Miracle-dz/UniFAR-FTUs huggingface dataset:
Prime+Probe 1, JavaScript 0: Overcoming Browser-based Side-Channel Defenses

The "eternal war in cache" has reached browsers, with multiple cache-based side-channel attacks and countermeasures being suggested. A common approach for countermeasures is to disable or restrict JavaScript features deemed essential for carrying out attacks. To assess the effectiveness of this approach, in this work we seek to identify those JavaScript features which are essential for carrying out a cache-based attack. We develop a sequence of attacks with progressively decreasing dependency on JavaScript features, culminating in the first browser-based side-channel attack which is constructed entirely from Cascading Style Sheets (CSS) and HTML, and works even when script execution is completely blocked. We then show that avoiding JavaScript features makes our techniques architecturally agnostic, resulting in microarchitectural website fingerprinting attacks that work across hardware platforms including Intel Core, AMD Ryzen, Samsung Exynos, and Apple M1 architectures. As a final contribution, we evaluate our techniques in hardened browser environments including the Tor browser, Deter-Fox (Cao el al., CCS 2017), and Chrome Zero (Schwartz et al., NDSS 2018). We confirm that none of these approaches completely defend against our attacks. We further argue that the protections of Chrome Zero need to be more comprehensively applied, and that the performance and user experience of Chrome Zero will be severely degraded if this approach is taken.
Reliability-Aware Quantization for Anti-Aging NPUs

Transistor aging is one of the major concerns that challenges designers in advanced technologies. It profoundly degrades the reliability of circuits during its lifetime as it slows down transistors resulting in errors due to timing violations unless large guardbands are included, which leads to considerable performance losses. When it comes to Neural Processing Units (NPUs), where increasing the inference speed is the primary goal, such performance losses cannot be tolerated. In this work, we are the first to propose a reliability-aware quantization to eliminate aging effects in NPUs while completely removing guardbands. Our technique delivers a graceful inference accuracy degradation over time while compensating for the aging-induced delay increase of the NPU. Our evaluation, over ten state-of-the-art neural network architectures trained on the ImageNet dataset, demonstrates that for an entire lifetime of 10 years, the average accuracy loss is merely 3%. In the meantime, our technique achieves 23% higher performance due to the elimination of the aging guardband.
Efficient Model Performance Estimation via Feature Histories

An important step in the task of neural network design, such as hyper-parameter optimization (HPO) or neural architecture search (NAS), is the evaluation of a candidate model's performance. Given fixed computational resources, one can either invest more time training each model to obtain more accurate estimates of final performance, or spend more time exploring a greater variety of models in the configuration space. In this work, we aim to optimize this exploration-exploitation trade-off in the context of HPO and NAS for image classification by accurately approximating a model's maximal performance early in the training process. In contrast to recent accelerated NAS methods customized for certain search spaces, e.g., requiring the search space to be differentiable, our method is flexible and imposes almost no constraints on the search space. Our method uses the evolution history of features of a network during the early stages of training to build a proxy classifier that matches the peak performance of the network under consideration. We show that our method can be combined with multiple search algorithms to find better solutions to a wide range of tasks in HPO and NAS. Using a sampling-based search algorithm and parallel computing, our method can find an architecture which is better than DARTS and with an 80% reduction in wall-clock search time.
On a log-symmetric quantile tobit model applied to female labor supply data

The study of female labor supply has been a topic of relevance in the economic literature. Generally, the data are left-censored and the classic tobit model has been extensively used in the modeling strategy. This model, however, assumes normality for the error distribution and is not recommended for data with positive skewness, heavy-tails and heteroscedasticity, as is the case of female labor supply data. Moreover, it is well-known that the quantile regression approach accounts for the influences of different quantiles in the estimated coefficients. We take all these features into account and propose a parametric quantile tobit regression model based on quantile log-symmetric distributions. The proposed method allows one to model data with positive skewness (which is not suitable for the classic tobit model), to study the influence of the quantiles of interest, and to account for heteroscedasticity. The model parameters are estimated by maximum likelihood and a Monte Carlo experiment is performed to evaluate alternative estimators. The new method is applied to two distinct female labor supply data sets. The results indicate that the log-symmetric quantile tobit model fits better the data than the classic tobit model.
Low-Rank Isomap Algorithm

The Isomap is a well-known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete eigenvalue decomposition. Although the reduction of the computational complexity of the graphing stage has been investigated, yet the eigenvalue decomposition stage remains a bottleneck in the problem. In this paper, we propose the Low-Rank Isomap algorithm by introducing a projection operator on the embedded graph from the ambient space to a low-rank latent space to facilitate applying the partial eigenvalue decomposition. This approach leads to reducing the complexity of Isomap to a linear order while preserving the structural information during the dimensionality reduction process. The superiority of the Low-Rank Isomap algorithm compared to some state-of-art algorithms is experimentally verified on facial image clustering in terms of speed and accuracy.
Tensor Laplacian Regularized Low-Rank Representation for Non-Uniformly Distributed Data Subspace Clustering

Low-Rank Representation (LRR) suffers from discarding the locality information of data points in subspace clustering. We propose a hypergraph-based model by incorporation of a variable number of adjacent nodes, locality information, and sparsity of subspaces. An optimization problem is defined and solved by developing a tensor Laplacian-based algorithm.The outperformance of the proposed method is substantial when the inherent structure of the data exploits severe nonlinearity, geometrical overlapping, and outliers.
An automated approach to mitigate transcription errors in braille texts for the Portuguese language

The quota system in Brazil made it possible to include blind studentsin higher education. Teachers’ lack of knowledge about the braillesystem can represent a barrier between them and students who useit for writing and reading. Computer-vision-based transcriptionsolutions represent mechanisms for reducing understanding restrictionson this system. However, such tools face nuisances inherentto image processing systems, e.g., illumination, noise, and scale,harming the result. This paper presents an automated approachto mitigate transcription errors in braille texts for the Portugueselanguage. We propose a selection function, combined with dictionaries,that provides the best correspondence of words based ontheir braille representation. We validated our proposal on a datasetof synthetic images by submitting them to different noise levelsand testing the proposal’s robustness. Experimental results confirmthe effectiveness of the solution compared to a standard approach.As a contribution of this paper, we expect to provide a method tosupport robust and adaptable solutions to real use conditions.
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm

Merging into the highway from the on-ramp is an essential scenario for automated driving. The decision-making under the scenario needs to balance the safety and efficiency performance to optimize a long-term objective, which is challenging due to the dynamic, stochastic, and adversarial characteristics. The Rule-based methods often lead to conservative driving on this task while the learning-based methods have difficulties meeting the safety requirements. In this paper, we propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called the Shielded Distributional Soft Actor-critic (SDSAC). The SDSAC adopts the policy evaluation with safety consideration and a safety shield parameterized with the barrier function in its offline training and online correction, respectively. These two measures support each other for better safety while not damaging the efficiency performance severely. We verify the SDSAC on an on-ramp merge scenario in simulation. The results show that the SDSAC has the best safety performance compared to baseline algorithms and achieves efficient driving simultaneously.
Novel Basic Mesoporous Catalysts for the Friedländer Reaction from 2‐Aminoaryl Ketones: Quinolin‐2(1H)‐ones versus Quinolines

The quinoline ring is present in a number of natural and synthetic products often exhibiting interesting pharmacological activities or physical properties. Different synthetic approaches for the preparation of quinolines have been reported; the Friedl nder reaction (FR) being one of the simplest and most efficient methods. FR is a baseor acid-catalyzed condensation of an aromatic 2-amino-substituted carbonyl compound (aldehyde or ketone) with a carbonyl derivative containing a reactive a-methylene group followed by cyclodehydration (Scheme 1). Generally, the annulation takes place
ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building

Scan data of urban environments often include representations of dynamic objects, such as vehicles, pedestrians, and so forth. However, when it comes to constructing a 3D point cloud map with sequential accumulations of the scan data, the dynamic objects often leave unwanted traces in the map. These traces of dynamic objects act as obstacles and thus impede mobile vehicles from achieving good localization and navigation performances. To tackle the problem, this letter presents a novel static map building method called ERASOR, Egocentric RAtio of pSeudo Occupancy-based dynamic object Removal, which is fast and robust to motion ambiguity. Our approach directs its attention to the nature of most dynamic objects in urban environments being inevitably in contact with the ground. Accordingly, we propose the novel concept called pseudo occupancy to express the occupancy of unit space and then discriminate spaces of varying occupancy. Finally, Region-wise Ground Plane Fitting (R-GPF) is adopted to distinguish static points from dynamic points within the candidate bins that potentially contain dynamic points. As experimentally verified on SemanticKITTI, our proposed method yields promising performance against state-of-the-art methods overcoming the limitations of existing ray tracing-based and visibility-based methods.
On characterization of Dirichlet-to-Neumann map of Riemannian surface with boundary

Let (M,g) be a smooth compact orientable two-dimensional Riemannian manifold (surface) with a smooth metric tensor g and smooth connected boundary Γ. Its DN-map Λg : C ∞(Γ) → C∞(Γ) is associated with the (forward) elliptic problem ∆gu = 0 in M \ Γ, u = f on Γ, and acts by Λgf := ∂νu f on Γ, where ∆g is the BeltramiLaplace operator, u = uf (x) is the solution, ν is the outward normal to Γ. The corresponding inverse problem is to determine the surface (M,g) from its DN-map Λg. We provide the necessary and sufficient conditions on an operator acting in C∞(Γ) to be the DN-map of a surface. In contrast to the known conditions by G.Henkin and V.Michel in terms of multidimensional complex analysis, our ones are based on the connections of the inverse problem with commutative Banach algebras.
Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods

Stochastically controlled stochastic gradient (SCSG) methods have been proved to converge efficiently to first-order stationary points which, however, can be saddle points in nonconvex optimization. It has been observed that a stochastic gradient descent (SGD) step introduces anistropic noise around saddle points for deep learning and non-convex half space learning problems, which indicates that SGD satisfies the correlated negative curvature (CNC) condition for these problems. Therefore, we propose to use a separate SGD step to help the SCSG method escape from strict saddle points, resulting in the CNC-SCSG method. The SGD step plays a role similar to noise injection but is more stable. We prove that the resultant algorithm converges to a second-order stationary point with a convergence rate of $\tilde{O}( \epsilon^{-2} log( 1/\epsilon))$ where $\epsilon$ is the pre-specified error tolerance. This convergence rate is independent of the problem dimension, and is faster than that of CNC-SGD. A more general framework is further designed to incorporate the proposed CNC-SCSG into any first-order method for the method to escape saddle points. Simulation studies illustrate that the proposed algorithm can escape saddle points in much fewer epochs than the gradient descent methods perturbed by either noise injection or a SGD step.
Separation of Lead, Cadmium, and Iron in Analyses of Soil Samples

The conditions of sorption of chloride complexes of Pb 2+ , Cd 2+ , Fe 2+ , and Fe 3+ ions from media of hydrochloric acid have been studied with a strongly basic anion exchanger - Ostion LG AT. The experiments used an exchanger microcolumn connected with atomic absorption spectrometer as a selective detector of metal ions. From the relation found between retention of the ions studied and hydrochloric acid concentration, conditions of separation have been suggested and used for separations of small amounts of lead and cadmium from many times higher amounts of iron and aluminium in soil extract before determination of both the foreign metals by means of AAS method. The yield of separation exceeded 95%.
Coagulation bath composition and desiccation environment as tuning parameters to prepare skinless membranes via diffusion induced phase separation

Diffusion Induced Phase Separation (DIPS) is a currently used technique to produce porous membranes for a large variety of applications. A strong limitation is represented by the occurrence of a dense skin, which is formed during the process, highly reducing the membrane permeability. To overcome this issue, two modifications of the standard DIPS protocol were investigated: the use of coagulation baths composed by a solvent/nonsolvent mixture and the desiccation in a controlled environment, by modulating the partial pressure of nonsolvent vapor. An appropriate choice of coagulation bath composition, together with an appropriate desiccation protocol (i.e., the use of a nonsolvent vapor), will produce a skinless membrane, and offers the chance to control the morphology of both membrane surfaces. These results underline the importance of post-treatment stage in membrane preparation via phase separation, thus suggesting that membrane washing/drying stage will affect the final morphology. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42151.
Synthesis and physico‐chemical characterization of networks based on methacryloxypropyl‐grafted nano‐silica and methyl methacrylate

Model hybrid materials were prepared by using a well-known four-step procedure, including (a) preparation of spherical silica particles of nanometre size and narrow size-distribution, (b) grafting of 3-methacryloxypropyl units (MOPGS) at the surface of the particles, (c) formation of stable dispersions of the grafted particles in methyl methacrylate (MMA), and (d) copolymerization of MMA and MOPGS, initiated by UV irradiation, and then completed by thermal reaction. Progressive changes in network architecture were obtained by changing the particle diameter, d, the volume fraction of filler, ϕ, the number of MOP units grafted per surface unit of silica particle, n, and the nature of the grafting agent bearing the MOP unit. Materials of good transparency were obtained, even at high volume fractions of filler. This feature is the result of an excellent dispersion of the particles within the polymer matrix, as confirmed by transmission electron microscopy and atomic force microscopy. Swelling experiments were carried out to define the conditions, and function of d, ϕ, and n for preparing networks, in which the sol phase is almost lacking. The results were rationalized by defining a new parameter: ξD = 6ϕn/(1–ϕ)d. DSC plots were used as a first characterization of the materials. The presence of filler was accompanied by a slight increase in the glass transition onset temperature, T, and a marked broadening of the transition range, ΔTg. Both T and ΔTg were shown to increase linearly as a function of ξD. Copyright © 2004 Society of Chemical Industry
Blended cognitive behavioural therapy for adolescents with panic disorder co-morbid with autism spectrum disorder: a case study

Abstract Cognitive behavioural therapy (CBT) is an effective treatment for improving anxiety symptoms in patients with autism spectrum disorder (ASD). However, some patients with ASD take extra time for learning, and they can find it hard to change their thinking styles and behaviour due to cognitive deficits. The therapist must be creative when implementing CBT in this context. Here, it may be helpful for the patient with ASD to understand CBT’s concepts by using visual aid material. Blended CBT during which the patient is shown visual aid material with his or her therapist has been suggested as effective for adults without ASD to reduce anxiety. Blended CBT combines face-to-face treatment with internet guided support and resource. Blended CBT may facilitate an understanding of essential knowledge and help people with ASD and anxiety acquire skills based on cognitive behavioural science. However, as far as we know, no previous studies have reported on the use of blended CBT for patients with panic disorder co-morbid with ASD. This study, therefore, consecutively performed 16 blended CBT sessions on a biweekly basis to treat panic disorder (PD) in an adolescent Japanese female co-morbid with ASD. The patient exhibited improvements in PD symptoms and agoraphobia after treatment: the Panic Disorder Severity Scale score decreased from 18 to 2. These results indicate that visual aid-assisted treatment may help patients with impaired imagination and social cognition related to ASD. Furthermore, this study’s therapist notes the need for paced treatments and repeated psychoeducation for patients with impairments in central coherence and cognitive flexibility. Key learning aims (1) Blended CBT may patients with panic disorder (PD) co-morbid with autism spectrum disorder (ASD) to understand concepts based on cognitive behavioural science and symptoms. (2) Blended CBT sessions can each be conducted in approximately 20 min (about one-third of the time needed for typical 45- to 90-min CBT sessions); in other words, it is less burdensome for the patient and therapist. (3) How to adjust blended CBT for those who have low average intelligence quotients (IQ) and/or ASD.
Proton Magnetic Resonance Chemical Shift of Free (Gaseous) and Associated (Liquid) Hydride Molecules

In order to study the factors giving rise to proton resonance chemical shifts of free molecules, as well as the association shifts due to hydrogen bonding, proton resonance measurements were carried out for a variety of simple hydride molecules in both the liquid and gaseous states. It is found that the proton shifts in the gaseous state can be interpreted in terms of the combined effect of the electronegativity and magnetic anisotropy of the atom to which the proton is bonded. The proton signals measured in the liquid state near the melting point, which correspond to maximum association, show large shifts to lower magnetic field relative to the corresponding gas signals. It is suggested that the association, or hydrogen bond shifts, in a system Y ——— H — X can be interpreted largely in terms of the reduction of the diamagnetic circulation in the H — X bond by the electrostatic field of the Y donor. Further possible contributions in certain anomalous cases are also considered.
Synthesis and Characterization of a ZrO2/AC Composite as a Novel Adsorbent for Dibenzothiophene

Zirconium dioxide was impregnated into a commercial activated carbon (AC) by hydrothermal treatment employing an aqueous ZrOCl2•8H2O solution. The ZrO2/AC composites thus prepared were tested as adsorbents for dibenzothiophene (DBT) from a model diesel fuel. In comparison to the pure AC, impregnation of ZrO2 was found to lead to a ca. 17% increase in the adsorption capacity. The optimal ZrO2 loading and hydrothermal pH were determined as 0.1 mmol/g and 7, respectively. The spent adsorbents were regenerated by toluene washing, with the ZrO2/AC composite exhibiting a higher adsorptive capacity than pure AC even after three such recycles. The ZrO2/AC composite was also characterized by the physical adsorption of nitrogen and Boehm titrations. The results indicated that surface acidic sites on the impregnated ZrO2 may play an important role in the improved desulphurization performance of the composite.
Soluble polycyclosilane–polysiloxane hybrid material and silicon thin film with optical properties at 193 nm and etch selectivity

A polycyclosilane precursor was synthesized to develop soluble silicon materials and silicon thin films with optical properties at 193 nm, high silicon content, and etch selectivity for O2 and CFx plasmas. A new class of polycyclosilane–polysiloxane hybrid materials and their thin films exhibited good etch selectivity and good optical properties at 193 nm without organic absorbents.