Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Simulations of Heating Torques on a Luminous Body in an Accretion Disk

A luminous body embedded in an accretion disk can generate asymmetric density perturbations that lead to a net torque and thus orbital migration of the body. Linear theory has shown that this heating torque gives rise to a migration term linear in the body’s mass that can oppose or even reverse that arising from the sum of gravitational Lindblad and co-orbital torques. We use high-resolution local three-dimensional shearing sheet simulations of a zero-mass test particle in an unstratified disk to assess the accuracy and domain of applicability of the linear theory. We find agreement between analytic and simulation results to better than 10% in the low-luminosity, low thermal conductivity regime but measure deviations in both the nonlinear (high-luminosity) and high thermal conductivity regimes. In the nonlinear regime, linear theory overpredicts the acceleration due to the heating torque, potentially due to the neglect of nonlinear terms in the heat flux. In the high thermal conductivity regime, linear theory underpredicts the acceleration, which scales with a power-law index of −1 rather than −3/2, although here both nonlinear and computational constraints play a role. We discuss the impact of the heating torque for the evolution of low-mass planets in protoplanetary disks and massive stars or accreting compact objects embedded in active galactic nucleus disks. For the latter case, we show that the thermal torque is likely to be the dominant physical effect at disk radii where the optical depth drops below .
On Training and Evaluation of Neural Network Approaches for Model Predictive Control

The contribution of this paper is a framework for training and evaluation of Model Predictive Control (MPC) implemented using constrained neural networks. Recent studies have proposed to use neural networks with differentiable convex optimization layers to implement model predictive controllers. The motivation is to replace real-time optimization in safety critical feedback control systems with learnt mappings in the form of neural networks with optimization layers. Such mappings take as the input the state vector and predict the control law as the output. The learning takes place using training data generated from off-line MPC simulations. However, a general framework for characterization of learning approaches in terms of both model validation and efficient training data generation is lacking in literature. In this paper, we take the first steps towards developing such a coherent framework. We discuss how the learning problem has similarities with system identification, in particular input design, model structure selection and model validation. We consider the study of neural network architectures in PyTorch with the explicit MPC constraints implemented as a differentiable optimization layer using CVXPY. We propose an efficient approach of generating MPC input samples subject to the MPC model constraints using a hit-and-run sampler. The corresponding true outputs are generated by solving the MPC offline using OSOP. We propose different metrics to validate the resulting approaches. Our study further aims to explore the advantages of incorporating domain knowledge into the network structure from a training and evaluation perspective. Different model structures are numerically tested using the proposed framework in order to obtain more insights in the properties of constrained neural networks based MPC.
Topological origin of quantized transport in non-Hermitian Floquet chains

We show that non-Hermiticity enables topological phases with unidirectional transport in one-dimensional Floquet chains. The topological signatures of these phases are non-contractible loops in the spectrum of the Floquet propagator that are separated by an imaginary gap. Such loops occur exclusively in non-Hermitian Floquet systems. We define the corresponding topological invariant as the winding number of the Floquet propagator relative to the imaginary gap. To relate topology to transport, we introduce the concept of regularized dynamics of non-Hermitian chains. We establish that, under the conditions of regularized dynamics, transport is quantized in so far as the charge transferred over one period equals the topological winding number. We illustrate these theoretical findings with the example of a Floquet chain that features a topological phase transition and acts as a charge pump in the non-trivial topological phase. We finally discuss whether these findings justify the notion that non-Hermitian Floquet chains support topological transport.
Point Cloud Completion by Skip-Attention Network With Hierarchical Folding

Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffers from the information loss of structure details on local regions of incomplete point cloud. To address this problem, we propose Skip-Attention Network (SA-Net) for 3D point cloud completion. Our main contributions lie in the following two-folds. First, we propose a skip-attention mechanism to effectively exploit the local structure details of incomplete point clouds during the inference of missing parts. The skip-attention mechanism selectively conveys geometric information from the local regions of incomplete point clouds for the generation of complete ones at different resolutions, where the skip-attention reveals the completion process in an interpretable way. Second, in order to fully utilize the selected geometric information encoded by skip-attention mechanism at different resolutions, we propose a novel structure-preserving decoder with hierarchical folding for complete shape generation. The hierarchical folding preserves the structure of complete point cloud generated in upper layer by progressively detailing the local regions, using the skip-attentioned geometry at the same resolution. We conduct comprehensive experiments on ShapeNet and KITTI datasets, which demonstrate that the proposed SA-Net outperforms the state-of-the-art point cloud completion methods.
Planarity and Genus of Sparse Random Bipartite Graphs

The genus of the binomial random graph $G(n,p)$ is well understood for a wide range of $p=p(n)$. Recently, the study of the genus of the random bipartite graph $G(n_1,n_2,p)$, with partition classes of size $n_1$ and $n_2$, was initiated by Mohar and Ying, who showed that when $n_1$ and $n_2$ are comparable in size and $p=p(n_1,n_2)$ is significantly larger than $(n_1n_2)^{-\frac{1}{2}}$ the genus of the random bipartite graph has a similar behaviour to that of the binomial random graph. In this paper we show that there is a threshold for planarity of the random bipartite graph at $p=(n_1n_2)^{-\frac{1}{2}}$ and investigate the genus close to this threshold, extending the results of Mohar and Ying. It turns out that there is qualitatively different behaviour in the case where $n_1$ and $n_2$ are comparable, when whp the genus is linear in the number of edges, than in the case where $n_1$ is asymptotically smaller than $n_2$, when whp the genus behaves like the genus of a sparse random graph $G(n_1,q)$ for an appropriately chosen $q=q(p,n_1,n_2)$.
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems

We address the problem of resource allocation (RA) in a cognitive radio (CR) communication system with multiple secondary operators sharing spectrum with an incumbent primary operator. The key challenge of the RA problem is the inter-operator coordination arising in the optimization problem so that the aggregated interference at the primary users (PUs) does not exceed the target threshold. While this problem is easily solvable if a centralized unit could access information of all secondary operators, it becomes challenging in a realistic scenario. In this paper, considering a satellite setting, we alleviate this problem by proposing two approaches to reduce the information exchange level among the secondary operators. In the first approach, we formulate an RA scheme based on a partial information sharing method which enables distributed optimization across secondary operators. In the second approach, instead of exchanging secondary users (SUs) information, the operators only exchange their contributions of the interference-level and RA is performed locally across secondary operators. These two approaches, for the first time in this context, provide a trade-off between performance and level of inter-operator information exchange. Through the numerical simulations, we explain this trade-off and illustrate the penalty resulting from partial information exchange.
Non-Orthogonal Multiple Access for Hybrid VLC-RF Networks With Imperfect Channel State Information

This paper proposes a general framework for the energy efficiency analysis of a hybrid visible light communication (VLC) and Radio Frequency (RF) wireless system, in which both VLC and RF subsystems utilize non-orthogonal multiple access (NOMA) technology. The proposed framework is based on realistic communication scenarios as it takes into account the mobility of users and assumes imperfect channel-state information (CSI). In this context, tractable closed-form expressions are derived for the corresponding average sum-rate of NOMA-VLC and orthogonal frequency division multiple access (OFDMA)-VLC. It is shown extensively that incurred CSI errors have a considerable impact on the average energy efficiency of both NOMA-VLC and OFDMA-VLC systems. Hence, they should be taken into detailed account in the design of practical systems. We further demonstrate that the average energy efficiency of the hybrid NOMA-VLC-RF system outperforms the NOMA-VLC system under imperfect CSI. Respective computer simulations corroborate the derived analytic results, and interesting theoretical and practical insights are provided, which will be useful in the effective design and deployment of conventional VLC and hybrid VLC-RF systems.
Blind Backdoors in Deep Learning Models

We investigate a new method for injecting backdoors into machine learning models, based on poisoning the loss-value computation in the model-training code. We use it to demonstrate new classes of backdoors strictly more powerful than those in prior literature: single-pixel and physical backdoors in ImageNet models, backdoors that switch the model to a covert, privacy-violating task, and backdoors that do not require inference-time input modifications. Our attack is \emph{blind}: the attacker cannot modify the training data, nor observe the execution of his code, nor access the resulting model. Blind backdoor training uses multi-objective optimization to achieve high accuracy on both the main and backdoor tasks. Finally, we show how the blind attack can evade all known defenses, and propose new ones.
On Inexact Accelerated Proximal Gradient Methods with Relative Error Rules

One of the most popular and important first-order iterations that provides optimal complexity of the classical proximal gradient method (PGM) is the "Fast Iterative Shrinkage/Thresholding Algorithm" (FISTA). In this paper, two inexact versions of FISTA for minimizing the sum of two convex functions are studied. The proposed schemes inexactly solve their subproblems by using relative error criteria instead of exogenous and diminishing error rules. When the evaluation of the proximal operator is difficult, inexact versions of FISTA are necessary and the relative error rules proposed here may have certain advantages over previous error rules. The same optimal convergence rate of FISTA is recovered for both proposed schemes. Some numerical experiments are reported to illustrate the numerical behavior of the new approaches.
AppAngio: Revealing Contextual Information of Android App Behaviors by API-Level Audit Logs

Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps’ audit logs is one of the essential methods for the security analysts of various companies to unveil the underlying maliciousness within apps. We propose and implement AppAngio, a novel system that reveals contextual information in Android app behaviors by API-level audit logs. Our goal is to help security analysts understand how the target apps worked and facilitate the identification of the maliciousness within apps. The key module of AppAngio is identifying the path matched with the logs on the app’s control-flow graphs (CFGs). The challenge, however, is that the limited-quantity logs may incur high computational complexity in the log matching, where there are a large number of candidates caused by the coupling relation of successive logs. To address the challenge, we propose a divide and conquer strategy that precisely positions the nodes matched with log records on the corresponding CFGs and connects the nodes with as few backtracks as possible. Our experiments show that AppAngio reveals contextual information of behaviors in real-world apps. Moreover, the revealed results assist the analysts in identifying the maliciousness of app behaviors and complement existing analysis schemes. Meanwhile, AppAngio incurs negligible performance overhead on the real device in the experiments.
EASILY MEASURED CALL ATTRIBUTES CAN DETECT VOCAL DIFFERENCES BETWEEN WEDDELL SEALS FROM TWO AREAS

ABSTRACT Underwater vocalisations of Weddell seals Leptonychotes weddellii were recorded approximately 1400 km apart at Casey and Davis, Antarctica. Recordings were made during the 1992 and 1997 breeding seasons at Davis and during the 1997 season at Casey. Two observers independently analysed four attributes (start and end frequency, duration and number of elements) of narrow bandwidth calls from each location and time. There were few observer differences when the calls were grouped into four broad types (Trills, Descending Whistles, Ascending Whistles and Mews). Ascending Whistles and Mews were rare at Casey but common at Davis. Descending Whistles occurred significantly more often at Davis. Except for Trills, discriminant function analyses indicated less variation between the call attributes at Davis in 1992 and 1997 than between either of the Davis data sets and that from Casey. Vocalisation differences between Weddell seals from different areas can be detected by measuring common attributes of narrow bandwidth calls.
Fermentation of goat's milk by two DL-type mixed strain starters

Summary Variations in the biochemical performance of 2 DL-type mixed strain starters CH 01 and FDn grown in a sample of skimmed goat's milk I (obtained 2–3 weeks after parturition) and of goat's milk II (8 months after parturition) were studied, and results compared with those of the cultures grown in skimmed cow's milk. The initial levels of citrate differed in the 3 milks, with goat's milk II containing 0·7 mg/ml, goat's milk I 1·4 mg/ml and cow's milk 1·8 mg/ml. The production of diacetyl, α-acetolactic acid and acetoin was very low in goat's milk II. In goat's milk I the production of these compounds was higher, but still considerably lower than in cow's milk. Production of ethanol (EtOH) was similar in the 3 milks incubated with starter CH 01, but with FDn the production of EtOH was distinctly higher in goat's milk II. The total amount of CO2 produced corresponded with the initial level of citrate; consequently the highest production was found in cow's milk and the lowest in goat's milk II. The use of different starters for fermented products made from goat's milk in early and late lactation is suggested.
Drying of conidia of Penicillium oxalicum, a biological control agent against Fusarium wilt of tomato

The effects of freeze drying, spray drying, and fluid bed drying on the viability of Penicillium oxalicum conidia were compared. Conidia of P. oxalicum can be dried by fluid bed drying and freeze drying maintaining 100% viability after both processes, but protective additives must be added for obtaining this viability in the case of freeze drying. The best viabilities were obtained after freeze drying with non-fat skim milk (NFSM) + Tween 20, NFSM + peptone and NFSM + sucrose. However, NFSM + glycerol had only a 0.5% viability after freeze drying. Freeze-dried P. oxalicum conidia with or without additives did not maintain viability over time at room temperature, while conidia dried in a fluidized bed drier had a 40-50% viability after 180 days of storage in these conditions. Penicillium oxalicum conidial viability after spray-drying was lower than 20%. Penicillium oxalicum conidia dried by fluid bed drying were effective in reducing the incidence of Fusarium wilt of tomato under glasshouse and field conditions.
Multi-omics study revealing tissue-dependent putative mechanisms of SARS-CoV-2 drug targets on viral infections and complex diseases

Drug target prioritisation for new targets and drug repurposing of existing drugs for COVID-19 treatment are urgently needed for the current pandemic. Here we pooled 353 candidate drug targets of COVID-19 from clinical trial registries and the literature and estimated their putative causal effects in 11 SARS-CoV-2 related tissues on 622 complex human diseases. By constructing a disease atlas of drug targets for COVID-19, we prioritise 726 target-disease associations as evidence of causality using robust Mendelian randomization (MR) and colocalization evidence (http://epigraphdb.org/covid-19/ctda/). Triangulating these MR findings with historic drug trial information and the druggable genome, we ranked and prioritised three genes DHODH, ITGB5 and JAK2 targeted by three marketed drugs (Leflunomide, Cilengitide and Baricitinib) which may have repurposing potential with careful risk assessment. This study evidences the value of our integrative approach in prioritising and repurposing drug targets, which will be particularly applicable when genetic association studies of COVID-19 are available in the near future.
Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International Cohorts

Background: Suicidal behavior is highly heterogeneous and complex. A better understanding of its biological substrates and mechanisms could inform the design of more effective suicide prevention and intervention strategies. Neuroimaging studies of suicidality have so far been conducted in small samples, prone to biases and false-positive associations, yielding inconsistent results. The ENIGMA-MDD working group aims to address the issues of poor replicability and comparability by coordinating harmonized analyses across neuroimaging studies of major depressive disorder and related phenotypes, including suicidal behavior. Methods: Here, we pool data from eighteen international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy controls and 6,448 people with depression, of whom 694 had attempted suicide). We compare regional cortical thickness and surface area, and measures of subcortical, lateral ventricular and intracranial volumes between suicide attempters, clinical controls (non-attempters with depression) and healthy controls. Findings: We identified 25 regions of interest with statistically significant (FDR<0.05) differences between groups. Post-hoc examinations identified neuroimaging markers associated with suicide attempt over and above the effects of depression, including smaller volumes of the left and right thalamus and the right pallidum, and lower surface area of the left inferior parietal lobe. Interpretation: This study addresses the lack of replicability and consistency in several previously published neuroimaging studies of suicide attempt, and further demonstrates the need for well-powered samples and collaborative efforts to avoid reaching biased or misleading conclusions. Our results highlight the potential involvement of the thalamus, a structure viewed historically as a passive gateway in the brain, and the pallidum, a region linked to reward response and positive affect. Future functional and connectivity studies of suicidality may focus on understanding how these regions relate to the neurobiological mechanisms of suicide attempt risk.
The majority of male patients with COVID-19 present low testosterone levels on admission to Intensive Care in Hamburg, Germany: a retrospective cohort study.

Background. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread worldwide and pose a major public health burden. There is increasing evidence that men are more likely to die from SARS-CoV-2 infection than women. However, underlying factors that mediate the observed sex bias in coronavirus disease 2019 (COVID-19) remain unknown. Methods. In this retrospective cohort, we included COVID-19 patients who were admitted to an Intensive Care Unit at the University Hospital Hamburg-Eppendorf, Germany. We obtained demographic data of all patients who were discharged or had died by 29th April 2020. We systematically analyzed sex hormones as well as cytokine and chemokine responses in male and female patients with laboratory-confirmed SARS-CoV-2 infections upon hospital admission. We used uni- and multivariable linear regression methods to identify potential risk factors for disease severity in males and females. Findings. All enrolled patients (n=45; n=35 males and n=10 females) presented comorbidities with hypertension being the most common (45.7% in males; 40% in females), followed by cancer (35% in males; 40% in females), obesity (34.3% in males and 30% in females), type II diabetes (25.7% in males and 20% in females) and chronic heart diseases (8.6% in males and 0% in females). We detected that the vast majority of male COVID-19 patients present low testosterone (68.6%) and low dihydrotestosterone (48.6%) levels. In contrast, most female COVID-19 patients have elevated testosterone levels (60%) without alterations in dihydrotestosterone levels. Both, female and male COVID-19 patients may present elevated estradiol levels (45.7% in males and 40% in females). Disease severity defined by SOFA score correlates with elevated cytokine responses (e.g. IL-6) in males and IL-2 in females. In male COVID-19 patients, testosterone levels negatively correlate with inflammatory IL-2 and IFN-{gamma}, whereas estradiol levels positively correlate with the inflammatory cytokine IL-6. Vice versa, in female COVID-19 patients, testosterone levels positively correlate with inflammatory cytokines (e.g. IL-6). Interpretation. We here show that critically ill male COVID-19 patients suffer from severe testosterone and dihydrotestosterone deficiencies. Both androgens are required to mount antiviral immune responses to combat infection in males.