Miracle-dz/UniFAR-FTUs huggingface dataset:
A SURVEY ON SECURITY ISSUES AND SOLUTIONS IN VIRTUAL PRIVATE NETWORK

— Cloud computing is a paradigm which modernize the world around us and ease the human efforts in day-to-day life. In recent years, the cloud computing has became a promising platform for business concepts and it is one of the fastest growing factor in IT industry. The money exchange operations have undergone incredible evolutions from physical transfer to digital transfer via cloud. As the technology is developing and implementing new facilities in the cloud to perform the money transfer, the security issue remains constant. The secret data used for authenticating the money transfer remains insecure due to in between attacks makes cloud computing an insecure one. This paper investigates and presents a survey report on categories of threats and assault involved in cloud computing and various research measures taken to solve the same.
StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectories

Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural norms. It is known that socially aware AVs can be designed if there exist a mechanism to understand the behaviors of human drivers. We present a notion of Machine Theory of Mind (M-ToM) to infer the behaviors of human drivers by observing the trajectory of their vehicles. Our M-ToM approach, called StylePredict, is based on trajectory analysis of vehicles, which has been investigated in robotics and computer vision. StylePredict mimics human ToM to infer driver behaviors, or styles, using a computational mapping between the extracted trajectory of a vehicle in traffic and the driver behaviors using graph-theoretic techniques, including spectral analysis and centrality functions. We use StylePredict to analyze driver behavior in different cultures in the USA, China, India, and Singapore, based on traffic density, heterogeneity, and conformity to traffic rules and observe an inverse correlation between longitudinal (overspeeding) and lateral (overtaking, lane-changes) driving styles.
Impedance Optimization for Uncertain Contact Interactions Through Risk Sensitive Optimal Control

This letter addresses the problem of computing optimal impedance schedules for legged locomotion tasks involving complex contact interactions. We formulate the problem of impedance regulation as a trade-off between disturbance rejection and measurement uncertainty. We extend a stochastic optimal control algorithm known as Risk Sensitive Control to take into account measurement uncertainty and propose a formal way to include such uncertainty for unknown contact locations. The approach can efficiently generate optimal state and control trajectories along with local feedback control gains, i.e. impedance schedules. Extensive simulations demonstrate the capabilities of the approach in generating meaningful stiffness and damping modulation patterns before and after contact interaction. For example, contact forces are reduced during early contacts, damping increases to anticipate a high impact event and tracking is automatically traded-off for increased stability. In particular, we show a significant improvement in performance during jumping and trotting tasks with a simulated quadruped robot.
On K‐stability of some del Pezzo surfaces of Fano index 2

For every integer a⩾2$a \geqslant 2$ , we relate the K‐stability of hypersurfaces in the weighted projective space P(1,1,a,a)$\mathbb {P}(1,1,a,a)$ of degree 2a$2a$ with the GIT stability of binary forms of degree 2a$2a$ . Moreover, we prove that such a hypersurface is K‐polystable and not K‐stable if it is quasi‐smooth.
The properties of dwarf spheroidal galaxies in the Cen A group

Dwarf spheroidal galaxies (dSphs) have been extensively investigated in the Local Group, but their low luminosity and surface brightness make similar work in more distant galaxy groups challenging. Modern instrumentation unlocks the possibility of scrutinizing these faint systems in other environments, expanding the parameter space of group properties. We use MUSE spectroscopy to study the properties of 14 known or suspected dSph satellites of Cen A. Twelve targets are confirmed to be group members based on their radial velocities. Two targets are background galaxies at ∼50 Mpc: KK 198 is a face-on spiral galaxy, and dw1315−45 is an ultra-diffuse galaxy with an effective radius of ∼2300 pc. The 12 confirmed dSph members of the Cen A group have old and metal-poor stellar populations and follow the stellar metallicity-luminosity relation defined by the dwarf galaxies in the Local Group. In the three brightest dwarf galaxies (KK 197, KKs 55, and KKs 58), we identify globular clusters, as well as a planetary nebula in KK 197, although its association with this galaxy and/or the extended halo of Cen A is uncertain. Using four discrete tracers, we measure the velocity dispersion and dynamical mass of KK 197. This dSph appears dark matter dominated and lies on the radial acceleration relation of star-forming galaxies within the uncertainties. It also is consistent with predictions stemming from modified Newtonian dynamics. Surprisingly, in the dwarf KK 203 we find an extended Hα ring. Careful examination of Hubble Space Telescope photometry reveals a very low level of star formation at ages between 30 and 300 Myr. The Hα emission is most likely linked to a ∼40 Myr old supernova remnant, although other possibilities for its origin cannot be entirely ruled out.
Cost of reclaimed municipal wastewater for applications in seasonally stressed semi-arid regions

E. Gonzalez-Serrano The effectiveness and the cost of wastewater treatment options appropriate for different uses of reclaimed water in seasonally stressed regions are evaluated. Data are for applications in locales of the semi-arid regions of the Mediterranean rim and islands. These regions experience water demand stresses mainly from seasonally intense tourism and agriculture. With precipitation- derived resources fixed, recycled treated wastewater is a non-traditional, potentially cost- effective alternative water source that can be secured to meet certain demands. Different levels of wastewater treatment have been studied, Ti, satisfying effluent quality standards corresponding to four final effluent destinations: T1, for a sea outfall or surface waters (zero- alternative, for comparison); T2, for irrigation (USEPA guidelines); T3, for aquifer recharge by infiltration from spreading fields (guidelines of the California Department of Health Services) and T4, for aquifer recharge by direct injection (USA Drinking Water Standards). The result is a set of cost functions for investment I and for operation and maintenance O&M developed based on the technical design and the cost estimation of the unit processes and operations of the studied wastewater treatment plant (WWTP).
Planning under Uncertainty to Goal Distributions

Goal spaces for planning problems are typically conceived of as subsets of the state space. It is common to select a particular goal state to plan to, and the agent monitors its progress to the goal with a distance function defined over the state space. Due to numerical imprecision, state uncertainty, and stochastic dynamics, the agent will be unable to arrive at a particular state in a verifiable manner. It is therefore common to consider a goal achieved if the agent reaches a state within a small distance threshold to the goal. This approximation fails to explicitly account for the agent's state uncertainty. Point-based goals further do not accommodate goal uncertainty that arises when goals are estimated in a data-driven way. We argue that goal distributions are a more appropriate goal representation and present a novel approach to planning under uncertainty to goal distributions. We use the unscented transform to propagate state uncertainty under stochastic dynamics and use cross-entropy method to minimize the Kullback-Leibler divergence between the current state distribution and the goal distribution. We derive reductions of our cost function to commonly used goal-reaching costs such as weighted Euclidean distance, goal set indicators, chance-constrained goal sets, and maximum expectation of reaching a goal point. We explore different combinations of goal distributions, planner distributions, and divergence to illustrate behaviors achievable in our framework.
Generalized LSTM-based End-to-End Text-Independent Speaker Verification

The increasing amount of available data and more affordable hardware solutions have opened a gate to the realm of Deep Learning (DL). Due to the rapid advancements and ever-growing popularity of DL, it has begun to invade almost every field, where machine learning is applicable, by altering the traditional state-of-the-art methods. While many researchers in the speaker recognition area have also started to replace the former state-of-the-art methods with DL techniques, some of the traditional i-vector-based methods are still state-of-the-art in the context of text-independent speaker verification (TI-SV). In this paper, we discuss the most recent generalized end-to-end (GE2E) DL technique based on Long Short-term Memory (LSTM) units for TI-SV by Google and compare different scenarios and aspects including utterance duration, training time, and accuracy to prove that our method outperforms the traditional methods.
Sodium channel Nav1.2-L1342P variant displaying complex biophysical properties renders hyperexcitability of cortical neurons derived from human iPSCs

With wide adoption of whole-exome sequencing in children with seizures, an increasing number of SCN2A variants has been revealed as genetic causes of epilepsy. Voltage-gated sodium channel Nav1.2, encoded by gene SCN2A, is strongly expressed in the pyramidal excitatory neurons and supports action potential firing. One recurrent SCN2A variant is L1342P, which was identified in multiple patients with early-onset encephalopathy and intractable seizures. Despite its recurrence, how this variant affects the functions of human neurons causing severe epileptic symptoms is largely elusive. To understand the core phenotype of the L1342P variant in human neurons, here we took advantage of a reference human induced pluripotent stem cell (hiPSC) line, in which L1342P was engineered by CRISPR/Cas9 mediated genome editing. Using patch-clamping and micro-electrode array (MEA) recording, we found that the cortical neurons derived from hiPSCs engineered with heterozygous L1342P variant have increased intrinsic excitability, higher current density, and enhanced synchronous network firing and bursting, showing a strong hyperexcitability phenotype. We further demonstrated that the L1342P neuronal culture has a degree of resistance to the anti-seizure medication (phenytoin), likely recapitulating aspects of clinical observation of patients carrying the L1342P variant. We further studied the efficacy of a Nav1.2 specific blocker Phrixotoxin-3 (PTx-3), which showed strong potency in reducing spontaneous and chemically induced excitability of neurons with the L1342P variant. Our results reveal a possible pathogenic mechanism underlying L1342P mediated seizures, and demonstrate the utility of genome-edited hiPSCs as in vitro platform to advance personalized phenotyping and drug discovery.
A Distributionally Robust Optimization Approach to Unit Commitment in Microgrids

This paper proposes a distributionally robust unit commitment approach for microgrids under net load and electricity market price uncertainty. The key thrust of the proposed approach is to leverage the Kullback-Leibler divergence to construct an ambiguity set of probability distributions and formulate an optimization problem that minimizes the expected cost brought about by the worst-case distribution in the ambiguity set. The proposed approach effectively exploits historical data and capitalizes on the k-means clustering algorithm-in conjunction with the soft dynamic time warping score-to form the nominal probability distribution and its associated support. A two-level decomposition method is developed to enable the efficient solution of the devised problem. We carry out representative studies and quantify the relative merits of the proposed approach vis-à-vis a stochastic optimization-based model under different divergence tolerance values.
Modelling aspects of consciousness: a topological perspective.

Attention Schema Theory (AST) is a recent proposal to provide a scientific explanation for the basis of subjective awareness. In AST, the brain constructs a representation of attention taking place in its own (and others') mind ('the attention schema'). Moreover, this representation is incomplete for efficiency reasons. This inherent incompleteness of the attention schema results in the inability of humans to understand how their own subjective awareness arises (related to the so-called 'hard problem' of consciousness). Given this theory, the present paper asks whether a mind (either human or machine-based) that incorporates attention, and that contains a representation of its own attention, can ever have a complete representation. Using a simple yet general model and a mathematical argument based on classical topology, we show that a complete representation of attention is not possible, since it cannot faithfully represent streams of attention. In this way, the study supports one of the core aspects of AST, that the brain's representation of its own attention is necessarily incomplete.
On Counting (Quantum-)Graph Homomorphisms in Finite Fields

We study the problem of counting the number of homomorphisms from an input graph $G$ to a fixed (quantum) graph $\bar{H}$ in any finite field of prime order $\mathbb{Z}_p$. The subproblem with graph $H$ was introduced by Faben and Jerrum~[ToC'15] and its complexity is still uncharacterised despite active research, e.g. the very recent work of Focke, Goldberg, Roth, and Zivný~[SODA'21]. Our contribution is threefold. First, we introduce the study of quantum graphs to the study of modular counting homomorphisms. We show that the complexity for a quantum graph $\bar{H}$ collapses to the complexity criteria found at dimension 1: graphs. Second, in order to prove cases of intractability we establish a further reduction to the study of bipartite graphs. Lastly, we establish a dichotomy for all bipartite $(K_{3,3}\backslash\{e\},\, {domino})$-free graphs by a thorough structural study incorporating both local and global arguments. This result subsumes all results on bipartite graphs known for all prime moduli and extends them significantly. Even for the subproblem with $p=2$ this establishes new results.
Efficient Algorithms for Stochastic Repeated Second-price Auctions

Developing efficient sequential bidding strategies for repeated auctions is an important practical challenge in various marketing tasks. In this setting, the bidding agent obtains information, on both the value of the item at sale and the behavior of the other bidders, only when she wins the auction. Standard bandit theory does not apply to this problem due to the presence of action-dependent censoring. In this work, we consider second-price auctions and propose novel, efficient UCB-like algorithms for this task. These algorithms are analyzed in the stochastic setting, assuming regularity of the distribution of the opponents' bids. We provide regret upper bounds that quantify the improvement over the baseline algorithm proposed in the literature. The improvement is particularly significant in cases when the value of the auctioned item is low, yielding a spectacular reduction in the order of the worst-case regret. We further provide the first parametric lower bound for this problem that applies to generic UCB-like strategies. As an alternative, we propose more explainable strategies which are reminiscent of the Explore Then Commit bandit algorithm. We provide a critical analysis of this class of strategies, showing both important advantages and limitations. In particular, we provide a minimax lower bound and propose a nearly minimax-optimal instance of this class.
Point Cloud Registration Based on Consistency Evaluation of Rigid Transformation in Parameter Space

We can use a method called registration to integrate some point clouds that represent the shape of the real world. In this paper, we propose highly accurate and stable registration method. Our method detects keypoints from point clouds and generates triplets using multiple descriptors. Furthermore, our method evaluates the consistency of rigid transformation parameters of each triplet with histograms and obtains the rigid transformation between the point clouds. In the experiment of this paper, our method had minimul errors and no major failures. As a result, we obtained sufficiently accurate and stable registration results compared to the comparative methods.
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning*

For autonomous vehicles, effective behavior planning is crucial to ensure safety of the ego car. In many urban scenarios, it is hard to create sufficiently general heuristic rules, especially for challenging scenarios that some new human drivers find difficult. In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments. Application of the hierarchical structure [1] allows the various layers of the behavior planning system to be satisfied. Our algorithms can perform better than heuristic-rule-based methods for elective decisions such as when to turn left between vehicles approaching from the opposite direction or possible lane-change when approaching an intersection due to lane blockage or delay in front of the ego car. Such behavior is hard to evaluate as correct or incorrect, but some aggressive expert human drivers handle such scenarios effectively and quickly. On the other hand, compared to traditional RL methods, our algorithm is more sample-efficient, due to the use of a hybrid reward mechanism and heuristic exploration during the training process. The results also show that the proposed method converges to an optimal policy faster than traditional RL methods.
Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation

We propose a method for incorporating object interaction and human body dynamics into the task of 3D ego-pose estimation using a head-mounted camera. We use a kinematics model of the human body to represent the entire range of human motion, and a dynamics model of the body to interact with objects inside a physics simulator. By bringing together object modeling, kinematics modeling, and dynamics modeling in a reinforcement learning (RL) framework, we enable object-aware 3D ego-pose estimation. We devise several representational innovations through the design of the state and action space to incorporate 3D scene context and improve pose estimation quality. We also construct a fine-tuning step to correct the drift and refine the estimated human-object interaction. This is the first work to estimate a physically valid 3D full-body interaction sequence with objects (e.g., chairs, boxes, obstacles) from egocentric videos. Experiments with both controlled and in-the-wild settings show that our method can successfully extract an object-conditioned 3D ego-pose sequence that is consistent with the laws of physics.
Multi-document Summarization via Deep Learning Techniques: A Survey

Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our survey, the first of its kind, systematically overviews the recent deep-learning-based MDS models. We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state of the art. We highlight the differences between various objective functions that are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting field.
Safe Trajectory Planning Using Reinforcement Learning for Self Driving

Self-driving vehicles must be able to act intelligently in diverse and difficult environments, marked by high-dimensional state spaces, a myriad of optimization objectives and complex behaviors. Traditionally, classical optimization and search techniques have been applied to the problem of self-driving; but they do not fully address operations in environments with high-dimensional states and complex behaviors. Recently, imitation learning has been proposed for the task of self-driving; but it is labor-intensive to obtain enough training data. Reinforcement learning has been proposed as a way to directly control the car, but this has safety and comfort concerns. We propose using model-free reinforcement learning for the trajectory planning stage of self-driving and show that this approach allows us to operate the car in a more safe, general and comfortable manner, required for the task of self driving.
Enhancing Low-Quality Voice Recordings Using Disentangled Channel Factor and Neural Waveform Model

High-quality speech corpora are essential foundations for most speech applications. However, such speech data are expensive and limited since they are collected in professional recording environments. In this work, we propose an encoder-decoder neural network to automatically enhance low-quality recordings to professional high-quality recordings. To address channel variability, we first filter out the channel characteristics from the original input audio using the encoder network with adversarial training. Next, we disentangle the channel factor from a reference audio. Conditioned on this factor, an auto-regressive decoder is then used to predict the target-environment Mel spectrogram. Finally, we apply a neural vocoder to synthesize the speech waveform. Experimental results show that the proposed system can generate a professional high-quality speech waveform when setting high-quality audio as the reference. It also improves speech enhancement performance compared with several state-of-the-art baseline systems.
Efficiency Models for GaN-Based Light-Emitting Diodes: Status and Challenges

Light-emitting diodes (LEDs) based on Gallium Nitride (GaN) have been revolutionizing various applications in lighting, displays, biotechnology, and other fields. However, their energy efficiency is still below expectations in many cases. An unprecedented diversity of theoretical models has been developed for efficiency analysis and GaN-LED design optimization, including carrier transport models, quantum well recombination models, and light extraction models. This invited review paper provides an overview of the modeling landscape and pays special attention to the influence of III-nitride material properties. It thereby identifies some key challenges and directions for future improvements.