Social Media Monitoring for IoT Cyber-Threats

The rapid development of IoT applications and their use in various fields of everyday life has resulted in an escalated number of different possible cyber-threats, and has consequently raised the need of securing IoT devices. Collecting Cyber-Threat Intelligence (e.g., zero-day vulnerabilities or trending exploits) from various online sources and utilizing it to proactively secure IoT systems or prepare mitigation scenarios has proven to be a promising direction. In this work, we focus on social media monitoring and investigate real-time Cyber-Threat Intelligence detection from the Twitter stream. Initially, we compare and extensively evaluate six different machine-learning based classification alternatives trained with vulnerability descriptions and tested with real-world data from the Twitter stream to identify the best-fitting solution. Subsequently, based on our findings, we propose a novel social media monitoring system tailored to the IoT domain; the system allows users to identify recent/trending vulnerabilities and exploits on IoT devices. Finally, to aid research on the field and support the reproducibility of our results we publicly release all annotated datasets created during this process.

Fast Simulation of a High Granularity Calorimeter by Generative Adversarial Networks

We present the 3DGAN for the simulation of a future high granularity calorimeter output as three-dimensional images. We prove the efficacy of Generative Adversarial Networks (GANs) for generating scientific data while retaining a high level of accuracy for diverse metrics across a large range of input variables. We demonstrate a successful application of the transfer learning concept: we train the network to simulate showers for electrons from a reduced range of primary energies, we then train further for a five times larger range (the model could not train for the larger range directly). The same concept is extended to generate showers for other particles (photons and neutral pions) depositing most of their energies in electromagnetic interactions. In addition, the generation of charged pion showers is also explored, a more accurate effort would require additional data from other detectors not included in the scope of the current work. Our further contribution is a demonstration of using GAN-generated data for a practical application. We train a third-party network using GAN-generated data and prove that the response is similar to a network trained with data from the Monte Carlo simulation. The showers generated by GAN present accuracy within $10%$ of Monte Carlo for a diverse range of physics features, with three orders of magnitude speedup. The speedup for both the training and inference can be further enhanced by distributed training.

Counting Twisted Tame Fourier-Mukai Partners of an Ordinary K3 Surface

In this article, we prove that a tame twisted K3 surface over an algebraically closed field of positive characteristic has only finitely many tame twisted Fourier-Mukai partners and we give a counting formula in case we have an ordinary tame untwisted K3 surface. We also show that every tame twisted Fourier Mukai partner of a K3 surface of finite height is a moduli space of twisted sheaves over it.

Magnetic Interactions Between Radical Pairs in Chiral Graphene Nanoribbons

Magnetic graphene nanoribbons (GNRs) have become promising candidates for future applications, including quantum technologies. Here, we characterize magnetic states hosted by chiral graphene nanoribbons (chGNRs). The substitution of a hydrogen atom at the chGNR edge by a ketone group effectively adds one p_z electron to the {pi}-electron network, thus producing an unpaired {pi} radical. A closely related scenario occurs for regular ketone-functionalized chGNRs in which one oxygen atom is missing. Two such radical states can interact via exchange coupling and we study those interactions as a function of their relative position, which includes a remarkable dependence on the chirality, as well as on the nature of the surrounding GNR, i.e., with or without ketone functionalization. In addition, we determine the parameters whereby this type of systems with oxygen heteroatoms can be adequately described within the widely used mean-field Hubbard model. Altogether, we provide new insights to both theoretically model and devise GNR-based nanostructures with tunable magnetic properties.

Maximum Force and Black Hole Thermodynamic Instability

We show that the notion of the maximum force conjecture $F leqslant 1/4$ in general relativity, when applied to asymptotically flat singly spinning Myer-Perry black holes in any dimension, reveals the underlying thermodynamic instability in a number of ways. In particular, the Hookean force law $F_1=kx$, suitably defined, is bounded by the conjectured limit, but in $dgeqslant 6$ it is further bounded by a dimensional dependent value less than $1/4$, which remarkably corresponds to the Emparan-Myers fragmentation (splitting of a black hole into two becomes thermodynamically preferable). Furthermore, we define another force as the square of the angular momentum to entropy ratio ($F_2=J^2/S^2$). In dimensions $dgeqslant 6$, the positive Ruppeiner scalar curvature region in the thermodynamic phase space is marked by the upper boundary $F_2=frac{1}{12}left(frac{d-3}{d-5}right)$ and the lower boundary $F_2=frac{1}{4}left(frac{d-3}{d-5}right)$, the latter corresponds to a black hole that suffers from Gregory-Laflamme instability. Surprisingly, the upper and lower boundaries correspond to $F=1/4$ when $d=6$ and $dto infty$, respectively. We discuss how the maximum force may be relevant to the underlying black hole microstructure and its relationship to cosmic censorship.

Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation

Timely detection of horse pain is important for equine welfare. Horses express pain through their facial and body behavior, but may hide signs of pain from unfamiliar human observers. In addition, collecting visual data with detailed annotation of horse behavior and pain state is both cumbersome and not scalable. Consequently, a pragmatic equine pain classification system would use video of the unobserved horse and weak labels. This paper proposes such a method for equine pain classification by using multi-view surveillance video footage of unobserved horses with induced orthopaedic pain, with temporally sparse video level pain labels. To ensure that pain is learned from horse body language alone, we first train a self-supervised generative model to disentangle horse pose from its appearance and background before using the disentangled horse pose latent representation for pain classification. To make best use of the pain labels, we develop a novel loss that formulates pain classification as a multi-instance learning problem. Our method achieves pain classification accuracy better than human expert performance with 60% accuracy. The learned latent horse pose representation is shown to be viewpoint covariant, and disentangled from horse appearance. Qualitative analysis of pain classified segments shows correspondence between the pain symptoms identified by our model, and equine pain scales used in veterinary practice.

Ammonium adsorption, desorption and recovery by acid and alkaline treated zeolite

In this study, the suitability of zeolite as a possible medium for ammonium adsorption, desorption and recovery from wastewater was investigated. Specifically, batch adsorption and desorption studies with solutions enriched in NH$_4^+$ were conducted employing zeolite to evaluate how the chemical treatment and contact time affect adsorption and desorption. Several experimental tests were carried out considering both untreated and treated zeolite. Untreated and HCl-Na treated zeolite adsorbed up to 11.8 mg NH$_4^+$ g$^{-1}$ and showed the highest efficiency in recovering NH$_4^+$ from aqueous solution. Regardless of pre-treatment, treatments with NaCl resulted in higher and faster adsorption of NH$_4^+$ than treatments with CaCl$_2$ and MgCl$_2$.

The Impact of Powerful Jets on the Far-infrared Emission of an Extreme Radio Quasar at z~6

The interactions between radio jets and the interstellar medium play a defining role for the co-evolution of central supermassive black holes and their host galaxies, but observational constraints on these feedback processes are still very limited at redshifts $z > 2$. We investigate the radio-loud quasar PSO J352.4034-15.3373 at $z sim 6$ at the edge of the Epoch of Reionization. This quasar is among the most powerful radio emitters and the first one with direct evidence of extended radio jets ($sim$1.6 kpc) at these high redshifts. We analyze NOEMA and ALMA millimeter data targeting the CO (6-5) and [CII] far-infrared emission lines, respectively, and the underlying continuum. The broad $440pm 80$ km s$^{-1}$ and marginally resolved [CII] emission line yields a systemic redshift of $z!=!5.832 pm 0.001$. Additionally, we report a strong 215 MHz radio continuum detection, $88pm 7$ mJy, using the GMRT. This measurement significantly improves the constraints at the low-frequency end of the spectral energy distribution of this quasar. In contrast to what is typically observed in high-redshift radio-quiet quasars, we show that cold dust emission alone cannot reproduce the millimeter continuum measurements. This is evidence that the strong synchrotron emission from the quasar contributes substantially to the emission even at millimeter (far-infrared in the rest-frame) wavelengths. This quasar is an ideal system to probe the effects of radio jets during the formation of a massive galaxy within the first Gyr of the Universe.

Progress in Nuclear Astrophysics of East and Southeast Asia

Nuclear astrophysics is an interdisciplinary research field of nuclear physics and astrophysics, seeking for the answer to a question, how to understand the evolution of the Universe with the nuclear processes which we learn. We review the research activities of nuclear astrophysics in east and southeast Asia which includes astronomy, experimental and theoretical nuclear physics and astrophysics. Several hot topics such as the Li problems, critical nuclear reactions and properties in stars, properties of dense matter, r-process nucleosynthesis and $ u$-process nucleosynthesis are chosen and discussed in further details. Some future Asian facilities, together with physics perspectives, are introduced.

In-plane Isotropy of the Low Energy Phonon Anomalies in YBa$_{2}$Cu$_{3}$O$_{6+x}$

We study the temperature dependence of the low energy phonons in the $(H, 0, L)$ reciprocal plane of the highly ordered ortho-II YBa$_2$Cu$_3$O$_{6.55}$ cuprate high temperature superconductor by means of high-resolution inelastic x-ray scattering. Anomalies associated with the emergence of long-range charge density wave (CDW) fluctuations are observed, and are qualitatively similar to those previously observed in the $(0, K, L)$ plane. This confirms the unconventional nature of this bi-dimensional CDW, which is not soft-phonon driven. With the support of first principles calculations, the symmetry of the anomalous phonon is identified and is found to match that of the charge modulation. This suggests in turn that these anomalies originate from a direct coupling between the phonons and the collective CDW excitations.