Miracle-dz/UniFAR-FTUs huggingface dataset:
Vulnerability of the Neural Networks Against Adversarial Examples: A Survey

With further development in the fields of computer vision, network security, natural language processing and so on so forth, deep learning technology gradually exposed certain security risks. The existing deep learning algorithms cannot effectively describe the essential characteristics of data, making the algorithm unable to give the correct result in the face of malicious input. Based on current security threats faced by deep learning, this paper introduces the problem of adversarial examples in deep learning, sorts out the existing attack and defense methods of the black box and white box, and classifies them. It briefly describes the application of some adversarial examples in different scenarios in recent years, compares several defense technologies of adversarial examples, and finally summarizes the problems in this research field and prospects for its future development. This paper introduces the common white box attack methods in detail, and further compares the similarities and differences between the attack of the black and white box. Correspondingly, the author also introduces the defense methods, and analyzes the performance of these methods against the black and white box attack.
ShadowNet: A Secure and Efficient System for On-device Model Inference

On-device machine learning (ML) is getting more and more popular as fast evolving AI accelerators emerge on mobile and edge devices. Consequently, thousands of proprietary ML models are being deployed on billions of untrusted devices, raising a serious security concern about the model privacy. However, how to protect the model privacy without losing access to the AI accelerators is a challenging problem. This paper presents a novel on-device model inference system called ShadowNet, which protects the model privacy with TEE while securely outsourcing the heavy linear layers of the model onto the hardware accelerators without trusting them. ShadowNet achieves it by transforming the weights of the linear layers before outsourcing them and restoring the results inside the TEE. The nonlinear layers are also kept secure inside the TEE. The transformation of the weights and the restoration of the results are designed in a way that can be implemented efficiently. We have built a ShadowNet prototype based on TensorFlow Lite and applied it on three popular CNNs including AlexNet, MiniVGG and MobileNets. Our evaluation shows that the ShadowNet transformed models have comparable performance with the original models, offering a promising solution for secure on-device model inference.
Shortcuts to dynamic polarization

Dynamic polarization protocols aim to hyperpolarize a spin bath by transferring spin polarization from a well-controlled qubit. Building on techniques from shortcuts to adiabaticity, the authors characterize and design efficient dynamic polarization protocols in central spin models. These protocols maximize the transfer of polarization via bright states through local counterdiabatic driving, efficiently depopulate dark states, and can be experimentally realized through Floquet engineering provided a speed limit is satisfied.
Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts

Aspect Based Sentiment Analysis (ABSA) deals with extracting aspects from a given text and then allocate each aspect a sentiment level (positive, negative or neutral) [1]. A number of researchers have addressed ABSA in different domains except for the legal domain. In this study, we explore how the concepts related to aspect-based sentiment analysis can be used in the legal domain to extract valuable information from legal opinion text. To this regard, we propose a rule based approach to perform aspect-based sentiment analysis in order to figure out the sentiment level of a sentence in relation to each legal party related to a court case (considering the legal parties as the aspects).
A back-linked Fabry–Pérot interferometer for space-borne gravitational wave observations

Direct observations of gravitational waves at frequencies below 10 Hz will play a crucial role in fully exploiting the potential of gravitational wave astronomy. One approach to pursue this direction is the utilization of laser interferometers equipped with Fabry–Pérot optical cavities in space. However, there are a number of practical challenges in following this path. In particular, the implementation of precision control for cavity lengths and the suppression of laser phase noises may prevent a practical detector design. To circumvent such difficulties, we propose a new interferometer topology, called a back-linked Fabry–Pérot interferometer, where precision length controls are not required and an offline subtraction scheme for laser phase noises is readily applicable. This article presents the principle idea and the associated sensitivity analyses. Despite additional noises, a strain sensitivity of $$7\times10^{-23}\,\textrm{Hz}^{-1/2}$$ may be attainable in the decihertz band. Several technological developments must occur and studies must be carried out to pave the way for the implementation.
Teaching deep learning causal effects improves predictive performance

Causal inference is a powerful statistical methodology for explanatory analysis and individualized treatment effect (ITE) estimation, a prominent causal inference task that has become a fundamental research problem. ITE estimation, when performed naively, tends to produce biased estimates. To obtain unbiased estimates, counterfactual information is needed, which is not directly observable from data. Based on mature domain knowledge, reliable traditional methods to estimate ITE exist. In recent years, neural networks have been widely used in clinical studies. Specifically, recurrent neural networks (RNN) have been applied to temporal Electronic Health Records (EHR) data analysis. However, RNNs are not guaranteed to automatically discover causal knowledge, correctly estimate counterfactual information, and thus correctly estimate the ITE. This lack of correct ITE estimates can hinder the performance of the model. In this work we study whether RNNs can be guided to correctly incorporate ITE-related knowledge and whether this improves predictive performance. Specifically, we first describe a Causal-Temporal Structure for temporal EHR data; then based on this structure, we estimate sequential ITE along the timeline, using sequential Propensity Score Matching (PSM); and finally, we propose a knowledge-guided neural network methodology to incorporate estimated ITE. We demonstrate on real-world and synthetic data (where the actual ITEs are known) that the proposed methodology can significantly improve the prediction performance of RNN.
Magnon-assisted photon-phonon conversion in the presence of structured environments

Quantum conversion or interface is one of the most prominent protocols in quantum information processing and quantum state engineering. We propose a photon-phonon conversion protocol in a hybrid magnomechanical system comprising a microwave optical mode, a driven magnon mode and a mechanical-vibrating mode. The microwave photons in the optical cavity are coupled to the magnons by the magnetic-dipole interaction, and the latter are coupled to the mechanical phonons by the magnetostrictive interaction. With strong photon-magnon interaction and strong driving on magnon, an effective Hamiltonian is constructed to describe the conversion between photons and phonons nearby their resonant point. The cavity-magnon system can then play the role of a quantum memory. Moreover, the faithfulness of the photon-phonon conversion is estimated in terms of fidelities for state evolution and state-independent transfer. The former is discussed in the Lindblad master equation taking account the leakages of photon, phonon and magnon into consideration. The latter is derived by the Heisenberg-Langevin equation considering the non-Markovian noise from the structured environments for both optical and mechanical modes. The state-evolution fidelity is found to be robust to the weak leakage. The transfer fidelity can be maintained by the Ohmic and sub-Ohmic environments of the photons and is insensitive to the $1/f$ noise of the phonons. Our work thus provides an interesting application for the magnon system as a photon-phonon converter in the microwave regime.
Modelling of Milk Solids Extraction from Tigernut (Cyperus Esculentus L) Tubers Using Response Surface Methodology

Vegetable milk solids from black and brown varieties of tiger nut (Cyperus esculentus L) tubers were extracted using water. Response surface methodology was used to determine the optimum conditions for extraction. The factors and their levels were boiling time (10.125, 240 minutes), milling time (5, 17.5, 30 minutes), and tuber: water (1: 2, 1:5, and 1: 8). The data obtained were fitted to second order polynomial models to enable the determination of variables for the estimation and prediction of the extraction of milk solids. The optimum extraction conditions were for the black cultivar; milling time, 23.8 minutes; tuber: water, 1 g: 7.8 ml; boiling time, 240 minutes and for the brown cultivar, milling time, 22.0 minutes; tuber: water, 1 g: 7.2 ml; boiling time, 10 minutes. Under the experimental conditions the expected milk solids for the black tubers was 65.07 (0.40) % against the predicted value of 70.66 % and that for the brown, 74.84 (1.84) % against the predicted value of 80.99 %.
Long-lived nonthermal states in pumped one-dimensional systems of hard-core bosons

We study a unitary time evolution of a symmetry-broken state in a form of a charge density wave in a finite system of interacting hard-core bosons, which can be mapped onto the XXZ Heisenberg chain. Moreover, we introduce a spatially-homogenous and time-dependent vector potential that mimics a short laser pulse. We establish the range of amplitudes of the vector potential for which the onset of charge density wave order can be controlled. We propose a protocol that reveals non-thermal long-lived states, which are characterized by a non-zero charge density wave order translated by one lattice site with respect to its initial formation. The life times of these states are large in comparison to all typical times given by the parameters of the system. They increase with the number of lattice sites, but are significantly suppressed by the integrablility breaking perturbations. In view of these findings, we speculate that the long-lived non-thermal states exist in the thermodynamic limit.
Dirichlet Policies for Reinforced Factor Portfolios

This article aims to combine factor investing and reinforcement learning (RL). The agent learns through sequential random allocations which rely on firms' characteristics. Using Dirichlet distributions as the driving policy, we derive closed forms for the policy gradients and analytical properties of the performance measure. This enables the implementation of REINFORCE methods, which we perform on a large dataset of US equities. Across a large range of implementation choices, our result indicates that RL-based portfolios are very close to the equally-weighted (1/N) allocation. This implies that the agent learns to be agnostic with regard to factors. This is partly consistent with cross-sectional regressions showing a strong time variation in the relationship between returns and firm characteristics.
Collaborative Augmented Reality on Smartphones via Life-long City-scale Maps

In this paper we present the first published end-to-end production computer-vision system for powering city-scale shared augmented reality experiences on mobile devices. In doing so we propose a new formulation for an experience-based mapping framework as an effective solution to the key issues of city-scale SLAM scalability, robustness, map updates and all-time all-weather performance required by a production system. Furthermore, we propose an effective way of synchronising SLAM systems to deliver seamless real-time localisation of multiple edge devices at the same time. All this in the presence of network latency and bandwidth limitations. The resulting system is deployed and tested at scale in San Francisco where it delivers AR experiences in a mapped area of several hundred kilometers. To foster further development of this area we offer the data set to the public, constituting the largest of this kind to date
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery

Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia, a country with one of the highest deforestation rates in the world. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size. We curate a dataset of Landsat 8 satellite images of known forest loss events paired with driver annotations from expert interpreters. We use the dataset to train and validate the models and demonstrate that ForestNet substantially outperforms other standard driver classification approaches. In order to support future research on automated approaches to deforestation driver classification, the dataset curated in this study is publicly available at this https URL .
Representations, extensions and deformations of $n$-BiHom-Lie algebras

In this paper we define and discuss the representations of $n$-BiHom-Lie algebra. We also introduce $T_{\theta}$-extensions and $T_{\theta}^{\ast}$-extensions of $n$-BiHom-Lie algebras and prove the necessary and sufficient conditions for a $2m$-dimensional quadratic $n$-Bihom-Lie algebra to be isomorphic to a $T_{\theta}^{\ast}$-extension. Moreover, we develop the one-parameter formal deformations of $n$-BiHom-Lie algebras, and we proved that the first and second cohomology groups are suitable to the deformation theory involving infinitesimals, equivalent deformations, and rigidity
Accelerated Probabilistic State Estimation in Distribution Grids via Model Order Reduction

This paper applies a custom model order reduction technique to the distribution grid state estimation problem. Specifically, the method targets the situation where, due to pseudo-measurement uncertainty, it is advantageous to run the state estimation solver potentially thousands of times over sampled input perturbations in order to compute probabilistic bounds on the underlying system state. This routine, termed the Accelerated Probabilistic State Estimator (APSE), efficiently searches for the solutions of sequential state estimation problems in a low dimensional subspace with a reduced order model (ROM). When a sufficiently accurate solution is not found, the APSE reverts to a conventional QR factorization-based Gauss-Newton solver. The resulting solution is then used to preform a basis expansion of the low-dimensional subspace, thus improving the reduced model solver. Simulated test results, collected from the unbalanced three-phase 8500-node distribution grid, show the resulting algorithm to be almost an order of magnitude faster than a comparable full-order Gauss-Newton solver and thus potentially fast enough for real-time use.
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines

Purpose: Coronavirus 2019 (COVID-19), which emerged in Wuhan, China and affected the whole world, has cost the lives of thousands of people. Manual diagnosis is inefficient due to the rapid spread of this virus. For this reason, automatic COVID-19 detection studies are carried out with the support of artificial intelligence algorithms. Methods: In this study, a deep learning model that detects COVID-19 cases with high performance is presented. The proposed method is defined as Convolutional Support Vector Machine (CSVM) and can automatically classify Computed Tomography (CT) images. Unlike the pre-trained Convolutional Neural Networks (CNN) trained with the transfer learning method, the CSVM model is trained as a scratch. To evaluate the performance of the CSVM method, the dataset is divided into two parts as training (%75) and testing (%25). The CSVM model consists of blocks containing three different numbers of SVM kernels. Results: When the performance of pre-trained CNN networks and CSVM models is assessed, CSVM (7x7, 3x3, 1x1) model shows the highest performance with 94.03% ACC, 96.09% SEN, 92.01% SPE, 92.19% PRE, 94.10% F1-Score, 88.15% MCC and 88.07% Kappa metric values. Conclusion: The proposed method is more effective than other methods. It has proven in experiments performed to be an inspiration for combating COVID and for future studies.
Desires and Motivation: The Computational Rule, the Underlying Neural Circuitry, and the Relevant Clinical Disorders

As organism is a dissipative system. The process from multi desires to exclusive motivation is of great importance among all sensory-action loops. In this paper we argued that a proper Desire-Motivation model should be a continuous dynamic mapping from the dynamic desire vector to the sparse motivation vector. Meanwhile, it should at least have specific stability and adjustability of motivation intensity. Besides, the neuroscience evidences suggest that the Desire-Motivation model should have dynamic information acquisition and should be a recurrent neural network. A five-equation model is built based on the above arguments, namely the Recurrent Gating Desire-Motivation (RGDM) model. Additionally, a heuristic speculation based on the RGDM model about corresponding brain regions is carried out. It believes that the tonic and phasic firing of ventral tegmental area dopamine neurons should execute the respective and collective feedback functions of recurrent processing. The analysis about the RGMD model shows the expectations about individual personality from three dimensions, namely stability, intensity, and motivation decision speed. These three dimensions can be combined and create eight different personalities, which is correspondent to Jung's personality structure theorem. Furthermore, the RGDM model can be used to predict three different brand-new types of depressive disorder with different phenotypes. Moreover, it can also explain several other psychiatry disorders from new perspectives.
Simulating Autonomous Driving in Massive Mixed Urban Traffic

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents SUMMIT, a high-fidelity simulator that facilitates the development and testing of crowd-driving algorithms. SUMMIT simulates dense, unregulated urban traffic at any worldwide locations as supported by the OpenStreetMap. The core of SUMMIT is a multi-agent motion model, GAMMA, that models the behaviours of heterogeneous traffic agents, and a real-time POMDP planner, Context-POMDP, that serves as a driving expert. SUMMIT is built as an extension of CARLA and inherits from it the physical and visual realism for autonomous driving simulation. SUMMIT supports a wide range of applications, including perception, vehicle control or planning, and end-to-end learning. We validate the realism of our motion model using its traffic motion prediction accuracy on various real-world data sets. We also provide several real-world benchmark scenarios to show that SUMMIT simulates complex, realistic traffic behaviors, and Context-POMDP drives safely and efficiently in challenging crowd-driving settings.
Unique Decoding of Explicit $\varepsilon$-balanced Codes Near the Gilbert-Varshamov Bound

The Gilbert-Varshamov bound (non-constructively) establishes the existence of binary codes of distance <tex>$1/2-\varepsilon$</tex> and rate <tex>$\Omega(\varepsilon^{2})$</tex> (where an upper bound of <tex>$O(\varepsilon^{2}\log(1/\varepsilon))$</tex> is known). Ta-Shma [STOC 2017] gave an explicit construction of <tex>$\varepsilon$</tex>-balanced binary codes, where any two distinct codewords are at a distance between <tex>$1/2-\varepsilon/2$</tex> and <tex>$1/2+\varepsilon/2$</tex>, achieving a near optimal rate of <tex>$\Omega(\varepsilon^{2+\beta})$</tex>, where <tex>$\beta\rightarrow 0$</tex> as <tex>$\varepsilon\rightarrow 0$</tex>. We develop unique and list decoding algorithms for (a slight modification of) the family of codes constructed by Ta-Shma, in the adversarial error model. We prove the following results for <tex>$\varepsilon$</tex>-balanced codes with block length <tex>$N$</tex> and rate <tex>$\Omega(\varepsilon^{2+\beta})$</tex> in this family: –For all <tex>$\varepsilon,\beta > 0$</tex>, there are explicit codes which can be uniquely decoded up to an error of half the minimum distance in time <tex>$N^{O_{\varepsilon,\beta}(1)}$</tex>. –For any fixed constant <tex>$\beta$</tex> independent of <tex>$\varepsilon$</tex>, there is an explicit construction of codes which can be uniquely decoded up to an error of half the minimum distance in time <tex>$(\log(1/\varepsilon))^{O(1)}\cdot N^{O_{\beta}(1)}$</tex>. –For any <tex>$\varepsilon > 0$</tex>, there are explicit <tex>$\varepsilon$</tex>-balanced codes with rate <tex>$\Omega(\varepsilon^{2+\beta})$</tex> which can be list decoded up to error <tex>$1/2-\varepsilon^{\prime}$</tex> in time <tex>$N^{\mathrm{O}_{\varepsilon,\varepsilon^{\prime},\beta}(1)}$</tex>, where <tex>$\varepsilon^{\prime},\beta\rightarrow 0$</tex> as <tex>$\varepsilon\rightarrow 0$</tex>. The starting point of our algorithms is the framework for list decoding direct-sum codes develop in Alev et al. [SODA 2020], which uses the Sum-of-Squares SDP hierarchy. The rates obtained there were quasipolynomial in <tex>$\varepsilon$</tex>. Here, we show how to overcome the far from optimal rates of this framework obtaining unique decoding algorithms for explicit binary codes of near optimal rate. These codes are based on simple modifications of Ta-Shma's construction.
Improved two-point correlation function estimates using glass-like distributions as a reference sample

All estimators of the two-point correlation function are based on a random catalogue, a set of points with no intrinsic clustering following the selection function of a survey. High-accuracy estimates require the use of large random catalogues, which imply a high computational cost. We propose to replace the standard random catalogues by glass-like point distributions or glass catalogues, which are characterized by a power spectrum $P(k)\propto k^4$ and exhibit significantly less power than a Poisson distribution with the same number of points on scales larger than the mean inter-particle separation. We show that these distributions can be obtained by iteratively applying the technique of Zeldovich reconstruction commonly used in studies of baryon acoustic oscillations (BAO). We provide a modified version of the widely used Landy-Szalay estimator of the correlation function adapted to the use of glass catalogues and compare its performance with the results obtained using random samples. Our results show that glass-like samples do not add any bias with respect to the results obtained using Poisson distributions. On scales larger than the mean inter-particle separation of the glass catalogues, the modified estimator leads to a significant reduction of the variance of the Legendre multipoles $\xi_\ell(s)$ with respect to the standard Landy-Szalay results with the same number of points. The size of the glass catalogue required to achieve a given accuracy in the correlation function is significantly smaller than when using random samples. Even considering the small additional cost of constructing the glass catalogues, their use could help to drastically reduce the computational cost of configuration-space clustering analysis of future surveys while maintaining high-accuracy requirements.