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A crystalline solid is a periodic sequence of identical cells, each containing one or more atoms. If the constituting unit cell is not centrosymmetric, charge may distribute unevenly between the atoms, resulting in internal electric polarization. This effect serves as the basis for numerous ferroelectric, piezoelectric, and pyroelectric phenomena. In nearly all polar materials, including multilayered van der Waals stacks that were recently found to exhibit interfacial polarization, inversion symmetry is broken by having two or more atomic species within the unit cell. Here, we show that even elemental crystals, consisting of one type of atom, and composed of non-polar centrosymmetric layers, exhibit electric polarization if arranged in an appropriate three-dimensional architecture. This concept is demonstrated here for inversion and mirror asymmetric mixed-stacking tetra-layer polytypes of non-polar graphene sheets. Furthermore, we find that the room temperature out-of-plane electric polarization increases with external electrostatic doping, rather than decreases owing to screening. Using first-principles calculations, as well as tight-binding modeling, we unveil the origin of polytype-induced polarization and its dependence on doping. Extension of this idea to graphene multilayers suggests that solely by lateral shifts of constituent monolayers one can obtain multiple meta-stable interlayer stacking sequences that may allow for even larger electrical polarization.

In recent years, REST API fuzzing has emerged to explore errors on a cloud service. Its performance highly depends on the sequence construction and request generation. However, existing REST API fuzzers have trouble generating long sequences with well-constructed requests to trigger hard-to-reach states in a cloud service, which limits their performance of finding deep errors and security bugs. Further, they cannot find the specific errors caused by using undefined parameters during request generation. Therefore, in this paper, we propose a novel hybrid data-driven solution, named MINER, with three new designs working together to address the above limitations. First, MINER collects the valid sequences whose requests pass the cloud service's checking as the templates, and assigns more executions to long sequence templates. Second, to improve the generation quality of requests in a sequence template, MINER creatively leverages the state-of-the-art neural network model to predict key request parameters and provide them with appropriate parameter values. Third, MINER implements a new data-driven security rule checker to capture the new kind of errors caused by undefined parameters. We evaluate MINER against the state-of-the-art fuzzer RESTler on GitLab, Bugzilla, and WordPress via 11 REST APIs. The results demonstrate that the average pass rate of MINER is 23.42% higher than RESTler. MINER finds 97.54% more unique errors than RESTler on average and 142.86% more reproducible errors after manual analysis. We have reported all the newly found errors, and 7 of them have been confirmed as logic bugs by the corresponding vendors., Comment: Accepted as a full paper at USENIX Security '23

There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive. At the same time, many of these algorithms need an environment to train and optimize. Simulation is a valuable and meaningful solution with training and testing functions, and it can say that simulation is a critical link in the autonomous driving world. There are also many different applications or systems of simulation from companies or academies such as SVL and Carla. These simulators flaunt that they have the closest real-world simulation, but their environment objects, such as pedestrians and other vehicles around the agent-vehicle, are already fixed programmed. They can only move along the pre-setting trajectory, or random numbers determine their movements. What is the situation when all environmental objects are also installed by Artificial Intelligence, or their behaviors are like real people or natural reactions of other drivers? This problem is a blind spot for most of the simulation applications, or these applications cannot be easy to solve this problem. The Neurorobotics Platform from the TUM team of Prof. Alois Knoll has the idea about "Engines" and "Transceiver Functions" to solve the multi-agents problem. This report will start with a little research on the Neurorobotics Platform and analyze the potential and possibility of developing a new simulator to achieve the true real-world simulation goal. Then based on the NRP-Core Platform, this initial development aims to construct an initial demo experiment. The consist of this report starts with the basic knowledge of NRP-Core and its installation, then focus on the explanation of the necessary components for a simulation experiment, at last, about the details of constructions for the autonomous driving system, which is integrated object detection and autonomous control., Comment: 25 pages, 8 figures

The goal of automated feature generation is to liberate machine learning experts from the laborious task of manual feature generation, which is crucial for improving the learning performance of tabular data. The major challenge in automated feature generation is to efficiently and accurately identify effective features from a vast pool of candidate features. In this paper, we present OpenFE, an automated feature generation tool that provides competitive results against machine learning experts. OpenFE achieves high efficiency and accuracy with two components: 1) a novel feature boosting method for accurately evaluating the incremental performance of candidate features and 2) a two-stage pruning algorithm that performs feature pruning in a coarse-to-fine manner. Extensive experiments on ten benchmark datasets show that OpenFE outperforms existing baseline methods by a large margin. We further evaluate OpenFE in two Kaggle competitions with thousands of data science teams participating. In the two competitions, features generated by OpenFE with a simple baseline model can beat 99.3% and 99.6% data science teams respectively. In addition to the empirical results, we provide a theoretical perspective to show that feature generation can be beneficial in a simple yet representative setting. The code is available at https://github.com/ZhangTP1996/OpenFE., Comment: 22 pages, 3 figures, accepted by ICML2023

Let $\mathbb{F}_q$ denote the finite field of $q$ elements with characteristic $p$. Let $\mathbb{Z}_q$ denote the unramified extension of the $p$-adic integers $\mathbb{Z}_p$ with residue field $\mathbb{F}_q$. In this paper, we investigate the $q$-divisibility for the number of solutions of a polynomial system in $n$ variables over the finite Witt ring $\mathbb{Z}_q/p^m\mathbb{Z}_q$, where the $n$ variables of the polynomials are restricted to run through a combinatorial box lifting $\mathbb{F}_q^n$. The introduction of the combinatorial box makes the problem much more complicated. We prove a $q$-divisibility theorem for any box of low algebraic complexity, including the simplest Teichm\"uller box.This extends the classical Ax-Katz theorem over finite field $\mathbb{F}_q$ (the case $m=1$). Taking $q=p$ to be a prime, our result extends and improves a recent combinatorial theorem of Grynkiewicz. Our different approach is based on the addition operation of Witt vectors and is conceptually much more transparent.

Multivariate time series forecasting has seen widely ranging applications in various domains, including finance, traffic, energy, and healthcare. To capture the sophisticated temporal patterns, plenty of research studies designed complex neural network architectures based on many variants of RNNs, GNNs, and Transformers. However, complex models are often computationally expensive and thus face a severe challenge in training and inference efficiency when applied to large-scale real-world datasets. In this paper, we introduce LightTS, a light deep learning architecture merely based on simple MLP-based structures. The key idea of LightTS is to apply an MLP-based structure on top of two delicate down-sampling strategies, including interval sampling and continuous sampling, inspired by a crucial fact that down-sampling time series often preserves the majority of its information. We conduct extensive experiments on eight widely used benchmark datasets. Compared with the existing state-of-the-art methods, LightTS demonstrates better performance on five of them and comparable performance on the rest. Moreover, LightTS is highly efficient. It uses less than 5% FLOPS compared with previous SOTA methods on the largest benchmark dataset. In addition, LightTS is robust and has a much smaller variance in forecasting accuracy than previous SOTA methods in long sequence forecasting tasks., Comment: datasets and code: https://tinyurl.com/5993cmus

Ferroelectricity in atomically thin bilayer structures has been recently predicted1 and measured[2-4] in two-dimensional (2D) materials with hexagonal non-centrosymmetric unit-cells. Interestingly, the crystal symmetry translates lateral shifts between parallel 2D layers to a change of sign in their out-of-plane electric polarization, a mechanism referred to as "Slide-Tronics"[4]. These observations, however, have been restricted to switching between only two polarization states under low charge carrier densities[5-12], strongly limiting the practical application of the revealed phenomena[13]. To overcome these issues, one needs to explore the nature of the polarization that arises in multi-layered van der Waals (vdW) stacks, how it is governed by intra- and inter-layer charge redistribution, and to which extent it survives the introduction of mobile charge carriers, all of which are presently unknown14. To explore these questions, we conduct surface potential measurements of parallel WSe2 and MoS2 multi-layers with aligned and anti-aligned configurations of the polar interfaces. We find evenly spaced, nearly decoupled potential steps, indicating highly confined interfacial electric fields, which provide means to design multi-state "ladder ferroelectrics". Furthermore, we find that the internal polarization remains significant upon electrostatic doping of a mobile charge carrier density as high as 1013 cm-2, with substantial in-plane conductivity. Using first-principles calculations based on density functional theory (DFT), we trace the extra charge redistribution in real and momentum space and identify an eventual doping-induced depolarization mechanism.

A large number of novel two-dimensional (2D) materials are constantly discovered and deposed into the databases. Consolidate implementation of machine learning algorithms and density functional theory (DFT) based predictions have allowed creating several databases containing an unimaginable amount of 2D samples. The next step in this chain, the investigation leads to a comprehensive study of the functionality of the invented materials. In this work, a family of transition metal dichlorides has been screened out for systematical investigation of their structural stability, fundamental properties, structural defects, and environmental stability via DFT based calculations. The work highlights the importance of using the potential of the invented materials and proposes a comprehensive characterization of a new family of 2D materials.

Speech emotion recognition systems have high prediction latency because of the high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level features. Mel-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to increase feature processing speed and tolerance to adversity. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions. The syllable level predictions help to achieve the real-time latency and lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives real-time latency while predicting with state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE., Comment: Significant revisions

Reactive magnesium oxide (MgO)-based cement (RMC) can play a key role in carbon capture processes. However, knowledge on the driving forces that control the degree of carbonation and hydration and rate of reactions in this system remains limited. In this work, density functional theory-based simulations are used to investigate the physical nature of the reactions taking place during the fabrication of RMCs under ambient conditions. Parametric indicators such as adsorption energies, charge transfer, electron localization function, adsorption/dissociation energy barriers and the mechanisms of interaction of H2O and CO2 molecules with MgO and brucite (Mg(OH)2) clusters are considered. The following hydration and carbonation interactions relevant to RMCs are evaluated i) carbonation of MgO, ii) hydration of MgO, carbonation of hydrated MgO, iii) carbonation of Mg(OH)2, iv) hydration of Mg(OH)2 and v) hydration of carbonated Mg(OH)2. A comparison of the energy barriers and reaction pathways of these mechanisms shows that the carbonation of MgO is hindered by presence of H2O molecules, while the carbonation of Mg(OH)2 is hindered by the formation of initial carbonate and hydrate layers as well as presence of excessed H2O molecules. To compare these finding to bulk mineral surfaces, the interactions of the CO2 and H2O molecules with the MgO(001) and Mg(OH)2 (001) surfaces are studied. Therefore, this work presents deep insights into the physical nature of the reactions and the mechanisms involved in hydrated magnesium carbonates production that can be beneficial for its development.

Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting. In this paper, we introduce a deep expansion learning framework, DEPTS, for PTS forecasting. DEPTS starts with a decoupled formulation by introducing the periodic state as a hidden variable, which stimulates us to make two dedicated modules to tackle the aforementioned two challenges. First, we develop an expansion module on top of residual learning to perform a layer-by-layer expansion of those complicated dependencies. Second, we introduce a periodicity module with a parameterized periodic function that holds sufficient capacity to capture diversified periods. Moreover, our two customized modules also have certain interpretable capabilities, such as attributing the forecasts to either local momenta or global periodicity and characterizing certain core periodic properties, e.g., amplitudes and frequencies. Extensive experiments on both synthetic data and real-world data demonstrate the effectiveness of DEPTS on handling PTS. In most cases, DEPTS achieves significant improvements over the best baseline. Specifically, the error reduction can even reach up to 20% for a few cases. Finally, all codes are publicly available., Comment: ICLR22 Spotlight