Miracle-dz/UniFAR-FTUs huggingface dataset:
Key-Aggregate Searchable Encryption (KASE) Through trapdoor for Group Data Sharing via Cloud Storage

Data distribution is an important functionality in cloud storage. In this article, we show how to securely, efﬁciently, and ﬂexibly share data with others in cloud storage. We describe new public-key cryptosystems which produce constant-size ciphertexts such that efﬁcient delegation of decryption rights for any set of ciphertexts are possible. The innovation is that one can aggregate any set of secret keys and make them as compact as a single key, but surrounding the power of all the keys being aggregated. In other words, the secret key holder can release a constant-size aggregate key for ﬂexible choices of ciphertext set in cloud storage, but the other encrypted ﬁles outside the set remain conﬁdential. This compact aggregate key can be expediently sent to others or be stored in a smart card with very limited secure storage. We provide formal security analysis of our schemes in the standard model. We also describe other application of our schemes. In particular, our schemes give the ﬁrst public-key patient-controlled encryption for ﬂexible hierarchy, which was yet to be known.
Gastroprotective effect of tarantula cubensis extract in the indomethacin-induced peptic ulcer model in rats

Background: Gastric ulcers are the most common gastrointestinal disease, due to several factors in the industrialized world. They may also occur after many pharmacological agents, combined with gastroprotective agents such as proton pump inhibitors (PPI) or anti-acids, are used as a preferred approach to maintaining gastrointestinal health. Unexpected adverse effects in these combinations make natural products an important alternative option. Methods: Therefore, the main aim of this study is to investigate one natural compound, the Tarantula cubensis extract (TCE), in the experimental peptic ulcer model which was created by a single administration of indomethacin (40 mg/kg, body weight) to fed state Wistar-albino rats. The animals were pre-treated with two-doses of the TCE (0.2 ml/kg) before the indomethacin administration. After six hours, they were euthanized and the stomach tissue was isolated for biochemical and immunohistochemical analysis. Total antioxidant status/total oxidant status (TAS/ TOS), prostaglandin E2 (PGE2), and nuclear factor kappa-B (NF-κB) levels were determined with ELISA in tissue homogenates. Caspase-3, cyclooxygenase-2 (COX- 2), inducible nitric oxide synthase (iNOS) and tumor necrosis factor-alpha (TNF-α), were visualized with immunohistochemistry in intact tissues. Results: Pre-treatment with TCE increased PGE2 levels and decreased total oxidative status (TAS/TOS). Additionally, TCE alleviated the increase of (NF-κB) levels due to the indomethacin administration. Histopathological and immunostaining results showed that TCE mitigated elevated immunoreactivity of the caspase-3, COX-2, iNOS, and TNF-α which were the results of the indomethacin administration. Conclusion: Our study demonstrated that pre-TCE treatment ameliorated indomethacin-induced peptic ulcers via antioxidant and anti-inflammatory actions.
Formation of singularities in plasma ion dynamics

We study the formation of singularity for the Euler–Poisson system equipped with the Boltzmann relation, which describes the dynamics of ions in an electrostatic plasma. In general, it is known that smooth solutions to nonlinear hyperbolic equations fail to exist globally in time. We establish criteria for C 1 blow-up of the Euler–Poisson system, both for the isothermal and pressureless cases. In particular, our blow-up condition for the pressureless model does not require that the gradient of velocity is negatively large. In fact, our result particularly implies that the smooth solutions can break down even if the gradient of initial velocity is trivial. For the isothermal case, we prove that smooth solutions leave C 1 class in a finite time when the gradients of the Riemann functions are initially large.
Every free basic convex semi-algebraic set has an LMI representation

The (matricial) solution set of a Linear Matrix Inequality (LMI) is a convex basic non-commutative semi-algebraic set. The main theorem of this paper is a converse, a result which has implications for both semidefinite programming and systems engineering. For p(x) a non-commutative polynomial in free variables x= (x1, . xg) we can substitute a tuple of symmetric matrices X= (X1, . Xg) for x and obtain a matrix p(X). Assume p is symmetric with p(0) invertible, let Ip denote the set {X: p(X) is an invertible matrix}, and let Dp denote the component of Ip containing 0. THEOREM: If the set Dp is uniformly bounded independent of the size of the matrix tuples, then Dp has an LMI representation if and only if it is convex. Linear engineering systems problems are called "dimension free" if they can be stated purely in terms of a signal flow diagram with L2 performance measures, e.g., H-infinity control. Conjecture: A dimension free problem can be made convex if and only it can be made into an LMI. The theorem here settles the core case affirmatively.
Augmenting Netflix Search with In-Session Adapted Recommendations

We motivate the need for recommendation systems that can cater to the members’ in-the-moment intent by leveraging their interactions from the current session. We provide an overview of an end-to-end in-session adaptive recommendations system in the context of Netflix Search. We discuss the challenges and potential solutions when developing such a system at production scale.
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference

With the ever increasing complexity of speciﬁcations, manual sizing for analog circuits recently became very challenging. Especially for innovative, large-scale circuits designs, with tens of design variables, operating conditions and conﬂicting objectives to be optimized, design engineers spend many weeks, running time-consuming simulations, in their attempt at ﬁnding the right conﬁguration. Recent years brought machine learning and optimization techniques to the ﬁeld of analog circuits design, with evolutionary algorithms and bayesian models showing good results for circuit sizing. In this context, we introduce a design optimization method based on Generalized Diﬀerential Evolution 3 (GDE3) and Gaussian Processes (GPs). The proposed method is able to perform sizing for complex circuits with a large number of design variables and many conﬂicting objectives to be optimized. While state-of-the-art methods reduce multi-objective problems to single-objective optimization and potentially induce a priori bias, we search directly over the multiobjective space using Pareto dominance and ensure that diverse solutions are provided to the designers to choose from. To the best of our knowledge, the proposed method is the ﬁrst to speciﬁcally address the diversity of the solutions, while also focusing on minimizing the number of simulations required to reach feasible conﬁgurations. We evaluate the introduced method on two voltage regu-lators showing diﬀerent levels of complexity and we highlight that the proposed innovative candidate selection method and survival policy leads to obtaining feasible solutions, with a high degree of diversity, much faster than with GDE3 or Bayesian Optimization-based algorithms.
Conversation Group Detection With Spatio-Temporal Context

In this work, we propose an approach for detecting conversation groups in social scenarios like cocktail parties and networking events, from overhead camera recordings. We posit the detection of conversation groups as a learning problem that could benefit from leveraging the spatial context of the surroundings, and the inherent temporal context in interpersonal dynamics which is reflected in the temporal dynamics in human behavior signals, an aspect that has not been addressed in recent prior works. This motivates our approach which consists of a dynamic LSTM-based deep learning model that predicts continuous pairwise affinity values indicating how likely two people are in the same conversation group. These affinity values are also continuous in time, since relationships and group membership do not occur instantaneously, even though the ground truths of group membership are binary. Using the predicted affinity values, we apply a graph clustering method based on Dominant Set extraction to identify the conversation groups. We benchmark the proposed method against established methods on multiple social interaction datasets. Our results showed that the proposed method improves group detection performance in data that has more temporal granularity in conversation group labels. Additionally, we provide an analysis in the predicted affinity values in relation to the conversation group detection. Finally, we demonstrate the usability of the predicted affinity values in a forecasting framework to predict group membership for a given forecast horizon.
Local boundary behaviour and the area integral of generalized harmonic functions associated with root systems

The rational Dunkl operators are commuting differential-reflection operators on the Euclidean space R associated with a root system. The aim of the paper is to study local boundary behaviour of generalized harmonic functions associated with the Dunkl operators. We introduce a Lusin-type area integral operator S by means of Dunkl’s generalized translation and the Dunkl operators. The main results are on characterizations of local existence of non-tangential boundary limits of a generalized harmonic function u in the upper half-space R + associated with the Dunkl operators, and for a subset E of R invariant under the reflection group generated by the root system, the equivalence of the following three assertions are proved: (i) u has a finite non-tangential limit at (x, 0) for a.e. x ∈ E; (ii) u is non-tangentially bounded for a.e. x ∈ E; (iii) (Su)(x) is finite for a.e. x ∈ E. 2020 MS Classification: 42B99(Primary), 31B25, 51F15(Secondary)
Direct numerical simulation of thermo-hydro-mechanical processes at Soultz-sous-For\^ets

Porosity and permeability alteration due to the thermo-poro-elastic stress field disturbance from the cold fluid injection is a deciding factor for longer, economic and safer heat extraction from an enhanced geothermal system (EGS). In the Soultz-sous-Forêts geothermal system, faulted zones are the main flow paths and the resulting porosity-permeability development over time due to stress reorientation is more sensitive in comparison with the regions without faulted zones. Available operational and field data are combined through a validated numerical simulation model to examine the mechanical impact on the pressure and temperature evolution. Results shows that near the injection wellbore zones, permeability and porosity values are strongly affected by stress field changes and permeability changes will affect the overall temperature and pressure of the system demonstrating a fully coupled phenomenon. In some regions inside the faulted zones and close to injection wellbores, porosity doubles whereas permeability may enhance up to 30 times. From the sensitivity analysis on the two unknown parameters from the mechanical aspect is performed and results shows that only one of the parameters impacts significantly on the porosity-permeability changes. Further experimental and field works on this parameter will help to model the heat extraction more precisely than before.
The host galaxy - AGN connection at low and high redshift

The properties of radio loud active galaxies (radiogalaxies, BL Lac objects and radio loud quasar) at low and high redshift are briefly reviewed and compared. The recently derived empirical relations between central black hole mass and host properties allow one to explore the demographics of massive black hole in active galaxies. It is shown that all classes of active galaxies considered exhibit very similar host properties and, consequently, have central black holes of comparable mass. The future observing capabilities would allow the investigation of the evolution of the host galaxy properties up to z $\sim$ 3, and will give insight into the joint formation of black holes and massive spheroids.
Toward Sustainable Transportation: Accelerating Vehicle Electrification With Dynamic Charging Deployment

Electric vehicles (EVs) are being actively adopted as a solution to sustainable transportation. However, a bottleneck remains with charging, where two of the main problems are the long charging time and the range anxiety of EV drivers. In this research, we investigate the deployment of dynamic charging systems, i.e., electrified roads that wirelessly charge EVs on the go, with a view to accelerating EV adoption rate. We propose a traffic-based deployment strategy, statistically quantify its impact, and apply the strategy to two case studies of real traffic in New York City (USA) and Xi’an (China). We find that our analytical estimates not only closely match the real data, but they also suggest that dynamic charging considerably extends the driving range of popular EV models in urban mobility. For example, when only 5% of the existing roads in New York City are equipped with this technology, an EV model such as the Nissan Leaf will approximately maintain its battery level without stopping to recharge. If the percentage of charging roads is increased to 10%, then the Leaf will gain nearly 10% of its battery after every 40 kilometers of driving. Our framework provides a solution to public and private organizations that support and facilitate vehicle electrification through charging infrastructure.
Quasisymmetric harmonics of the exterior algebra

Abstract We study the ring of quasisymmetric polynomials in n anticommuting (fermionic) variables. Let $R_n$ denote the ring of polynomials in n anticommuting variables. The main results of this paper show the following interesting facts about quasisymmetric polynomials in anticommuting variables: (1) The quasisymmetric polynomials in $R_n$ form a commutative subalgebra of $R_n$ . (2) There is a basis of the quotient of $R_n$ by the ideal $I_n$ generated by the quasisymmetric polynomials in $R_n$ that is indexed by ballot sequences. The Hilbert series of the quotient is given by $$ \begin{align*}\text{Hilb}_{R_n/I_n}(q) = \sum_{k=0}^{\lfloor{n/2}\rfloor} f^{(n-k,k)} q^k\,,\end{align*} $$ where $f^{(n-k,k)}$ is the number of standard tableaux of shape $(n-k,k)$ . (3) There is a basis of the ideal generated by quasisymmetric polynomials that is indexed by sequences that break the ballot condition.
Hamiltonian formalism for dynamics of particles in MOG

MOG as a modified gravity theory is designed to be replaced with dark matter. In this theory, in addition to the metric tensor, a massive vector is a gravity field where each particle has a charge proportional to the inertial mass and couples to the vector field through the four-velocity of a particle. In this work, we present the Hamiltonian formalism for the dynamics of particles in this theory. The advantage of Hamiltonian formalism is a better understanding and analyzing the dynamics of massive and massless particles. The massive particles deviate from the geodesics of space–time and photons follow the geodesics. We also study the dynamics of particles in the Newtonian and post-Newtonian regimes for observational purposes. An important result of Hamiltonian formalism is that while lensing on large scales is compatible with the observations, however the deflection angle from stellar size lensing is larger than General Relativity. This result can rule out this theory unless we introduce a screening mechanism to change the effective gravitational constant near compact objects like stars.
Perturbation formulae for quenched random dynamics with applications to open systems and extreme value theory

We consider quasi-compact linear operator cocycles Lω := Lσn−1ω ◦ · · · ◦ Lσω ◦ Lω driven by an invertible ergodic process σ : Ω → Ω, and their small perturbations Lω,ε. We prove an abstract ω-wise first-order formula for the leading Lyapunov multipliers λω,ε = λω − θω∆ω,ε + o(∆ω,ε), where ∆ω,ε quantifies the closeness of Lω,ε and Lω. We then consider the situation where Lω is a transfer operator cocycle for a random map cocycle T ω := Tσn−1ω ◦ · · · ◦ Tσω ◦ Tω and the perturbed transfer operators Lω,ε are defined by the introduction of small random holes Hω,ε in [0, 1], creating a random open dynamical system. We obtain a first-order perturbation formula in this setting, which reads λω,ε = λω − θωμω(Hω,ε) + o(μω(Hω,ε)), where μω is the unique equivariant random measure (and equilibrium state) for the original closed random dynamics. Our new machinery is then deployed to create a spectral approach for a quenched extreme value theory that considers random dynamics with general ergodic invertible driving, and random observations. An extreme value law is derived using the first-order terms θω. Further, in the setting of random piecewise expanding interval maps, we establish the existence of random equilibrium states and conditionally invariant measures for random open systems via a random perturbative approach. Finally we prove quenched statistical limit theorems for random equilibrium states arising from contracting potentials. We illustrate the theory with a variety of explicit examples. Date: June 7, 2022. 1 ar X iv :2 20 6. 02 47 1v 1 [ m at h. D S] 6 J un 2 02 2 2 JASON ATNIP, GARY FROYLAND, CECILIA GONZÁLEZ-TOKMAN, AND SANDRO VAIENTI
Geometrically-Motivated Primary-Ambient Decomposition With Center-Channel Extraction

A geometrically-motivated method for primary-ambient decomposition is proposed and evaluated in an up-mixing application. The method consists of two steps, accommo-dating a particularly intuitive explanation. The first step consists of signal-adaptive rotations applied on the input stereo scene, which translate the primary sound sources into the center of the rotated scene. The second step applies a center-channel extraction method, based on a simple signal model and optimal in the mean-squared-error sense. The performance is evaluated by using the estimated ambient component to enable surround sound starting from real-world stereo signals. The participants in the reported listening test are asked to adjust the audio scene envelopment and find the audio settings that pleases them the most. The possibility for up-mixing enabled by the proposed method is used extensively, and the user satisfaction is significantly increased compared to the original stereo mix.
Data-driven Construction of Hierarchical Matrices with Nested Bases

Hierarchical matrices provide a powerful representation for significantly reducing the computational complexity associated with dense kernel matrices. For general kernel functions, interpolation-based methods are widely used for the efficient construction of hierarchical matrices. In this paper, we present a fast hierarchical data reduction (HiDR) procedure with $O(n)$ complexity for the memory-efficient construction of hierarchical matrices with nested bases where $n$ is the number of data points. HiDR aims to reduce the given data in a hierarchical way so as to obtain $O(1)$ representations for all nearfield and farfield interactions. Based on HiDR, a linear complexity $\mathcal{H}^2$ matrix construction algorithm is proposed. The use of data-driven methods enables {better efficiency than other general-purpose methods} and flexible computation without accessing the kernel function. Experiments demonstrate significantly improved memory efficiency of the proposed data-driven method compared to interpolation-based methods over a wide range of kernels. Though the method is not optimized for any special kernel, benchmark experiments for the Coulomb kernel show that the proposed general-purpose algorithm offers competitive performance for hierarchical matrix construction compared to several state-of-the-art algorithms for the Coulomb kernel.
3D Convolutional with Attention for Action Recognition

Human action recognition is one of the challenging tasks in computer vision. The current action recognition methods use computationally expensive models for learning spatio-temporal dependencies of the action. Models utilizing RGB channels and optical flow separately, models using a two-stream fusion technique, and models consisting of both convolutional neural network (CNN) and long-short term memory (LSTM) network are few examples of such complex models. Moreover, fine-tuning such complex models is computationally expensive as well. This paper proposes a deep neural network architecture for learning such dependencies consisting of a 3D convolutional layer, fully connected (FC) layers, and attention layer, which is simpler to implement and gives a competitive performance on the UCF-101 dataset. The proposed method first learns spatial and temporal features of actions through 3D-CNN, and then the attention mechanism helps the model to locate attention to essential features for recognition.
First semi-empirical test of the white dwarf mass-radius relationship using a single white dwarf via astrometric microlensing

In November 2019 the nearby single, isolated DQ-type white dwarf LAWD 37 (WD 1142-645) aligned closely with a distant background source and caused an astrometric microlensing event. Leveraging astrometry from Gaia and followup data from the Hubble Space Telescope we measure the astrometric deflection of the background source and obtain a gravitational mass for LAWD 37. The main challenge of this analysis is in extracting the lensing signal of the faint background source whilst it is buried in the wings of LAWD 37’s point spread function. Removal of LAWD 37’s point spread function induces a significant amount of correlated noise which we find can mimic the astrometric lensing signal. We find a deflection model including correlated noise caused by the removal of LAWD 37’s point spread function best explains the data and yields a mass for LAWD 37 of 0.56 ± 0.08 M⊙. This mass is in agreement with the theoretical mass-radius relationship and cooling tracks expected for CO core white dwarfs. Furthermore, the mass is consistent with no or trace amounts of hydrogen that is expected for objects with helium-rich atmospheres like LAWD 37. We conclude that further astrometric followup data on the source is likely to improve the inference on LAWD 37’s mass at the ≈3 percent level and definitively rule out purely correlated noise explanations of the data. This work provides the first semi-empirical test of the white dwarf mass-radius relationship using a single, isolated white dwarf and supports current model atmospheres of DQ white dwarfs and white dwarf evolutionary theory.