Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study

Deep Neural Networks (DNNs) are increasingly applied in the real world in safety critical applications like advanced driver assistance systems. An example for such use case is represented by traffic sign recognition systems. At the same time, it is known that current DNNs can be fooled by adversarial attacks, which raises safety concerns if those attacks can be applied under realistic conditions. In this work we apply different black-box attack methods to generate perturbations that are applied in the physical environment and can be used to fool systems under different environmental conditions. To the best of our knowledge we are the first to combine a general framework for physical attacks with different black-box attack methods and study the impact of the different methods on the success rate of the attack under the same setting. We show that reliable physical adversarial attacks can be performed with different methods and that it is also possible to reduce the perceptibility of the resulting perturbations. The findings highlight the need for viable defenses of a DNN even in the black-box case, but at the same time form the basis for securing a DNN with methods like adversarial training which utilizes adversarial attacks to augment the original training data.
Comparison of two magnetic resonance imaging spectroscopy postprocessing methods.

OBJECTIVE The aim of this study was to compare the results obtained using SpectroView® (SV) and J-Magnetic Resonance User Interface (jMRUI) from the same magnetic resonance (MR) spectroscopy of hydrogen data. METHODS Data from 23 males with alcohol use disorder (AUD) and 23 healthy non-AUD males were acquired by a 1.5 Tesla MR using a PRESS sequence (TE=30 ms) in four voxels located in the right frontal and left frontal (RF and LF) lobes, and posterior cingulate (AC and PC). The ratio of the signals from both N-acetyl-aspartate (NAA) and choline (Cho) over creatine (Cr) was calculated automatically using SV and semiautomatically by an expert neuroradiologist using jMRUI. The software' agreement was calculated by the 95% limits of agreement (LoA) of the ratio of the obtained values. RESULTS The standard deviation was greater in jMRUI than in SV. Although there was a correlation between the results from both methods, it was not possible to predict their variance from one another. Additionally, the 95% LoA showed that jMRUI values were expected to vary from 38 to 190% of those obtained using SV for NAA/Cr in RF of AUD subjects and from 48 to 196% for NAA/Cr in CA of non-AUD individuals. CONCLUSIONS The difference between the methods may represent clinically significant magnitudes. We suggest the use of the same method when comparing spectroscopic data. We also suggest that in clinical practice, the automatic method should be preferred.
TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data

Radar Sounders (RSs) are nadir-looking sensors operating in high frequency (HF) or very high frequency (VHF) bands that profile subsurface targets to retrieve miscellaneous scientific information. Due to complex electromagnetic interaction between back-scattered returns, the interpretation of RS data is challenging. The investigations of ice-sheet subsurface structures require automatic techniques to account for both the sequential spatial distribution of subsurface targets and relevant statistical properties embedded in RS signals. Automatic techniques exist for characterizing these targets either related to probabilistic inference models or convolutional neural network (CNN) deep learning methods. Unfortunately, CNN-based methods capture local spatial context and merely model the global spatial context. In contrast to CNN, the Transformer-based models are reliable architectures for capturing long-range sequence-to-sequence global spatial contextual prior. Motivated by the aforementioned fact, we propose a novel Transformer-based semantic segmentation architecture named TransSounder to effectively encode the sequential structures of the RS signals. The TransSounder was constructed on a hybrid TransUNet-TransFuse architectural framework to systematically augment the modules from TransUNet and TransFuse architectures. Experimental results obtained using the Multichannel Coherent Radar Depth Sounder (MCoRDS) dataset confirms the robustness and capability of Transformers to accurately characterize the different subsurface targets.
Assimilating near-real-time mass balance stake readings into a model ensemble using a particle filter

Abstract. Short-term glacier variations can be important for water supplies or hydropower production, and glaciers are important indicators of climate change. This is why the interest in near-real-time mass balance nowcasting is considerable. Here, we address this interest and provide an evaluation of continuous observations of point mass balance based on online cameras transmitting images every 20 min. The cameras were installed on three Swiss glaciers during summer 2019, provided 352 near-real-time point mass balances in total, and revealed melt rates of up to 0.12 m water equivalent per day (mw.e.d-1) and of more than 5 mw.e. in 81 d. By means of a particle filter, these observations are assimilated into an ensemble of three TI (temperature index) and one simplified energy-balance mass balance models. State augmentation with model parameters is used to assign temporally varying weights to individual models. We analyze model performance over the observation period and find that the probability for a given model to be preferred by our procedure is 39 % for an enhanced TI model, 24 % for a simple TI model, 23 %, for a simplified energy balance model, and 14 % for a model employing both air temperature and potential solar irradiation. When compared to reference forecasts produced with both mean model parameters and parameters tuned on single mass balance observations, the particle filter performs about equally well on the daily scale but outperforms predictions of cumulative mass balance by 95 %–96 %. A leave-one-out cross-validation on the individual glaciers shows that the particle filter is also able to reproduce point observations at locations not used for model calibration. Indeed, the predicted mass balances is always within 9 % of the observations. A comparison with glacier-wide annual mass balances involving additional measurements distributed over the entire glacier mostly shows very good agreement, with deviations of 0.02, 0.07, and 0.24 mw.e.
Resistivity of nano-devices based on MoS2 in low temperature

ABSTRACT We present a detailed theoretical study of the electronic transport properties of nano-devices based on MoS2. The transport resistivity is calculated on the basis of the usual momentum-balance equation derived from the semi-classical Boltzmann equation. Moreover, the expression of electron–electron screening is obtained within the random phase approximation. It shows that the resistivity R sub-linearly depends on the impurity density, and is inversely proportional to the electron densities. We obtain low resistivity or high conductivity of nano-devices based on MoS2 by tuning electron density or impurity density. This study is relevant to the application of nano-devices based on MoS2, such as MoS2 field-effect transistors.
SAND 2009-0842 Unlimited Release Printed February 2009 Using DEDICOM for Completely Unsupervised Part-of-Speech Tagging

A standard and widespread approach to part-of-speech tagging is based on Hidden Markov Models (HMMs). An alternative approach, pioneered by Schütze (1993), induces parts of speech from scratch using singular value decomposition (SVD). We introduce DEDICOM as an alternative to SVD for part-of-speech induction. DEDICOM retains the advantages of SVD in that it is completely unsupervised: no prior knowledge is required to induce either the tagset or the associations of terms with tags. However, unlike SVD, it is also fully compatible with the HMM framework, in that it can be used to esti-mate emissionand transition-probability matrices which can then be used as the input for an HMM. We apply the DEDICOM method to the CONLL corpus (CONLL 2000) and compare the output of DEDICOM to the part-of-speech tags given in the corpus, and find that the cor-relation (almost 0.5) is quite high. Using DEDICOM, we also estimate part-of-speech ambiguity for each term, and find that these estimates correlate highly with part-of-speech ambiguity as measured in the original corpus (around 0.88). Finally, we show how the output of DEDICOM can be evaluated and compared against the more familiar output of supervised HMM-based tagging.
Flexible tactile sensor with 2D structure based on Fiber Bragg grating

A distributed flexible tactile sensor based on 2D fiber Bragg grating (FBG) sensing is designed and produced in this study to solve the error of the single FBG flexible tactile sensor in identifying any two pressure positions symmetrical to the z-axis with grating center as the origin. This study also aims to obtain accurate pressure information on the surface of the electronic skin. A new type of tactile sensing application scheme composed of multiple FBG sensing elements with high spatial resolution is determined by analyzing the characteristics of FBG flexible sensing and by using ANSYS software for dynamic simulation. Simulation and experimental results show that the flexible tactile sensor proposed in this study has high linearity and sensitivity. In addition, the spatial resolution of the proposed sensor is increased by 8 times compared with that of a single FBG sensor with the same length. Research results can be applied to the tactile detection of the flexible electronic skin for robots. This application, which provides a feasible solution to develop and promote flexible electronic skin, has a wide range of prospects.
Changes in physical properties of 4C pyrrhotite (Fe7S8) across the 32 K Besnus transition

Abstract Pyrrhotite, Fe7S8, is a common sulfide mineral in the Earth’s crust and mantle, as well as in a range of meteorites and is of interest to a wide variety of disciplines including economic geology, geophysics, and material science. The 4C variety of pyrrhotite shows a dramatic change in magnetic properties at T ≈ 30 K, known as the Besnus transition. Although this transition is frequently used to detect pyrrhotite in geologic samples, the underlying mechanism driving the transition has not yet been identified. This study presents a high-resolution view of the changes in heat capacity, magnetic, and electronic properties of a natural single crystal of nearly pure, monoclinic 4C pyrrhotite across the Besnus transition. Contrary to previous studies, all of these properties show clear evidence of the Besnus transition, specific heat, in particular, revealing a clear transition at 32 K, apparently of second-order nature. Small-angle neutron scattering data are also presented, demonstrating an unusual change in short-range magnetic scattering at the transition. Furthermore, a magnetic field dependence of the transition temperature can be seen in both induced magnetization and electrical resistivity. These new observations help narrow the possible nature of the phase transition, clearly showing that interactions between intergrown coexisting 4C and 5C∗ superstructures, as suggested in some literature, are not necessary for the Besnus transition. In fact, the changes seen here in both the specific heat and the electronic transport properties are considerably larger than those seen in samples with intergrown superstructures. To further constrain the mechanism underlying the Besnus transition, we identify five separate potential models and evaluate them within the context of existing observations, thereby proposing experimental approaches that may help resolve ongoing ambiguities.
Comment on acp-2021-766

This study (acp-2021-766) reported a one-year eBC measurement study in Poland using a multiwavelength absorption black carbon instrument (MABI). MABI is a newly developed offline filter-based absorption photometer that provides more wavelengths (7λ) than the existing OT-21 (2λ) and MWAA (3λ). The application of MABI could provide more information to study the spectral characteristics of light-absorbing aerosols. However, the following concerns need to be addressed.
An Optimal Modulation for a Semi Dual-Bridge Resonant Converter with a SCC-based Tunable Tank

To enhance the efficiency of a semi dual-bridge resonant converter(SDBRC), a control strategy to minimize the root mean square(RMS) current and eliminate the backflow power is proposed. An optimal operating point is precisely selected to reduce the conduction loss due to backflow power and high rms current. The converter is enabled to work with the optimal operating point in the whole load range by adjusting the switch-controlled capacitor (SCC) based tunable resonant tank continuously. Soft-switching of all switches can be maintained along the control route as well. A lab prototype converter designed at 300 W is tested to validate the SCC-based topology and proposed modulation scheme. The experimental result has demonstrated that the proposed topology and modulation scheme can improve the converter efficiency over the whole load range, especially in the light load condition.
Title : Hydrologic Variability of the Cosumnes River Floodplain

Natural floodplain ecosystems are adapted to highly variable hydrologic regimes, which include periodic droughts, infrequent large floods, and relatively frequent periods of inundation. To more effectively manage water resources and maintain ecosystem services provided by floodplains – and associated aquatic, riparian, and wetland habitats – requires an understanding of seasonal and inter-annual hydrologic variability of floodplains. The Cosumnes River, the largest river on the west-slope Sierra Nevada mountains without a major dam, provides a pertinent test case to develop a systematic classification of hydrologic variability. By examining the dynamics of its relatively natural flow regime, and a 98-year streamflow record (1908 – 2005), we identified 12 potential flood types. We identified four duration thresholds, defined as short (S), medium (M), long (L), and very long (V). We then intersected the flood duration division by three magnitude classes, defined as small-medium (1), large (2), and very large (3). Of the 12 possible flood types created by this classification matrix, the Cosumnes River streamflow record populated 10 such classes. To assess the robustness of our classification, we employed discriminant analysis to test class fidelity based on independent measures of flood capability, such as start date. Lastly, we used
Optimization of Lead Base Perovskite Solar Cell with ZnO and CuI as Electron Transport Material and Hole Transport Material Using SCAPS-1D

Perovskite solar cells (PSCs) research is substantially drawing attention because of the fast improvement in their power conversion efficiency (PCE), cheapness, possibility to tune the bandgap, low recombination rate, high open circuit voltage, excellent ambipolar charge carrier transport and strong and broad optical absorption. In this research, Zinc oxide as electron transport material (ETM) and copper iodide as hole transport material (HTM) have been optimized using SCAPS-1D simulation software. The thickness, bandgap, of ZnO (ETM) and CuI (HTM) was investigated. Results shows that the thickness and bandgap were found to strongly influence the PCE of perovskite solar cell. ZnO/CuI was found to be a better replacement to TiO2/Cu2O for stability and low degradation rate. It was observed that the maximum efficiency is 22.04%, Voc of 0.84V, JSC of 32.83mA/cm2 and FF of 79.79% was obtained when the thickness of ETM and HTM layer of (CH3NH3PbI3) PSCs which was found to be optimum at 0. 2μm for the final optimization.
Terahertz image segmentation of occluded objects based on mean clustering

Terahertz imaging of occluded objects is a popular technology, which is an important feature superior to visible light imaging. However, the quality of the target image is obviously damaged due to factors such as discontinuity and absorption of the occlusion, which makes it difficult to segment the image. It is especially difficult to segment digital holographic reconstruction images of small objects with high resolution. In this paper, clustering segmentation algorithms are compared for terahertz images of metal objects which is occluded by paper. K-means, fuzzy C-means (FCM) and fuzzy c-means clustering with spatial constraints (FCM-S) algorithms were used respectively. Since the minimum horizontal target is only three pixels, the mean template size in these algorithms is all 3*3. The experimental results show that FCM-S of the segmentation effect obtained is the best among the three algorithms, because FCM-S considers the pixel neighborhood information.
Intra and interobservers concordance analysis of face fractures detection through computed tomography

1) Mestrando pelo Curso de Pós-graduação em Ciências da Saúde do Hospital Heliópolis HOSPHEL, São Paulo. Especialista em Ortodontia e Ortopedia Funcional dos Maxilares e em Cirurgia e Traumatologia BucoMaxilo-Facial. Professor do Curso de Especialização em Cirurgia e Traumatologia Buco-Maxilo-Facial do Centro de Pesquisas Odontológicas São Leopoldo Mandic. 2) Livre Docente pelo Departamento de Cirurgia da Faculdade de Medicina da Universidade de São Paulo. Coordenador do Curso de Pós-graduação em Ciências da Saúde do Hospital Heliópolis HOSPHEL, São Paulo. 3) Doutor em Radiologia pela Faculdade de Medicina da Universidade de São Paulo. Docente do Curso de Pós-graduação em Ciências da Saúde do Hospital Heliópolis HOSPHEL, São Paulo. 4) Doutor em Cirurgia e Traumatologia Buco-Maxilo-Facial, Professor Titular da Disciplina de Cirurgia e Traumatologia Buco-Maxilo-Facial da Faculdade de Odontologia São Leopoldo Mandic. Coordenador dos Cursos de Aperfeiçoamento e Especialização em Cirurgia e Traumatologia Buco-Maxilo-Facial e Especialização em Cirurgia Bucal Avançada do Centro de Pesquisas Odontológicas São Leopoldo Mandic. 5) Especialista em Cirurgia e Traumatologia Buco-Maxilo-Facial. Professor dos Cursos de Especialização em Cirurgia e Traumatologia Buco-Maxilo-Facial e Cirurgia Bucal Avançada do Centro de Pesquisas Odontológicas São Leopoldo Mandic. Instituição: Centro de Pesquisas Odontológicas São Leopoldo Mandic Correspondência: Rua Iramaia, 136 – 01450-020 São Paulo,SP, Brasil. E-mail: arapoport@terra.com. br Recebido em: 20/08/2008; aceito para publicação em: 15/12/2008; publicado online em: 10/03/2009. Conflito de interesse: nenhum. Fonte de fomento: nenhuma. Artigo Original
Prediction and analysis of water resources demand in Taiyuan City based on principal component analysis and BP neural network

Water is a fundamental natural and strategic economic resource that plays a vital role in promoting economic and social development. With the accelerated urbanization and industrialization in China, the potential demand for water resources will be enormous. Therefore, accurate prediction of water resources demand is important for the formulation of industrial and agricultural policies, development of economic plans, and many other aspects. In this study, we develop a model based on principal component analysis (PCA) and back propagation (BP) neural network to predict water resources demand in Taiyuan, Shanxi Province, a city with severe water shortage in China. The prediction accuracy is then compared with PCA-ANN, ARIMA, NARX, Grey–Markov, serial regression, and LSTM models, and the results showed that the PCA-BP model outperformed other models in many evaluation factors. The proposed PCA-BP model reduces the dimensionality of high-dimensional variables by PCA and transformed them into uncorrelated composite data, which can make them easier to compute. More importantly, BP and weight threshold adjustment in model training further improve the prediction accuracy of the model. The model analysis will provide an important reference for water demand assessment and optimal water allocation in other regions.
Superselective transarterial radioembolization for the treatment of hepatocellular carcinoma

Superselective transarterial radioembolization (TARE) refers the delivery of Y90 at the tumor-feeding segmental or subsegmental arteries. The pur- pose of TARE would be delivering high dose in the tumor while saving as much normal parenchyma as possible. This concept is similar to the radiation segmentectomy. To identify all tumor feeders, thorough angiography and cone-beam computed tomography are essential. Reported target dose of radiation segmentectomy ranges 222.6–521 Gy. After superselective TARE, the treated segments can show persistent enhancement, and then atrophy combined with capsular retraction. Superselective TARE promises favorable outcomes. It’s reported objective response rates range 64%–95% and toxicities are tolerable in most cases. In conclusion, superselective TARE is a very safe modality for the treatment of HCCs with favorable outcome close to the curative-intent treatments in selected patients.
CORE SELF-EVALUATION AND JOB SATISFACTION: MEDIATING ROLE OF CAREER COMMITMENT

This study aims to determine the effect of core self-evaluation on job satisfaction mediated by career commitment in IBM-JTI employees. The sampling technique was convenience sampling, using 86 samples. This research's data analysis method is the mediation test using the Preacher-Hayes The Simple Mediation Model. The results of data processing using SPSS 22 for windows with macros and syntax. This study's results indicate that core self-evaluation and career commitment have a direct effect on job satisfaction. The effect of core self-evaluation on job satisfaction is not mediated by career commitment. Core self-evaluation does not affect career commitment. Keywords: Core Self-Evaluation; Career Commitment; Job Satisfaction
Selective Foveated Ray Tracing for Head-Mounted Displays

Although ray tracing produces significantly more realistic images than traditional rasterization techniques, it is still considered computationally burdensome when implemented on a head-mounted display (HMD) system that demands both wide field of view and high rendering rate. A further challenge is that to present high-quality images on an HMD screen, a sufficient number of ray samples should be taken per pixel for effective antialiasing to reduce visually annoying artifacts. In this paper, we present a novel foveated real-time rendering framework that realizes classic Whitted-style ray tracing on an HMD system. In particular, our method proposes combining the selective supersampling technique by Jin et al. [8] with the foveated rendering scheme, resulting in perceptually highly efficient pixel sampling suitable for HMD ray tracing. We show that further enhanced by foveated temporal antialiasing, our ray tracer renders nontrivial 3D scenes in real time on commodity GPUs at high sampling rates as effective as up to 36 samples per pixel (spp) in the foveal area, gradually reducing to at least 1 spp in the periphery.