Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-gravitating strange dark matter halos around galaxies

A new family of nonrelativistic, Newtonian, non-quantum equilibrium configurations describing galactic halos is introduced, by considering strange quark matter conglomerates with masses larger than about 8 GeV as new possible components of the dark matter. Originally introduced to explain the state of matter in neutron stars, such conglomerates may also form in the high-density and temperature conditions of the primordial Universe and then decouple from ordinary baryonic matter, providing the fundamental components of dark matter for the formation of pristine gravitational potential wells and the subsequent evolution of cosmic structures. The obtained results for halo mass and radius are consistent with the rotational velocity curve observed in the Galaxy. Additionally, the average density of such dark matter halos is similar to that derived for halos of dwarf spheroidal galaxies, which can therefore be interpreted as downscaled versions of larger dark matter distributions around Milky Way-sized galaxies and hint for a common origin of the two families of cosmic structures.
RCModel, a Risk Chain Model for Risk Reduction in AI Services

With the increasing use of artificial intelligence (AI) services and products in recent years, issues related to their trustworthiness have emerged and AI service providers need to be prepared for various risks. In this policy recommendation, we propose a risk chain model (RCModel) that supports AI service providers in proper risk assessment and control. We hope that RCModel will contribute to the realization of trustworthy AI services.
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control

In this thesis, I propose a family of fully decentralized deep multi-agent reinforcement learning (MARL) algorithms to achieve high, real-time performance in network-level traffic signal control. In this approach, each intersection is modeled as an agent that plays a Markovian Game against the other intersection nodes in a traffic signal network modeled as an undirected graph, to approach the optimal reduction in delay. Following Partially Observable Markov Decision Processes (POMDPs), there are 3 levels of communication schemes between adjacent learning agents: independent deep Q-leaning (IDQL), shared states reinforcement learning (S2RL) and a shared states & rewards version of S2RL--S2R2L. In these 3 variants of decentralized MARL schemes, individual agent trains its local deep Q network (DQN) separately, enhanced by convergence-guaranteed techniques like double DQN, prioritized experience replay, multi-step bootstrapping, etc. To test the performance of the proposed three MARL algorithms, a SUMO-based simulation platform is developed to mimic the traffic evolution of the real world. Fed with random traffic demand between permitted OD pairs, a 4x4 Manhattan-style grid network is set up as the testbed, two different vehicle arrival rates are generated for model training and testing. The experiment results show that S2R2L has a quicker convergence rate and better convergent performance than IDQL and S2RL in the training process. Moreover, three MARL schemes all reveal exceptional generalization abilities. Their testing results surpass the benchmark Max Pressure (MP) algorithm, under the criteria of average vehicle delay, network-level queue length and fuel consumption rate. Notably, S2R2L has the best testing performance of reducing 34.55% traffic delay and dissipating 10.91% queue length compared with MP.
Time-periodic dynamics generates pseudo-random unitaries

How much does local and time-periodic dynamics resemble a random unitary? In the present work we address this question by using the Clifford formalism from quantum computation. We analyse a Floquet model with disorder, characterised by a family of local, time-periodic, random quantum circuits in one spatial dimension. We observe that the evolution operator enjoys an extra symmetry at times that are a half-integer multiple of the period. With this we prove that after the scrambling time, namely when any initial perturbation has propagated throughout the system, the evolution operator cannot be distinguished from a (Haar) random unitary when all qubits are measured with Pauli operators. This indistinguishability decreases as time goes on, which is in high contrast to the more studied case of (time-dependent) random circuits. We also prove that the evolution of Pauli operators displays a form of ergodicity. These results require the dimension of the local subsystem to be large. In the opposite regime our system displays a novel form of localisation, which can be characterised in terms of one-sided walls.
Discovery of recombining plasma associated with the candidate supernova remnant G189.6+3.3 with Suzaku

We present the results of an X-ray spectral analysis of the northeast region of the candidate supernova remnant G189.6+3.3 with Suzaku. K-shell lines from highly ionized Ne, Mg, Si, and S were detected in the spectrum for the first time. In addition, a radiative recombining continuum (RRC) from He-like Si was clearly seen near 2.5 keV. This detection of an RRC reveals for the first time that G189.6+3.3 possesses an X-ray-emitting recombining plasma (RP). The extracted X-ray spectrum in the 0.6-10.0 keV energy band is well fitted with a model consisting of a collisional ionization equilibrium plasma component (associated with the interstellar medium) and an RP component (associated with the ejecta). The spectral feature shows that G189.6+3.3 is most likely to be a middle-aged SNR with an RP.
A deep reinforcement learning model based on deterministic policy gradient for collective neural crest cell migration

Modeling cell interactions such as co-attraction and contact-inhibition of locomotion is essential for understanding collective cell migration. Here, we propose a novel deep reinforcement learning model for collective neural crest cell migration. We apply the deep deterministic policy gradient algorithm in association with a particle dynamics simulation environment to train agents to determine the migration path. Because of the different migration mechanisms of leader and follower neural crest cells, we train two types of agents (leaders and followers) to learn the collective cell migration behavior. For a leader agent, we consider a linear combination of a global task, resulting in the shortest path to the target source, and a local task, resulting in a coordinated motion along the local chemoattractant gradient. For a follower agent, we consider only the local task. First, we show that the self-driven forces learned by the leader cell point approximately to the placode, which means that the agent is able to learn to follow the shortest path to the target. To validate our method, we compare the total time elapsed for agents to reach the placode computed using the proposed method and the time computed using an agent-based model. The distributions of the migration time intervals calculated using the two methods are shown to not differ significantly. We then study the effect of co-attraction and contact-inhibition of locomotion to the collective leader cell migration. We show that the overall leader cell migration for the case with co-attraction is slower because the co-attraction mitigates the source-driven effect. In addition, we find that the leader and follower agents learn to follow a similar migration behavior as in experimental observations. Overall, our proposed method provides useful insight on how to apply reinforcement learning techniques to simulate collective cell migration.
Gradient Descent Converges to Ridgelet Spectrum

Deep learning achieves a high generalization performance in practice, despite the non-convexity of the gradient descent learning problem. Recently, the inductive bias in deep learning has been studied through the characterization of local minima. In this study, we show that the distribution of parameters learned by gradient descent converges to a spectrum of the ridgelet transform based on a ridgelet analysis, which is a wavelet-like analysis developed for neural networks. This convergence is stronger than those shown in previous results, and guarantees the shape of the parameter distribution has been identified with the ridgelet spectrum. In numerical experiments with finite models, we visually confirm the resemblance between the distribution of learned parameters and the ridgelet spectrum. Our study provides a better understanding of the theoretical background of an inductive bias theory based on lazy regimes.
Quenched invariance principle for long range random walks in balanced random environments

We establish via a probabilistic approach the quenched invariance principle for a class of long range random walks in independent (but not necessarily identically distributed) balanced random environments, with the transition probability from $x$ to $y$ on average being comparable to $|x-y|^{-(d+\alpha)}$ with $\alpha\in (0,2]$. We use the martingale property to estimate exit time from balls and establish tightness of the scaled processes, and apply the uniqueness of the martingale problem to identify the limiting process. When $\alpha\in (0,1)$, our approach works even for non-balanced cases. When $\alpha=2$, under a diffusive with the logarithmic perturbation scaling, we show that the limit of scaled processes is a Brownian motion.
High-resolution Spectroscopic Monitoring Observations of FU Orionis–type Object, V960 Mon

We present the results of high-resolution (R ≥ 30,000) optical and near-infrared (NIR) spectroscopic monitoring observations of an FU Orionis–type object (FUor), V960 Mon, which underwent an outburst in 2014 November. We have monitored this object with the Bohyunsan Optical Echelle Spectrograph and the Immersion GRating INfrared Spectrograph since 2014 December. Various features produced by a wind, disk, and outflow/jet were detected. The wind features varied over time and continually weakened after the outburst. We detected double-peaked line profiles in the optical and NIR, and the line widths tend to decrease with increasing wavelength, indicative of Keplerian disk rotation. The disk features in the optical and NIR spectra fit well with G-type and K-type stellar spectra convolved with a kernel to account for the maximum projected disk rotation velocities of about 40.3 ± 3.8 km s−1 and 36.3 ± 3.9 km s−1, respectively. We also report the detection of [S ii] and H2 emission lines, which are jet/outflow tracers and rarely found in FUors.
Dynamic Regret of Convex and Smooth Functions

We investigate online convex optimization in non-stationary environments and choose the dynamic regret as the performance measure, defined as the difference between cumulative loss incurred by the online algorithm and that of any feasible comparator sequence. Let $T$ be the time horizon and $P_T$ be the path-length that essentially reflects the non-stationarity of environments, the state-of-the-art dynamic regret is $\mathcal{O}(\sqrt{T(1+P_T)})$. Although this bound is proved to be minimax optimal for convex functions, in this paper, we demonstrate that it is possible to further enhance the dynamic regret by exploiting the smoothness condition. Specifically, we propose novel online algorithms that are capable of leveraging smoothness and replace the dependence on $T$ in the dynamic regret by problem-dependent quantities: the variation in gradients of loss functions, and the cumulative loss of the comparator sequence. These quantities are at most $\mathcal{O}(T)$ while could be much smaller in benign environments. Therefore, our results are adaptive to the intrinsic difficulty of the problem, since the bounds are tighter than existing results for easy problems and meanwhile guarantee the same rate in the worst case.
Economically Viable Randomness

We study the problem of providing blockchain applications with \emph{economically viable randomness} (EVR), namely, randomness that has significant economic consequences. Applications of EVR include blockchain-based lotteries and gambling. An EVR source guarantees (i) secrecy, assuring that the random bits are kept secret until some predefined condition indicates that they are safe to reveal (e.g., the lottery's ticket sale closes), and (ii) robustness, guaranteeing that the random bits are published once the condition holds. We formalize the EVR problem and solve it on top of an Ethereum-like blockchain abstraction, which supports smart contracts and a transferable native coin. Randomness is generated via a distributed open commit-reveal scheme by game-theoretic agents who strive to maximize their coin holdings. Note that in an economic setting, such agents might profit from breaking secrecy or robustness, and may engage in side agreements (via smart contracts) to this end. Our solution creates an incentive structure that counters such attacks. We prove that following the protocol gives rise to a stable state, called Coalition-Proof Nash Equilibrium, from which no coalition comprised of a subset of the players can agree to deviate. In this stable state, robustness and secrecy are satisfied. Finally, we implement our EVR source over Ethereum.
Synthesis and biological evaluation of new 2,5-dimethylthiophene/furan based N-acetyl pyrazolines as selective topoisomerase II inhibitors

Based on reported pharmacophores as topoisomerase inhibitors, 2,5-dimethylthiophene/furan based N-acetyl pyrazolines were designed and envisaged as topoisomerase inhibitors. The target compounds were synthesized and tested in vitro against human topoisomerases in decatenation, relaxation, cleavage complex and DNA intercalation assays. Out of 29 compounds, three (10, 11 and 29) showed potent and selective topoisomerase II inhibitory activity with no intercalation with DNA. Further, molecular docking studies also endorsed them as ATP dependent topoisomerase II catalytic inhibitors. These compounds exerted potential anticancer effects on breast, colon, lung and prostate cancer cell lines at a low micromolar level, as compared to etoposide, and low toxicity to normal cells. Apart from the topoisomerase II inhibition, these compounds also induced a reactive oxygen species (ROS) level in cancer cells. The cell cycle analyses showed their apoptotic effect at the G1 phase.
Genetic diversity and origin of slovene common bean (Phaseolus vulgaris L.) germplasm as revealed by AFLP markers and phaseolin analysis

There is a long tradition of common bean cultivation in Slovenia, which has resulted in the development of numerous landraces in addition to newly established cultivars. The genetic diversity of 100 accessions from the Genebank of the Agricultural Institute of Slovenia (AIS) were evaluated with amplifi ed fragment length polymor- phism (AFLP) markers and phaseolin seed protein. Twenty-seven standard accessions of known Mesoamerican and Andean origin, 10 wild Phaseolus vulgaris accessions and two related species, P. coccineus L. and P. lunatus L. , were also included. Ten AFLP primer combinations produced 303 polymorphic bands, indicating a relatively high level of genetic diversity. Based on the marker data, unweighted pair group method with arithmethic mean (UPGMA) analysis and principal coordinate analysis (PCoA) all P. vulgaris accessions were separated into three well-defi ned groups. Two groups consisted of accessions of Mesoamerican and Andean origin, while the third was comprised of only four wild P. vulgaris accessions. A set of Slovene accessions formed a well-defi ned sub-group within the Andean cluster, showing their unique genetic structure. These data were supported by phaseolin analysis, which also revealed additional vari- ants of "C" and "T" phaseolin types. The results are in agreement with previous fi ndings concerning diversifi cation of common bean germplasm introduced in Europe.
Cultural Convergence: Insights into the behavior of misinformation networks on Twitter

How can the birth and evolution of ideas and communities in a network be studied over time? We use a multimodal pipeline, consisting of network mapping, topic modeling, bridging centrality, and divergence to analyze Twitter data surrounding the COVID-19 pandemic. We use network mapping to detect accounts creating content surrounding COVID-19, then Latent Dirichlet Allocation to extract topics, and bridging centrality to identify topical and non-topical bridges, before examining the distribution of each topic and bridge over time and applying Jensen-Shannon divergence of topic distributions to show communities that are converging in their topical narratives.
Benefitting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image degradations and analytically modeling these realistic degradations can prove quite difficult. In this work, we propose to handle real-world SR by splitting this ill-posed problem into two comparatively more well-posed steps. First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs. Second, we take a generic SR network trained on bicubically downsampled images to super-resolve the transformed LR image. The first step of the pipeline addresses the problem by registering the large variety of degraded images to a common, well understood space of images. The second step then leverages the already impressive performance of SR on bicubically downsampled images, sidestepping the issues of end-to-end training on datasets with many different image degradations. We demonstrate the effectiveness of our proposed method by comparing it to recent methods in real-world SR and show that our proposed approach outperforms the state-of-the-art works in terms of both qualitative and quantitative results, as well as results of an extensive user study conducted on several real image datasets.
New physics at nuSTORM

In this work we investigate the usefulness of nuSTORM as a probe of two new-physics scenarios which are sterile neutrinos and non-unitarity of the neutrino mixing matrix. For the sterile neutrino we show the importance of the neutral current events when combined with the charged current events to constrain the effective mixing angle, $\theta_{\mu \mu}$, and the sterile mixing angles $\theta_{14}$ and $\theta_{24}$. We also study the role nuSTORM will play in the study of neutrino oscillation physics if the three generation neutrino mixing matrix is non-unitary. In this context we elucidate the role of nuSTORM, considering both charged current and neutral current events, in constraining the various non-unitarity parameters such as $\alpha_{11}$, $|\alpha_{21}|$ and $\alpha_{22}$.
Bayesian metric reconstruction with gravitational wave observations

Theories of gravity extending or modifying general relativity typically allow for black hole solutions different from the Schwarzschild/Kerr geometries. Electromagnetic observations have been used to place constraints on parametrized deviations from the Schwarzschild/Kerr metrics, in an effort to gain insight on the underlying gravitational theory. In this work, we show that observations of the gravitational quasi-normal modes by existing and future interferometers can be used to bound the same parametrized black hole metrics that are constrained by electromagnetic observations (e.g. by the Event Horizon Telescope). We argue that our technique is most sensitive to changes in the background black hole metric near the circular photon orbit, and that it is robust against the changes that a gravitational theory differing from general relativity necessarily introduces in the equations for the gravitational perturbations. We demonstrate our approach by reconstructing the background metric from a set of simulated observations using a Bayesian approach.
Are spoofs from latent fingerprints a real threat for the best state-of-art liveness detectors?

We investigated the threat level of realistic attacks using latent fingerprints against sensors equipped with state-of-art liveness detectors and fingerprint verification systems which integrate such liveness algorithms. To the best of our knowledge, only a previous investigation was done with spoofs from latent prints. In this paper, we focus on using snapshot pictures of latent fingerprints. These pictures provide molds, that allows, after some digital processing, to fabricate high-quality spoofs. Taking a snapshot picture is much simpler than developing fingerprints left on a surface by magnetic powders and lifting the trace by a tape. What we are interested here is to evaluate preliminary at which extent attacks of the kind can be considered a real threat for state-of-art fingerprint liveness detectors and verification systems. To this aim, we collected a novel data set of live and spoof images fabricated with snapshot pictures of latent fingerprints. This data set provide a set of attacks at the most favourable conditions. We refer to this method and the related data set as “ScreenSpoof”. Then, we tested with it the performances of the best liveness detection algorithms, namely, the three winners of the LivDet competition. Reported results point out that the ScreenSpoof method is a threat of the same level, in terms of detection and verification errors, than that of attacks using spoofs fabricated with the full consensus of the victim. We think that this is a notable result, never reported in previous work.
Prediction of 3-dimensional structure of EMV1, a group 1 late embryogenesis abundant protein of Vigna radiata Wilczek.

Late embryogenesis abundant proteins (LEA) are associated with desiccation tolerance among photosynthetic organisms and have been reported in mono and dicot plants as well as in nematodes, yeast, bacteria and cyanobacteria. Although the functional role of LEA proteins remains speculative, there is evidence supporting their participation in acclimation and/or in the adaptive response to stress. EMV1 is a Group 1 LEA protein isolated from Vigna radiata , which is speculated to impart desiccation tolerance in plants. The homology model of this protein was generated by using the LOOPP software based on available structural homologues in protein databases. The final model obtained by molecular mechanics and dynamics method was refined and assessed by PROCHECK and shown to be reliable. The generated model could be helpful in understanding functional characteristics of this important class of desiccation tolerant protein.