Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of thickness of SiO
 2
 layer on the performance of SINW sensors

In recent decades, silicon nanowire, one of the most widely used 1D nanomaterials, has been considered as the promising sensitive material with its unique advantages in the sensor area [1–5], owing to its distinct mechanical, electronical and optical properties [6–10]. The ultrahigh surface to volume ratios leads to the super-sensitivity and extremely low detection limits of silicon nanowires [11, 12]. Moreover, silicon nanowire can be easily modified by various biological and chemical molecules that can be used to specifically detect target molecules, including a wide range of chemical and biological species [13–15]. In addition, it is compatible with the scale of target biochemical molecular for silicon nanowires with diameters less than 100 nm, whose unique advantages in biochemical detection is given to silicon nanowires [16, 17]. In recent years, the applications of silicon nanowires in various fields have been widely studied, including solar cells, photodetectors, resonators, thermoelectric generator and so on [18–22]. Especially, silicon nanowires are designed as biosensors for the detection of various targets, which could detect nucleic acids, proteins, virus, and chemical species [23–28]. Owing to the application of MEMS technology, the silicon nanowires devices obtained advantages like small size, low price and easy to batch fabricated [29–31]. However, some drawbacks existed in the preparation process, in which the silicon nanowires were wrapped by silicon oxide due to selflimiting oxidation method [32–34]. Since silicon nanowires were employed as sensitive elements of sensors, the silicon nanowires must be exposed by etching away the silicon oxide using the buffered oxide etch (BOE) solution before being used for tests [35, 36]. This corrosion process, which was difficult to precisely control, would greatly influence the performance of silicon nanowire sensors. The corrosion process of silicon nanowire devices could be recognized as one of the most important challenge to control the silicon oxide layer thickness. In order to ensure high performance of devices for subsequent test experiments, this influence of silicon oxide thickness need to be investigated carefully. In this paper, we have investigated the influence of BOE etching process, i.e. silicon oxide layer thickness, on silicon
A BJT-Based CMOS Temperature Sensor With Duty-Cycle-Modulated Output and ±0.5°C (3σ) Inaccuracy From −40 °C to 125 °C

This brief presents a 0.65% relative inaccuracy CMOS temperature sensor with a duty-cycle-modulated (DCM) output. It uses a BJT-based front-end to generate a proportional to absolute temperature voltage (<inline-formula> <tex-math notation="LaTeX">$V_{PTAT}$ </tex-math></inline-formula>) and a complementary to absolute temperature voltage (<inline-formula> <tex-math notation="LaTeX">$V_{CTAT}$ </tex-math></inline-formula>), which are then modulated to a digital-friendly duty-cycle output. Dynamic element matching with Kelvin connection (KC-DEM) is applied to improve the accuracy of <inline-formula> <tex-math notation="LaTeX">$V_{PTAT}$ </tex-math></inline-formula>. To enhance the robustness of the sensor, a continuous-time dynamic single-threshold hysteresis comparator with high energy efficiency is proposed. Implemented in a standard 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text {m}$ </tex-math></inline-formula> CMOS process, the sensor has an active area of 0.086 mm<sup>2</sup> and achieves an inaccuracy of ±0.54 °C (<inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula>) from −40 °C to 125 °C.
THE INFLUENCE OF METEOROLOGICAL FACTORS ON THE LONG-PERIOD VARIATION OF THE KULLBACK MEASURE OF VOLTAGE FLUCTUATIONS ON THE ELECTROLYTIC CELLS

Приведены результаты сопоставления подвергнутых низкочастотной филь-трации записей мер Кульбака флуктуаций напряжения в малых объемах элек-тролита в двух независимых электролитических ячейках и метеорологическихфакторов. Установлена высокая корреляция значений мер Кульбака и темпе-ратуры точки росы для атмосферного воздуха. Рассчитаны коэффициентырегрессии мер Кульбака и значений различных метеорологических факторов. Показано, что при высокочастотной фильтрации сигналов коэффициенты ре-грессии уменьшаются в несколько раз. Предложена физическая модель, объ-ясняющая наблюдаемое воздействие на электролитические ячейки, котораяосновывается на предположении о влиянии производства энтропии при пере-излучении солнечного света поверхностью Земли.
COMPARING THE DISCONTINUOUS GALERKIN AND HIGH-ORDER DIAMOND DIFFERENCING METHODS FOR THE TRANSPORT EQUATION ON A LOZENGE-BASED HEXAGONAL GEOMETRY

This paper presents an implementation and a comparison of two spatial discretisation schemes over a hexagonal geometry for the two-dimensional discrete ordinates transport equation. The methods are a high-order Discontinuous Galerkin (DG) finite element scheme and a high-order Diamond Differencing (DD) scheme. The DG method has been, and is being, studied on the hexagonal geometry, also called a honeycomb mesh – but not the DD method. In this research effort, it was chosen to divide the hexagons into (at least) three lozenges. An affine transformation is then applied onto said lozenges to cast them into the reference quadrilaterals usually studied in finite elements. In practice, this effectively means that the equations used in Cartesian geometry have their terms and operators altered using the Jacobian matrix of the transformation. This was implemented in the discrete ordinates solver of the code DRAGON5. Two 2D benchmark problems were then used for the verification and validation, including one based on the Monju 3D reactor benchmark. It was found that the diamond-differencing scheme seemed better. It converged much faster towards the solution at comparable mesh refinements for first-order expansion of the flux. Even if this difference was not present for second-order, DG was slower, about two to four times slower.
Active mobile oscillators on a substrate: Density fluctuations and phase ordering

Astik Haldar, ∗ Apurba Sarkar, † Swarnajit Chatterjee, ‡ and Abhik Basu § Theory Division, Saha Institute of Nuclear Physics, HBNI, Calcutta 700064, West Bengal, India School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science, Kolkata-700032, West Bengal, India Center for Biophysics & Department for Theoretical Physics, Saarland University, 66123 Saarbrücken, Germany
Concurrent circulation of dengue serotype 1, 2 and 3 among acute febrile patients in Cameroon

Acute febrile patients presenting at hospitals in Douala, Cameroon between July and December 2020, were screened for dengue infections using RT-PCR on fragments of the 5 and 3 UTR genomic regions. In total, 12.8% (41/320) of cases examined were positive for dengue. Dengue virus 3 (DENV-3) was the most common serotype found (68.3%), followed by DENV-2 (19.5%) and DENV-1 (4.9%). Co-infections of DENV-3 and DENV-2 were found in 3 cases. Jaundice and headache were the most frequent clinical signs associated with infection and 176 cases (55%) were co-infections with malaria. Phylogenetic analysis of the envelope gene identified DENV-1 as belonging to genotype V, DENV-2 to genotype II and DENV-3 to genotype III. The simultaneous occurrence of three serotypes in Douala reveals dengue as a serious public health threat for Cameroon and highlights the need for further epidemiological studies in the major cities of this region.
Kudu: An Efficient and Scalable Distributed Graph Pattern Mining Engine

This paper proposes Kudu, a distributed execution engine with a well-defined abstraction that can be integrated with existing single-machine graph pattern mining (GPM) systems to provide efficiency and scalability at the same time. The key novelty is the extendable embedding abstraction which can express pattern enumeration algorithms, allow fine-grained task scheduling, and enable low-cost GPM-specific data reuse to reduce communication cost. The effective BFS-DFS hybrid exploration generates sufficient concurrent tasks for communication-computation overlapping with bounded memory consumption. Two scalable distributed GPM systems are implemented by porting Automine and GraphPi on Kudu. Our evaluation shows that Kudu based systems significantly outperform state-of-the-art distributed GPM systems with partitioned graphs by up to 75 . 5 × (on average 19 . 0 × ), achieve similar or even better performance compared with the fastest distributed GPM systems with replicated graph, and scale to massive graphs with more than one hundred billion edges with a commodity cluster. ,
Practical sufficient conditions for convergence of distributed optimisation algorithms over communication networks with interference

Information exchange over networks can be affected by various forms of delay. This causes challenges for using the network by a multi-agent system to solve a distributed optimisation problem. Distributed optimisation schemes, however, typically do not assume network models that are representative for real-world communication networks, since communication links are most of the time abstracted as lossless. Our objective is therefore to formulate a representative network model and provide practically verifiable network conditions that ensure convergence of distributed algorithms in the presence of interference and possibly unbounded delay. Our network is modelled by a sequence of directed-graphs, where to each network link we associate a process for the instantaneous signal-to-interference-plus-noise ratio. We then formulate practical conditions that can be verified locally and show that the age of information (AoI) associated with data communicated over the network is in $\mathcal{O}(\sqrt{n})$. Under these conditions we show that a penalty-based gradient descent algorithm can be used to solve a rich class of stochastic, constrained, distributed optimisation problems. The strength of our result lies in the bridge between practical verifiable network conditions and an abstract optimisation theory. We illustrate numerically that our algorithm converges in an extreme scenario where the average AoI diverges.
Development of a risk assessment method for sea trout in coastal areas exploited for aquaculture

The regulation of aquaculture production in Norway considers the potential impact of salmon lice on wild fish. However, most attention has been focused on impacts on wild Atlantic salmon, despite the fact that anadromous brown trout spend the majority of their marine phase in coastal waters, where salmon lice have the highest impact. In the present study, we first suggest changes in marine living area and marine feeding time as sustainability indicators for first-time migrant sea trout, as high salmon lice densities may exclude sea trout from otherwise usable habitat and force them to return early to freshwater. Further, a method based on a bio-hydrodynamic model was developed to serve as a proxy for these indicators. The method accounted for the size, migration timing and spatial extent of sea trout and was demonstrated in 2 Norwegian salmon aquaculture production areas, Hardangerfjord (PO3) and Romsdalsfjord (PO5), and 2 focal rivers from within each fjord. Based on these comparisons, we exemplify how the change in marine living area and marine feeding time differed between PO3 and PO5 and within the areas. Sea trout migrating to sea late (June 5) were always more affected by lice than those migrating early (April 24) or at intermediate dates (May 15). Our estimates revealed dramatic potential impacts of salmon lice on sea trout populations, which were greatly influenced by spatial and temporal aspects. Considering the negative impacts of salmon lice on sea trout, a holistic view of environmental interactions between aquaculture and wild species that depend on habitats exploited for production is necessary.
CASA-B: A Uniﬁed Framework of Model-Free Reinforcement Learning

Building on the breakthrough of reinforcement learning, this paper introduces a uniﬁed framework of model-free reinforcement learning, CASA-B, C ritic AS an A ctor with B andits Vote Algorithm. CASA-B is an actor-critic framework that estimates state-value, state-action-value and policy. An expectation-correct Doubly Robust Trace is introduced to learn state-value and state-action-value, whose convergence properties are guaranteed. We prove that CASA-B integrates a consistent path for the policy evaluation and the policy improvement. The policy evaluation is equivalent to a compensational policy improvement, which alleviates the function approximation error, and is also equivalent to an entropy-regularized policy improvement, which prevents the policy from collapsing to a suboptimal solution. Building on this design, we ﬁnd the entropy of the behavior policies’ and the target policy’s are disentangled. Based on this observation, we propose a progressive closed-form entropy control mechanism, which explicitly controls the behavior policies’ entropy to arbitrary range. Our experiments show that CASA-B is super sample efﬁcient and achieves State-Of-The-Art on Arcade Learning Environment. Our mean Human Normalized Score is 6456 . 63% and our median Human Normalized Score is 477 . 17% , under 200M training scale.
PolSAR Image Classification Using Hybrid Conditional Random Fields Model Based on Complex-Valued 3-D CNN

In this article, we propose a hybrid conditional random field model based on complex-valued 3-D convolutional neural network (C3NN), named as HCRF-C3NN, for polarimetric synthetic aperture radar (PolSAR) image classification. HCRF-C3NN combines the modeling power of random fields with the representation-learning ability of deep learning model for PolSAR image. By C3NN, HCRF-C3NN exploits deep amplitude and phase information of PolSAR data and, thus, measures the class probabilities in the framework of random fields. Additionally, HCRF-C3NN captures the spatial label interactions effectively in PolSAR image classification by the following work: 1) Based on the C3NN-based class probabilities, the relative entropy of class distributions is derived to describe the local structure and, thus, regulates the pairwise label interactions, which enhances the accuracy of edge location in the classification; 2) a product-of-expert potential is introduced to enforce label consistency, and, thus, HCRF-C3NN is more robust against speckle of PolSAR image and achieves smoother classification within the consistent class region. At last, to capture PolSAR image information in a more complete manner, the deep features and PolSAR scattering statistics are integrated into HCRF-C3NN based on Bayesian fusion. In this way, HCRF-C3NN effectively explores spatial correlation, scattering statistics, and deep features of PolSAR data in the framework of random fields. The experimental results demonstrate the superiority of HCRF-C3NN over the recent deep learning models for PolSAR image classification.
Robust Structure Identification of Industrial Cyber-Physical System From Sparse Data: A Network Science Perspective

Industrial cyber-physical systems (ICPSs) are deployed in many high-value facilities recently, and the monitoring of ICPS is more and more important. However, the prerequisite of ICPS monitoring is how to obtain an accurate network structure. In addition, the structure of ICPS may change over time and the observations data are limited and noisy. These situations make the ICPS network structure identification more difficult. In this article, we proposed the algorithm of temporal network identification from sparse data (ATNISD) to address these two issues simultaneously. First, we established the temporal network analysis model from the aspect of state equation and observation equation. Then, we analyze the characteristics of temporal networks in both time domain and space domain and propose a general framework of temporal networks structure identification, which is a combinatorial optimization problem. To improve the accuracy and alleviate the computational complexity, we decompose the combinatorial problem into small independent simple problems, which can be solved efficiently. The performance of the proposed algorithm is verified on synthetic evolutionary game dynamics on both homogeneous and heterogeneous temporal networks. The experimental results show that the proposed method can efficiently solve the problem of temporal networks structure identification from sparse data. Note to Practitioners—This article addresses the importance of network structure identification in industrial cyber-physical systems (ICPSs). The proposed method can effectively cope with the task of ICPS network structure identification in time-varying environments by exploiting the spatial and temporal features of networks in both time domain and space domain. The proposed algorithm can be implemented in typical slowing changing ICPS with or without observation noise, and it can decompose the combinatorial problem into small independent simple problems to improve the accuracy and release the computational complexity. Extensive simulation experiments demonstrate the accuracy and robustness of the proposed method for solving the structure identification task of temporal networks.
A Simple yet Universal Strategy for Online Convex Optimization

Recently, several universal methods have been proposed for online convex optimization, and attain minimax rates for multiple types of convex functions simultaneously. However, they need to design and optimize one surrogate loss for each type of functions, which makes it difficult to exploit the structure of the problem and utilize the vast amount of existing algorithms. In this paper, we propose a simple strategy for universal online convex optimization, which avoids these limitations. The key idea is to construct a set of experts to process the original online functions, and deploy a meta-algorithm over the \emph{linearized} losses to aggregate predictions from experts. Specifically, we choose Adapt-ML-Prod to track the best expert, because it has a second-order bound and can be used to leverage strong convexity and exponential concavity. In this way, we can plug in off-the-shelf online solvers as black-box experts to deliver problem-dependent regret bounds. Furthermore, our strategy inherits the theoretical guarantee of any expert designed for strongly convex functions and exponentially concave functions, up to a double logarithmic factor. For general convex functions, it maintains the minimax optimality and also achieves a small-loss bound.
A new thermo-elastic model for Thermal Radiation Pressure

Une approche complementaire a celle suivie jusqu'a present sur ce sujet est presentee. A l'aide d'une extension des variables independantes de l'energie du systeme, un nouveau modele thermoelastique pour la Pression Thermique de Rayonnement est introduit. Il s'avere compatible avec le deuxieme principe de la thermodynamique
Neural Text Generation with Part-of-Speech Guided Softmax

Neural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity only by exploiting contextual features, but rarely do they consider incorporating syntactic structure clues. In this work, we propose using linguistic annotation, i.e., part-of-speech (POS), to guide the text generation. In detail, we introduce POS Guided Softmax (POSG-Softmax) to explicitly model two posterior probabilities: (i) next-POS, and (ii) next-token from the vocabulary of the target POS. A POS guided sampling strategy is further proposed to address the low-diversity problem by enriching the diversity of POS. Extensive experiments and human evaluations demonstrate that, compared with existing state-of-the-art methods, our proposed methods can generate more diverse text while maintaining comparable quality.
RETRACTED: Income Shocks and Child Mortality Rates: Evidence from Fluctuations in Oil Prices

Abstract Previous studies show that children in lower socioeconomic status families reveal higher rates of mortality. We complement the income-mortality literature by establishing a causal link between income and child mortality. Our instrument for income is based on time-series global shocks to oil prices combined with the cross-sectional share of employment in manufacturing across US states as their exposure to oil price changes. Using the universe of death records between the years 1975-2004, we find the OLS results of income-child-mortality relationships are under-biased. The 2SLS-IV results suggest that a $1,000 increase in income per capita at the state level reduces child mortality and infant mortality by 0.87 and 0.53 fewer incidences per 1,000 population of age-specific children.
MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer With One Transformer VAE

Transformers and variational autoencoders (VAE) have been extensively employed for symbolic (e.g., MIDI) domain music generation. While the former boast an impressive capability in modeling long sequences, the latter allow users to willingly exert control over different parts (e.g., bars) of the music to be generated. In this paper, we are interested in bringing the two together to construct a single model that exhibits both strengths. The task is split into two steps. First, we equip Transformer decoders with the ability to accept segment-level, time-varying conditions during sequence generation. Subsequently, we combine the developed and tested in-attention decoder with a Transformer encoder, and train the resulting MuseMorphose model with the VAE objective to achieve style transfer of long pop piano pieces, in which users can specify musical attributes including rhythmic intensity and polyphony (i.e., harmonic fullness) they desire, down to the bar level. Experiments show that MuseMorphose outperforms recurrent neural network (RNN) based baselines on numerous widely-used metrics for style transfer tasks.
Validation of image systems simulation technology using a Cornell Box

We describe and experimentally validate an end-to-end simulation of a digital camera. The simulation models the spectral radiance of 3D-scenes, formation of the spectral irradiance by multi-element optics, and conversion of the irradiance to digital values by the image sensor. We quantify the accuracy of the simulation by comparing real and simulated images of a precisely constructed, three-dimensional high dynamic range test scene. Validated end-to-end software simulation of a digital camera can accelerate innovation by reducing many of the time-consuming and expensive steps in designing, building and evaluating image systems.
Agriculture environment monitoring: rapid soil fertility evaluation by means of near infrared spectroscopy

In agricultural practices, the development of environmental controlling programs is essential in maintaining soil fertility. It should be monitored constantly in real time situation by predicting soil properties such as nutrients and mineral contents. The main purpose of this presented paper is employ the near infrared spectroscopy (NIRS) technique in determining nitrogen (N) and magnesium (Mg) content of agricultural soil rapidly and simultaneously. Near infrared spectra data of soil samples were obtained by acquiring absorbance data in wavelength range from 1000 to 2500 nm using a portable sensing device NIR with a photodiode array detector. Spectra data were enhanced using mean normalization approach to improve prediction accuracy. Further, prediction models were developed by means of partial least square regression (PLSR) followed by cross validation. The results showed that both soil fertility properties (N and Mg) can be predicted by NIRS with maximum correlation coefficient (r) were 0.93 and 0.91 for N and Mg respectively. It may conclude that NIRS technique was able to be used in agriculture environment monitoring especialy to predict soil fertility properties.