link in the network path. We call this platform ``middlebox zero'' or MBZ. By being on-board, MBZ also leverages local context as it processes the traffic and complements the network-wide view of standard middleboxes. We discuss the challenges of the MBZ approach, sketch a working design, and illustrate its potential with some concrete examples.
Optimizing MARVEL for the radial velocity follow up of TESS and PLATO transiting exoplanets

The space missions TESS and PLATO plan to double the number of 4000 exoplanets already discovered and will measure the size of thousands of exoplanets around the brightest stars in the sky, allowing ground-based radial velocity spectroscopy follow-up to determine the orbit and mass of the detected planets. The new facility we are developing, MARVEL (Raskin et al. this conference1 ), will enable the ground-based follow-up of large numbers of exoplanet detections expected from TESS and PLATO, which cannot be carried out only by the current facilities that achieve the necessary radial velocity accuracy of 1 ms-1 or less. This paper presents the MARVEL observation strategy and performance analysis based on predicted PLATO transit detection yield simulations. The resulting observation scenario baseline will help in the instrument design choices and demonstrate the effectiveness of MARVEL as a TESS and PLATO science enabling facility.
EPR of hydrogen in lithium oxide

The EPR spectrum of interstitial H0 has been observed in X-irradiated Li2O. Transferred hyperfine structure for 7Li nuclei shows that at 4 KH0 is strongly coupled to four equivalent 7Li neighbours, whereas at 300 K it is coupled to eight 7Li neighbours. It is suggested that in the low-temperature site, H0 is displaced from the centre of a cube formed by eight Li+ ions towards the centre of one face, and that temperature-induced tunnelling between the six such sites produces the high-temperature site.
Characterisation of the genetic architecture of immune mediated disease through informed dimension reduction

Genome-wide association studies (GWAS) have uncovered pervasive genetic overlap between common clinically related immune-mediated diseases (IMD). To distinguish axes of IMD risk, and extend genetic knowledge of rare IMDs and subtypes, we developed a Bayesian shrinkage approach to perform a disease-focused decomposition of IMD GWAS summary statistics. We derive 13 components which summarise the multidimensional patterns of IMD genetic risk including those related to raised eosinophil count and serum IP-10. Projection of UK Biobank data demonstrated the IMD-specificity and accuracy of our reduced dimension basis in independent datasets. By projecting 22 rare IMD or IMD subtypes onto the basis we were able to identify disease-discriminating components and suggest novel associations. Requiring only summary level data, our approach allows the genetic architectures across any range of clinically-related traits to be characterised in fewer dimensions, facilitating the analysis of studies with modest sample size, where classical GWAS approaches are challenging.
CRAN Task Views: The Next Generation

CRAN Task Views have been available on the Comprehensive R Archive Network since 2005. They provide guidance about which CRAN packages are relevant for tasks related to a certain topic, and can also facilitate automatic installation of all corresponding packages. Motivated by challenges from the growth of CRAN and the R community as a whole since 2005, all of the task views infrastructure and workflows were rethought and relaunched in 2021/22 in order to facilitate maintenance and to foster deeper interactions with the R community. The redesign encompasses the establishment of a group of CRAN Task View Editors, moving all task view sources to dedicated GitHub repositories, adopting well-documented workflows with a code of conduct, and leveraging R/Markdown files (rather than XML) for the content of the task views.
An overview of SPSA: recent development and applications

There is an increasing need in solving high-dimensional optimization problems under non-deterministic environment. The simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted considerable attention for solving high-dimensional optimization problems where the analytical formula cannot be attained. SPSA is designed to estimate the gradient by applying perturbation on a random subset of dimensions at each iteration. SPSA can be easily implemented and is highly efficient in that that it relies on measurements of the objective function, not on measurements of the gradient of the objective function. Since its invention, SPSA has been implemented in various fields such as reinforcement learning, production optimization etc. The paper briefly discuss the recent development of SPSA and its applications.
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings

Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain. In this regard, the implicit assumption is that there exists (at least approximately) a ground-truth bijection such that a given input from either domain can be accurately reconstructed from successive application of the respective mappings. But in many applications no such bijection can be expected to exist and large reconstruction errors can compromise the success of cycle-consistent training. As one important instance of this limitation, we consider practically-relevant situations where there exists a many-to-one or surjective mapping between domains. To address this regime, we develop a conditional variational autoencoder (CVAE) approach that can be viewed as converting surjective mappings to implicit bijections whereby reconstruction errors in both directions can be minimized, and as a natural byproduct, realistic output diversity can be obtained in the one-to-many direction. As theoretical motivation, we analyze a simplified scenario whereby minima of the proposed CVAE-based energy function align with the recovery of ground-truth surjective mappings. On the empirical side, we consider a synthetic image dataset with known ground-truth, as well as a real-world application involving natural language generation from knowledge graphs and vice versa, a prototypical surjective case. For the latter, our CVAE pipeline can capture such many-to-one mappings during cycle training while promoting textural diversity for graph-to-text tasks. Our code is available at this http URL
Mid-infrared Outbursts in Nearby Galaxies (MIRONG). I. Sample Selection and Characterization

Optical time-domain astronomy has grown rapidly in the past decade, but the dynamic infrared sky is rarely explored. Aiming to construct a sample of mid-infrared outbursts in nearby galaxies (MIRONG), we have conducted a systematical search of low-redshift (z < 0.35) Sloan Digital Sky Survey spectroscopic galaxies that have experienced recent mid-infrared (MIR) flares using their Wide-field Infrared Survey Explorer (WISE) light curves. A total of 137 galaxies have been selected by requiring a brightening amplitude of 0.5 mag in at least one WISE band with respect to their quiescent phases. Only a small fraction (10.9%) has corresponding optical flares. Except for the four supernovae (SNe) in our sample, the MIR luminosities of the remaining sources (L 4.6 μm > 1042 erg s−1) are markedly brighter than known SNe, and their physical locations are very close to the galactic center (median <0.″1). Only four galaxies are radio-loud, indicating that synchrotron radiation from relativistic jets could contribute to MIR variability. We propose that these MIR outbursts are dominated by the dust echoes of transient accretion onto supermassive black holes, such as tidal disruption events (TDEs) and turn-on (changing-look) active galactic nuclei. Moreover, the inferred peak MIR luminosity function is generally consistent with the X-ray and optical TDEs at the high end, albeit with large uncertainties. Our results suggest that a large population of transients has been overlooked by optical surveys, probably due to dust obscuration or intrinsically optical weakness. Thus, a search in the infrared band is crucial for us to obtain a panoramic picture of nuclear outburst. The multiwavelength follow-up observations of the MIRONG sample are in progress and will be presented in a series of subsequent papers.
Largest similar copies of convex polygons amidst polygonal obstacles

Given a convex polygon $P$ with $k$ vertices and a polygonal domain $Q$ consisting of polygonal obstacles with total size $n$ in the plane, we study the optimization problem of finding a largest similar copy of $P$ that can be placed in $Q$ without intersecting the obstacles. We improve the time complexity for solving the problem to $O(k^2n^2\lambda_4(k)\log{n})$. This is progress of improving the previously best known results by Chew and Kedem [SoCG89, CGTA93] and Sharir and Toledo [SoCG91, CGTA94] on the problem in more than 25 years.
Mask-Align: Self-Supervised Neural Word Alignment

Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence. In this paper, we propose Mask-Align, a self-supervised word alignment model that takes advantage of the full context on the target side. Our model masks out each target token and predicts it conditioned on both source and the remaining target tokens. This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned. We also introduce an attention variant called leaky attention, which alleviates the problem of unexpected high cross-attention weights on special tokens such as periods. Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.
Physicochemical characterization of hydrogels based on polyvinyl alcohol-vinyl acetate blends

Hydrogels made of polyvinyl alcohol–vinyl acetate and its blends with water soluble polymer were studied in terms of swelling behavior, microstructure, and dynamic mechanical properties. Hydrogels prepared by blending polyvinyl alcohol–vinyl acetate with either polyacrylic acid or poly(4-vinyl pyridine) exhibited a strong pH dependency. When poly(vinyl pyrrolidone) was used for blending, an unusual pH dependency was observed. An increase in the equilibrium water content in all systems resulted in an increase in the freezable water as determined by DSC. Critical point drying led to a striated surface on polyacrylic acid–polyvinyl alcohol–vinyl acetate hydrogels, whereas a porous structure was observed on the freeze-dried poly(vinyl pyrrolidone)–polyvinyl alcohol–vinyl acetate gels. Hydrogels with elevated storage modulus were obtained when either polyvinyl alcohol–vinyl acetate alone or polyacrylic acid–polyvinyl alcohol–vinyl acetate blends were thermally treated at high temperatures (i.e., 150°C). Low storage modulus was observed for both poly(vinyl pyrrolidone) and poly(4-vinyl pyridine)-containing hydrogels. Temperature dependency of storage modulus from 20 to 60°C was observed only for poly(4-vinyl pyridine)–polyvinyl alcohol–vinyl acetate hydrogels. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 82: 3578–3590, 2001
Reversible pH-Induced Formation and Disruption of Unimolecular Micelles of an Amphiphilic Polyelectrolyte

Reversible pH-induced formation and disruption of a unimolecular micelle (unimer micelle) of a random copolymer of sodium 2-(acrylamido)-2-methylpropanesulfonate and 11-acrylamidoundecanoic acid (50 mol %) were investigated by static light scattering (SLS), quasi-elastic light scattering, viscometric, 1H NMR spin−spin relaxation, and fluorescence probe techniques. The weight-average molecular weight (Mw) value of the copolymer determined by SLS in a 0.1 M NaCl aqueous solution at pH 7 was virtually the same as that determined in a methanol solution, Mw in 0.1 M NaCl being practically independent of pH in the range 3 < pH < 9. These findings indicate that the copolymer exists as a single molecular state (unimer) in 0.1 M NaCl over the range 3 < pH < 9. At pH < 5, the copolymer exhibited very small values of the radius of gyration, hydrodynamic radius, reduced viscosity, and spin−spin relaxation time, indicative of the formation of a unimer micelle under acidic conditions. All these values increased signifi.
Active Learning for Node Classification: The Additional Learning Ability from Unlabelled Nodes

Node classification on graph data is an important task on many practical domains. However, it requires labels for training, which can be difficult or expensive to obtain in practice. Given a limited labelling budget, active learning aims to improve performance by carefully choosing which nodes to label. Our empirical study shows that existing active learning methods for node classification are considerably outperformed by a simple method which randomly selects nodes to label and trains a linear classifier with labelled nodes and unsupervised learning features. This indicates that existing methods do not fully utilize the information present in unlabelled nodes as they only use unlabelled nodes for label acquisition. In this paper, we utilize the information in unlabelled nodes by using unsupervised learning features. We propose a novel latent space clustering-based active learning method for node classification (LSCALE). Specifically, to select nodes for labelling, our method uses the K-Medoids clustering algorithm on a feature space based on the dynamic combination of both unsupervised features and supervised features. In addition, we design an incremental clustering module to avoid redundancy between nodes selected at different steps. We conduct extensive experiments on three public citation datasets and two co-authorship datasets, where our proposed method LSCALE consistently and significantly outperforms the state-of-the-art approaches by a large margin.
Clickbait in Hindi News Media : A Preliminary Study

A corpus of Hindi news headlines shared on Twitter was created by collecting tweets of 5 mainstream Hindi news sources for a period of 4 months. 7 independent annotators were recruited to mark the 20 most retweeted news posts by each of the 5 news sources on its clickbait nature. The clickbait score hence generated was assessed for its correlation with interactions on the platform (retweets, favorites, reader replies), tweet word count, and normalized POS (part-of-speech) tag counts in tweets. A positive correlation was observed between readers' interactions with tweets and tweets' clickbait score. Significant correlations were also observed for POS tag counts and clickbait score. The prevalence of clickbait in mainstream Hindi news media was found to be similar to its prevalence in English news media. We hope that our observations would provide a platform for discussions on clickbait in mainstream Hindi news media.
Learning Contextual Causality from Time-consecutive Images

Causality knowledge is crucial for many artificial intelligence systems. Conventional textual-based causality knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causality knowledge records (e.g., ConceptNet) typically do not take the context into consideration. To explore a more scalable way of acquiring causality knowledge, in this paper, we jump out of the textual domain and investigate the possibility of learning contextual causality from the visual signal. Compared with pure text-based approaches, learning causality from the visual signal has the following advantages: (1) Causality knowledge belongs to the commonsense knowledge, which is rarely expressed in the text but rich in videos; (2) Most events in the video are naturally time-ordered, which provides a rich resource for us to mine causality knowledge from; (3) All the objects in the video can be used as context to study the contextual property of causal relations. In detail, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representation models as well as enough training signals, it is possible to automatically discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists, taking which into consideration might be crucial if we want to use the causality knowledge in real applications, and the visual signal could serve as a good resource for learning such contextual causality.
Cotorsion pairs in Hopfological algebra

In an intriguing paper arXiv:math/0509083 Khovanov proposed a generalization of homological algebra, called Hopfological algebra. Since then, several attempts have been made to import tools and techiniques from homological algebra to Hopfological algebra. For example, Qi arXiv:1205.1814 introduced the notion of cofibrant objects in the category $\mathbf{C}_{A,H}^{H}$ of $H$-equivariant modules over an $H$-module algebra $A$, which is a counterpart to the category of modules over a dg algebra, although he did not define a model structure on $\mathbf{C}_{A,H}^{H}$. In this paper, we show that there exists an Abelian model structure on $\mathbf{C}_{A,H}^{H}$ in which cofibrant objects agree with Qi's cofibrant objects under a slight modification. This is done by constructing cotorsion pairs in $\mathbf{C}_{A,H}^{H}$ which form a Hovey triple in the sense of Gillespie arXiv:1512.06001. This can be regarded as a Hopfological analogues of the works of Enochs, Jenda, and Xu and of Avramov, Foxby, and Halperin. By restricting to compact cofibrant objects, we obtain a Waldhausen category $\mathcal{P}\mathrm{erf}_{A,H}^{H}$ of perfect objects. By taking invariants of this Waldhausen category, such as algebraic $K$-theory, Hochschild homology, cyclic homology, and so on, we obtain Hopfological analogues of these invariants.