Miracle-dz/UniFAR-FTUs huggingface dataset:
Enriching Vulnerability Reports Through Automated and Augmented Description Summarization

Security incidents and data breaches are increasing rapidly, and only a fraction of them is being reported. Public vulnerability databases, e.g., national vulnerability database (NVD) and common vulnerability and exposure (CVE), have been leading the effort in documenting vulnerabilities and sharing them to aid defenses. Both are known for many issues, including brief vulnerability descriptions. Those descriptions play an important role in communicating the vulnerability information to security analysts in order to develop the appropriate countermeasure. Many resources provide additional information about vulnerabilities, however, they are not utilized to boost public repositories. In this paper, we devise a pipeline to augment vulnerability description through third party reference (hyperlink) scrapping. To normalize the description, we build a natural language summarization pipeline utilizing a pretrained language model that is fine-tuned using labeled instances and evaluate its performance against both human evaluation (golden standard) and computational metrics, showing initial promising results in terms of summary fluency, completeness, correctness, and understanding.
PlaneDepth: Plane-Based Self-Supervised Monocular Depth Estimation

—Self-supervised monocular depth estimation refers to training a monocular depth estimation (MDE) network using only RGB images to overcome the difﬁculty of collecting dense ground truth depth. Many previous works addressed this problem using depth classiﬁcation or depth regression. However, depth classiﬁcation tends to fall into local minima due to the bilinear interpolation search on the target view. Depth classiﬁcation overcomes this problem using pre-divided depth bins, but those depth candidates lead to discontinuities in the ﬁnal depth result, and using the same probability for weighted summation of color and depth is ambiguous. To overcome these limitations, we use some predeﬁned planes that are parallel to the ground, allowing us to automatically segment the ground and predict continuous depth for it. We further model depth as a mixture Laplace distribution, which provides a more certain objective for optimization. Previous works have shown that MDE networks only use the vertical image position of objects to estimate the depth and ignore relative sizes. We address this problem for the ﬁrst time in both stereo and monocular training using resize cropping data augmentation. Based on our analysis of resize cropping, we combine it with our plane deﬁnition and improve our training strategy so that the network could learn the relationship between depth and both the vertical image position and relative size of objects. We further combine the self-distillation stage with post-processing to provide more accurate supervision and save extra time in post-processing. We conduct extensive experiments to demonstrate the effectiveness of our analysis and improvements.
Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices

Stress is considered to be the epidemic of the 21st-century. Yet, mobile apps cannot directly evaluate the impact of their content and services on user stress. We introduce the Beam AI SDK to address this issue. Using our SDK, apps can monitor user stress through the selfie camera in real-time. Our technology extracts the user's pulse wave by analyzing subtle color variations across the skin regions of the user's face. The user's pulse wave is then used to determine stress (according to the Baevsky Stress Index), heart rate, and heart rate variability. We evaluate our technology on the UBFC dataset, the MMSE-HR dataset, and Beam AI's internal data. Our technology achieves 99.2%, 97.8% and 98.5% accuracy for heart rate estimation on each benchmark respectively, a nearly twice lower error rate than competing methods. We further demonstrate an average Pearson correlation of 0.801 in determining stress and heart rate variability, thus producing commercially useful readings to derive content decisions in apps. Our SDK is available for use at www.beamhealth. ai.
Blockchain-Based Decentralized Knowledge Marketplace Using Active Inference

A knowledge market can be described as a type of market where there is a consistent supply of data to satisfy the demand for information and is responsible for the mapping of potential problem solvers with the entities which need these solutions. It is possible to define them as value-exchange systems in which the dynamic features of the creation and exchange of intellectual assets serve as the fundamental drivers of the frequency, nature, and outcomes of interactions among various stakeholders. Furthermore, the provision of financial backing for research is an essential component in the process of developing a knowledge market that is capable of enduring over time, and it is also an essential driver of the progression of scientific investigation. This paper underlines flaws associated with the conventional knowledge-based market, including but not limited to excessive financing concentration, ineffective information exchange, a lack of security, mapping of entities, etc. The authors present a decentralized framework for the knowledge marketplace incorporating technologies such as blockchain, active inference, zero-knowledge proof, etc. The proposed decentralized framework provides not only an efficient mapping mechanism to map entities in the marketplace but also a more secure and controlled way to share knowledge and services among various stakeholders.
Hyperbolic Deep Reinforcement Learning

We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space. Sequential decision-making requires reasoning about the possible future consequences of current behavior. Consequently, capturing the relationship between key evolving features for a given task is conducive to recovering effective policies. To this end, hyperbolic geometry provides deep RL models with a natural basis to precisely encode this inherently hierarchical information. However, applying existing methodologies from the hyperbolic deep learning literature leads to fatal optimization instabilities due to the non-stationarity and variance characterizing RL gradient estimators. Hence, we design a new general method that counteracts such optimization challenges and enables stable end-to-end learning with deep hyperbolic representations. We empirically validate our framework by applying it to popular on-policy and off-policy RL algorithms on the Procgen and Atari 100K benchmarks, attaining near universal performance and generalization benefits. Given its natural fit, we hope future RL research will consider hyperbolic representations as a standard tool.
A numerical model preserving nontrivial steady-state solutions for predicting waves run-up on coastal areas

In this study, a numerical model preserving a class of nontrivial steady-state solutions is proposed to predict waves propagation and waves run-up on coastal zones. The numerical model is based on the Saint-Venant system with source terms due to variable bottom topography and bed friction effects. The resulting nonlinear system is solved using a Godunov-type finite volume method on unstructured triangular grids. A special piecewise linear reconstruction of the solution is implemented with a correction technique to ensure the accuracy of the method and the positivity of the computed water depth. Efficient semi-implicit techniques for the friction terms and a well-balanced formulation for the bottom topography are used to exactly preserve stationary steady-state s solutions. Moreover, we prove that the numerical scheme preserves a class of nontrivial steady-state solutions. To validate the proposed numerical model against experiments, we first demonstrate its ability to preserve nontrivial steady-state solutions and then we model several laboratory experiments for the prediction of waves run-up on sloping beaches. The numerical simulations are in good agreement with laboratory experiments which confirms the robustness and accuracy of the proposed numerical model in predicting waves propagation on coastal areas.
Maximum principle for discrete time mean-field stochastic optimal control problems

In this paper, we study the optimal control of a discrete-time stochastic dif-ferential equation (SDE) of mean-ﬁeld type, where the coeﬃcients can depend on both a function of the law and the state of the process. We establish a new version of the maximum principle for discrete-time stochastic optimal control problems. Moreover, the cost functional is also of the mean-ﬁeld type. This maximum principle diﬀers from the classical principle since we introduce new discrete-time backward (matrix) stochastic equations. Based on the discrete-time backward stochastic equations where the adjoint equations turn out to be discrete backward SDEs with mean ﬁeld, we obtain necessary ﬁrst-order and suﬃcient optimality conditions for the stochastic discrete optimal control problem. To verify, we apply the result to production and consumption choice optimization problem.
Learning in online MDPs: is there a price for handling the communicating case?

It is a remarkable fact that the same $O(\sqrt{T})$ regret rate can be achieved in both the Experts Problem and the Adversarial Multi-Armed Bandit problem albeit with a worse dependence on number of actions in the latter case. In contrast, it has been shown that handling online MDPs with communicating structure and bandit information incurs $\Omega(T^{2/3})$ regret even in the case of deterministic transitions. Is this the price we pay for handling communicating structure or is it because we also have bandit feedback? In this paper we show that with full information, online MDPs can still be learned at an $O(\sqrt{T})$ rate even in the presence of communicating structure. We first show this by proposing an efficient follow the perturbed leader (FPL) algorithm for the deterministic transition case. We then extend our scope to consider stochastic transitions where we first give an inefficient $O(\sqrt{T})$-regret algorithm (with a mild additional condition on the dynamics). Then we show how to achieve $O\left(\sqrt{\frac{T}{\alpha}}\right)$ regret rate using an oracle-efficient algorithm but with the additional restriction that the starting state distribution has mass at least $\alpha$ on each state.
Detecting and denoising gravitational wave signals from binary black holes using deep learning

We present a convolutional neural network, designed in the auto-encoder configuration that can detect and denoise astrophysical gravitational waves from merging black hole binaries, orders of magnitude faster than the conventional matched-filtering based detection that is currently employed at advanced LIGO (aLIGO). The Neural-Net architecture is such that it learns from the sparse representation of data in the time-frequency domain and constructs a non-linear mapping function that maps this representation into two separate masks for signal and noise, facilitating the separation of the two, from raw data. This approach is the first of its kind to apply machine learning based gravitational wave detection/denoising in the 2D representation of gravitational wave data. We applied our formalism to the first gravitational wave event detected, GW150914, successfully recovering the signal at all three phases of coalescence at both detectors. This method is further tested on the gravitational wave data from the second observing run ($O2$) of aLIGO, reproducing all binary black hole mergers detected in $O2$ at both the aLIGO detectors. The Neural-Net seems to have uncovered a pattern of 'ringing' after the ringdown phase of the coalescence, which is not a feature that is present in the conventional binary merger templates. This method can also interpolate and extrapolate between modeled templates and explore gravitational waves that are unmodeled and hence not present in the template bank of signals used in the matched-filtering detection pipelines. Faster and efficient detection schemes, such as this method, will be instrumental as ground based detectors reach their design sensitivity, likely to result in several hundreds of potential detections in a few months of observing runs.
On the possible implications of Dark Matter in the rings of Saturn: a conjecture

In this article we discuss some consequences of the well-known proposition of Fritz Zwicky [1], published in the nineteen thirties, that Dark Matter `mimics' the inertia-gravitational behaviour of usual matter. In particular, we consider some special dynamical regions such as those of the Ring Systems of the gaseous giants at the edge of the Planetary System. This article is a continuation of an earlier paper [2], where it was shown that gravitationally interacting particles may remain near the Lagrange Points L4 and L5 for many thousands of years. This provides enough time for the Dark Matter, if present there, to interact with the usual matter. We discuss also a number of questions related to places which might be considered singular in the mathematical sense.
Robust Multi-Agent Coordination from CaTL+ Specifications

We consider the problem of controlling a heterogeneous multi-agent system required to satisfy temporal logic requirements. Capability Temporal Logic (CaTL) was recently proposed to formalize such specifications for deploying a team of autonomous agents with different capabilities and cooperation requirements. In this paper, we extend CaTL to a new logic CaTL+, which is more expressive than CaTL and has semantics over a continuous workspace shared by all agents. We define a novel robustness metric for CaTL+, which is sound, differentiable almost everywhere and eliminates masking, which is one of the main limitations of existing traditional robustness metrics. We formulate a control synthesis problem to maximize CaTL+ robustness and propose a two-step optimization method to solve this problem. Simulation results are included to illustrate the increased expressivity of CaTL+ and the efficacy of the proposed control synthesis approach.
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification

Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and that generalizes to previously unseen re-identification tasks. The main challenge is that at inference time the representation must generalize to previously unseen identities. To address this problem, we apply continual meta metric learning to lifelong object re-identification. To prevent forgetting of previous tasks, we use knowledge distillation and explore the roles of positive and negative pairs. Based on our observation that the distillation and metric losses are antagonistic, we propose to remove positive pairs from distillation to robustify model updates. Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on extensive intra-domain experiments on person and vehicle re-identification datasets, as well as inter-domain experiments on the LReID benchmark. Our experiments demonstrate that DwoPP significantly outperforms the state-of-the-art. The code is here: https://github.com/wangkai930418/DwoPP_code
A Framework for Web Services Retrieval Using Bio Inspired Clustering

Efficiently discovering relevant Web services with respect to a specific user query has become a growing challenge owing to the incredible growth in the field of web technologies. In previous works, different clustering models have been used to address these issues. But, most of the traditional clustering techniques are computationally intensive and fail to address all the problems involved. Also, the current standards fail to incorporate the semantic relatedness of Web services during clustering and retrieval resulting in decreased performance. In this paper, we propose a framework for web services retrieval that uses a bottom-up, decentralized and self organising approach to cluster available services. It also provides online, dynamic computation of clusters thus overcoming the drawbacks of traditional clustering methods. We also use the semantic similarity between Web services for the clustering process to enhance the precision and lower the recall.
UltraBots: Large-Area Mid-Air Haptics for VR with Robotically Actuated Ultrasound Transducers

We introduce UltraBots, a system that combines ultrasound haptic feedback and robotic actuation for large-area mid-air haptics for VR. Ultrasound haptics can provide precise mid-air haptic feedback and versatile shape rendering, but the interaction area is often limited by the small size of the ultrasound devices, restricting the possible interactions for VR. To address this problem, this paper introduces a novel approach that combines robotic actuation with ultrasound haptics. More specifically, we will attach ultrasound transducer arrays to tabletop mobile robots or robotic arms for scalable, extendable, and translatable interaction areas. We plan to use Sony Toio robots for 2D translation and/or commercially available robotic arms for 3D translation. Using robotic actuation and hand tracking measured by a VR HMD (ex: Oculus Quest), our system can keep the ultrasound transducers underneath the user’s hands to provide on-demand haptics. We demonstrate applications with workspace environments, medical training, education and entertainment.
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts

In this work we present a Mixture of Task-Aware Experts Network for Machine Reading Comprehension on a relatively small dataset. We particularly focus on the issue of commonsense learning, enforcing the common ground knowledge by speciﬁcally training different expert networks to capture different kinds of relationships between each passage, question and choice triplet. Moreover, we take inspiration on the recent advancements of multi-task and transfer learning by training each network a relevant focused task. By making the mixture-of-networks aware of a speciﬁc goal by enforcing a task and a relationship, we achieve state-of-the-art results and reduce over-ﬁtting.
The elemental composition of the Sun: II. The iron group elements Sc to Ni

We redetermine the abundances of all iron group nuclei in the Sun, based on neutral and singly-ionised lines of Sc, Ti, V, Mn, Fe, Co and Ni in the solar spectrum. We employ a realistic 3D hydrodynamic model solar atmosphere, corrections for departures from local thermodynamic equilibrium (NLTE), stringent line selection procedures and high quality observational data. We have scoured the literature for the best quality oscillator strengths, hyperfine constants and isotopic separations available for our chosen lines. We find log ∈ = 3.16 ± 0.04, log ∈ = 4.93 ± 0.04, log ∈ = 3.89 ± 0.08, log ∈ = 5.62 ± 0.04, log ∈ = 5.42 ± 0.04, log ∈ = 7.47 ± 0.04, log ∈ = 4.93 ± 0.05 and log ∈ = 6.20 ± 0.04. Our uncertainties factor in both statistical and systematic errors (the latter estimated for possible errors in the model atmospheres and NLTE line formation). The new abundances are generally in good agreement with the CI meteoritic abundances but with some notable exceptions. This analysis constitutes both a full exposition and a slight update of the preliminary results we presented in Asplund et al. (2009, ARA&A, 47, 481), including full line lists and details of all input data we employed.
Plasticity Neural Network Based on Astrocytic eﬀects at Critical Period, Synaptic Competition and Strength Rebalance by Current and Mnemonic Brain Plasticity and Synapse Formation

phagocytosis of diﬀerent variables and synaptic positions is revoked is determined by the correlation coeﬃcient of the corresponding time range, proves simple and eﬀective. Therefore, is it possible to reduce the number of animal experiments and their suﬀering by simulating and planning the factors of biological experiments through Deep Learning? And our question is can model construction, formula derivation and algorithm testing serve to promote the development of computational neuroscience and brain cognition?
The hidden factor: accounting for covariate effects in power and sample size computation for a binary trait

Abstract Motivation Accurate power and sample size estimation is crucial to the design and analysis of genetic association studies. When analyzing a binary trait via logistic regression, important covariates such as age and sex are typically included in the model. However, their effects are rarely properly considered in power or sample size computation during study planning. Unlike when analyzing a continuous trait, the power of association testing between a binary trait and a genetic variant depends, explicitly, on covariate effects, even under the assumption of gene–environment independence. Earlier work recognizes this hidden factor but the implemented methods are not flexible. We thus propose and implement a generalized method for estimating power and sample size for (discovery or replication) association studies of binary traits that (i) accommodates different types of nongenetic covariates E, (ii) deals with different types of G–E relationships, and (iii) is computationally efficient. Results Extensive simulation studies show that the proposed method is accurate and computationally efficient for both prospective and retrospective sampling designs with various covariate structures. A proof-of-principle application focused on the understudied African sample in the UK Biobank data. Results show that, in contrast to studying the continuous blood pressure trait, when analyzing the binary hypertension trait ignoring covariate effects of age and sex leads to overestimated power and underestimated replication sample size. Availability and implementation The simulated datasets can be found on the online web-page of this manuscript, and the UK Biobank application data can be accessed at https://www.ukbiobank. ac.uk. The R package SPCompute that implements the proposed method is available at CRAN. The genome-wide association studies are carried out using the software PLINK 2.0 [Purcell et al. (Plink: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559–75.)].
Effect of tau neutrino contribution to muon signals at neutrino factories

We discuss precision measurements of the leading atmospheric parameters at a standard neutrino factory with a detector that is sensitive to muons alone. The oscillation of the muon and electron neutrinos in the neutrino factory beam to tau neutrinos adds to the muon events sample (both right sign and wrong sign) via leptonic decays of the taus produced through charge-current interactions in the detector. In particular, we study how this affects a precision measurement of the atmospheric mixing parameters and the deviation of ${\ensuremath{\nu}}_{\ensuremath{\mu}}\ensuremath{\leftrightarrow}{\ensuremath{\nu}}_{\ensuremath{\tau}}$ mixing from maximality. In spite of the enhancement of the number of events due to the additional tau contribution, the determination of the atmospheric mixing angle and the deviation from maximality will be poorer. We show that it is impossible to devise satisfactory cuts to remove this tau contamination. Neglect of these tau contributions will lead to an incorrect conclusion about the precision obtainable at a neutrino factory.