Miracle-dz/UniFAR-FTUs huggingface dataset:
Face, Body, Voice: Video Person-Clustering with Multiple Modalities

The objective of this work is person-clustering in videos – grouping characters according to their identity. Previous methods focus on the narrower task of face-clustering, and for the most part ignore other cues such as the person’s voice, their overall appearance (hair, clothes, posture), and the editing structure of the videos. Similarly, most current datasets evaluate only the task of face-clustering, rather than person-clustering. This limits their applicability to downstream applications such as story understanding which require person-level, rather than only face-level, reasoning.In this paper we make contributions to address both these deficiencies: first, we introduce a Multi-Modal High-Precision Clustering algorithm for person-clustering in videos using cues from several modalities (face, body, and voice). Second, we introduce a Video Person-Clustering dataset, for evaluating multi-modal person-clustering. It contains body-tracks for each annotated character, face-tracks when visible, and voice-tracks when speaking, with their associated features. The dataset is by far the largest of its kind, and covers films and TV-shows representing a wide range of demographics. Finally, we show the effectiveness of using multiple modalities for person-clustering, explore the use of this new broad task for story understanding through character co-occurrences, and achieve a new state of the art on all available datasets for face and person-clustering.
Moderate deviation principles for bifurcating Markov chains: case of functions dependent of one variable

The main purpose of this article is to establish moderate deviation principles for additive functionals of bifurcating Markov chains. Bifurcating Markov chains are a class of processes which are indexed by a regular binary tree. They can be seen as the models which represent the evolution of a trait along a population where each individual has two offsprings. Unlike the previous results of Bitseki, Djellout \&Guillin (2014), we consider here the case of functions which depend only on one variable. So, mainly inspired by the recent works of Bitseki \&Delmas (2020) about the central limit theorem for general additive functionals of bifurcating Markov chains, we give here a moderate deviation principle for additive functionals of bifurcating Markov chains when the functions depend on one variable. This work is done under the uniform geometric ergodicity and the uniform ergodic property based on the second spectral gap assumptions. The proofs of our results are based on martingale decomposition recently developed by Bitseki \&Delmas (2020) and on results of Dembo (1996), Djellout (2001) and Puhalski (1997).
Does Hubble tension signal a breakdown in FLRW cosmology?

The tension between early and late Universe probes of the Hubble constant has motivated various new FLRW cosmologies. Here, we reanalyse the Hubble tension with a recent age of the Universe constraint. This allows us to restrict attention to matter and a dark energy sector that we treat without assuming a specific model. Assuming analyticity of the Hubble parameter H(z), and a generic low redshift modification to flat ΛCDM, we find that low redshift data (z ≲ 2.5) and well-motivated priors only permit a dark energy sector close to the cosmological constant Λ. This restriction rules out late Universe modifications within FLRW. We show that early Universe physics that alters the sound horizon can yield an upper limit of H 0 ∼ 71 ± 1 km s−1 Mpc−1. Since various local determinations may be converging to H 0 ∼ 73 km s−1 Mpc−1, a breakdown of the FLRW framework is a plausible resolution. We outline how future data, in particular strongly lensed quasar data, could also provide further confirmations of such a resolution.
Comments on foliated gauge theories and dualities in 3+1d

We investigate the properties of foliated gauge fields and construct several foliated field theories in 3+1d that describe foliated fracton orders both with and without matter, including the recent hybrid fracton models. These field theories describe Abelian or non-Abelian gauge theories coupled to foliated gauge fields, and they fall into two classes of models that we call the electric models and the magnetic models. We show that these two classes of foliated field theories enjoy a duality. We also construct a model (using foliated gauge fields and an exactly solvable lattice Hamiltonian model) for a subsystem-symmetry protected topological (SSPT) phase, which is analogous to a one-form symmetry protected topological phase, with the subsystem symmetry acting on codimension-two subregions. We construct the corresponding gauged SSPT phase as a foliated two-form gauge theory. Some instances of the gauged SSPT phase are a variant of the X-cube model with the same ground state degeneracy and the same fusion, but different particle statistics.
Noncommutative Riemann hypothesis

In this note, making use of noncommutative $l$-adic cohomology, we extend the generalized Riemann hypothesis from the realm of algebraic geometry to the broad setting of geometric noncommutative schemes in the sense of Orlov. As a first application, we prove that the generalized Riemann hypothesis is invariant under derived equivalences and homological projective duality. As a second application, we prove the noncommutative generalized Riemann hypothesis in some new cases.
Approximation Algorithms For The Dispersion Problems in a Metric Space

In this article, we consider the $c$-dispersion problem in a metric space $(X,d)$. Let $P=\{p_{1}, p_{2}, \ldots, p_{n}\}$ be a set of $n$ points in a metric space $(X,d)$. For each point $p \in P$ and $S \subseteq P$, we define $cost_{c}(p,S)$ as the sum of distances from $p$ to the nearest $c $ points in $S \setminus \{p\}$, where $c\geq 1$ is a fixed integer. We define $cost_{c}(S)=\min_{p \in S}\{cost_{c}(p,S)\}$ for $S \subseteq P$. In the $c$-dispersion problem, a set $P$ of $n$ points in a metric space $(X,d)$ and a positive integer $k \in [c+1,n]$ are given. The objective is to find a subset $S\subseteq P$ of size $k$ such that $cost_{c}(S)$ is maximized. We propose a simple polynomial time greedy algorithm that produces a $2c$-factor approximation result for the $c$-dispersion problem in a metric space. The best known result for the $c$-dispersion problem in the Euclidean metric space $(X,d)$ is $2c^2$, where $P \subseteq \mathbb{R}^2$ and the distance function is Euclidean distance [ Amano, K. and Nakano, S. I. , Away from Rivals, CCCG, pp.68-71, 2018 ]. We also prove that the $c$-dispersion problem in a metric space is $W[1]$-hard.
Content-adaptive Representation Learning for Fast Image Super-resolution

Deep convolutional networks have attracted great attention in image restoration and enhancement. Generally, restoration quality has been improved by building more and more convolutional block. However, these methods mostly learn a specific model to handle all images and ignore difficulty diversity. In other words, an area in the image with high frequency tend to lose more information during compressing while an area with low frequency tends to lose less. In this article, we adrress the efficiency issue in image SR by incorporating a patch-wise rolling network(PRN) to content-adaptively recover images according to difficulty levels. In contrast to existing studies that ignore difficulty diversity, we adopt different stage of a neural network to perform image restoration. In addition, we propose a rolling strategy that utilizes the parameters of each stage more flexible. Extensive experiments demonstrate that our model not only shows a significant acceleration but also maintain state-of-the-art performance.
Empirical Analysis of Image Caption Generation using Deep Learning

Automated image captioning is one of the applications of Deep Learning which involves fusion of work done in computer vision and natural language processing, and it is typically performed using Encoder-Decoder architectures. In this project, we have implemented and experimented with various flavors of multi-modal image captioning networks where ResNet101, DenseNet121 and VGG19 based CNN Encoders and Attention based LSTM Decoders were explored. We have studied the effect of beam size and the use of pretrained word embeddings and compared them to baseline CNN encoder and RNN decoder architecture. The goal is to analyze the performance of each approach using various evaluation metrics including BLEU, CIDEr, ROUGE and METEOR. We have also explored model explainability using Visual Attention Maps (VAM) to highlight parts of the images which has maximum contribution for predicting each word of the generated caption.
Microwave irradiation accelerated rapid, efficient and high yield esterification of Boc-amino acid to Merrifield resin mediated by KF

A rapid and efficient procedure for the attachment of Boc-amino acid to Merrifield resin in the presence of potassium fluoride by flash heating under microwave irradiation has been described. Esterification rate acceleration and higher loadings are obtained significantly. Also, reaction times are reduced from the conventional 24 hr to 5-6 min duration under microwave irradiation.
A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars

Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of devices to a centralized server, it its own implementation challenges such as robustness, centralized server security, communication resources constraints, and privacy leakage due to the capability of a malicious aggregation server to infer sensitive information of end-devices. To address the aforementioned limitations, a dispersed federated learning (DFL) framework for autonomous driving cars is proposed to offer robust, communication resource-efficient, and privacy-aware learning. A mixed-integer non-linear (MINLP) optimization problem is formulated to jointly minimize the loss in federated learning model accuracy due to packet errors and transmission latency. Due to the NP-hard and non-convex nature of the formulated MINLP problem, we propose the Block Successive Upper-bound Minimization (BSUM) based solution. Furthermore, the performance comparison of the proposed scheme with three baseline schemes has been carried out. Extensive numerical results are provided to show the validity of the proposed BSUM-based scheme.
Joint Face Image Restoration and Frontalization for Recognition

In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination, low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to restore frontalized high-quality faces from the given low-quality ones under arbitrary facial poses, with three distinct novelties. First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart. Second, MDFR introduces a pose residual learning strategy along with a 3D-based Pose Normalization Module (PNM), which can perceive the pose gap between the input initial pose and its real-frontal pose to guide the face frontalization. Finally, MDFR can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration.
Gamma-ray bursts as probes of high-redshift Lyman-alpha emitters and radiative transfer models

Aims. We present the updated census and statistics of Lyman- α emitting long gamma-ray burst host galaxies (LAE-LGRBs). We investigate the properties of a subsample of LAE-LGRBs and test the shell model that is commonly used to ﬁt Lyman- α to LAE Lyman continuum (LyC) leakers in the literature in terms of the properties that are identiﬁed as possible indirect indicators of LyC leakage. We ﬁnd that only one LGRB (GRB021004) would likely be a strong LyC leaker and discuss the validity of these indicators at high redshift. While our work shows that LGRBs are useful tools for probing LAEs and radiative transfer models, larger statistics are required to strengthen our ﬁndings.
Stratified Data Integration

We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual(where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language(where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge(in the form of a graph where nodes are entity types and links are properties), and data(in the form of a graph of entities populating the previous knowledge graph). This allows us to state the problem of semantic heterogeneity as a problem of Representation Diversity where the different types of heterogeneity, viz. Conceptual, Language, Knowledge, and Data, are uniformly dealt within each single layer, independently from the others. In this paper we describe the proposed stratified representation of data and the process by which data are first transformed into the target representation, then suitably integrated and then, finally, presented to the user in her preferred format. The proposed framework has been evaluated in various pilot case studies and in a number of industrial data integration problems.
Monoclonal Immunoglobulin-Associated Renal Lesions in Patients with Newly Diagnosed Multiple Myeloma: A Report from a Single Center

Background Monoclonal immunoglobulin-associated renal lesions in patients with newly diagnosed myeloma vary. We aimed to determine the pathological spectrum and analyze associated prognostic factors. Methods Fifty-six patients with newly diagnosed multiple myeloma and biopsy-proven renal lesions were enrolled. Kidney biopsies were reanalyzed, and the baseline clinical characteristics, treatments and outcomes were recorded. Results Fifty-one patients had monoclonal immunoglobulin-associated renal lesions, with myeloma cast nephropathy (MCN) being the most common pattern. We divided our cohort into pure MCN, MCN+ other pathologies and non-MCN. Patients with MCN had more severe renal injury than those with non-MCN. In our cohort, none of the patients with pure MCN or MCN + other pathologies presented with nephrotic syndrome. Patients with non-MCN had better renal and overall survival than those with pure MCN but similar survivals to those with MCN + other pathologies. Number of myeloma casts (HR 1.08, p = 0.012) was the only independent prognostic factor for renal survival. Male sex (HR: 3.64; p = 0.015) and number of casts (HR: 1.17; p = 0.001) were independent prognostic factors for overall survival. Conclusion Patients with MCN had more severe renal injury than those with non-MCN. Patients with non-MCN had better renal and overall outcomes than those with pure MCN, but their outcomes were similar to those with MCN + other pathologies. The independent predictors of overall survival were male sex and number of myeloma casts.
Green Synthesis of Silver Nanoparticles Using the Flower Extract of Abelmoschus esculentus for Cytotoxicity and Antimicrobial Studies

Background Abelmoschus esculentus (L.) Moench, an economically important malvaceous vegetable crop popularly known as okra, is used in various culinary preparations and is rich in vitamins, minerals, and nutrients. The biological properties of okra flowers in relation to nanoparticle synthesis have not yet been reported. Materials and Methods In the current study, silver nanoparticles (AgNPs) were synthesized using extracts of the flowers of A. esculentus. The characteristics of the AgNPs were studied using a UV-vis spectrometer, Fourier transmission infrared spectrophotometer (FTIR), X-ray diffractometer (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM) and energy dispersive X-ray spectrometer (EDX). Antibacterial activity screening was performed using the agar well diffusion method, and cytotoxicity and cell viability studies were conducted using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Results The synthesized AgNPs were spherical and ranged in size from 5.52 to 31.96 nm, with an average size of 16.19 nm, as determined by UV-vis spectroscopy, FTIR, XRD, TEM and EDX. A. esculentus flower extract-mediated silver nanoparticles (AME-AgNPs) exhibited excellent activities in vitro studies, particularly in vitro cytotoxic and antiproliferative studies against cancer cell lines, such as the TERT-4 and A-549 cell lines. The antibacterial effects on the Gram-positive pathogens Bacillus subtilis, Staphylococcus aureus, Staphylococcus epidermidis, and Streptococcus pyogenes and the Gram-negative pathogens Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, Proteus vulgaris, Salmonella typhimurium and Shigella sonnei were tested. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values varied with the bacterial strain. The IC50 values of the synthesized NPs for the tested cell lines were close to that of a standard drug. Conclusion Compared to other NPs the NPs synthesized in this study were smaller in size and exhibited a higher level of antibacterial activity, cytotoxicity and apoptosis at minimal concentrations, and this is the first study on okra flower-induced anticancer and antimicrobial activities.
A Baseline Score to Predict Response to Ranibizumab Treatment in Neovascular Age-Related Macular Degeneration

Purpose What are the patient characteristics predictive of response to ranibizumab treatment? Methods Model-based characterization of best-corrected visual acuity (BCVA) time profiles of patients with neovascular age-related macular degeneration under ranibizumab or sham treatment based on 24-month observations of BCVA in 2419 patients from randomized multicenter phase 3 trials of ranibizumab: ANCHOR, MARINA, PIER, and HARBOR. Goodness-of-fit plots and precision of parameter estimates were used for measure of accuracy. Results The model incorporates a long-term effect on disease progression and an additive and more potent short-term effect of ranibizumab. Response to ranibizumab treatment and progression of the disease were found to be a function of seven baseline characteristics (visual acuity, age, leakage size, central retinal lesion thickness, presence or absence of cyst, type of choroidal neovascularization (CNV), and size of pigment epithelium detachment). A composite score of these seven baseline characteristics was derived and used to categorize response to ranibizumab treatment. The ranibizumab treatment arms of two proof-of-concept studies held out from the model development were used to validate the methodology. Conclusions A composite score based on seven patient characteristics prior to treatment could be used to discriminate patients with predicted insufficient response to anti–vascular endothelial growth factor treatment. Translational Relevance The method could be used to create a virtual ranibizumab treatment arm in clinical trials or to reduce the size of a ranibizumab active control arm.
Chemotherapy-Free Management of Follicular and Marginal Zone Lymphoma

Abstract Many patients with follicular (FL) or marginal zone lymphoma (MZL) are not eligible to receive immunochemotherapy due to advanced age or comorbidities. Recent innovations in the treatment of these indolent lymphomas provide options for multiple lines of chemotherapy-free management. More research is needed to determine which older patients are best served by a chemotherapy-free approach in the context of geriatric vulnerabilities. In the first line, regardless of disease burden, rituximab monotherapy can provide high rates of disease control with minimal toxicity, while judicious use of brief maintenance extends the duration of response. Radioimmunotherapy using ibritumomab tiuxetan is an effective and safe post-rituximab consolidation for older patients who have <25% bone marrow involvement. The combination of rituximab and lenalidomide, although “chemotherapy-free”, does not improve tolerability over immunochemotherapy. However, studies support lower doses and shorter duration of lenalidomide exposure as a means to improve safety without materially compromising efficacy for older individuals. Extranodal MZL can often be effectively controlled with low-dose radiation therapy, and splenic MZL has excellent outcomes with rituximab monotherapy. For many patients with relapsed FL/MZL, simple retreatment with anti-CD20 antibodies will prove sufficient. Other currently available options for relapsed/refractory disease include ibritumomab tiuxetan, lenalidomide with rituximab, umbralisib as a potentially less toxic PI3K inhibitor, ibrutinib (for MZL), and tazemetostat (for FL, especially with EZH2 mutation). Emerging data with novel forms of immunotherapy (antibody-drug conjugates like polatuzumab vedotin or loncastuximab tesirine; T-cell-engaging bispecific antibodies like mosunetuzumab or epcoritamab; and chimeric antigen receptor CAR T-cells like axicabtagene ciloleucel) suggest that immune-directed approaches can produce very high and potentially durable responses in FL/MZL with limited toxicities, further obviating the need for chemotherapy.
Optimized and Non-Optimized Personal Protective Equipment Use during the COVID-19 Pandemic in Thailand: A National Cross-Sectional Survey in a Resource-Limited Setting

Introduction: Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV2). COVID-19 is highly contagious, potentially fatal, and a global public health concern. Combining optimized personal protective equipment (PPE) use and hand hygiene is the best strategy for preventing COVID-19 in health care workers (HCWs). Methods: We conducted a national cross-sectional web-based survey of HCWs in the infection control program (IPC) in Thailand between May 5, 2020 and May 15, 2020. The primary objective was the prevalence of optimized PPE use amongst HCWs. The secondary objective was identification of the independent predictors of optimized PPE use. Results: We received a response from 46% of HCWs (756/1650), and all those who responded were nurse or HCWs who were registered in the IPC network. Five HCWs were excluded because of missing data, and 751 were included in the final analysis. The prevalences of PPE use were 22% (168/751) for optimized PPE use, 78% (583/751) for non-optimized PPE use, 35% (263/751) for PPE overuse, and 43% (320/751) for PPE underused. In univariate analysis, optimized PPE use was significantly associated with age, education level, knowledge of appropriate negative pressure room selection, and knowledge of apparently milder symptom severity in children than adults. In multivariate analysis, independent predictors of optimized PPE use were knowledge of appropriate negative pressure room selection (aOR = 1.95, 95% CI = 1.18-3.22), the difference in symptom severity between children and adults (aOR = 0.55, 95% CI = 0.37-0.81), and education level (aOR = 1.54, 95% CI = 1.04-2.27). Conclusion: The prevalence of optimized PPE use amongst HCWs was 22%. Independent predictors of optimized PPE use were COVID-19 knowledge-based factors and education level. Therefore, the continued education training program should be implemented to ensure maintenance of appropriate practices during the COVID-19 pandemic.