Miracle-dz/UniFAR-FTUs huggingface dataset:
Properties of PZT thick films made on LTCC

Purpose – To find properties of screen printed PZT (PbZr0.53Ti0.47O3 with 6 per cent of PbO and 2 per cent of Pb5Ge3O11) thick films layers on LTCC substrate.Design/methodology/approach – The influence of PZT firing time and electrode materials on electrical characteristics and microstructure were examined. A scanning electron microscope (SEM) equipped with an energy‐dispersive X‐ray (EDS) analyser was used for the microstructural and compositional analysis.Findings – Microstructural and compositional analyses have shown the diffusion of SiO2 from LTCC into PZT layers and the diffusion of PbO in the opposite direction. SiO2 presumably forms low permitivity lead based silicates in PZT layer. The new phase deteriorates the piezoelectric properties. The amount of diffused materials was dependent upon the electrode material and increased with increasing firing time. Better properties, i.e. higher remanent polarisation and dielectric constant were achieved for samples with PdAg electrodes and shorter firing ti.
The Neurophysiology Behind Trauma-Focused Therapy Modalities Used to Treat Post-Traumatic Stress Disorder Across the Life Course: A Systematic Review

This review presents the current state of understanding of trauma-informed modalities in light of current research in neuroscience, analyzing which brain structures and processes are impacted by these modalities. Studies included in the present review met the inclusion criteria of 1) addressing post-traumatic stress disorder (PTSD) in a specific population, 2) treatment of PTSD using any of the evidence-based trauma-informed modalities considered in this review, and 3) presenting functional magnetic resonance imagery (fMRI) data, derived from BOLD signals and voxel-compression maps, of brain structures impacted by these trauma-informed modalities. Articles for this review were collated through PubMed and MEDLINE, using key terms in descending order, such as ‘childhood trauma’, ‘adolescent trauma’, and ‘adulthood trauma’, to ‘PTSD’, ‘fMRI’, and so on, depending on the modality in question. Based on these criteria and research methods, 37 studies remained for inclusion in the present review. Among a number of critical findings, this review demonstrates that eye movement desensitization and reprocessing (EMDR) and mindfulness therapy effectively deactivate hindbrain regions implicated in the downregulation of autonomic nervous system (ANS) hyperarousal. This review also shows that trauma-focused cognitive behavioral therapy (TF-CBT) and EMDR activate the hippocampus, anterior cingulate cortex (ACC), medial prefrontal cortex (mPFC), and orbitofrontal cortex (OFC)—areas that are implicated in crucial cognitive, affective, and behavioral processes that aid trauma survivors in navigating their challenges.
Do They Think Alike? Perception Analysis on Quality Environment (QE) Audit Effectiveness: A Malaysian Case

Prior studies have shown that Quality Environment (QE) auditing is an effective management tools, i.e. in improving housekeeping and environmental performance. There are limited studies aimed at identifying factors contributing to the effectiveness of QE auditing. Based on the framework studying factors contributing to internal audit effectiveness, this paper attempts to examine such factors in QE context. By identifying the potential differences of priority factors that lead to effectiveness, result of this study contributes to the improvement of quality audit process and management of QE certification auditing. This paper explores the differences of perception between management and executive levels on the prioritization factors that contribute to the audit effectiveness of Quality Environment (QE) audit. The study was conducted at an organization that is responsible for promoting QE certification both in private and government sectors in the Malaysian environment. Although the results show mixed response between the levels, the researchers found there is some similarity of prioritization on the top three factors that contribute to the QE audit effectiveness between management and executive level. Since this study only focuses on perception of the factor that contributes to the effectiveness, another focus such as measuring the effectiveness will be an interesting area to explore for future research. Furthermore, a larger scale of study would contribute to the generalization of quality auditing series.
Learning Reinforced Dynamic Representations for Sequential Recommendation

Recently, sequential recommendation systems are important in solving the information overload in many online services. Current meth-ods in sequential recommendation focus on learning a fixed number of representations for each user at any time, with a single representation or multi-interest representations for the user. However, when a user is exploring items on an e-commerce recommendation system, the number of this user’s interests may change overtime (e.g. increase/reduce one interest), affected by the user’s evolving self needs. Moreover, different users may have various number of interests. In this paper, we argue that it is meaningful to explore a personalized dynamic number of user interests, and learn a dynamic group of user interest representations accordingly. We propose a Reinforced sequential model with dynamic number of interest representations for recommendation systems (RDRSR). Specifically, RDRSR is composed of a dynamic interest discriminator (DID) module and a dynamic interest allocator (DIA) module. The DID module explores the number of a user’s interests by learning the over-all sequential characteristics with bi-directional self-attention and Gumble-Softmax. The DIA module allocates the historical clicked items into a group of sub-sequences and constructs user’s dynamic interest representations. We formalize the allocation problem in the form of Markov Decision Process(MDP), and sample an action from policy 𝜋 for each item to determine which sub-sequence it belongs to. Additionally, experiments on the real-world datasets demonstrates our model’s effectiveness.
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks

We propose a new algorithm for training deep neural networks (DNNs) with binary weights. In particular, we first cast the problem of training binary neural networks (BiNNs) as a bilevel optimization instance and subsequently construct flexible relaxations of this bilevel program. The resulting training method shares its algorithmic simplicity with several existing approaches to train BiNNs, in particular with the straight-through gradient estimator successfully employed in BinaryConnect and subsequent methods. Infact, our proposed method can be interpreted as an adaptive variant of the original straight-through estimator that conditionally (but not always) acts like a linear mapping in the backward pass of error propagation. Experimental results demonstrate that our new algorithm offers favorable performance compared to existing approaches.11This work was partially supported by theWallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.
Mesoscale Modelling of the Tolman Length in Multi-component Systems

In this paper we analyze the curvature corrections to the surface tension in the context of the Shan-Chen (SC) multi-component Lattice Boltzmann method (LBM). We demonstrate that the same techniques recently applied in the context of the Shan-Chen multi-phase model can be applied to multi-component mixtures. We implement, as a new application, the calculation of the surface of tension radius $R_s$ through the minimization of the generalized surface tension $\sigma[R]$. In turn we are able to estimate the Tolman length, i.e. the first order coefficient of the curvature expansion of the surface tension $\sigma(R)$, as well as the higher order corrections, i.e. the curvature- and the Gaussian-rigidity coefficients. The SC multi-component model allows to model both fully-symmetric as well as asymmetric interactions among the components. By performing an extensive set of simulations we present a first example of tunable Tolman length in the mesoscopic model, being zero for symmetric interactions and different from zero otherwise. This result paves the way for controlling such interface properties which are paramount in presence of thermal fluctuations. All reported results can be independently reproduced through the"idea.deploy"framework available at https://github.com/lullimat/idea.deploy.
Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning

In this paper, we propose a gradient boosting algorithm called adaptive boosting histogram transform (ABHT ) for regression to illustrate the local adaptivity of gradient boosting algorithms in histogram transform ensemble learning. From the theoretical perspective, when the target function lies in a locally Hölder continuous space, we show that our ABHT can filter out the regions with different orders of smoothness. Consequently, we are able to prove that the upper bound of the convergence rates of ABHT is strictly smaller than the lower bound of parallel ensemble histogram transform (PEHT ). In the experiments, both synthetic and real-world data experiments empirically validate the theoretical results, which demonstrates the advantageous performance and local adaptivity of our ABHT.
Voronoi Diagrams in the Hilbert Metric

The Hilbert metric is a distance function defined for points lying within a convex body. It generalizes the Cayley-Klein model of hyperbolic geometry to any convex set, and it has numerous applications in the analysis and processing of convex bodies. In this paper, we study the geometric and combinatorial properties of the Voronoi diagram of a set of point sites under the Hilbert metric. Given any convex polygon $K$ bounded by $m$ sides, we present two algorithms (one randomized and one deterministic) for computing the Voronoi diagram of an $n$-element point set in the Hilbert metric induced by $K$. Our randomized algorithm runs in $O(m n + n (\log n)(\log m n))$ expected time, and our deterministic algorithm runs in time $O(m n \log n)$. Both algorithms use $O(m n)$ space. We show that the worst-case combinatorial complexity of the Voronoi diagram is $\Theta(m n)$.
Design of a High-Efficient Grid-Connected Three-Phase Three-Level T-Type PV Power System

This paper intends to design a high-efficient grid connected three-phase three-level T-type photovoltaic inverter system which is operated with conjunction with grid. Different from the two-level topology mainly used previous, switch elements are increased by 6 pieces, it has the advantages such as the efficiency of the inverter, less stress in the switching elements, and less harmonic component in the output current. This paper will suggest the boost convert always supplying constant DC voltage for a three-phase three-level T-type photovoltaic inverter, and also presents the stability and excellence of the 3 level boost converter through experiments. Also, it offers the stable and high-efficient MPPT algorism and through solar irradiation change experiment and MPPT efficiency assessment, the excellence and efficacy of three-level T-type inverter was verified from experiments.
A discrete memristor model and its application in the Rulkov neuron

Continuous time memristor have been widely used in fields such as chaotic oscillating circuitsand neuromorphic computing systems, but research on the application of discretememristors haven't been noticed yet. In this paper, we designed a new chaoticneuron by applying the discrete model to two-dimensional Rulkov chaotic neuron,and analyzed its dynamical behaviors by extensive experiments involves phasediagram, bifurcation diagram, and spectral entropy complexity algorithm. The experimental results show that the charge of the memristor has an importanteffect on the system dynamics, delaying the occurrence of bifurcation, and evenin the case of full memory, leading to the disappearance of the bifurcation thus makethe system reach a fixed point. Besides, the increase of the current magnification,can also bring about the increase in discharge frequency ofneurons, and a wider and larger range of spectral entropy complexity. The results of our study show the performance of Rulkov chaotic neuronis improved by applying thediscrete memristor, and may provide new insights into the mechanism of memoryand cognition in the nervous.
Player of Games

Games have a long history of serving as a benchmark for progress in artiﬁcial intelligence. Recently, approaches using search and learning have shown strong performance across a set of perfect information games, and approaches using game-theoretic reasoning and learning have shown strong performance for speciﬁc imperfect information poker variants. We introduce Player of Games, a general-purpose algorithm that uniﬁes previous approaches, combining guided search, self-play learning, and game-theoretic reasoning. Player of Games is the ﬁrst algorithm to achieve strong empirical performance in large perfect and imperfect information games — an important step towards truly general algorithms for arbitrary environments. We prove that Player of Games is sound, converging to perfect play as available computation time and approximation capacity increases. Player of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold’em poker (Slumbot), and defeats the state-of-the-art agent in Scotland Yard, an imperfect information game that illustrates the value of guided search, learning, and game-theoretic reasoning.
Efficient FPGA-based ECDSA Verification Engine for Permissioned Blockchains

Permissioned blockchain platforms heavily depend on cryptography to provide a layer of trust within the blockchain network, thus verification of cryptographic signatures often becomes the bottleneck. ECDSA is the most commonly used cryptographic scheme in permissioned blockchains. In this work, we propose an efficient implementation of ECDSA signature verification on FPGA, in order to improve performance of permissioned blockchains that aim to use FPGA-based hardware accelerators. We propose several optimizations for modular arithmetic (e.g., custom multipliers and fast modular reduction) and point arithmetic (e.g., reduced number of point double and addition operations, and optimal width NAF representation). Based on these optimized modular and point arithmetic modules, we propose an ECDSA verification engine that can be used by any application for fast verification of signatures. We further optimize our ECDSA verification engine for Hyperledger Fabric (one of the most widely used blockchain platforms) by moving carefully selected operations to a precomputation block, thus simplifying the critical path of ECDSA signature verification. Our ECDSA engine running at 250MHz on Xilinx Alveo U250 accelerator card can perform a verification in $760\mu s$ with a throughput of 1, 315 verifications/sec, which is $\sim 2.5$ times faster than state-of-the-art FPGA-based implementations. Our Hyperledger Fabric-specific ECDSA engine can perform a verification in $368\mu s$, and 2, 717 verifications/sec. With 10 engines, Hyperledger Fabric can achieve a throughput of 7, 520 transactions/sec.
Incentive Compatible Pareto Alignment for Multi-Source Large Graphs

In this paper, we focus on learning effective entity matching models over multi-source large-scale data. For real applications, we relax typical assumptions that data distributions/spaces, or entity identities are shared between sources, and propose a Relaxed Multi-source Large-scale Entity-matching (RMLE) problem. Challenges of the problem include 1) how to align large-scale entities between sources to share information and 2) how to mitigate negative transfer from joint learning multi-source data. What's worse, one practical issue is the entanglement between both challenges. Specifically, incorrect alignments may increase negative transfer; while mitigating negative transfer for one source may result in poorly learned representations for other sources and then decrease alignment accuracy. To handle the entangled challenges, we point out that the key is to optimize information sharing first based on Pareto front optimization, by showing that information sharing significantly influences the Pareto front which depicts lower bounds of negative transfer. Consequently, we proposed an Incentive Compatible Pareto Alignment (ICPA) method to first optimize cross-source alignments based on Pareto front optimization, then mitigate negative transfer constrained on the optimized alignments. This mechanism renders each source can learn based on its true preference without worrying about deteriorating representations of other sources. Specifically, the Pareto front optimization encourages minimizing lower bounds of negative transfer, which optimizes whether and which to align. Comprehensive empirical evaluation results on four large-scale datasets are provided to demonstrate the effectiveness and superiority of ICPA. Online A/B test results at a search advertising platform also demonstrate the effectiveness of ICPA in production environments.
Intelligent trading systems: a sentiment-aware reinforcement learning approach

The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers. Reinforcement Learning (RL) and Natural Language Processing have gained notoriety in these single-asset trading tasks, but only a few works have explored their combination. Moreover, some issues are still not addressed, such as extracting market sentiment momentum through the explicit capture of sentiment features that reflect the market condition over time and assessing the consistency and stability of RL results in different situations. Filling this gap, we propose the Sentiment-Aware RL (SentARL) intelligent trading system that improves profit stability by leveraging market mood through an adaptive amount of past sentiment features drawn from textual news. We evaluated SentARL across twenty assets, two transaction costs, and five different periods and initializations to show its consistent effectiveness against baselines. Subsequently, this thorough assessment allowed us to identify the boundary between news coverage and market sentiment regarding the correlation of price-time series above which SentARL's effectiveness is outstanding.
ED2: Environment Dynamics Decomposition World Models for Continuous Control

Model-based reinforcement learning (MBRL) achieves significant sample efficiency in practice in comparison to model-free RL, but its performance is often limited by the existence of model prediction error. To reduce the model error, standard MBRL approaches train a single well-designed network to fit the entire environment dynamics, but this wastes rich information on multiple sub-dynamics which can be modeled separately, allowing us to construct the world model more accurately. In this paper, we propose the Environment Dynamics Decomposition (ED2), a novel world model construction framework that models the environment in a decomposing manner. ED2 contains two key components: sub-dynamics discovery (SD2) and dynamics decomposition prediction (D2P). SD2 discovers the sub-dynamics in an environment automatically and then D2P constructs the decomposed world model following the sub-dynamics. ED2 can be easily combined with existing MBRL algorithms and empirical results show that ED2 significantly reduces the model error, increases the sample efficiency, and achieves higher asymptotic performance when combined with the state-of-the-art MBRL algorithms on various continuous control tasks. Our code is open source and available at https://github.com/ED2-source-code/ED2.
V2X Misbehavior and Collective Perception Service: Considerations for Standardization

Connected and Automated Vehicles (CAV) use sensors and wireless communication to improve road safety and efficiency. However, attackers may target Vehicle-to-Everything (V2X) communication. Indeed, an attacker may send authenticated-but-wrong data to send false location information, alert incorrect events or report a bogus object endangering other CAVs' safety. Currently, Standardization Development Organi-zations are working on developing security standards against such attacks. Unfortunately, current standardization efforts do not include misbehavior specifications for advanced V2X services such as Collective Perception (CP) yet. This work assesses the security of CP Messages and proposes inputs for consideration in existing standards.
Functional Regularization for Reinforcement Learning via Learned Fourier Features

We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis and show that it improves the sample efficiency of both state-based and image-based RL. We perform infinite-width analysis of our architecture using the Neural Tangent Kernel and theoretically show that tuning the initial variance of the Fourier basis is equivalent to functional regularization of the learned deep network. That is, these learned Fourier features allow for adjusting the degree to which networks underfit or overfit different frequencies in the training data, and hence provide a controlled mechanism to improve the stability and performance of RL optimization. Empirically, this allows us to prioritize learning low-frequency functions and speed up learning by reducing networks' susceptibility to noise in the optimization process, such as during Bellman updates. Experiments on standard state-based and image-based RL benchmarks show clear benefits of our architecture over the baselines. Website at https://alexanderli.com/learned-fourier-features
Eternal Domination in Trees

Mobile guards on the vertices of a graph are used to defend the graph against an infinite sequence of attacks on vertices. A guard must move from a neighboring vertex to an attacked vertex (we assume attacks happen only at vertices containing no guard). More than one guard is allowed to move in response to an attack. The $m$-eternal domination number is the minimum number of guards needed to defend the graph. We characterize the trees achieving several upper and lower bounds on the $m$-eternal domination number.
Hardware Trojan Insertion in Finalized Layouts: a Silicon Demonstration

—Owning a high-end semiconductor foundry is a luxury very few companies can afford. Thus, fabless design companies outsource integrated circuit fabrication to third parties. Within foundries, rogue elements may gain access to the customer’s layout and perform malicious acts, including the insertion of a hardware trojan (HT). Many works focus on the structure/effects of a HT, while very few have demonstrated the viability of their HTs in silicon. Even fewer disclose how HTs are inserted or the time required for this activity. Our work details, for the ﬁrst time, how effortlessly a HT can be inserted into a ﬁnalized layout by presenting an insertion framework based on the engineering change order ﬂow. For validation, we have built an ASIC prototype in 65nm CMOS technology comprising of four trojaned cryptocores. A side-channel HT is inserted in each core with the intent of leaking the cryptokey over a power channel. Moreover, we have determined that the entire attack can be mounted in a little over one hour. We also show that the attack was successful for all tested samples. Finally, our measurements demonstrate the robustness of our SCT against skews in the manufacturing process.