Miracle-dz/UniFAR-FTUs huggingface dataset:
Black-Box Saliency Map Generation Using Bayesian Optimisation

Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches to saliency map generation are available, but most require access to model parameters. This work proposes an approach for saliency map generation for black-box models, where no access to model parameters is available, using a Bayesian optimisation sampling method. The approach aims to find the global salient image region responsible for a particular (black-box) model’s prediction. This is achieved by a sampling-based approach to model perturbations that seeks to localise salient regions of an image to the black-box model. Results show that the proposed approach to saliency map generation outperforms grid-based perturbation approaches, and performs similarly to gradient-based approaches which require access to model parameters.
The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition

This paper presents a new approach for the recognition of elements in floor plan layouts. Besides of elements with common shapes, we aim to recognize elements with irregular shapes such as circular rooms and inclined walls. Furthermore, the reduction of noise in the semantic segmentation of the floor plan is on demand. To this end, we propose direction-aware, learnable, additive kernels in the application of both the context module and common convolutional blocks. We apply them for high performance of elements with both common and irregular shapes. Besides, an adversarial network with two discriminators is proposed to further improve the accuracy of the elements and to reduce the noise of the semantic segmentation. Experimental results demonstrate the superiority and effectiveness of the proposed network over the state-of-the-art methods.
Numerical investigation of plasma-driven superradiant instabilities

Photons propagating in a plasma acquire an effective mass μ, which is given by the plasma frequency and which scales with the square root of the plasma density. As noted previously in the literature, for electron number densities ne ∼ 10−3 cm−3 (such as those measured in the interstellar medium) the effective mass induced by the plasma is μ ∼ 10−12 eV. This would cause superradiant instabilities for spinning black holes of a few tens of solar masses. An obvious problem with this picture is that densities in the vicinity of black holes are much higher than in the interstellar medium because of accretion, and possibly also pair production. We have conducted numerical simulations of the superradiant instability in spinning black holes surrounded by a plasma with density increasing closer to the black hole, in order to mimic the effect of accretion. While we confirm that superradiant instabilities appear for plasma densities that are sufficiently low near the black hole, we find that astrophysically realistic accretion disks are unlikely to trigger such instabilities.
Rigidity of Elliptic Genera: From Number Theory to Geometry and Back

In this paper, we derive topological and number theoretical consequences of the rigidity of elliptic genera, which are special modular forms associated to compact almost complex manifolds. On the geometry side, we prove that rigidity implies relations between the Betti numbers and the index of a compact symplectic manifold admitting a Hamiltonian action of a circle with isolated fixed points. We investigate the case of maximal index and toric actions. On the number theoretical side, we prove that from each compact almost complex manifold of index greater than one, that can be endowed with the action of a circle with isolated fixed points, one can derive non-trivial relations among Eisenstein series. We give explicit formulas coming from the standard action on ${{\mathbb{C}}} P^n$.
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of existing and future accelerator facilities, such as the Electron-Ion Collider.
Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.
The Herschel view of the dense core population in the Ophiuchus molecular cloud

Context. Herschel observations of nearby clouds in the Gould Belt support a paradigm for low-mass star formation, starting with the generation of molecular filaments, followed by filament fragmentation, and the concentration of mass into self-gravitating prestellar cores. In the case of the Ophiuchus molecular complex, a rich star formation activity has been documented for many years inside the clumps of L1688, the main and densest cloud of the complex, and in the more quiescent twin cloud L1689 thanks to extensive surveys at infrared and other wavelengths. Aims. With the unique far-infrared and submillimeter continuum imaging capabilities of the Herschel Space observatory, the closeby (d = 139 pc) Ophiuchus cloud was extensively mapped at five wavelengths from 70 to 500 μm with the aim of providing a complete census of dense cores in this region, including unbound starless cores, bound prestellar cores, and protostellar cores. Methods. Taking full advantage of the high dynamic range and multi-wavelength nature of the Herschel data, we used the multi-scale decomposition algorithms getsources and getfilaments to identify an essentially complete sample of dense cores and filaments in the cloud and study their properties. Results. The densest clouds of the Ophiuchus complex, L1688 and L1689, which thus far are only indirectly described as filamentary regions owing to the spatial distribution of their young stellar objects, are now confirmed to be dominated by filamentary structures. The tight correlation observed between prestellar cores and filamentary structures in L1688 and L1689 supports the view that solar-type star formation occurs primarily in dense filaments. While the sub clouds of the complex show some disparities, L1689 being apparently less efficient than L1688 at forming stars when considering their total mass budgets, both sub clouds share almost the same prestellar core formation efficiency in dense molecular gas. We also find evidence in the Herschel data for a remarkable concentric geometrical configuration in L1688 which is dominated by up to three arc-like compression fronts and has presumably been created by shockwave events emanating from the Sco OB2 association, including the neighboring massive (O9V) star σ Sco. Conclusions. Our Herschel study of the well-documented Ophiuchus region has allowed us to further analyze the influence of several early-type (OB) stars surrounding the complex, thus providing positive feedback and enhancing star formation activity in the dense central part of the region, L1688.
Correcting for Selection Bias in Learning-to-rank Systems

Click data collected by modern recommendation systems are an important source of observational data that can be utilized to train learning-to-rank (LTR) systems. However, these data suffer from a number of biases that can result in poor performance for LTR systems. Recent methods for bias correction in such systems mostly focus on position bias, the fact that higher ranked results (e.g., top search engine results) are more likely to be clicked even if they are not the most relevant results given a user’s query. Less attention has been paid to correcting for selection bias, which occurs because clicked documents are reflective of what documents have been shown to the user in the first place. Here, we propose new counterfactual approaches which adapt Heckman’s two-stage method and accounts for selection and position bias in LTR systems. Our empirical evaluation shows that our proposed methods are much more robust to noise and have better accuracy compared to existing unbiased LTR algorithms, especially when there is moderate to no position bias.
Braid rigidity for path algebras

Path algebras are a convenient way of describing decompositions of tensor powers of an object in a tensor category. If the category is braided, one obtains representations of the braid groups $B_n$ for all $n\in \N$. We say that such representations are rigid if they are determined by the path algebra and the representations of $B_2$. We show that besides the known classical cases also the braid representations for the path algebra for the 7-dimensional representation of $G_2$ satisfies the rigidity condition, provided $B_3$ generates $\End(V^{\otimes 3})$. We obtain a complete classification of ribbon tensor categories with the fusion rules of $\g(G_2)$ if this condition is satisfied.
The Bail-in effect: Does bond yield follow the hierarchy of risk after BRRD?

On January 1 2016 the BRRD introduced the Bail-in mechanism which limits government assistance to the Euroarea banks in the case of crisis. Our analysis, based on a sample of 3,886 bonds of 50 banks from 9 Euro-area countries during the period January 31 2006 to May 31 2017, investigates the impact of the introduction of Bailin on bank bonds yields. The research compares three different groups of bonds: i) bailinable and non-bailinable bonds; ii) bailinable senior bonds and bailinable subordinated bonds; iii) bailinable subordinated bonds included in Tier 1, Upper Tier 2 or Lower Tier 2 capital. To do this we use Difference-in-Difference methodology. Our results underline that after the Bail-in introduction: i) in the period before BRRD, current bailinable bonds show a higher yield than non-bailinable bonds, which increases significantly after 1 January 2016; ii) although subordinated bonds display higher yields before 2016 than senior unsecured bonds, after the Bail-in introduction senior unsecured bonds show a higher increase in bond yields, more than that displayed by unsecured subordinated; finally, iii) with regard to the classification between unsecured subordinated bonds, all three classes yield more than the non-bailinable bonds, both before and after the Bail-in introduction. However, bonds with intermediate risks (Upper Tier 2) show a higher increase than bonds with the highest risk (Tier 1) in the period
Gene Expression Profiling of Mediators Associated with the Inflammatory Pathways in the Intestinal Tissue from Patients with Ulcerative Colitis

Background Multiple genes have been associated with IBD, and many of these can be linked to alterations in autophagy, UPR, ubiquitination, and metabolic and immune response pathways. The aim of this study was to analyze a transcriptomic panel of mediators associated with the inflammatory pathways in the colonic mucosa of UC patients. Patients and Methods. We studied a total of 100 patients with definitive diagnosis of UC (50 active and 50 in remission) and a control group (50 subjects) without endoscopic evidence of intestinal inflammation. Colonic mucosal biopsies were taken by colonoscopy and preserved in RNA later. Gene expression were measured by real-time polymerase chain reaction (RT-PCR). Results The gene expressions of XBP1, AGR2, HSPA5, UBE2L3, TNFRSF14, LAMP3, FCGR2A, LSP1, CTLA4, SOD2, TDO2, and ALDOB mRNA levels were significantly higher in the colonic mucosa from UC patients (both quiescent and active) as compared to the control group (P < 0.05). Conversely, IRGM, ORDML3, UBD, CUL2, CYLD, FOXC2, FOXO4, DOK3, and SNX20 mRNA levels were found to be significantly lower in patients with active disease, as compared to those with active disease (P < 0.05). Gene expressions of IRGM, CTLA4, FOXO4, SLC26A3, SLC39A4, SOD2, TDO2, and ALDOB were associated with clinical outcomes, such as medical treatment in response to aminosalicylates, histological remission, clinical course, and evolution. Conclusions : The gene expressions of FOXO4, ALDOB, SOD2, TOD2, SLC26A3, and SLC39A4 were associated with the clinical course and histological activity and are of relevance since these provide the utility of new prognostic markers in IBD. Gene expression signature showed dysregulation in mediators associated with autophagy, ubiquitination, ER stress, oxidative stress, carbohydrate metabolism, solute transport, and T cell regulation in the colonic mucosa from patients with UC, suggesting that these genes could be involved in the pathogenesis of UC.
Local tomography and the role of the complex numbers in quantum mechanics

Various reconstructions of finite-dimensional quantum mechanics result in a formally real Jordan algebra A and a last step remains to conclude that A is the self-adjoint part of a C*-algebra. Using a quantum logical setting, it is shown that this can be achieved by postulating that there is a locally tomographic model for a composite system consisting of two copies of the same system. Local tomography is a feature of classical probability theory and quantum mechanics; it means that state tomography for a multipartite system can be performed by simultaneous measurements in all subsystems. The quantum logical definition of local tomography is sufficient, but it is less restrictive than the prevalent definition in the literature and involves some subtleties concerning the so-called spin factors.
Exploring the dispersion measure of the Milky Way halo

Fast radio bursts offer the opportunity to place new constraints on the mass and density profile of hot and ionized gas in galactic haloes. We test here the X-ray emission and dispersion measure predicted by different gas profiles for the halo of the Milky Way. We examine a range of models, including entropy stability conditions and external pressure continuity. We find that incorporating constraints from X-ray observations leads to favouring dispersion measures on the lower end of the range given by these models. We show that the dispersion measure of the Milky Way halo could be less than 10 cm$^{-3}$ pc in the most extreme model we consider, which is based on constraints from O VII absorption lines. However, the models allowed by the soft X-ray constraints span more than an order of magnitude in dispersion measures. Additional information on the distribution of gas in the Milky Way halo could be obtained from the signature of a dipole in the dispersion measure of fast radio bursts across the sky, but this will be a small effect for most cases.
Analysis of Y00 Protocol Under Quantum Generalization of a Fast Correlation Attack: Toward Information-Theoretic Security

In our previous work, it was demonstrated that the attacker could not pin-down the correct keys to start the Y00 protocol with a probability of one under the assistance of unlimitedly long known-plaintext attacks and optimal quantum measurements on the attacker’s quantum memory. However, there were several assumptions that the Y00 system utilized linear-feedback shift registers as pseudo-random-number generators, and a fast correlation attack was disabled by irregular mapping on the Y00 systems. This study generalizes such an attack to remove the assumptions of the previous work. The framework of the security analyses of this study reiterates two well-known results from the past: (1) Y00 systems would be cryptanalyzed when the system is not designed well; (2) the system is possibly information-theoretically secure when the system is designed well, although the attacker’s confidence in the correct key increases over time but the success probability of key recovery does not reach unity in finite time; (3) the breach probability of the shared keys increases with time. Hence, a key-refreshment procedure for the Y00 protocol is provided herein. Such security analyses are important not only in key refreshment but also in initial key agreement.
NCVis: Noise Contrastive Approach for Scalable Visualization

Modern methods for data visualization via dimensionality reduction, such as t-SNE, usually have performance issues that prohibit their application to large amounts of high-dimensional data. In this work, we propose NCVis – a high-performance dimensionality reduction method built on a sound statistical basis of noise contrastive estimation. We show that NCVis outperforms state-of-the-art techniques in terms of speed while preserving the representation quality of other methods. In particular, the proposed approach successfully proceeds a large dataset of more than 1 million news headlines in several minutes and presents the underlying structure in a human-readable way. Moreover, it provides results consistent with classical methods like t-SNE on more straightforward datasets like images of hand-written digits. We believe that the broader usage of such software can significantly simplify the large-scale data analysis and lower the entry barrier to this area.
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.
Measurement-induced dynamics of many-body systems at quantum criticality

We consider a dynamic protocol for quantum many-body systems, which enables to study the interplay between unitary Hamiltonian driving and random local projective measurements. While the unitary dynamics tends to increase entanglement, local measurements tend to disentangle, thus favoring decoherence. Close to a quantum transition where the system develops critical correlations with diverging length scales, the competition of the two drivings is analyzed within a dynamic scaling framework, allowing us to identify a regime (dynamic scaling limit) where the two mechanisms develop a nontrivial interplay. We perform a numerical analysis of this protocol in a measurement-driven Ising chain, which supports the scaling laws we put forward. The local measurement process generally tends to suppress quantum correlations, even in the dynamic scaling limit. The power law of the decay of the quantum correlations turns out to be enhanced at the quantum transition.
Internet Addiction a Global Concern: A Cross-Sectional Appraisal amongst Imminent Medical Doctors of National Defence University of Malaysia

Background: Recent evolution in terms of technology and availability of the internet has brought several innovations that made work and access to information straightforward. Nonetheless, the danger of pathological internet addiction and its adverse consequences are frightening. Objectives: The primary goal of this research is to determine the level of internet addiction among undergraduate medical students at National Defence University, Malaysia. Methods: A structured questionnaire comprising of section A consisting of student demographic information and section B made up of Dr. Kimberly Young’s 20 items internet addiction test (IAT) in 5-point Likert’s scale. Internet addiction scores were classified based on Young’s scale. Results: The response rate obtained from this study was 89.6%. Out of the 224 respondents, only 4 (1.8%) were found to be regular internet users, 98 (43.8%) were established to be mild addicts, 110 (49.1%) were moderate addicts and finally, 12 (5.4%) were severe internet addicts. There was a statistically significant difference in the mean internet addiction score between males and females, with males having higher ratings. Conclusion: The prevalence of internet addiction among undergraduate medical students is high and needs to be controlled. As such, the use of the internet at colleges and universities may need to be regulated.
English and Japanese Compliments in Book Reviews of Academic Sociolinguistic Journals

Compliments have been previously examined along with linguistic politeness (Brown and Levinson, 1987) as an important speech act for creating solidarity among speakers. In written texts, writers need to negotiate a relationship with readers and thus maintain a level of concern about politeness. Book reviews in academic journals publicly display both positive and critical evaluation, and reviewers need to create solidarity with authors and readers alike. Therefore, this study examines the way in which compliments are used in English and Japanese book reviews in sociolinguistic journals in order to reveal the cultural differences in written compliments between the two languages as well as their similarities. In this paper, previous studies of compliments in written texts, particularly in academic book reviews in English and other foreign languages, will be discussed first. Second, the methodology of this study will be introduced. After the findings of English and Japanese compliments in book reviews are revealed, similarities as well as differences between English and Japanese compliments in sociolinguistic book reviews in written texts will be discussed along with linguistic politeness. In both languages, compliments were used to positively evaluate a book and its authors and to make authors and readers feel good. However, the way in which reviewers in English and Japanese express compliments towards a book sometimes differed between the two languages. Reviewers in English tended to compliment more directly to the authors and the book itself, including contents and analysis, than reviewers in Japanese. Instead, the benefits to readers and style of the book tended to be more emphasised English and Japanese Compliments in Book Reviews of Academic Sociolinguistic Journals Kayo Fujimura-Wilson