Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Accurate and Compact Architectures via Neural Architecture Transformer

Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods. However, even a well-designed/searched architecture may still contain many nonsignificant or redundant modules/operations (e.g., some intermediate convolution or pooling layers). Such redundancy may not only incur substantial memory consumption and computational cost but also deteriorate the performance. Thus, it is necessary to optimize the operations inside an architecture to improve the performance without introducing extra computational cost. To this end, we have proposed a Neural Architecture Transformer (NAT) method which casts the optimization problem into a Markov Decision Process (MDP) and seeks to replace the redundant operations with more efficient operations, such as skip or null connection. Note that NAT only considers a small number of possible replacements/transitions and thus comes with a limited search space. As a result, such a small search space may hamper the performance of architecture optimization. To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization. Specifically, we present a two-level transition rule to obtain valid transitions, i.e., allowing operations to have more efficient types (e.g., convolution<inline-formula><tex-math notation="LaTeX">${\to }$</tex-math><alternatives><mml:math><mml:mo>→</mml:mo></mml:math><inline-graphic xlink:href="tan-ieq1-3086914.gif"/></alternatives></inline-formula>separable convolution) or smaller kernel sizes (e.g., <inline-formula><tex-math notation="LaTeX">$5{\times }5 {\to } 3{\times }3$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>5</mml:mn><mml:mo>×</mml:mo><mml:mn>5</mml:mn><mml:mo>→</mml:mo><mml:mn>3</mml:mn><mml:mo>×</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="tan-ieq2-3086914.gif"/></alternatives></inline-formula>). Note that different operations may have different valid transitions. We further propose a Binary-Masked Softmax (BMSoftmax) layer to omit the possible invalid transitions. Last, based on the MDP formulation, we apply policy gradient to learn an optimal policy, which will be used to infer the optimized architectures. Extensive experiments show that the transformed architectures significantly outperform both their original counterparts and the architectures optimized by existing methods.
Effect of burning on soil organic matter content and N mineralization under shifting cultivation system of Karen people in Northern Thailand

Abstract In the traditional shifting cultivation system practiced by the Karen people in northern Thailand, the effects of burning on the content of extractable organic matter, microbial biomass, and N mineralization process of the soils were studied. Five plots (5×5 m2 quadrat) with 0, 10, 20, 50, and 100 Mg ha-1 of slashed materials were arranged and burned. Ten to 20 Mg ha-1 of slashed biomass corresponded to the amount commonly burned by the Karen people. During the burning process, the soil temperature at the depth of 2.5 cm in the 100 Mg ha-1 plot almost evenly increased to 300°C while the temperature in the 10 to 50 Mg ha-1 plots increased with large variations from 50 to 300°C. Burning caused a conspicuous increase in the contents of organic C and (organic + mineral)-N extracted at room temperature and a simultaneous decrease in the contents of microbial biomass C and N, especially in the soil of the 100 Mg ha-1 plot. In the rainy season, the values of the changes induced by burning reverted to the values recorded before burning, except for the microbial biomass in the 100 Mg ha-1 plot, which still remained lower. Based on an incubation experiment, N mineralization rate was higher in the soils taken just after burning, especially in the 100 Mg ha-1 plot, than in the soils taken during the rainy season. However, the soil in the 100 Mg ha-1 plot was considered to have the lowest ability to supply mineral N among the soils in the rainy season. Burning of 10 to 20 Mg ha-1 biomass corresponding to the values recorded in Karen peoples' shifting cultivation system was more compatible with soil ecology in terms of N supply at the initial stage of crop growth and of microbial biomass recovery during the rainy season, compared to the burning of 100 Mg ha-1 biomass corresponding to the value recorded in a natural forest. Thus, the shifting cultivation system implemented by the Karen people can be considered to be a well-balanced agricultural system.
Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI

We consider the challenges in extracting stimulus-related neural dynamics from other intrinsic processes and noise in naturalistic functional magnetic resonance imaging (fMRI). Most studies rely on inter-subject correlations (ISC) of low-level regional activity and neglect varying responses in individuals. We propose a novel, data-driven approach based on low-rank plus sparse ( $\text{L}+\text{S}$ ) decomposition to isolate stimulus-driven dynamic changes in brain functional connectivity (FC) from the background noise, by exploiting shared network structure among subjects receiving the same naturalistic stimuli. The time-resolved multi-subject FC matrices are modeled as a sum of a low-rank component of correlated FC patterns across subjects, and a sparse component of subject-specific, idiosyncratic background activities. To recover the shared low-rank subspace, we introduce a fused version of principal component pursuit (PCP) by adding a fusion-type penalty on the differences between the columns of the low-rank matrix. The method improves the detection of stimulus-induced group-level homogeneity in the FC profile while capturing inter-subject variability. We develop an efficient algorithm via a linearized alternating direction method of multipliers to solve the fused-PCP. Simulations show accurate recovery by the fused-PCP even when a large fraction of FC edges are severely corrupted. When applied to natural fMRI data, our method reveals FC changes that were time-locked to auditory processing during movie watching, with dynamic engagement of sensorimotor systems for speech-in-noise. It also provides a better mapping to auditory content in the movie than ISC.
Elementary superexpressive activations

We call a finite family of activation functions superexpressive if any multivariate continuous function can be approximated by a neural network that uses these activations and has a fixed architecture only depending on the number of input variables (i.e., to achieve any accuracy we only need to adjust the weights, without increasing the number of neurons). Previously, it was known that superexpressive activations exist, but their form was quite complex. We give examples of very simple superexpressive families: for example, we prove that the family {sin, arcsin} is superexpressive. We also show that most practical activations (not involving periodic functions) are not superexpressive.
Dimensions of Timescales in Neuromorphic Computing Systems

This article is a public deliverable of the EU project"Memory technologies with multi-scale time constants for neuromorphic architectures"(MeMScales, https://memscales.eu, Call ICT-06-2019 Unconventional Nanoelectronics, project number 871371). This arXiv version is a verbatim copy of the deliverable report, with administrative information stripped. It collects a wide and varied assortment of phenomena, models, research themes and algorithmic techniques that are connected with timescale phenomena in the fields of computational neuroscience, mathematics, machine learning and computer science, with a bias toward aspects that are relevant for neuromorphic engineering. It turns out that this theme is very rich indeed and spreads out in many directions which defy a unified treatment. We collected several dozens of sub-themes, each of which has been investigated in specialized settings (in the neurosciences, mathematics, computer science and machine learning) and has been documented in its own body of literature. The more we dived into this diversity, the more it became clear that our first effort to compose a survey must remain sketchy and partial. We conclude with a list of insights distilled from this survey which give general guidelines for the design of future neuromorphic systems.
Influence of Polyethylene Mulch, Irrigation Regime, and Potassium Rates on Field Cucumber Yield and Related Traits

ABSTRACT Cucumber (Cucumis sativus) was field grown from April 2001–July 2001 to determine the effects of mulch, irrigation regime, and potassium (K) rates on yield and related traits (i.e., leaf relative water content, water-use efficiency, and macronutrition). This was a factorial experiment with two irrigation levels (125% A pan daily versus 75% A pan every three days), two mulch levels (mulched versus unmulched), and three K2O levels (20, 40, or 60 g/m2). Plants receiving reduced water application (75% A pan every 3 days) showed significant reductions in all parameters when compared with well-watered plants (125% A pan daily). The use of black polyethylene mulch (BPM) covers improved the plant dry matter, chlorophyll concentrations, fruit yield, and relative water content in leaves of well-watered plants and also improved K availability to the plants by keeping soil moisture higher than that of stressed plants without mulch. Using BPM increased plant water-use efficiency compared with that under the reduced water (RW) treatment. Reduced water application enhanced electrolyte leakage compared with that recorded under the well-watered (WW) treatment. Mulching decreased electrolyte leakage under the RW treatment. Increased K rates significantly enhanced leaf K in the mulched and WW plants. However, increased K rates did not increase leaf K in the RW plants. Reduced water application reduced leaf concentrations of all nutrients tested, i.e., nitrogen (N), phosphorus (P), K, calcium (Ca), and magnesium (Mg). However, mulching enhanced the concentrations of these elements, although their concentrations were still lower than those under the WW treatment. These results clearly indicate that field-grown number plants under mulched treatments were less stressed under semi-arid conditions and also that mulched treatments increased K availability to the plants.
Detecting transition between Abelian and non-Abelian topological orders through symmetric tensor networks

We propose a unified scheme to identify phase transitions out of the $\mathbb{Z}_2$ Abelian topological order, including the transition to a non-Abelian chiral spin liquid. Using loop gas and and string gas states [H. -Y. Lee, R. Kaneko, T. Okubo, N. Kawashima, Phys. Rev. Lett. 123, 087203 (2019)] on the star lattice Kitaev model as an example, we compute the overlap of minimally entangled states through transfer matrices. We demonstrate that, similar to the anyon condensation, continuous deformation of a $\mathbb{Z}_2$-injective projected entangled-pair state (PEPS) also allows us to study the transition between Abelian and non-Abelian topological orders. We show that the charge and flux anyons defined in the Abelian phase transmute into the $\sigma$ anyon in the non-Abelian topological order. Furthermore, we show that contrary to the claim in [Phys. Rev. B 101, 035140 (2020)], both the LG and SG states have infinite correlation length in the non-Abelian regime, consistent with the no-go theorem that a chiral PEPS has a gapless parent Hamiltonian.
Wireless sensor network for in situ soil moisture monitoring

We discuss the history and lessons learned from a series of deployments of environmental sensors measuring soil parameters and CO2 fluxes over the last fifteen years, in an outdoor environment. We present the hardware and software architecture of our current Gen-3 system, and then discuss how we are simplifying the user facing part of the software, to make it easier and friendlier for the environmental scientist to be in full control of the system. Finally, we describe the current effort to build a large-scale Gen-4 sensing platform consisting of hundreds of nodes to track the environmental parameters for urban green spaces in Baltimore, Maryland.
Person Re-identification based on Robust Features in Open-world

Deep learning technology promotes the rapid development of person re-identifica-tion (re-ID). However, some challenges are still existing in the open-world. First, the existing re-ID research usually assumes only one factor variable (view, clothing, pedestrian pose, pedestrian occlusion, image resolution, RGB/IR modality) changing, ignoring the complexity of multi-factor variables in the open-world. Second, the existing re-ID methods are over depend on clothing color and other apparent features of pedestrian, which are easily disguised or changed. In addition, the lack of benchmark datasets containing multi-factor variables is also hindering the practically application of re-ID in the open-world. In this paper, we propose a low-cost and high-efficiency method to solve shortcomings of the existing re-ID research, such as unreliable feature selection, low efficiency of feature extraction, single research variable, etc. Our approach based on pose estimation model improved by group convolution to obtain the continuous key points of pedestrian, and utilize dynamic time warping (DTW) to measure the similarity of features between different pedestrians. At the same time, to verify the effectiveness of our method, we provide a miniature dataset which is closer to the real world and includes pedestrian changing clothes and cross-modality factor variables fusion. Extensive experiments are conducted and the results show that our method achieves Rank-1: 60.9%, Rank-5: 78.1%, and mAP: 49.2% on this dataset, which exceeds most existing state-of-art re-ID models.
Review of recent progress in chemical stability of perovskite solar cells

In recent years, the record efficiency of perovskite solar cells (PSCs) has been updated from 9.7% to 20.1%. However, there has been very little study of the issue of stability, which restricts the outdoor application of PSCs. The issues of the degradation of perovskite and the stability of PSC devices should be urgently addressed to achieve good reproducibility and long lifetimes for PSCs with high conversion efficiency. Without studies on stability, exciting achievements cannot be transferred from the laboratory to industry and outdoor applications. In order to improve their stability, a basic understanding of the degradation process of PSCs in different conditions should be acquired via thorough study. This review summarizes recent studies of the relationship of the chemical stability of PSCs with their environment (oxygen and moisture, UV light, solution process, temperature) and corresponding possible solutions.
Stochastic AC Network-constrained Scheduling of CAES and Wind Power Generation in Joint Energy and reserve market: Toward More Realistic Results

In this paper, a two-stage stochastic day-ahead (DA) scheduling model is proposed incorporating wind power units and compressed air energy storage (CAES) to clear a co-optimized energy and reserve market. The two-stage stochastic programming method is employed to deal with the wind power generation uncertain nature. A linearized AC optimal power flow (LAC-OPF) approach with consideration of network losses, reactive power, and voltage magnitude constraints is utilized in the proposed two-stage stochastic DA scheduling model. Using an engineering insight, a two-level LAC-OPF (TL-LAC-OPF) approach is proposed to (i) reduce the number of binary variables of the LAC-OPF approach which decreases the computational burden, and (ii) obtain LAC-OPF pre-defined parameters adaptively so that the accuracy of LAC-OPF approach is increased as a result of reducing artificial losses. Furthermore, as the CAES efficiency depends on its thermodynamic and operational conditions, the proposed two-stage stochastic DA scheduling model is developed by considering its thermodynamic characteristics to obtain a more realistic market decision at the first place. The proposed model is applied to IEEE 30-bus and 57-bus test systems using GAMS software, and is compared with three traditional approaches, i.e., AC-OPF, DC-OPF, and LAC-OPF. Simulation results demonstrate effectiveness of the proposed methodology.
Phase slips dynamics in gated Ti and V all-metallic supercurrent nano-transistors

The effect of electrostatic gating on metallic elemental superconductors was recently demonstrated in terms of modulation of the switching current and control of the current phase relation in superconducting quantum interferometers. The latter suggests the existence of a direct connection between the macroscopic quantum phase (φ) in a superconductor and the applied gate voltage. The measurement of the switching current cumulative probability distributions is a convenient and powerful tool to analyze such relation. In particular, the comparison between the conventional Kurkijärvi–Fulton–Dunkleberger model and the gate-driven distributions give useful insights into the microscopic origin of the gating effect. In this paper, we summarize the main results obtained in the analysis of the phase slip events in elemental gated superconducting weak-links in a wide range of temperatures between 20 mK and 3.5 K. Such a large temperature range demonstrates both that the gating effect is robust as the temperature increases, and that fluctuations induced by the electric field are not negligible in a wide temperature range.
InaudibleKey: Generic Inaudible Acoustic Signal based Key Agreement Protocol for Mobile Devices

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present InaudibleKey, an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey exploits the acoustic channel frequency response of two legitimate devices as a common secret to generating keys. InaudibleKey employs several novel technologies to significantly improve its performance. We conduct extensive experiments to evaluate the proposed system in different real environments. Compared to state-of-the-art works, InaudibleKey improves key generation rate by 3 times, extends pairing distance by 3.2 times, and reduces information reconciliation counts by 2.5 times. Security analysis demonstrates that InaudibleKey is resilient to a number of malicious attacks. We also implement InaudibleKey on modern smartphones and resource-limited IoT devices. Results show that it is energy-efficient and can run on both powerful and resource-limited IoT devices without incurring excessive resource consumption.
Overview of the Phase Space Formulation of Quantum Mechanics with Application to Quantum Technologies

The phase‐space formulation of quantum mechanics has recently seen increased use in testing quantum technologies, including methods of tomography for state verification and device validation. Here, an overview of quantum mechanics in phase space is presented. The formulation to generate a generalized phase‐space function for any arbitrary quantum system is shown, such as the Wigner and Weyl functions along with the associated Q and P functions. Examples of how these different formulations are used in quantum technologies are provided, with a focus on discrete quantum systems, qubits in particular. Also provided are some results that, to the authors' knowledge, have not been published elsewhere. These results provide insight into the relation between different representations of phase space and how the phase‐space representation is a powerful tool in understanding quantum information and quantum technologies.
Nonlinear Dynamics of Wave Packets in Tunnel-Coupled Harmonic-Oscillator Traps

We consider a two-component linearly coupled system with the intrinsic cubic nonlinearity and the harmonic-oscillator (HO) confining potential. The system models binary settings in BEC and optics. In the symmetric system, with the HO trap acting in both components, we consider Josephson oscillations (JO) initiated by an input in the form of the HO’s ground state (GS) or dipole mode (DM), placed in one component. With the increase of the strength of the self-focusing nonlinearity, spontaneous symmetry breaking (SSB) between the components takes place in the dynamical JO state. Under still stronger nonlinearity, the regular JO initiated by the GS input carries over into a chaotic dynamical state. For the DM input, the chaotization happens at smaller powers than for the GS, which is followed by SSB at a slightly stronger nonlinearity. In the system with the defocusing nonlinearity, SSB does not take place, and dynamical chaos occurs in a small area of the parameter space. In the asymmetric half-trapped system, with the HO potential applied to a single component, we first focus on the spectrum of confined binary modes in the linearized system. The spectrum is found analytically in the limits of weak and strong inter-component coupling, and numerically in the general case. Under the action of the coupling, the existence region of the confined modes shrinks for GSs and expands for DMs. In the full nonlinear system, the existence region for confined modes is identified in the numerical form. They are constructed too by means of the Thomas–Fermi approximation, in the case of the defocusing nonlinearity. Lastly, particular (non-generic) exact analytical solutions for confined modes, including vortices, in one- and two-dimensional asymmetric linearized systems are found. They represent bound states in the continuum.
Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis

A software vulnerability could be exploited without any visible symptoms. When no source code is available, although such silent program executions could cause very serious damage, the general problem of analyzing silent yet harmful executions is still an open problem. In this work, we propose a graph neural network (GNN) assisted data flow analysis method for spotting silent buffer overflows in execution traces. The new method combines a novel graph structure (denoted DFG+) beyond data-flow graphs, a tool to extract {\tt DFG+} from execution traces, and a modified Relational Graph Convolutional Network as the GNN model to be trained. The evaluation results show that a well-trained model can be used to analyze vulnerabilities in execution traces (of previously-unseen programs) without support of any source code. Our model achieves 94.39\% accuracy on the test data and successfully locates 29 out of 30 real-world silent buffer overflow vulnerabilities. Leveraging deep learning, the proposed method is, to our best knowledge, the first general-purpose analysis method for silent buffer overflows. It is also the first method to spot silent buffer overflows in global variables, stack variables, or heap variables without crossing the boundary of allocated chunks.