Miracle-dz/UniFAR-FTUs huggingface dataset:
High Photosensitivity in Band-to-Band Tunneling Regime of Carbon Nanotube Field-Effect Phototransistor: Numerical Investigation

In this paper, we show how the band-to-band tunneling (BTBT) regime of ultra-scaled carbon nanotube fieldeffect transistor (CNTFET) can be exploited to get ultra-high photosensitivity to low infrared (IR) optical power. The CNT-based phototransistor has been computationally assessed by solving self-consistently the Poisson solver including the photovoltage with the non-equilibrium Green’s function (NEGF) formalism in the ballistic limit. This quantum simulation approach is adopted to take into account the BTBT mechanisms on which the photosensing principle is based in the proposed nanodevice. The light-induced gate photovoltage is adopted as photosensing principle. It has been found that the BTBT CNTFET can exhibit subthermionic optical swing. In addition, high photosensitivity can be recorded in BTBT regime to low gate photovoltages induced by weak IR incident optical powers. The obtained results indicate that the proposed nanoscale CNT phototransistor operating in BTBT photosensing regime can serve the modern optoelectronics, which is in dire need for ultra-sensitive phototransistors with low detection limits.
A CNN-enhanced Stereo Matching Method and its Application to Bin Picking Problem for Tiny Cubic Workpieces

This paper proposes a stereo matching method enhanced by instance segmentation results obtained by a deep convolutional neural network. The system configuration and bin picking process flow are newly suggested by using the proposed method and it is applied to the bin picking problem for tiny cubic workpieces. The picking plan is generated by applying Harris corner detector to the point-cloud in the 3-dimensional map. In the experiments, two kinds of piles consisting of cubes with a side of 10 [mm] or 5 [mm] are tested for bin picking. In these bin picking problems, all cubes are successfully picked up and the results show the feasibility of proposed method. As future work, not only cubes but also other arbitrarily shaped workpieces should be recognized.
Energy-Efficient Multiuser Localization in the RIS-Assisted IoT Networks

In various location-based Internet of Things (IoT) services, it is required to localize a large number of energy-limited devices simultaneously and accurately. In order to achieve this goal, a reconfigurable intelligent surface (RIS)-assisted positioning method for multiple IoT devices is proposed, where the signals transmitted by the users reach the base station (BS) along the direct path and the reflection path via the RIS. The difference in the propagation delay of the two paths is essential in the proposed triangulation-based localization framework, which is estimated via the cross-correlation function of the received signals. Based on the orthogonality of the transmitted signals, the optimization of the multiantenna BS and the RIS, with the goal of minimizing the total transmission power of the IoT devices, is constructed. For the orthogonal signal case, the nonconvex optimization problem for the RIS is recast into a convex problem via the semidefinite relaxation (SDR). For the nonorthogonal signal case, the zero-forcing (ZF) combining vectors at the BS are adopted to eliminate interferences among multiple users, and the block coordinate descent (BCD) algorithm is used to decouple the combining vectors and the RIS phases. Numerical results show that by using the proposed optimization method, decimeter-level positioning accuracy can be achieved with low-power consumption, and significant power gain can be achieved compared to the unoptimized RIS-assisted localization.
Exploring Statistical Backbone Filtering Techniques in the Air Transportation Network

The dense nature of transportation networks expands the challenge of their visualization and processing. Several statistical backbone extraction techniques are proposed to reduce their size while keeping essential information. Here, we perform a comparative evaluation of seven prominent statistical backbone extraction techniques in the USA weighted air transportation network. One can classify the airports into hubs, spokes, and focus airports based on the business models used by the airlines. We compare the extracted backbones using various performance measures. We consider the number of components, sizes, the fraction of airport type, edge type, and weights preserved by each method. Results show that the Enhanced Configuration Model (ECM) Filter tends to preserve edges between spoke airports uncovering the infrastructure connecting the regional spoke airports. In contrast, the alternative filters (Disparity, Polya Urn, Marginal Likelihood, Noise Corrected, Global Statistical Significance (GLOSS), Locally Adaptive Network Sparsification (LANS)) highlight edges between the hub and spoke, focus and spoke, and spoke and spoke airports revealing more of the hub and spoke foundation used by airlines. Moreover, the Disparity Filter, Marginal Likelihood Filter, and Noise Corrected Filter preserve the highest proportion of weights while Polya Urn Filter and ECM Filter keep the lowest. The GLOSS and LANS Filters maintain a moderate fraction of weights between the two extremes.
Near-Field Cosmology with Extremely Metal-Poor Stars

The oldest, most metal-poor stars in the Galactic halo and satellite dwarf galaxies present an opportunity to explore the chemical and physical conditions of the earliest star-forming environments in the Universe. We review the fields of stellar archaeology and dwarf galaxy archaeology by examining the chemical abundance measurements of various elements in extremely metal-poor stars. Focus on the carbon-rich and carbon-normal halo star populations illustrates how these provide insight into the Population III star progenitors responsible for the first metal enrichment events. We extend the discussion to near-field cosmology, which is concerned with the formation of the first stars and galaxies, and how metal-poor stars can be used to constrain these processes. Complementary abundance measurements in high-redshift gas clouds further help establish the early chemical evolution of the Universe. The data appear consistent with the existence of two distinct channels of star formation at the earliest times.
Predictive maintenance and Structural Health Monitoring via IoT system

Reinforced concrete buildings have proven the need to monitor the concrete and steel parts over time. The topic of structural monitoring of a building is becoming more topical with time, and many buildings from the 1960s and 1970s are under observation. The current challenge is to monitor structures effectively and continuously, applying the meaning of preventive maintenance, a concept well developed in engineering disciplines. New technologies allow us to assess the impact of time, wear, and tear, which in the long term can challenge the safety of buildings by monitoring the natural vibrations of a building. However, traditional monitoring systems in the civil infrastructure sector have always been expensive and undervalued. Therefore, borrowing innovations from computer science, a sensor system based on the new paradigms of the Internet of Things (IoT) was developed to provide a valuable alternative to proven vibration monitoring systems. The proposed system consists of a microprocessor (Raspberry Pi) and a low-cost accelerometer for microelectromechanical systems (MEMS), this type of lower costs sensor allows for investment in the safety of structures. The architecture of the monitoring system and the visualization of the vibrational model and its operation mechanism are presented. The performance of the monitoring system and the data collected are then integrated with Deep Learning techniques to obtain possible future scenarios and forecasts practically to perform tests that are as close as possible to reality and thus be able to act with the necessary maintenance to prevent undesired structural effects.
Performance of Uplink Underwater Optical Wireless Communications in the Presence of Random Sea Surface

This paper studies the effects of the random sea surface on an up-link underwater optical wireless communication (UOWC) system. Both light-emitting diode (LED) and laser diode (LD)-based links are considered under the influence of the 3-dimensional random motion of the sea surface. The simplified form of beam spread function (BSF) is used to analyse the performance of the LD-based link while the Lambertian model is used for the LED-based link. Performance deteriorations caused by sea surface motion are estimated.
Experimental Implementation of a Laser Model with Cavity Loss Modulation

Experimental evidence of chaos and generalized multistability in a laser model with cavity loss modulation was first found in 1982, and recently revisited in a theoretical framework. Here we propose and investigate an electronic implementation of such laser model. Although a chaotic behavior has not been observed yet, the analysis provides insights in the difficulties of an experimental implementation of the model. These difficulties appear to be mainly due to the laser intensity, indeed a non-negative quantity, being simulated by a voltage, which can take on both positive and negative values.
Adaptive Visible Light Positioning with MSE Inner Loop for Underwater Environment

The Internet of Underwater Things is a paradigm coming up beside well-known Internet of Things, but focusing on a specific environment that is the underwater scenario. Due to high attenuation and then limited performance, communication links in this harsh scenario are provided by dedicated wireless technologies such as optical wireless communication, acoustic communication. The high data rate achievable with underwater optical wireless makes it an enabling candidate technology for effective underwater communication systems. One of the most prominent topics related with underwater communication, is localisation, for all those underwater applications requiring high accuracy, such as marine ecology, seabed monitoring, etc. However, the high dynamic characteristics of the underwater environment can limit the localisation accuracy, thus resulting in high estimation errors. In this paper, we present an adaptive localisation algorithm that exploits optical wireless connectivity, in the visible range. It takes into account information about water turbidity and is able to dynamically correct the estimation error in case of variable water conditions. The effectiveness of the proposed approach has been compared to a previous technique, which does not exploit information about the environment and then results to be effective only if exists an apriori knowledge of the real water conditions.
Optimal Localization Design for Indoor Non-interfering LED-based System

Visible Light Communications (VLC) systems are designed to guarantee the twofold paradigm of both illumination and communications, thus resulting as a green technology. Indeed, it is possible to reduce cost and energy consumption by minimizing the number of Light Emitting Diodes (LEDs), while still guaranteeing network performance. However, adapting existing VLC systems to offer localization services needs a few considerations, as localization requirements do not overlap with the communication ones. In order to guarantee localization services, Visible Light Positioning (VLP) systems should be designed accordingly in order to provide at least three optical signals per unknown location. In this paper, we design an indoor VLP network able to guarantee accurate localization estimate with a minimum number of LED devices. The proposed optimal LED deployment technique is designed for VLP network. Numerical results achieving a 20 cm localization accuracy are obtained for different geometrical settings.
A Simple Self-labeling Method for Semi-supervised Medical Image Segmentation

Leveraging a few labeled images and a large number of unlabeled images is crucial for medical image segmentation since labeling the medical data can be very expensive and time-consumed. Therefore we introduce a naïve but simple method to utilize the massive unlabeled medical images for better training. We first use all the labeled data to train a basic model, then use this pre-trained model to infer the unlabeled images to get pseudo-labels, and finally use all the obtained pseudo-labels and the original labels as the ground truth of all images, and retrain the model from scratch to acquire the final model. We believe this is a simple but effective way to utilize the massive number of unlabeled images and experiments were performed to evaluate such method.
Parallel Data Helps Neural Entity Coreference Resolution

Coreference resolution is the task of finding expressions that refer to the same entity in a text. Coreference models are generally trained on monolingual annotated data but annotating coreference is expensive and challenging. Hardmeier et al.(2013) have shown that parallel data contains latent anaphoric knowledge, but it has not been explored in end-to-end neural models yet. In this paper, we propose a simple yet effective model to exploit coreference knowledge from parallel data. In addition to the conventional modules learning coreference from annotations, we introduce an unsupervised module to capture cross-lingual coreference knowledge. Our proposed cross-lingual model achieves consistent improvements, up to 1.74 percentage points, on the OntoNotes 5.0 English dataset using 9 different synthetic parallel datasets. These experimental results confirm that parallel data can provide additional coreference knowledge which is beneficial to coreference resolution tasks.
Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data

Land cover area estimates can be derived via design-based approaches using a probability (random) reference sample. The collection of samples is usually costly and requires an effective sampling design. Earth-Observation-based mapping approaches do not have this requirement but can be biased in providing area estimates. Combining reference samples with remote sensing products can reduce sampling efforts and provide a more effective method to estimate land cover. The Copernicus High-Resolution Layer (HRL) provides remote-sensing-based data across Europe to support area estimation. Different methods are tested to estimate areas of imperviousness in four selected countries in Europe to demonstrate the use and shortcomings of existing reference information from the LUCAS survey program and the HRL Imperviousness products from 2015 and 2018.
On Detecting COVID-Risky Behavior from Smartphones

Detecting risky behavior using smartphones and other mobile devices may help mitigate the spread of infectious diseases. However, the privacy concerns introduced by individualized activity recognition may counteract potential benefits. As an example, consider a public health official gauging their message’s efficacy. Machine learning and data mining methods may help them understand how their local population’s behavior changes, but aggressive surveillance can severely hinder individual privacy and cause disproportionate harm to disadvantaged groups. In this work, we benchmark a series of machine learning algorithms predicting high-risk behaviors—going to bars and gyms, attending parties, and riding on buses—given only low-level smartphone sensor data. We find that models trained to perform these challenging tasks are largely unreliable and should be avoided in practice, though their predictions are significantly better than random.
Enhancing Multi-hop Connectivity for Graph Convolutional Networks

Graph Convolutional Network and many of its variants are known to suffer from the dilemma between model depth and over-smoothing issues. Stacking layers of GCN usually lead to the ex-ponential expansion of the receptive field (i.e., high-order neighbors). In order to incorporate the information from high-order neighbors to learn node representations without drastically increasing the number of graph convolution layers, we propose a simple and effective pre-processing technique to increase graph connectivity. Our approach selectively inserts connections between center nodes and informative high-order neighbors, with learnable weights to control the information flow through the connection. Experiments show that our approach improves the performance of GCN, and reduce the depth of GCNII without sacrificing its performance. Besides, our proposed homophily-based weight assignment can mitigate the effect of graph structural attacks.
On the Estimation of Ground-Motion Duration Models with an Application to the M9 Simulations

Different parameterizations, related to mixed-effects structure, of ground-motion models for the duration of strong shaking are revisited. A new parameterization is proposed which can better account for the separation of source and path duration in additive ground-motion duration models. In addition, different distributions for the likelihood of ground-motion duration given the predictor variables are investigated. Traditionally, duration has been modeled as lognormally distributed, which makes sense for a multiplicative model, but less so for an additive model. Models using a lognormal and Gamma likelihood are compared, using the M9 simulations (Frankel et al., 2018) as an underlying data set. In general, differences between models in terms of predictions are small, but the models using a Gamma distribution perform slightly better than the ones employing a lognormal distribution. Furthermore, the new parameterization outperforms traditional models. All models are estimated via Bayesian inference, accounting for epistemic uncertainty through the posterior distribution of the parameters.
Analyst Earnings Forecast Optimism during the COVID-19 Pandemic: Evidence from China

: Analysts are important participants in the capital market, but there are relatively few studies on the impact of the COVID-19 outbreak on analysts’ behaviors. This article examines the impact of the COVID-19 outbreak on the analysts’ earnings forecast optimism by using a sample of visits to Chinese listed ﬁrms during 2019–2020. We ﬁnd that the analysts’ earnings forecasts become less optimistic and show pessimism after the outbreak of COVID-19. This result is consistent with past research ﬁndings that major natural disasters lead to analysts’ forecasts pessimism. However, we also ﬁnd that the earnings forecasts issued by analysts with on-site visits are more optimistic after the COVID-19 outbreak. The increase in optimism is associated with accounting information transparency, the proportion of tangible assets, and the revenue geographical concentration of the visited companies. Further analysis shows that higher optimism in visiting analysts’ earnings forecasts after the COVID-19 outbreak leads to a positive market response, suggesting that optimism in visiting analysts’ forecasts misleads the market’s resource allocation. We also ﬁnd that the higher level of optimism in visiting analysts’ earnings forecasts disappeared after the COVID-19 outbreak was well controlled. Overall, our study enriches the study of the impact of COVID-19 on capital markets from the perspective of analysts’ forecast optimism. Investors in other countries should also be aware of the impact of similar phenomena.
Two-component flux explanation for the high energy neutrino events at IceCube

Understanding the spectral and flavor composition of the astrophysical neutrino flux responsible for the recently observed ultra-high energy events at IceCube is of great importance for both astrophysics and particle physics. We perform a statistical likelihood analysis to the 3-year IceCube data and derive the allowed range of the spectral index and flux normalization for various well-motivated physical flavor compositions at source. While most of the existing analyses so far assume the flavor composition of the neutrinos at an astrophysical source to be (1:2:0), it seems rather unnatural to assume only one type of source, once we recognize the possibility of at least two physical sources. Bearing this in mind, we entertain the possibility of a two-component source for the analysis of IceCube data. It appears that our two component hypothesis explains some key features of the data better than a single-component scenario, i.e it addresses the apparent energy gap between 400 TeV to about 1 PeV and easily accommodates the observed track to shower ratio. Given the extreme importance of the flavor composition for the correct interpretation of the underlying astrophysical processes as well as for the ramification for particle physics, this two-component flux should be tested as more data is accumulated.
Distilling Causal Metaknowledge from Massive Knowledge Graphs

In recent years, the growing information over-001 load facilitates the access to billions of rela-002 tional facts in the world, which are usually in-003 tegrated in all manner of knowledge graphs. 004 The metaknowledge, defined as the knowledge 005 about knowledge, reveals the inner principle 006 of arising these factual knowledge, and hence 007 is of vital importance to be discovered for the 008 understanding, exploiting and completion of 009 knowledge. In this paper, we focus on captur-010 ing the causal component of metaknowledge, 011 that is a metarule with causal semantic. For 012 the propose, we devise an efficient causal rule 013 discovery algorithm called CaRules that distills 014 the causal rules between two knowledge graph 015 schemata abstracted from instances from mas-016 sive knowledge graphs. Extensive experiments 017 demonstrate that the quality and interpretabil-018 ity of the causation-based rules outperform the 019 correlation-based rules, especially in the out-020 of-distribution tasks. 021
T reu : A Trainable Evaluation Metric for Natural Language Rationales

Explanable AI (XAI) and Natural Language 001 Processing (NLP) researchers often rely on 002 humans to annotate both labels and natural 003 language rationales (explanations) with the 004 goal that models can utilize these rationales 005 to improve model performance, or can gener-006 ate human-understandable explanations. How-007 ever, human-annotated rationales are very sub-008 jective and could be low-quality, as some re-009 cent works discovered. The vital question 010 arises: how can we evaluate the quality of 011 the human-annotated natural language ra-012 tionales? In this paper, we propose T reu , a 013 t rainable e valuation metric that can evaluate 014 the helpfulness of natural language r ationales 015 towards models’ prediction performances for a 016 wide range of NLP tasks and models with the 017 help of a u nified data structure. Our evaluation 018 experiment on five popular datasets with two 019 di ff erent model architectures demonstrates that 020 T reu can coherently and faithfully evaluate the 021 quality of rationales among datasets while the 022 Simulatability metric fails. T reu score can 023 also reveal rationale’s quality towards specific 024 classes in a multi-class classification task. 025
Good theories for better models and policies

Despite the very strong shocks of the last 15 years, the same paradigm continued to inspire economic policies, which envisages: a single long-term path of development, substantially insensitive to short-term policies; and an economic system composed of almost homogeneous representative agents that react to stimuli in an almost linear way. Policies based on these models can only support, if not amplify, the spontaneous trend of the market and tend to waste huge public resources with inefficient incentive systems. For a sustainable and lasting recovery, it is necessary to adopt a new interpretative paradigm and use different models. The ultra-expansionary monetary policy and the suspension of the Stability and Growth Pact are heading in the right direction, but they are not enough.