Miracle-dz/UniFAR-FTUs huggingface dataset:
Ravnest: Decentralized Asynchronous Training on Heterogeneous Devices

Modern deep learning models, growing larger and more complex, have demonstrated exceptional generalization and accuracy due to training on huge datasets. This trend is expected to continue. However, the increasing size of these models poses challenges in training, as traditional centralized methods are limited by memory constraints at such scales. This paper proposes an asynchronous decentralized training paradigm for large modern deep learning models that harnesses the compute power of regular heterogeneous PCs with limited resources connected across the internet to achieve favourable performance metrics. Ravnest facilitates decentralized training by efficiently organizing compute nodes into clusters with similar data transfer rates and compute capabilities, without necessitating that each node hosts the entire model. These clusters engage in $\textit{Zero-Bubble Asynchronous Model Parallel}$ training, and a $\textit{Parallel Multi-Ring All-Reduce}$ method is employed to effectively execute global parameter averaging across all clusters. We have framed our asynchronous SGD loss function as a block structured optimization problem with delayed updates and derived an optimal convergence rate of $O\left(\frac{1}{\sqrt{K}}\right)$. We further discuss linear speedup with respect to the number of participating clusters and the bound on the staleness parameter.
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM with Joint Semantic Encoding

We propose DDN-SLAM, a real-time dense neural implicit semantic SLAM system designed for dynamic scenes. While existing neural implicit SLAM systems perform well in static scenes, they often encounter challenges in real-world environments with dynamic interferences, leading to ineffective tracking and mapping. DDN-SLAM utilizes the priors provided by the deep semantic system, combined with conditional probability fields, for segmentation.By constructing depth-guided static masks and employing joint multi-resolution hashing encoding, we ensure fast hole filling and high-quality mapping while mitigating the effects of dynamic information interference. To enhance tracking robustness, we utilize sparse feature points validated with optical flow and keyframes, enabling loop closure detection and global bundle optimization. Furthermore, DDN-SLAM supports monocular, stereo, and RGB-D inputs, operating robustly at a frequency of 20-30Hz. Extensive experiments on 6 virtual/real datasets demonstrate that our method out-performs state-of-the-art approaches in both dynamic and static scenes.
Energy balance and damage for brittle fracture: nonlocal formulation

A nonlocal model of peridynamic type for dynamic brittle damage is introduced consisting of two phases, one elastic and the other inelastic. Evolution from the elastic to the inelastic phase depends on material strength. Existence and uniqueness of the displacement-failure set pair follow from the initial value problem. The displacement-failure pair satisfies energy balance. The length scale of nonlocality $\epsilon$ is taken to be small relative to the domain in $\mathbb{R}^d$, $d=2,3$. The new nonlocal model delivers a two point strain dynamics on a subset of $\mathbb{R}^d\times\mathbb{R}^d$. This dynamics provides an energy that interpolates between volume energy corresponding to elastic behavior and surface energy corresponding to failure. The deformation energy resulting in material failure over a region $R$ is given by a $d-1$ dimensional integral that is uniformly bounded as $\epsilon\rightarrow 0$. For fixed $\epsilon$, this energy is nonzero for $d-1$ dimensional regions $R$ associated with flat crack surfaces. The failure energy is the Griffith fracture energy for a given crack $R$ in terms of its area for $d=3$ (or length for $d=2$). Simulations illustrate fracture evolution through generation of an internal traction free boundary as a wake left behind a moving strain concentration. Crack paths are seen to follow a maximal strain energy density criterion.
The Entangled Quantum Polynomial Hierarchy Collapses

We introduce the entangled quantum polynomial hierarchy $\mathsf{QEPH}$ as the class of problems that are efficiently verifiable given alternating quantum proofs that may be entangled with each other. We prove $\mathsf{QEPH}$ collapses to its second level. In fact, we show that a polynomial number of alternations collapses to just two. As a consequence, $\mathsf{QEPH} = \mathsf{QRG(1)}$, the class of problems having one-turn quantum refereed games, which is known to be contained in $\mathsf{PSPACE}$. This is in contrast to the unentangled quantum polynomial hierarchy $\mathsf{QPH}$, which contains $\mathsf{QMA(2)}$. We also introduce a generalization of the quantum-classical polynomial hierarchy $\mathsf{QCPH}$ where the provers send probability distributions over strings (instead of strings) and denote it by $\mathsf{DistributionQCPH}$. Conceptually, this class is intermediate between $\mathsf{QCPH}$ and $\mathsf{QPH}$. We prove $\mathsf{DistributionQCPH} = \mathsf{QCPH}$, suggesting that only quantum superposition (not classical probability) increases the computational power of these hierarchies. To prove this equality, we generalize a game-theoretic result of Lipton and Young (1994) which says that the provers can send distributions that are uniform over a polynomial-size support. We also prove the analogous result for the polynomial hierarchy, i.e., $\mathsf{DistributionPH} = \mathsf{PH}$. These results also rule out certain approaches for showing $\mathsf{QPH}$ collapses. Finally, we show that $\mathsf{PH}$ and $\mathsf{QCPH}$ are contained in $\mathsf{QPH}$, resolving an open question of Gharibian et al. (2022).
Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data

Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Hypothesizing that SSL models would learn more generic, hence less biased, representations, this study explores the impact of pre-training and fine-tuning strategies on fairness (i.e., performing equally on different demographic breakdowns). Motivated by human-centric applications on real-world timeseries data, we interpret inductive biases on the model, layer, and metric levels by systematically comparing SSL models to their supervised counterparts. Our findings demonstrate that SSL has the capacity to achieve performance on par with supervised methods while significantly enhancing fairness--exhibiting up to a 27% increase in fairness with a mere 1% loss in performance through self-supervision. Ultimately, this work underscores SSL's potential in human-centric computing, particularly high-stakes, data-scarce application domains like healthcare.
Sharp weighted inequalities for iterated commutators of a class of multilinear operators

In this paper, the sharp quantitative weighted bounds for the iterated commutators of a class of multilinear operators were systematically studied. This class of operators contains multilinear Calder\'{o}n-Zygmund operators, multilinear Fourier integral operators, and multilinear Littlewood-Paley square operators as its typical examples. These were done only under two pretty much general assumptions of pointwise sparse domination estimates. We first established local decay estimates and quantitative weak $A_\infty$ decay estimates for iterated commutators of this class of operators. Then, we considered the corresponding Coifman-Fefferman inequalities and the mixed weak type estimates associated with Sawyer's conjecture. Beyond that, the Fefferman-Stein inequalities with respect to arbitrary weights and weighted modular inequalities were also given. As applications, it was shown that all the conclusions aforementioned can be applied to multilinear $\omega$-Calder\'{o}n-Zygmund operators, multilinear maximal singular integral operators, multilinear pseudo-differential operators, Stein's square functions, and higher order Calder\'{o}n commutators.
5-Engel Lie algebras

We prove that 5-Engel Lie algebras over a field of characteristic zero, or over a field of prime characteristic $p>7$, are nilpotent of class at most 11. We also prove that if $G$ is a finite 5-Engel $p$-group for $p>7$ then $G$ is nilpotent of class at most 10.
Glance and Focus: Memory Prompting for Multi-Event Video Question Answering

Video Question Answering (VideoQA) has emerged as a vital tool to evaluate agents' ability to understand human daily behaviors. Despite the recent success of large vision language models in many multi-modal tasks, complex situation reasoning over videos involving multiple human-object interaction events still remains challenging. In contrast, humans can easily tackle it by using a series of episode memories as anchors to quickly locate question-related key moments for reasoning. To mimic this effective reasoning strategy, we propose the Glance-Focus model. One simple way is to apply an action detection model to predict a set of actions as key memories. However, these actions within a closed set vocabulary are hard to generalize to various video domains. Instead of that, we train an Encoder-Decoder to generate a set of dynamic event memories at the glancing stage. Apart from using supervised bipartite matching to obtain the event memories, we further design an unsupervised memory generation method to get rid of dependence on event annotations. Next, at the focusing stage, these event memories act as a bridge to establish the correlation between the questions with high-level event concepts and low-level lengthy video content. Given the question, the model first focuses on the generated key event memory, then focuses on the most relevant moment for reasoning through our designed multi-level cross-attention mechanism. We conduct extensive experiments on four Multi-Event VideoQA benchmarks including STAR, EgoTaskQA, AGQA, and NExT-QA. Our proposed model achieves state-of-the-art results, surpassing current large models in various challenging reasoning tasks. The code and models are available at https://github.com/ByZ0e/Glance-Focus.
Parabolic Anderson model in bounded domains of recurrent metric measure spaces

A metric measure space equipped with a Dirichlet form is called recurrent if its Hausdorff dimension is less than its walk dimension. In bounded domains of such spaces we study the parabolic Anderson models \[ \partial_{t} u(t,x) = \Delta u(t,x) + \beta u(t,x) \, \dot{W}_\alpha(t,x) \] where the noise $W_\alpha$ is white in time and colored in space when $\alpha>0$ while for $\alpha=0$ it is also white in space. Both Dirichlet and Neumann boundary conditions are considered. Besides proving existence and uniqueness in the It\^o sense we also get precise $L^p$ estimates for the moments and intermittency properties of the solution as a consequence. Our study reveals new exponents which are intrinsically associated to the geometry of the underlying space and the results for instance apply in metric graphs or fractals like the Sierpi\'nski gasket for which we prove scaling invariance properties of the models.
Observations Favor the Redshift-evolutionary L
 X–L
 UV Relation of Quasars from Copula

We compare, with data from quasars, Hubble parameter measurements, and Pantheon+ type Ia supernova, three different relations between X-ray luminosity (L X) and ultraviolet luminosity (L UV) of quasars. These three relations consist of the standard and two redshift-evolutionary L X–L UV relations, which are constructed, respectively, by considering a redshift-dependent correction to the luminosities of quasars and using the statistical tool copula. By employing the PAge approximation for a cosmological model–independent description of the cosmic background evolution and dividing the quasar data into the low-redshift and high-redshift parts, we find that the constraints on the PAge parameters from the low-redshift and high-redshift data, which are obtained with the redshift-evolutionary relations, are consistent with each other, while they are not when the standard relation is considered. If the data are used to constrain the coefficients of the relations and the PAge parameters simultaneously, then the observations support the redshift-evolutionary relations at more than 3σ. The Akaike and Bayes information criteria indicate that there is strong evidence against the standard relation and mild evidence against the redshift-evolutionary relation constructed by considering a redshift-dependent correction to the luminosities of quasars. This suggests that the redshift-evolutionary L X–L UV relation of quasars constructed from copula is favored by the observations.
Quantum Polynomial Hierarchies: Karp-Lipton, error reduction, and lower bounds

The Polynomial-Time Hierarchy ($\mathsf{PH}$) is a staple of classical complexity theory, with applications spanning randomized computation to circuit lower bounds to ''quantum advantage'' analyses for near-term quantum computers. Quantumly, however, despite the fact that at least \emph{four} definitions of quantum $\mathsf{PH}$ exist, it has been challenging to prove analogues for these of even basic facts from $\mathsf{PH}$. This work studies three quantum-verifier based generalizations of $\mathsf{PH}$, two of which are from [Gharibian, Santha, Sikora, Sundaram, Yirka, 2022] and use classical strings ($\mathsf{QCPH}$) and quantum mixed states ($\mathsf{QPH}$) as proofs, and one of which is new to this work, utilizing quantum pure states ($\mathsf{pureQPH}$) as proofs. We first resolve several open problems from [GSSSY22], including a collapse theorem and a Karp-Lipton theorem for $\mathsf{QCPH}$. Then, for our new class $\mathsf{pureQPH}$, we show one-sided error reduction for $\mathsf{pureQPH}$, as well as the first bounds relating these quantum variants of $\mathsf{PH}$, namely $\mathsf{QCPH}\subseteq \mathsf{pureQPH} \subseteq \mathsf{EXP}^{\mathsf{PP}}$.
On internal categories and crossed objects in the category of monoids

It is a well-known fact that the category $\mathsf{Cat}(\mathbf{C})$ of internal categories in a category $\mathbf{C}$ has a description in terms of crossed modules, when $\mathbf{C}=\mathbf{Gr}$ is the category of groups. The proof of this result heavily uses the fact that any split epimorphism decomposes as a semi-direct product. An equivalent statement does not hold in the category $\mathbf{Mon}$ of monoids. In a previous work on quadratic algebras, I constructed an internal category in the category of monoids, see Section 6. Based on this construction, this paper will introduce the notion of a crossed semi-bimodule and show that it gives rise to an object in $\mathsf{Cat}(\mathbf{Mon})$. I will also relate this new notion to the crossed semi-modules introduced earlier by A. Patchkoria.
Non-associative Frobenius algebras of type $E_7$

Recently, Maurice Chayet and Skip Garibaldi introduced a class of commutative non-associative algebras. In previous work, we gave an explicit description of these algebras for groups of type $G_2,F_4$ and certain forms of $E_6$ in terms of octonion and Albert algebras. In this paper, we extend this further by dealing with $E_7$ in terms of generalised Freudenthal triple systems.
Pathwise-random trees and models of second-order arithmetic *

A tree is pathwise-random if all of its paths are algorithmically random. We show that: (a) an algorithmically random real computes a perfect pathwise-random tree if and only if it computes the halting problem; it follows that the class of perfect pathwise-random trees is null, with respect to any computable measure; (b) there exists a positive-measure pathwise-random tree which does not compute any complete extension of Peano arithmetic; and (c) there exists a perfect pathwise-random tree which does not compute any tree of positive measure and ﬁnite randomness deﬁciency. We then obtain models of second-order arithmetic that separate compactness principles below weak K¨onigs lemma, answering questions by Chong et al . (2019).
Ultrasound Changes in Salivary Glands after Radioactive Iodine Treatment in Benign Diseases and Differentiated Cancer of Thyroid Glands in Consideration of Dose and Time Dependency

Abstract Purpose To assess ultrasound (US) features observed in salivary glands after radioactive iodine treatment (RAIT) in relation to the dose and time interval after RAIT. Materials and Methods A retrospective analysis of US findings regarding the salivary glands of patients presenting after RAIT due to thyroid cancer (Group 1, n=99) or benign thyroid diseases (Group 2, n=25). The control group consisted of randomly selected patients (no RAIT, Group 3, n=100). Groups were compared regarding RAIT dose, symptoms, and US findings (duct dilation, hypoechoic/heterogeneous parenchyma, parenchymal loss). The association of the presence of US parameters after RAIT with various dose levels (2000–15000MBq) and time intervals (24, 60, 120 months) and the differences between the groups was evaluated. Results Significant differences between US parameters were noted when comparing Group 1 with Group 2 or 3. Nothing of relevant significance was noted when Groups 2 and 3 were compared. US features indicating a slight or moderate sialadenitis showed the most significant associations with doses <4000MBq and time intervals <24 months after RAIT. US changes indicating a serious sialadenitis or even gland atrophy showed the most significant association at doses between >7000–9000MBq and when US was performed >60 months after RAIT. Conclusion Our results point to a dose and time dependency of pathologic US findings in RAIT-induced effects on the major salivary glands. Based on the US findings, a better estimation of the current impact of RAIT on the salivary glands and the further prognosis appears possible.
Escherichia coli CadB is capable of promiscuously transporting muropeptides and contributing to peptidoglycan recycling

ABSTRACT The bacterial peptidoglycan (PG) cell wall is remodeled during growth and division, releasing fragments called muropeptides. Muropeptides can be internalized and reused in a process called PG recycling. Escherichia coli is highly devoted to recycling muropeptides and is known to have at least two transporters, AmpG and OppBCDF, that import them into the cytoplasm. While studying mutants lacking AmpG, we unintentionally isolated mutations that led to the altered expression of a third transporter, CadB. CadB is normally upregulated under acidic pH conditions and is an antiporter for lysine and cadaverine. Here, we explored if CadB was altering PG recycling to assist in the absence of AmpG. Surprisingly, CadB overexpression was able to restore PG recycling when both AmpG and OppBCDF were absent. CadB was found to import freed PG peptides, a subpopulation of muropeptides, through a promiscuous activity. Altogether, our data support that CadB is a third transporter capable of contributing to PG recycling. IMPORTANCE Bacteria produce a rigid mesh cell wall. During growth, the cell wall is remodeled, which releases cell wall fragments. If released into the extracellular environment, cell wall fragments can trigger inflammation by the immune system of a host. Gastrointestinal bacteria, like Escherichia coli, have dedicated pathways to recycle almost all cell wall fragments they produce. E. coli contains two known recycling transporters, AmpG and Opp, that we previously showed are optimized for growth in different environments. Here, we identify that a third transporter, CadB, can also contribute to cell wall recycling. This work expands our understanding of cell wall recycling and highlights the dedication of organisms like E. coli to ensure high recycling in multiple growth environments. Bacteria produce a rigid mesh cell wall. During growth, the cell wall is remodeled, which releases cell wall fragments. If released into the extracellular environment, cell wall fragments can trigger inflammation by the immune system of a host. Gastrointestinal bacteria, like Escherichia coli, have dedicated pathways to recycle almost all cell wall fragments they produce. E. coli contains two known recycling transporters, AmpG and Opp, that we previously showed are optimized for growth in different environments. Here, we identify that a third transporter, CadB, can also contribute to cell wall recycling. This work expands our understanding of cell wall recycling and highlights the dedication of organisms like E. coli to ensure high recycling in multiple growth environments.
Virology—the path forward

ABSTRACT In the United States (US), biosafety and biosecurity oversight of research on viruses is being reappraised. Safety in virology research is paramount and oversight frameworks should be reviewed periodically. Changes should be made with care, however, to avoid impeding science that is essential for rapidly reducing and responding to pandemic threats as well as addressing more common challenges caused by infectious diseases. Decades of research uniquely positioned the US to be able to respond to the COVID-19 crisis with astounding speed, delivering life-saving vaccines within a year of identifying the virus. We should embolden and empower this strength, which is a vital part of protecting the health, economy, and security of US citizens. Herein, we offer our perspectives on priorities for revised rules governing virology research in the US.
GROWTH AND DEVELOPMENT PATTERN OF BARLEY VARIETIES AS INFLUENCED BY DATE OF SOWING AND NITROGEN LEVELS

A field experiment was conducted at research farm, Department of Agronomy, Punjab Agricultural University, Ludhiana during rabi season 2012-13. A set of 27 treatment combinations including three dates of sowing viz. 16 th October (D 1 ), 15 th November (D 2 ) and 15 th December (D 3 ) in main plot, three varieties; PL 172, PL 426 and PL 807 in sub plot and three nitrogen levels; 75, 100 and 125 per cent of recommended dose of nitrogen (RDN 62.5 kg/ha) in sub-sub plot were evaluated. Treatments were replicated thrice as per split-split plot design. Crop sown on D1 attained significantly higher (plant height, dry matter accumulation at harvest). Crop sown on D3 attained significantly higher crop growth rate (CGR), relative growth rate (RGR) and net assimilation rate (NAR) at harvest. Varieties PL 807 performed better than all other varieties for growth parameters (plant height and dry matter accumulation) and growth pattern (CGR, RGR NAR). Application of 125 per cent of RDN resulted in improvement in most of the growth parameters (plant height, dry matter production and LAI) and growth pattern (CGR, RGR NAR). Crop sown on 16 th October took more number of days from sowing to heading than other
GIMNet: Assembly Plan Generation from Graphical Instruction Manual

Graphical instruction manuals are commonly employed for assembling various products, particularly furniture. However, these manuals often lack detailed written task instructions and rely primarily on a series of illustrated images to convey the assembly process. Consequently, in many cases, it becomes essential to comprehend the instructions solely based on the sequence of these illustrated images. In this work, we propose a deep neural network, GIMNet, for automatically generating assembly plans from graphical instruction manuals. We develop a hierarchical Swin Transformer model to predict actions and segment different parts in the instruction manual. The predicted actions and extracted information of corresponding parts is used to construct an assembly graph. We apply a functional object-oriented network to model the assembly plan. We evaluate our network on the IKEA-Manual dataset consisting of 102 IKEA objects paired with assembly manuals. We perform quantitative analysis to evaluate the efficacy of our approach. We compare our approach with the existing baseline and significantly outperform on all the metrics.