Miracle-dz/UniFAR-FTUs huggingface dataset:
Reversible Data Hiding in Encrypted Images Based on Reserving Room After Encryption and Multiple Predictors

This paper proposes a new vacating room after encryption reversible data hiding scheme. Hidden data is embedded into the encrypted host image by bit-flipping a preselected bitplane of a randomly formed pixel group. The major novelty of the paper is the use of multiple predictors in an adaptive procedure for detecting between original and modified pixels. Four predictors are used on a context of four neighbors, namely the average of the four pixels, a weighted average based on local gradients, the median and the midpoint. Experimental results are provided. Compared with the state-of-the-art reserving room after encryption schemes, the proposed approach provides higher embedding bit-rates at lower distortion.
Stochastic-Based Synapse and Soft-Limiting Neuron with Spintronic Devices for Low Power and Robust Artificial Neural Networks

We propose an innovative stochastic-based computing architecture to implement low-power and robust artificial neural network (S-ANN) with both magnetic tunneling junction (MTJ) and Domain Wall (DW) devices. Our mixed-model HSPICE simulation results have shown that, for a well-known pattern recognition task, a 34-neuron S-ANN implementation achieves more than 1.5 orders of magnitude lower energy consumption and 2.5 orders of magnitude less hidden layer chip area, when compared with its deterministic-based ANN counterparts which are implemented with digital and analog CMOS circuits. We believe that our S-ANN architecture achieves such a remarkable performance gain by leveraging two key ideas. First, because all neural signals are encoded as random bit streams, the standard weighted-sum synapses can be accomplished by stochastic bit writing and reading procedure. Second, we designed and implemented a novel multiple-phase pumping circuit structure to effectively realize the soft-limiting neural transfer function that is essential to improve the overall ANN capability and reduce its network complexity.
Zebrafish aggression on the sub-second time scale: evidence for mutual motor coordination and multi-functional attack manoeuvres

Most animals fight by repeating complex stereotypic behaviours, yet the internal structure of these behaviours has rarely been dissected in detail. We characterized the internal structure of fighting behaviours by developing a machine learning pipeline that measures and classifies the behaviour of individual unmarked animals on a sub-second time scale. This allowed us to quantify several previously hidden features of zebrafish fighting strategies. We found strong correlations between the velocity of the attacker and the defender, indicating a dynamic matching of approach and avoidance efforts. While velocity matching was ubiquitous, the spatial dynamics of attacks showed phase-specific differences. Contest-phase attacks were characterized by a paradoxical sideways attraction of the retreating animal towards the attacker, suggesting that the defender combines avoidance manoeuvres with display-like manoeuvres. Post-resolution attacks lacked display-like features and the defender was avoidance focused. From the perspective of the winner, game-theory modelling further suggested that highly energetically costly post-resolution attacks occurred because the winner was trying to increase its relative dominance over the loser. Overall, the rich structure of zebrafish motor coordination during fighting indicates a greater complexity and layering of strategies than has previously been recognized.
Impact of BRAF Mutation Class on Disease Characteristics and Clinical Outcomes in BRAF-mutant Lung Cancer

Purpose: BRAF mutations are divided into functional classes distinguished by signaling mechanism and kinase activity: V600-mutant kinase-activating monomers (class I), kinase-activating dimers (class II), and kinase-inactivating heterodimers (class III). The relationship between functional class and disease characteristics in BRAF-mutant non–small cell lung cancer (NSCLC) has not been fully explored. Experimental Design: We performed a retrospective analysis of BRAF-mutant NSCLCs treated at 2 institutions from 2005 to 2017 to determine clinicopathologic characteristics, progression-free survival (PFS) on chemotherapy, and overall survival (OS). Results: We identified 236 patients with BRAF-mutant NSCLC (n = 107 class I, n = 75 class II, and n = 54 class III). Patients with class II or III mutations were more likely to have brain metastases (P ≤ 0.01) and RAS coalterations (P ≤ 0.001) than class I. Compared with class I, PFS on chemotherapy was shorter for class II (P = 0.069) and class III (P = 0.034). OS was shorter for class II and III (class I, 40.1 months; class II, 13.9 months; and class III, 15.6 months; I vs. II, P < 0.001; I vs. III, P = 0.023); however, this difference was driven by fewer extrathoracic metastases and higher use of targeted therapies in class I patients. When patients treated with targeted therapy and those with thoracic-only metastases were excluded, there was no difference in OS across the 3 classes. Conclusions: BRAF-mutant NSCLC is a heterogeneous disease that encompasses 3 distinct functional classes. Classes II and III have more aggressive clinical features leading to less favorable outcomes. The distinct biological characteristics of class II and III tumors suggest that class-specific therapies may be necessary to effectively target these molecular subsets.
Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning

We introduce a novel type of representation learning to obtain a speaker invariant feature for zero-resource languages. Speaker adaptation is an important technique to build a robust acoustic model. For a zero-resource language, however, conventional model-dependent speaker adaptation methods such as constrained maximum likelihood linear regression are insufficient because the acoustic model of the target language is not accessible. Therefore, we introduce a model-independent feature extraction based on a neural network. Specifically, we introduce a multi-task learning to a bottleneck feature-based approach to make bottleneck feature invariant to a change of speakers. The proposed network simultaneously tackles two tasks: phoneme and speaker classifications. This network trains a feature extractor in an adversarial manner to allow it to map input data into a discriminative representation to predict phonemes, whereas it is difficult to predict speakers. We conduct phone discriminant experiments in Zero Resource Speech Challenge 2017. Experimental results showed that our multi-task network yielded more discriminative features eliminating the variety in speakers.
Direct Training for Spiking Neural Networks: Faster, Larger, Better

Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown competitive performance compared with artificial neural networks (ANNs), due to the lack of effective learning algorithms and efficient programming frameworks. We address this issue from two aspects: (1) We propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for deep SNNs. (2) Via narrowing the rate coding window and converting the leaky integrate-and-fire (LIF) model into an explicitly iterative version, we present a Pytorch-based implementation method towards the training of large-scale SNNs. In this way, we are able to train deep SNNs with tens of times speedup. As a result, we achieve significantly better accuracy than the reported works on neuromorphic datasets (N-MNIST and DVSCIFAR10), and comparable accuracy as existing ANNs and pre-trained SNNs on non-spiking datasets (CIFAR10). To our best knowledge, this is the first work that demonstrates direct training of deep SNNs with high performance on CIFAR10, and the efficient implementation provides a new way to explore the potential of SNNs.
From the Fundamental Attribution Error to the Truly Fundamental Attribution Error and Beyond: My Research Journey

This essay traces continuities and changes in focus of research and theory in my career. I describe early work on insensitivity to role-conferred advantages in self-presentation (and the personal experiences that prompted that work) and the subsequent identification and naming of the “fundamental attribution error. ” I next describe my work on the role that construal processes play in determining responses to various decision-making and attributional contexts. That work, in turn, culminated in identification and exploration of what I now deem the truly “fundamental attribution error”: the illusion of superior personal objectivity and its various consequences for interpersonal and intergroup interactions. I conclude with the lessons I have drawn from my applied work on conflict resolution.
Epstein–Barr Virus Lytic Reactivation Induces IgG4 Production by Host B Lymphocytes in Graves' Disease Patients and Controls: A Subset of Graves' Disease Is an IgG4-Related Disease-Like Condition

Immunoglobulin (Ig) G4-related disease (IgG4-RD) is a newly recognized systemic fibroinflammatory disease with characteristic histological findings and high serum IgG4 levels. Epstein-Barr virus (EBV) is a persistent herpesvirus in B lymphocytes, and we previously reported EBV reactivation-induced Ig production. We showed that EBV reactivation induced the production of thyrotropin receptor antibodies, the causative antibodies of Graves' disease. In the present study, we investigated whether EBV reactivation induced IgG4 production and if EBV-positive B cells or IgG4-positive plasma cells are present in the thyroid tissues of Graves' disease patients with lymphoplasmacytic infiltration. EBV-encoded small RNA1 (EBER1) in situ hybridization and immunohistochemistry for IgG and IgG4 were performed on seven resected thyroid tissues with lymphoplasmacytic infiltration collected from the thyroids of 11 Graves' disease patients. We then cultured the lymphocytes of 13 Graves' disease patients and 14 controls and induced EBV reactivation to measure IgG4 levels in culture fluids. We detected EBER1-positive cells and IgG4-positive plasma cells in the same area of thyroid tissues. EBV-reactivated cells with IgG4 on their surface were observed in culture cells, and IgG4 production was detected in culture fluids. The IgG4/IgG percentage was higher than that in normal serum level. A subset of Graves' disease is an IgG4-RD-like condition, not an IgG4-RD. EBV reactivation stimulates IgG4 production, which may result in high serum IgG4 levels and promote IgG4-positive plasma cell infiltration. EBER1 needs to be examined when an increase in IgG4-positive plasma cell numbers is noted.
A Reciprocal Formulation of Nonexponential Radiative Transfer. 2: Monte Carlo Estimation and Diffusion Approximation

Abstract When lifting the assumption of spatially-independent scattering centers in classical linear transport theory, collision rate is no longer proportional to angular flux/radiance because the macroscopic cross-section depends on the distance s to the previous collision or boundary. This creates a nonlocal relationship between collision rate and flux and requires revising a number of familiar deterministic and Monte Carlo methods. We generalize collision and track-length estimators to support unbiased estimation of either flux integrals or collision rates in generalized radiative transfer (GRT). To provide benchmark solutions for the Monte Carlo estimators, we derive the four Green’s functions for the isotropic point source in infinite media with isotropic scattering. Additionally, new moment-preserving diffusion approximations for these Green’s functions are derived, which reduce to algebraic expressions involving the first four moments of the free-path lengths between collisions.
Inhibition mechanism of Ca2+, Mg2+ and Fe3+ in fine cassiterite flotation using octanohydroxamic acid

The existence of metal ions should not be ignored in both hydrometallurgy and flotation. In this study, the effects of Ca2+, Mg2+ and Fe3+ on the flotation performance of cassiterite using octanohydroxamic acid (OHA) as the collector were investigated by micro-flotation tests, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared (FTIR) spectroscopy, contact angle, zeta (ζ) potential measurements and atomic force microscopy (AFM) imaging. The results of the flotation and contact angle experiments showed that the addition of Ca2+, Mg2+ and Fe3+ significantly decreased both the recovery and contact angle of cassiterite with pH ranged from 6.0 to 12.0 in the presence of OHA collector. ζ-Potential measurements, solution chemistry analysis and FTIR measurements indicated that the flotation recovery of the cassiterite declined due to the CaOH+, MgOH+ and Fe(OH)3 sites on the cassiterite surface. XPS results indicated that the chemisorption of OHA and calcium ions on the cassiterite surface finally changed its chemical properties. The AFM images also revealed that new species Fe(OH)3 of Fe3+ formed and adsorbed on the cassiterite surface at pH 9.0. The adsorption of Fe(OH)3 reduced the adsorption of OHA on the cassiterite surface, thus the hydrophobicity of cassiterite was deteriorated.
AC Impedance Modeling of Modular Multilevel Converters and Two-Level Voltage-Source Converters: Similarities and Differences

This paper presents a small-signal model for modular multilevel converters (MMCs) by using the harmonic state space (HSS) method. The ac output impedance of the MMC is then derived and compared with that of the commonly used two-level voltage-source converters (TL-VSCs). It turns out that the low frequency ac impedance of the MMC differs from that of the TL-VSC, due to the influences of internal dynamics, especially for the open-loop control and the voltage-mode control. In contrast, for the current-mode control, this difference can be minimized by the current regulator with a high-gain in the low frequency range. Moreover, despite the used control schemes, the high frequency ac impedance of the MMC is similar to that of the TL-VSC. The time-domain simulations are provided to validate the effectiveness of the theoretical analysis.
DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

This paper presents a novel computational optimization of the deceived non local means filter using moving average and symmetric weighting. The proposed optimization is compared with different approaches that reduce the computational cost of the deceived non local means filter. Furthermore, the impact of parallelizing different optimization approaches is assessed by evaluating the execution time and scalability in Xeon Phi KNL architecture. The proposed optimization for the sequential implementation achieved a 90x speedup, while its parallelized implementation yielded a speedup of up to 1662x.
The efficacy of sapacitabine in treating patients with acute myeloid leukemia

ABSTRACT Introduction: Acute myeloid leukemia (AML) remains a poor prognosis hematological malignancy. The introduction of aggressive chemotherapy with allogeneic stem cell transplantation has resulted in improved clinical outcomes in younger patients. However, the treatment results in unfit elderly AML population remain disappointing. New strategies should be introduced to improve the prognosis in this group of patients. Areas covered: This review presents and discusses the mechanism of action, safety and efficacy of sapacitabine in AML patients. Expert opinion: Sapacitabine, a novel nucleoside analog, seemed to be a promising new agent for AML treatment. Its oral bioavailability and tolerable toxicity profile allow the drug to be used in an outpatient setting, especially in elderly unfit patients. Sapacitabine is known to have antileukemic activity in randomized clinical trials. In AML patients, sapacitabine monotherapy offered no advantage over low-intensity cytarabine treatment, and the combination of sapacitabine with decitabine was not significantly more effective than decitabine alone. However, the oral administration of sapacitabine allows it to be used in AML maintenance therapy.
USP7 Cooperates with NOTCH1 to Drive the Oncogenic Transcriptional Program in T-Cell Leukemia

Purpose: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive disease, affecting children and adults. Chemotherapy treatments show high response rates but have debilitating effects and carry risk of relapse. Previous work implicated NOTCH1 and other oncogenes. However, direct inhibition of these pathways affects healthy tissues and cancer alike. Our goal in this work has been to identify enzymes active in T-ALL whose activity could be targeted for therapeutic purposes. Experimental Design: To identify and characterize new NOTCH1 druggable partners in T-ALL, we coupled studies of the NOTCH1 interactome to expression analysis and a series of functional analyses in cell lines, patient samples, and xenograft models. Results: We demonstrate that ubiquitin-specific protease 7 (USP7) interacts with NOTCH1 and controls leukemia growth by stabilizing the levels of NOTCH1 and JMJD3 histone demethylase. USP7 is highly expressed in T-ALL and is transcriptionally regulated by NOTCH1. In turn, USP7 controls NOTCH1 levels through deubiquitination. USP7 binds oncogenic targets and controls gene expression through stabilization of NOTCH1 and JMJD3 and ultimately H3K27me3 changes. We also show that USP7 and NOTCH1 bind T-ALL superenhancers, and inhibition of USP7 leads to a decrease of the transcriptional levels of NOTCH1 targets and significantly blocks T-ALL cell growth in vitro and in vivo. Conclusions: These results provide a new model for USP7 deubiquitinase activity through recruitment to oncogenic chromatin loci and regulation of both oncogenic transcription factors and chromatin marks to promote leukemia. Our studies also show that targeting USP7 inhibition could be a therapeutic strategy in aggressive leukemia.
Statistical Phrase/Accent Command Estimation Algorithm Utilizing Linguistic Information

The importance of extracting non-linguistic information has been highlighted in a growing variety of applications of speech signal processing. Among the audio features carrying such information, fundamental frequency $(F_{0})$ contours are considered primarily important. The Fujisaki model is a physical model that describes a $F_{0}$ contour with only a small number of parameters, namely, the timings and magnitudes of the phrase and accent commands, and a stochastic formulation and estimation algorithm have recently been proposed for it. However, the use of linguistic information has so far been limited, while it is known that accent commands are strongly related to linguistic information in many languages, and linguistic information could be obtained from the input audio signals by using speech recognition techniques. Against this background, this paper introduces a novel $F_{0}$ command parameter estimation method that incorporates linguistic information with the stochastic framework. Experiments using real speech data show that when linguistic information is appropriately utilized, the estimation accuracy of accent command parameters is improved by 43% under the proposed criteria.
Musculoskeletal Disorders: Prevalence and Associated Factors among District Hospital Nurses in Haiphong, Vietnam

Background Musculoskeletal disorders (MSDs) are a major occupational health problematic among healthcare workers, and the prevalence is especially high among nurses. In high income countries, the prevention of MSDs is an occupational health priority. But in Vietnam, there is no data available among health professionals. Objectives To determine the prevalence and associated factors of musculoskeletal disorders among district hospital nurses in Haiphong city. Material and Methods A cross-sectional study was conducted on 1179 nurses working in 15 district hospitals using the Standardized Nordic Questionnaire. Results A very high prevalence of MSDs in the past 12 months (74.7%) and during the last 7 days (41.1%), with the two most common sites being the low back (44.4%) and neck (44.1%), was found; 37.8% complained that MSDs symptoms limit their work. When analyzing factors related to MSDs, the results showed that women were 2.1 times more likely to develop MSDs than men; people with a previous history of MSDs were more likely to develop MSDs symptoms in the past 12 months than those with no history (OR = 7.1); nurses with symptoms of psychological distress and frequent absenteeism in the workplace had a higher prevalence of MSDs compared to the rest (p<0.001). Conclusions Due to the high prevalence of MSDs among nurses in district hospitals in Haiphong, preventive actions are needed to improve the working conditions and to raise the awareness of nurses about MSDs prevention.
High Stiffness in Teleoperated Comanipulation: Necessity or Luxury?

The present paper investigates if and in which conditions does the implementation of high stiffness controllers increase the performances of human dyads during comanipulative tasks in physical Human-Human Interaction (pHHI) settings. Two experiments are conducted which cover two fundamental aspects of pHHI: low-level interactions allowing interpersonal coordination, and high-level interactions allowing common decision-making and negotiation of strategies. The results of these experiments show that high stiffness is not necessary for good performances when the task only requires low-level interactions. On the contrary, when dealing with high-level interactions, higher stiffness increases task performance. The results presented highlight the importance of the quality of teleoperated control in setups used for the study of pHHI.
Deep Airway Inflammation and Respiratory Disorders in Nanocomposite Workers

Thousands of researchers and workers worldwide are employed in nanocomposites manufacturing, yet little is known about their respiratory health. Aerosol exposures were characterized using real time and integrated instruments. Aerosol mass concentration ranged from 0.120 mg/m3 to 1.840 mg/m3 during nanocomposite machining processes; median particle number concentration ranged from 4.8 × 104 to 5.4 × 105 particles/cm3. The proportion of nanoparticles varied by process from 40 to 95%. Twenty employees, working in nanocomposite materials research were examined pre-shift and post-shift using spirometry and fractional exhaled nitric oxide (FeNO) in parallel with 21 controls. Pro-inflammatory leukotrienes (LT) type B4, C4, D4, and E4; tumor necrosis factor (TNF); interleukins; and anti-inflammatory lipoxins (LXA4 and LXB4) were analyzed in their exhaled breath condensate (EBC). Chronic bronchitis was present in 20% of researchers, but not in controls. A significant decrease in forced expiratory volume in 1 s (FEV1) and FEV1/forced vital capacity (FVC) was found in researchers post-shift (p ˂ 0.05). Post-shift EBC samples were higher for TNF (p ˂ 0.001), LTB4 (p ˂ 0.001), and LTE4 (p ˂ 0.01) compared with controls. Nanocomposites production was associated with LTB4 (p ˂ 0.001), LTE4 (p ˂ 0.05), and TNF (p ˂ 0.001), in addition to pre-shift LTD4 and LXB4 (both p ˂ 0.05). Spirometry documented minor, but significant, post-shift lung impairment. TNF and LTB4 were the most robust markers of biological effects. Proper ventilation and respiratory protection are required during nanocomposites processing.