Miracle-dz/UniFAR-FTUs huggingface dataset:
Poly(oxymethylene) dimethyl ether synthesis – a combined chemical equilibrium investigation towards an increasingly efficient and potentially sustainable synthetic route

Polyoxymethylene dimethyl ethers (denoted hereon as OME) are potential sustainable fuels (e.g. as a diesel substitute). In this paper, the fundamental analysis of a potentially, sustainable synthetic OME system is presented (i.e. based on CH3OH synthesised from H2 and recycled CO2). In this context, a multicomponent thermodynamic vapour–liquid equilibrium model, based on CH3OH as the educt and source of H2CO for OME synthesis, is described. A thermodynamic equilibrium mathematical model for this complex (i.e. a 29 reaction network) CH3OH–H2CO equilibrium system is presented, capable of solving the sequential chemical and phase equilibrium, importantly considering all components in the reaction system including poly(oxymethylene) hemiformals and poly(oxymethylene) glycols. A theoretical efficiency evaluation indicates that the proposed anhydrous route is potentially more attractive than the conventional synthesis (i.e. based on dimethoxymethane and trioxane). To substantiate these theoretical investigations, a complimentary experimental batch OME synthesis is also presented, providing validation for the presented thermodynamic model. An initial kinetic analysis of the OME synthesis over different commercial catalysts is also highlighted. Our presented findings reliably describe the synthesis equilibrium with respect to our experimentally obtained results. The presented work supports further an operating OME synthesis framework based on CH3OH and H2CO and highlights the requirement for innovative process design regarding feed preparation, reactor technology, and product separation/fractions recycling.
Learning Log-Determinant Divergences for Positive Definite Matrices

Representations in the form of Symmetric Positive Definite (SPD) matrices have been popularized in a variety of visual learning applications due to their demonstrated ability to capture rich second-order statistics of visual data. There exist several similarity measures for comparing SPD matrices with documented benefits. However, selecting an appropriate measure for a given problem remains a challenge and in most cases, is the result of a trial-and-error process. In this paper, we propose to learn similarity measures in a data-driven manner. To this end, we capitalize on the <inline-formula><tex-math notation="LaTeX">$\alpha \beta$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>α</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="papanikolopoulos-ieq1-3073588.gif"/></alternatives></inline-formula>-log-det divergence, which is a meta-divergence parametrized by scalars <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="papanikolopoulos-ieq2-3073588.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math><alternatives><mml:math><mml:mi>β</mml:mi></mml:math><inline-graphic xlink:href="papanikolopoulos-ieq3-3073588.gif"/></alternatives></inline-formula>, subsuming a wide family of popular information divergences on SPD matrices for distinct and discrete values of these parameters. Our key idea is to cast these parameters in a continuum and learn them from data. We systematically extend this idea to learn vector-valued parameters, thereby increasing the expressiveness of the underlying non-linear measure. We conjoin the divergence learning problem with several standard tasks in machine learning, including supervised discriminative dictionary learning and unsupervised SPD matrix clustering. We present Riemannian gradient descent schemes for optimizing our formulations efficiently, and show the usefulness of our method on eight standard computer vision tasks.
FastS2S-VC: Streaming Non-Autoregressive Sequence-to-Sequence Voice Conversion

This paper proposes a non-autoregressive extension of our previously proposed sequence-to-sequence (S2S) model-based voice conversion (VC) methods. S2S model-based VC methods have attracted particular attention in recent years for their flexibility in converting not only the voice identity but also the pitch contour and local duration of input speech, thanks to the ability of the encoder-decoder architecture with the attention mechanism. However, one of the obstacles to making these methods work in real-time is the autoregressive (AR) structure. To overcome this obstacle, we develop a method to obtain a model that is free from an AR structure and behaves similarly to the original S2S models, based on a teacher-student learning framework. In our method, called"FastS2S-VC", the student model consists of encoder, decoder, and attention predictor. The attention predictor learns to predict attention distributions solely from source speech along with a target class index with the guidance of those predicted by the teacher model from both source and target speech. Thanks to this structure, the model is freed from an AR structure and allows for parallelization. Furthermore, we show that FastS2S-VC is suitable for real-time implementation based on a sliding-window approach, and describe how to make it run in real-time. Through speaker-identity and emotional-expression conversion experiments, we confirmed that FastS2S-VC was able to speed up the conversion process by 70 to 100 times compared to the original AR-type S2S-VC methods, without significantly degrading the audio quality and similarity to target speech. We also confirmed that the real-time version of FastS2S-VC can be run with a latency of 32 ms when run on a GPU.
Two-loop ﬁve-point integration-by-parts relations in a usable form

: In this paper, we show that with the state-of-art module intersection IBP reduction method and our improved Leinartas’ algorithm, IBP relations for very complicated Feynman integrals can be solved and the analytic reduction coeﬃcients can be dramatically simpliﬁed. We develop a large scale parallel implementation of our improved Leinartas’ al-gorithm, based on the Singular / GPI-Space framework. We demonstrate our method by the reduction of two-loop ﬁve-point Feynman integrals with degree-ﬁve numerators, with a simple and sparse IBP system. The analytic reduction result is then greatly simpliﬁed by our improved Leinartas’ algorithm to a usable form, with a compression ratio of two order of magnitudes. We further discover that the compression ratio increases with the complexity of the Feynman integrals.
Sentence Embeddings by Ensemble Distillation

This paper contributes a new State Of The Art (SOTA) for Semantic Textual Similarity (STS). We compare and combine a number of recently proposed sentence embedding methods for STS, and propose a novel and simple ensemble knowledge distillation scheme that improves on previous approaches. Our experiments demonstrate that a model trained to learn the average embedding space from multiple ensemble students outperforms all the other individual models with high robustness. Utilizing our distillation method in combination with previous methods, we significantly improve on the SOTA unsupervised STS, and by proper hyperparameter tuning of previous methods we improve the supervised SOTA scores.
TASAC: Temporally Abstract Soft Actor-Critic for Continuous Control

We propose temporally abstract soft actor-critic (TASAC), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner. TASAC adds a second-stage binary policy to choose between the previous action and the action output by an SAC actor. It has two beneﬁts compared to traditional off-policy RL algorithms: persistent exploration and an unbiased multi-step Q operator for TD learning. We demonstrate its advantages over several strong baselines across 5 different categories of 14 continuous control tasks, in terms of both sample efﬁciency and ﬁnal performance. Because of its simplicity and generality, TASAC can serve as a drop-in replacement for SAC when temporal abstraction is needed. Code is available at https://github.com/hnyu/tasac .
End-to-End Keyword Spotting Using Neural Architecture Search and Quantization

This paper introduces neural architecture search (NAS) for the automatic discovery of end-to-end keyword spotting (KWS) models for limited resource environments. We employ a differentiable NAS approach to optimize the structure of convolutional neural networks (CNNs) operating on raw audio waveforms. After a suitable KWS model is found with NAS, we conduct quantization of weights and activations to reduce the memory footprint. We conduct extensive experiments on the Google speech commands dataset. In particular, we compare our end-to-end models to mel-frequency cepstral coefficient (MFCC) based CNNs. For quantization, we compare fixed bit-width quantization and trained bit-width quantization. Using NAS only, we were able to obtain a highly efficient model with an accuracy of 95.55% using 75.7k parameters and 13.6M operations. Using trained bit-width quantization, the same model achieves a test accuracy of 93.76% while using on average only 2.91 bits per activation and 2.51 bits per weight.
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition

We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. First, we propose novel bounding algorithms based on Lagrangian Decomposition. Previous works have used off-the-shelf solvers to solve relaxations at each node of the BaB tree, or constructed weaker relaxations that can be solved efficiently, but lead to unnecessarily weak bounds. Our formulation restricts the optimization to a subspace of the dual domain that is guaranteed to contain the optimum, resulting in accelerated convergence. Furthermore, it allows for a massively parallel implementation, which is amenable to GPU acceleration via modern deep learning frameworks. Second, we present a novel activation-based branching strategy. By coupling an inexpensive heuristic with fast dual bounding, our branching scheme greatly reduces the size of the BaB tree compared to previous heuristic methods. Moreover, it performs competitively with a recent strategy based on learning algorithms, without its large offline training cost. Finally, we design a BaB framework, named Branch and Dual Network Bound (BaDNB), based on our novel bounding and branching algorithms. We show that BaDNB outperforms previous complete verification systems by a large margin, cutting average verification times by factors up to 50 on adversarial robustness properties.
Federated Generalized Face Presentation Attack Detection

Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. An fPAD model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks. In reality, training data (both real face images and spoof images) are not directly shared between data owners due to legal and privacy issues. In this article, with the motivation of circumventing this challenge, we propose a federated face presentation attack detection (FedPAD) framework that simultaneously takes advantage of rich fPAD information available at different data owners while preserving data privacy. In the proposed framework, each data owner (referred to as data centers) locally trains its own fPAD model. A server learns a global fPAD model by iteratively aggregating model updates from all data centers without accessing private data in each of them. Once the learned global model converges, it is used for fPAD inference. To equip the aggregated fPAD model in the server with better generalization ability to unseen attacks from users, following the basic idea of FedPAD, we further propose a federated generalized face presentation attack detection (FedGPAD) framework. A federated domain disentanglement strategy is introduced in FedGPAD, which treats each data center as one domain and decomposes the fPAD model into domain-invariant and domain-specific parts in each data center. Two parts disentangle the domain-invariant and domain-specific features from images in each local data center. A server learns a global fPAD model by only aggregating domain-invariant parts of the fPAD models from data centers, and thus, a more generalized fPAD model can be aggregated in server. We introduce the experimental setting to evaluate the proposed FedPAD and FedGPAD frameworks and carry out extensive experiments to provide various insights about federated learning for fPAD.
The role of tumor-associated macrophages in primary hepatocellular carcinoma and its related targeting therapy

Liver macrophages consist of ontogenically distinct populations termed Kupffer cells and monocyte-derived macrophages. Tumor-associated macrophages (TAMs) inhepatocellularcarcinoma (HCC) play a prominent role in tumormicroenvironment by presenting M1(induced by IFN γ along with LPS) and M2(induced by IL-4 and IL13) polarization. Although TAMs are involved in tumor immune surveillance during the course of HCC, they contribute to tumour progression at different levels by inhibiting the anti-tumor immune response, promoting the generation of blood vessels and lymphatic vessels, and supporting the proliferation and survival of tumor cells. In this paper, the multiple functions of TAMs in HCC were reviewed to provide assistance for future researches about therapeutic approaches.
Activity and Safety of Tegafur, Gimeracil, and Oteracil Potassium for Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis

In clinical practice, tegafur, gimeracil, and oteracil potassium (S-1) therapy is commonly administered to treat nasopharyngeal carcinoma (NPC). However, its efficacy and safety remain controversial in both randomized controlled trials (RCTs) and non-RCTs. We aimed to evaluate the efficacy and safety of S-1 treatment for NPC. We searched PubMed, Ovid, EMBASE, the Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, and VIP databases for RCTs of chemotherapy with or without S-1 for NPC, from 2001 to 2020. A meta-analysis was performed using RevMan5.3 and Stata15. Randomized controlled trials published in journals were included irrespective of blinding and language used. Patients were diagnosed with NPC through a clinicopathological examination; patients of all cancer stages and ages were included. Overall, 25 trials and 1858 patients were included. There were significant differences in the complete remission (OR = 2.42, 95% CI (1.88–3.10), P < 0.05) and overall response rate (OR = 2.68, 95% CI (2.08–3.45), P < 0.05) between the S-1 and non-S-1 groups. However, there was no significant difference in partial remission (OR = 1.10, 95% CI (0.87–1.39), P=0.42) and seven adverse reactions (leukopenia, thrombocytopenia, nausea and vomiting, diarrhea, dermatitis, oral mucositis, and anemia) between the S-1 and non-S-1 groups. Additionally, statistical analyses with six subgroups were performed. S-1 was found to be a satisfactory chemotherapeutic agent combined with radiotherapy, intravenous chemotherapy, or chemoradiotherapy for NPC. As an oral medicine, the adverse reactions of S-1, especially gastrointestinal reactions, can be tolerated by patients, thereby optimizing their quality of life. S-1 may be a better choice for the treatment of NPC. This trial is registered with CRD42019122041.
Outright Green Synthesis of Fluorescent Carbon Dots from Eutrophic Algal Blooms for In Vitro Imaging

Carbon dots (CDs) synthesized from biological sources have attracted much interest in bioimaging and biomedical applications due to their excellent biocompatibility, and thus, a facile synthesis of CDs with high fluorescence quantum yield (QY) is requisite for practical applications. In this work, we report a simple, rapid, and green approach to synthesize photoluminescent CDs using eutrophic algal blooms as the carbon source. This method offers a possibility for large scale production of highly luminescent CDs (QY = 13%) with the average particle size ∼8 nm. These CDs are highly water-soluble and exhibit nanosecond fluorescence lifetime with high photostability, luminescence stability in different environments, low cytotoxicity, and excellent cell permeability. Laser scanning confocal microscopy shows the uptake of CDs by MCF-7 cells, and the destined application of these CDs as a potential biomarker is demonstrated.
Effects of Quercetin and Resveratrol on Zinc Chloride- and Sodium Metavanadate-Induced Passive Avoidance Memory Retention Deficits in Male Mice

Quercetin and resveratrol are found in a variety of fruits and vegetables and have several biological and pharmacological properties. In this study, the effects of quercetin [50 mg/kg, intraperitoneal (i.p.)] and resveratrol (50 mg/kg, i.p.) on zinc chloride (ZnCl2; 75 mg/kg/d, 2 weeks oral gavage) and sodium metavanadate (SMV; 22.5 mg/kg/d, 2 weeks oral gavage) induced passive avoidance memory retention were investigated in step-through passive avoidance tasks. ZnCl2 was dissolved in saline and SMV was dissolved in drinking water. Mice received ZnCl2 or SMV orally for two weeks and were administered quercetin or resveratrol by i.p. injection on day 14, days 12 and 14, or days 10, 12, and 14. At the end of treatment, animals were trained for one day in a step-through passive avoidance task, then alterations in avoidance memory retention were evaluated after 24, 48, 96, and 168 h. Oral consumption of ZnCl2 and SMV decreased latency time compared with control groups. Both quercetin and resveratrol (50 mg/kg, i.p.) prevented ZnCl2- and SMV-induced avoidance memory retention impairments and did not significantly alter muscle strength, as demonstrated in rotarod tasks. No significant differences were observed between mice who received single, double, or triple doses of quercetin or resveratrol. The results suggest that quercetin and resveratrol may have preventive effects on ZnCl2- and SMV-induced memory impairment in male mice.
Phylogenetic studies of Newcastle disease virus isolated from poultry flocks in South Sulawesi Province, Indonesia, in 2019

Objective: Indonesia is one of the Newcastle disease (ND) endemic countries in the world. An outbreak of the ND virus (NDV) was first reported in Indonesia in 1926. This study aimed to detect, isolate, and classify the NDV by molecular approaches from poultry farms in South Sulawesi Province of Indonesia in 2019. Materials and Methods: As many as 36 pooling samples from the cloacal swab, trachea swab, proventriculus, and spleen tissues obtained from ND-suspected chickens were isolated in 11-day-old embryonated chicken eggs type-specific antibody-negative. The viruses were confirmed by reverse transcription-polymerase chain reaction (RT-PCR), followed by sequencing. Results: The results showed that 18 out of 36 pooling samples were NDV-positive based on the isolation result and RT-PCR test. The sequencing results showed that 10 NDV isolates had a motif 112R-R-Q-K-R-F117 in the fusion protein cleavage site region, which suggested that the NDV isolates were of virulent pathotype. The phylogenetic studies based on the F gene’s partial nucleotide sequence classified the study isolates into NDV virus genotype/subgenotype VII.2. Conclusion: These findings are expected to help provide the latest characteristic information of NDV in South Sulawesi Province to determine the seed vaccine for control strategies of ND.
Serotonin 2 Receptors, Agomelatine, and Behavioral and Psychological Symptoms of Dementia in Alzheimer's Disease

There are nearly 50 million Alzheimer's disease (AD) patients worldwide, 90% of whom develop behavioral and psychological symptoms of dementia (BPSD), which increase the mortality rate of patients, and impose an economic and care burden on families and society. As a neurotransmitter and neuromodulator, serotonin is involved in the regulation of psychoemotional, sleep, and feeding functions. Accumulating data support the importance of serotonin in the occurrence and development of BPSD. Studies have shown that reduction of serotonin receptors can increase depression and mental symptoms in AD patients. At present, there is no drug treatment for AD approved by the US Food and Drug Administration. Among them, agomelatine, as a new type of antidepressant, can act on serotonin 2 receptors to improve symptoms such as depression and anxiety. At present, research on BPSD is still in the preliminary exploratory stage, and there are still a lot of unknowns. This review summarizes the relationship between serotonin 2 receptors, agomelatine, and BPSD. It provides a new idea for the study of the pathogenesis and treatment of BPSD.
Resting-State fMRI in Studies of Acupuncture

Research exploring the mechanism of acupuncture has been a hot topic in medicine. Resting-state functional magnetic resonance imaging (rs-fMRI) research is a noninvasive and extensive method, which is aimed at the research of the mechanism of acupuncture. Researchers use fMRI technologies to inspect the acupuncture process. The authors reviewed the application of rs-fMRI in acupuncture research in recent 10 years from the aspects of studying acupoints, subjects, acupuncture methods, and intensities. The results found that the application of rs-fMRI in acupuncture research mainly includes research on the onset mechanism of acupuncture treatment; visual evidence of diagnosis and treatment of dominant diseases; efficacy assessments; physiological mechanism of acupoint stimulation; and specific visualization of acupoints.
Chemical Composition and Antibacterial Activities of Eight Plant Essential Oils from Morocco against Escherichia coli Strains Isolated from Different Turkey Organs

The aim of the present study was to determine the chemical composition of eight plant essential oils and evaluate their antibacterial activity against Escherichia coli strains isolated from different turkey organs. The essential oils were extracted by hydrodistillation and analyzed using gas chromatography-mass spectroscopy. All essential oil yielded high in a range between 2.2 and 3.12%. Gas chromatography-mass spectroscopy (GC-MS) revealed that the major constituents of Thymus vulgaris, Ocimum basilicum, Artemisia herba-alba, and Syzygium aromaticum oils were thymol (41.39%), linalool (37.16%), camphor (63.69%), and eugenol (80.83%), respectively. Results of the E. coli sensitivity evaluated by the standard antimicrobial sensitivity method varied depending on the organ of isolation. Similarly, the essential oils antimicrobial activity determined by the disc diffusion method varied all along within the organs of isolation. T. vulgaris essential oil showed the highest effective antibacterial activity against E. coli isolated from the throat with an inhibition zone diameter value of up to 23.33 mm. However, all the essential oils showed antibacterial activity and the MIC and MBC values were in the range of 1/3000 to 1/100 (v/v) and the ratios MBC/MIC were equal to 1. In conclusion, this study showed that the essential oils could be promising alternatives to overcome E. coli multiresistance in turkey.
Recent Applications of Molecularly Imprinted Polymers (MIPs) on Micro-extraction Techniques

Microextraction is considered as one of the most critical steps in the entire analytical process because it can effectively remove interference and pre-concentrate the target analytes. Molecularly imprinted polymers (MIPs) are synthetic polymers with a predetermined selectivity for a given analyte, or group of structurally related compounds, which are excellent materials for sample preparation in the process of microextraction owing to their high selectivity and ability. This review provides a critical overview of the synthesis and characterization of MIPs, with a focus on recent applications in the field of solid-phase microextraction (SPME) and liquid-phase microextraction (LPME). The advantages and drawbacks of the applications of MIPs used in SPME and LPME as well as the future expected trends are also discussed.