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Android malware attacks have posed a severe threat to mobile users, necessitating a significant demand for the automated detection system. Among the various tools employed in malware detection, graph representations (e.g., function call graphs) have played a pivotal role in characterizing the behaviors of Android apps. However, though achieving impressive performance in malware detection, current state-of-the-art graph-based malware detectors are vulnerable to adversarial examples. These adversarial examples are meticulously crafted by introducing specific perturbations to normal malicious inputs. To defend against adversarial attacks, existing defensive mechanisms are typically supplementary additions to detectors and exhibit significant limitations, often relying on prior knowledge of adversarial examples and failing to defend against unseen types of attacks effectively. In this paper, we propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware and remarkable robustness against adversarial attacks. Specifically, we introduce a masking mechanism into the Graph Neural Network (GNN) based framework, forcing MASKDROID to recover the whole input graph using a small portion (e.g., 20%) of randomly selected nodes.This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks. While capturing stable malicious semantics in the form of dependencies inside the graph structures, we further employ a contrastive module to encourage MASKDROID to learn more compact representations for both the benign and malicious classes to boost its discriminative power in detecting malware from benign apps and adversarial examples.

Transition metal dichalcogenides (TMDCs) possess valley pseudospin, allowing photon spin to be coupled to electron spin and enabling initialization and readout of both classical and quantum information. Rapid valley-dephasing processes have impeded the development of scalable, high-performance valleytronic devices operating at room temperature. Here we demonstrate that a chiral resonant metasurface can enable room-temperature valley-selective emission, even with linearly polarized excitation. This platform provides circular eigen-polarization states with a high quality factor (Q-factor) and strong chiral near-field enhancement, resulting in unitary emission circular dichroism (i.e. single-handed circularly polarized emission). Our fabricated Si chiral metasurfaces exhibit chiral electromagnetic modes with Q-factors up to 450 at visible wavelengths, spectrally tuned to the exciton energy of MoSe2 monolayers. Using spatially- and spectrally-resolved mapping from temperatures of 100 K to 294 K, we demonstrate degrees of circular polarization (DOP) as high as 0.5 at room temperature. Reciprocal space mapping of the exciton emission reveals the chiral q-BIC localizes valley-selective emission in the vicinity of the photonic gamma-point. Photon-spin and time-resolved photoluminescence measurements show that the high DOP can be attributed to the significantly increased chiroptical local density of states provided by the metasurface, which enhances valley-specific radiative transition rates by a factor of approximately 13, with lifetimes as short as 189 ps. Our work could facilitate the development of compact chiral classical and quantum light sources and the creation of molecular chiral polaritons for quantum enantioselective synthesis.

Safety is one of the fundamental challenges in control theory. Recently, multi-step optimal control problems for discrete-time dynamical systems were developed to ensure stability, while adhering to input constraints and safety-critical requirements. This was achieved by incorporating discrete-time Control Barrier Functions (CBFs) within a Model Predictive Control (MPC) framework. Existing work usually centers on the feasibility or safety of optimization problems when the boundaries of safe sets are clearly defined. Most of this research limits discussions to CBFs with relative degree one with respect to the system dynamics. Furthermore, real-time computation becomes challenging in MPC problems with large horizons. In this paper, we introduce a framework that addresses the safety-critical MPC problem through iterative optimization, applicable across CBFs of any relative degree. Our approach involves linearizing the nonlinear system dynamics and safety constraints, modeled as Discrete-time High-Order CBFs (DHOCBFs), at each time step. Additionally, when the boundaries of the safe sets are complex, we present a learning-based method to develop linear boundary equations for these safe sets. These equations are then converted into linearized DHOCBFs. The benefits of computational performance and safe avoidance of obstacles with diverse shapes are examined and confirmed through numerical results., Comment: 16 pages, 12 figures. arXiv admin note: text overlap with arXiv:2210.04361

Protecting system observability records (logs) from compromised OSs has gained significant traction in recent times, with several note-worthy approaches proposed. Unfortunately, none of the proposed approaches achieve high performance with tiny log protection delays. They also leverage risky environments for protection (\eg many use general-purpose hypervisors or TrustZone, which have large TCB and attack surfaces). HitchHiker is an attempt to rectify this problem. The system is designed to ensure (a) in-memory protection of batched logs within a short and configurable real-time deadline by efficient hardware permission switching, and (b) an end-to-end high-assurance environment built upon hardware protection primitives with debloating strategies for secure log protection, persistence, and management. Security evaluations and validations show that HitchHiker reduces log protection delay by 93.3--99.3% compared to the state-of-the-art, while reducing TCB by 9.4--26.9X. Performance evaluations show HitchHiker incurs a geometric mean of less than 6% overhead on diverse real-world programs, improving on the state-of-the-art approach by 61.9--77.5%.

Building on a previous work~\cite{Han:2024min}, we illustrate that the leading-twist light-cone distribution amplitudes (LCDAs) defined in heavy-quark effective theory (HQET) can be determined through lattice simulations of quasi-distribution amplitudes (quasi-DAs) with a large momentum component $P^z$. Exploiting heavy-quark spin symmetry, we show that the LCDAs for a heavy pseudoscalar and vector meson in the context of HQET exhibit degeneracy, and the degeneracy allows for the utilization of quasi DAs for both pseudoscalar and vector mesons on the lattice. We then derive the relevant short-distance coefficients for the matching between LCDAs defined with QCD fields and HQET LCDAs at the one-loop level. The incorporation of these three quasi DAs can not only confirm the methodology introduced in Ref.~\cite{Han:2024min} but also provides possible insight into power corrections. Discrepancies between the corresponding results offer a valuable perspective for estimating power corrections within the system which are imperative for precise investigations into heavy-meson LCDAs in the future particularly in the context of lattice QCD., Comment: 10 pages, 1 figure

With the advancements in 3D printing technologies, it is extremely important that the quality of 3D printed objects, and dimensional accuracies should meet the customer's specifications. Various factors during metal printing affect the printed parts' quality, including the power quality, the printing stage parameters, the print part's location inside the print bed, the curing stage parameters, and the metal sintering process. With the large data gathered from HP's MetJet printing process, AI techniques can be used to analyze, learn, and effectively infer the printed part quality metrics, as well as assist in improving the print yield. In-situ thermal sensing data captured by printer-installed thermal sensors contains the part thermal signature of fusing layers. Such part thermal signature contains a convoluted impact from various factors. In this paper, we use a multimodal thermal encoder network to fuse data of a different nature including the video data vectorized printer control data, and exact part thermal signatures with a trained encoder-decoder module. We explored the data fusing techniques and stages for data fusing, the optimized end-to-end model architecture indicates an improved part quality prediction accuracy.

Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer. The metal sintering process introduces large deformation varying from 25 to 50% depending on the green part porosity. In this paper, we use a graph-based deep learning approach to predict the part deformation, which can speed up the deformation simulation substantially at the voxel level. Running a well-trained Metal Sintering inferencing engine only takes a range of seconds to obtain the final sintering deformation value. The tested accuracy on example complex geometry achieves 0.7um mean deviation for a 63mm testing part.

We present a comprehensive investigation of leading-twist lightcone distribution amplitudes (LCDAs) and quasi distribution amplitudes (quasi-DAs) for light octet and decuplet baryons within large momentum effective theory. In LaMET, LCDAs can be factorized in terms of a hard kernel and quasi-DAs that are defined as spatial correlators and calculable on Lattice QCD. To renormalize quasi-DAs and eliminate the singular terms $\ln(\mu^2 z_i^2)'s$ in them, which undermine the efficacy in perturbative expansion, we adopt a hybrid renormalization scheme that combines the self-renormalization and ratio scheme. Through self-renormalization, we eliminate UV divergences and linear divergences at large spatial separations in quasi distribution amplitudes without introducing additional nonperturbative effects. By taking a ratio with respect to the zero-momentum matrix element, we effectively remove UV divergences at small spatial separations. Under the hybrid renormalization scheme, we calculate the hard kernels up to one-loop accuracy. It turns out that only four different hard kernels are needed for all leading-twist LCDAs of octet and decuplet baryons. These results are crucial for the lattice-based exploration of the LCDAs of a light baryon from the first principle of QCD.

Optimization-based approaches are widely employed to generate optimal robot motions while considering various constraints, such as robot dynamics, collision avoidance, and physical limitations. It is crucial to efficiently solve the optimization problems in practice, yet achieving rapid computations remains a great challenge for optimization-based approaches with nonlinear constraints. In this paper, we propose a geometric projector for dynamic obstacle avoidance based on velocity obstacle (GeoPro-VO) by leveraging the projection feature of the velocity cone set represented by VO. Furthermore, with the proposed GeoPro-VO and the augmented Lagrangian spectral projected gradient descent (ALSPG) algorithm, we transform an initial mixed integer nonlinear programming problem (MINLP) in the form of constrained model predictive control (MPC) into a sub-optimization problem and solve it efficiently. Numerical simulations are conducted to validate the fast computing speed of our approach and its capability for reliable dynamic obstacle avoidance.

Android malware detection serves as the front line against malicious apps. With the rapid advancement of machine learning (ML), ML-based Android malware detection has attracted increasing attention due to its capability of automatically capturing malicious patterns from Android APKs. These learning-driven methods have reported promising results in detecting malware. However, the absence of an in-depth analysis of current research progress makes it difficult to gain a holistic picture of the state of the art in this area. This paper presents a comprehensive investigation to date into ML-based Android malware detection with empirical and quantitative analysis. We first survey the literature, categorizing contributions into a taxonomy based on the Android feature engineering and ML modeling pipeline. Then, we design a general-propose framework for ML-based Android malware detection, re-implement 12 representative approaches from different research communities, and evaluate them from three primary dimensions, i.e., effectiveness, robustness, and efficiency. The evaluation reveals that ML-based approaches still face open challenges and provides insightful findings like more powerful ML models are not the silver bullet for designing better malware detectors. We further summarize our findings and put forth recommendations to guide future research.

Background and Objectives: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients. Methods and Study Design: We conducted a study of adult septic patients in Intensive Care Unit (ICU), collecting 47 indicators for 14 days. After data cleaning and preprocessing, we used stats to explore energy delivery in deceased and surviving patients. We filtered out nutrition-related features and divided the data into three metabolic phases: acute early, acute late, and rehabilitation. Models were built using data before September 2020 and validated on the rest. We then established optimal energy target models for each phase using deep learning. Results: A total of 277 patients and 3115 data were included in this study. The models indicated that the optimal energy targets in the three phases were 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy intake increased mortality rapidly in the early period of the acute phase. Insufficient energy in the late period of the acute phase significantly raised the mortality of septic patients. For the rehabilitation phase, too much or too little energy delivery both associated with high mortality. Conclusion: Our study established time-series prediction models for septic patients to optimize energy delivery in the ICU. This approach indicated the feasibility of developing nutritional tools for critically ill patients. We recommended permissive underfeeding only in the early acute phase. Later, increased energy intake may improve survival and settle energy debts caused by underfeeding.

We investigate the $K_1(1270)-K_1(1400)$ mixing caused by the flavor $SU(3)$ symmetry breaking. The mixing angle is expressed by a $K_{1A}\to K_{1B}$ matrix element induced by the operators that breaks flavor $SU(3)$ symmetry. The QCD contribution to this matrix element is assumed to be dominated and calculated by QCD sum rules. A three-point correlation function is defined and handled both at the hadron and quark-gluon levels. The quark-gluon level calculation is based on operator product expansion up to dimension-5 condensates. A detailed numerical analysis is performed to determine the Borel parameters, and the obtained mixing angle is $\theta_{K_1}=22^{\circ}\pm 7^{\circ}$ or $\theta_{K_1}=68^{\circ}\pm 7^{\circ}$., Comment: 14 pages, 7 figures and 1 table

We present a fast planning architecture called Hamilton-Jacobi-based bidirectional A* (HJBA*) to solve general tight parking scenarios. The algorithm is a two-layer composed of a high-level HJ-based reachability analysis and a lower-level bidirectional A* search algorithm. In high-level reachability analysis, a backward reachable tube (BRT) concerning vehicle dynamics is computed by the HJ analysis and it intersects with a safe set to get a safe reachable set. The safe set is defined by constraints of positive signed distances for obstacles in the environment and computed by solving QP optimization problems offline. For states inside the intersection set, i.e., the safe reachable set, the computed backward reachable tube ensures they are reachable subjected to system dynamics and input bounds, and the safe set guarantees they satisfy parking safety with respect to obstacles in different shapes. For online computation, randomized states are sampled from the safe reachable set, and used as heuristic guide points to be considered in the bidirectional A* search. The bidirectional A* search is paralleled for each randomized state from the safe reachable set. We show that the proposed two-level planning algorithm is able to solve different parking scenarios effectively and computationally fast for typical parking requests. We validate our algorithm through simulations in large-scale randomized parking scenarios and demonstrate it to be able to outperform other state-of-the-art parking planning algorithms., Comment: Resubmit

We explore the QED corrections to the $\Xi_c-\Xi_c^{\prime}$ mixing within the framework of light-front quark model (LFQM) in the three-quark picture. After explicitly investigating the relation between the $\Xi_c-\Xi_c^{\prime}$ mixing and the flavor $\rm {SU(3)}$ and heavy quark symmetry breaking, we derive the QED contributions to the mixing angle. Numerical results indicate the QED contribution is smaller than the one from the mass difference between the strange and up/down quark provided by a recent Lattice QCD analysis. Adding these contributions together we find that at this stage the $\Xi_c-\Xi_c^{\prime}$ mixing is small and still incapable to account for the large $\rm {SU(3)}$ symmetry breaking in the semi-leptonic $\Xi_c$ decays., Comment: 7 pages, 4figures

In this paper, we propose a safety-critical controller based on time-varying control barrier functions (CBFs) for a robot with an unicycle model in the continuous-time domain to achieve navigation and dynamic collision avoidance. Unlike previous works, our proposed approach can control both linear and angular velocity to avoid collision with obstacles, overcoming the limitation of confined control performance due to the lack of control variable. To ensure that the robot reaches its destination, we also design a control Lyapunov function (CLF). Our safety-critical controller is formulated as a quadratic program (QP) optimization problem that incorporates CLF and CBFs as constraints, enabling real-time application for navigation and dynamic collision avoidance. Numerical simulations are conducted to verify the effectiveness of our proposed approach., Comment: Accpeted by IECON 2023

In this paper, we focus on non-conservative obstacle avoidance between robots with control affine dynamics with strictly convex and polytopic shapes. The core challenge for this obstacle avoidance problem is that the minimum distance between strictly convex regions or polytopes is generally implicit and non-smooth, such that distance constraints cannot be enforced directly in the optimization problem. To handle this challenge, we employ non-smooth control barrier functions to reformulate the avoidance problem in the dual space, with the positivity of the minimum distance between robots equivalently expressed using a quadratic program. Our approach is proven to guarantee system safety. We theoretically analyze the smoothness properties of the minimum distance quadratic program and its KKT conditions. We validate our approach by demonstrating computationally-efficient obstacle avoidance for multi-agent robotic systems with strictly convex and polytopic shapes. To our best knowledge, this is the first time a real-time QP problem can be formulated for general non-conservative avoidance between strictly convex shapes and polytopes., Comment: 17 pages

We calculate the soft function using lattice QCD in the framework of large momentum effective theory incorporating the one-loop perturbative contributions. The soft function is a crucial ingredient in the lattice determination of light cone objects using transverse-momentum-dependent (TMD) factorization. It consists of a rapidity-independent part called intrinsic soft function and a rapidity-dependent part called Collins-Soper kernel. We have adopted appropriate normalization when constructing the pseudo-scalar meson form factor that is needed in the determination of the intrinsic part and applied Fierz rearrangement to suppress the higher-twist effects. In the calculation of CS kernel we consider a CLS ensemble other than the MILC ensemble used in a previous study. We have also compared the applicability of determining the CS kernel using quasi TMDWFs and quasi TMDPDFs. As an example, the determined soft function is used to obtain the physical TMD wave functions (WFs) of pion and unpolarized iso-vector TMD parton distribution functions (PDFs) of proton., Comment: 24 pages, 19 figures, published version

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years.

Momentum distributions of quarks/gluons inside a light baryon in a hard exclusive process are encoded in the light-cone distribution amplitudes (LCDAs). In this work, we point out that the leading twist LCDAs of a light baryon can be obtained through a simulation of a quasi-distribution amplitude calculable on lattice QCD within the framework of the large-momentum effective theory. We calculate the one-loop perturbative contributions to LCDA and quasi-distribution amplitudes and explicitly demonstrate the factorization of quasi-distribution amplitudes at the one-loop level. Based on the perturbative results, we derive the matching kernel in the $\overline{\rm MS}$ scheme and regularization-invariant momentum-subtraction scheme. Our result provides a first step to obtaining the LCDA from first principle lattice QCD calculations in the future., Comment: 19 pages, 2 figures

Obstacle avoidance for multi-robot navigation with polytopic shapes is challenging. Existing works simplify the system dynamics or consider it as a convex or non-convex optimization problem with positive distance constraints between robots, which limits real-time performance and scalability. Additionally, generating collision-free behavior for polytopic-shaped robots is harder due to implicit and non-differentiable distance functions between polytopes. In this paper, we extend the concept of velocity obstacle (VO) principle for polytopic-shaped robots and propose a novel approach to construct the VO in the function of vertex coordinates and other robot's states. Compared with existing work about obstacle avoidance between polytopic-shaped robots, our approach is much more computationally efficient as the proposed approach for construction of VO between polytopes is optimization-free. Based on VO representation for polytopic shapes, we later propose a navigation approach for distributed multi-robot systems. We validate our proposed VO representation and navigation approach in multiple challenging scenarios including large-scale randomized tests, and our approach outperforms the state of art in many evaluation metrics, including completion rate, deadlock rate, and the average travel distance., Comment: Accepted to IEEE Robotics and Automation Letters (RA-L) 2023, with open source repository released

This work introduces a novel control strategy called Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which aims to improve closed-loop performance with local trajectory optimization for iterative tasks in a dynamic environment. The proposed algorithm is reference-free and utilizes historical data from previous iterations to enhance the performance of the autonomous system. Unlike existing algorithms, the i2LQR computes the optimal solution in an iterative manner at each timestamp, rendering it well-suited for iterative tasks with changing constraints at different iterations. To evaluate the performance of the proposed algorithm, we conduct numerical simulations for an iterative task aimed at minimizing completion time. The results show that i2LQR achieves an optimized performance with respect to learning-based MPC (LMPC) as the benchmark in static environments, and outperforms LMPC in dynamic environments with both static and dynamics obstacles., Comment: Accepted by 2023 62nd IEEE Conference on Decision and Control (CDC)

We present a first lattice QCD calculation of the transverse-momentum-dependent wave functions (TMDWFs) of the pion using large-momentum effective theory. Numerical simulations are based on one ensemble with 2+1+1 flavors of highly improved staggered quarks action with lattice spacing $a=0.121$~fm from the MILC Collaboration, and one with 2 +1 flavor clover fermions and tree-level Symanzik gauge action generated by the CLS Collaboration with $a=0.098$~fm. As a key ingredient, the soft function is first obtained by incorporating the one-loop perturbative contributions and a proper normalization. Based on this and the equal-time quasi-TMDWFs simulated on the lattice, we extract the light-cone TMDWFs. The results are comparable between the two lattice ensembles and a comparison with phenomenological parametrization is made. Our studies provide a first attempt of $ab$ $initio$ calculation of TMDWFs which will eventually lead to crucial theory inputs for making predictions for exclusive processes under QCD factorization.

This paper presents a safety-critical locomotion control framework for quadrupedal robots. Our goal is to enable quadrupedal robots to safely navigate in cluttered environments. To tackle this, we introduce exponential Discrete Control Barrier Functions (exponential DCBFs) with duality-based obstacle avoidance constraints into a Nonlinear Model Predictive Control (NMPC) with Whole-Body Control (WBC) framework for quadrupedal locomotion control. This enables us to use polytopes to describe the shapes of the robot and obstacles for collision avoidance while doing locomotion control of quadrupedal robots. Compared to most prior work, especially using CBFs, that utilize spherical and conservative approximation for obstacle avoidance, this work demonstrates a quadrupedal robot autonomously and safely navigating through very tight spaces in the real world. (Our open-source code is available at github.com/HybridRobotics/quadruped_nmpc_dcbf_duality, and the video is available at youtu.be/p1gSQjwXm1Q.), Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 2023

Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples. Unfortunately, the distant supervision may induce noisy labels, thus undermining the robustness of the learned models and restricting the practical application. To relieve this problem, recent works adopt self-training teacher-student frameworks to gradually refine the training labels and improve the generalization ability of NER models. However, we argue that the performance of the current self-training frameworks for DS-NER is severely underestimated by their plain designs, including both inadequate student learning and coarse-grained teacher updating. Therefore, in this paper, we make the first attempt to alleviate these issues by proposing: (1) adaptive teacher learning comprised of joint training of two teacher-student networks and considering both consistent and inconsistent predictions between two teachers, thus promoting comprehensive student learning. (2) fine-grained student ensemble that updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise. To verify the effectiveness of our proposed method, we conduct experiments on four DS-NER datasets. The experimental results demonstrate that our method significantly surpasses previous SOTA methods., Comment: Accepted at AAAI 2023