Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxidant related biochemical traits are significant indices in triticale grain yield under drought stress condition

Abstract Two separate experiments, one as drought stress conditions and the other one as the normal irrigation conditions based on randomized complete block design with three replications were implemented to examine 20 triticale (Triticoseclae wittamack X) genotypes of which 19 genotypes have recently been generated by crossing different parent lines and the other one was Javaniloo, a common cultivar. Based on the results of different statistical techniques and comparing relationships among traits for normal irrigation and stress condition, enzymatic antioxidant could be used as criteria for screening tolerant genotypes of triticale. On the other hand, it is pointed out that superoxide dismutase are the most important criteria to achieve higher tolerant genotypes through indirect selection. Furthermore, hydrogen peroxide and malondialdehyde content having high relationship with rain yield are also the different possible criteria for screening triticale genotypes for water stress conditions in which genotypes with lower content of these traits could be screened.
Evolutionary Implications of the RNA N6-Methyladenosine Methylome in Plants

Abstract Epigenetic modifications play important roles in genome evolution and innovation. However, most analyses have focused on the evolutionary role of DNA modifications, and little is understood about the influence of posttranscriptional RNA modifications on genome evolution. To explore the evolutionary significance of RNA modifications, we generated transcriptome-wide profiles of N6-methyladenosine (m6A), the most prevalent internal modification of mRNA, for 13 representative plant species spanning over half a billion years of evolution. These data reveal the evolutionary conservation and divergence of m6A methylomes in plants, uncover the preference of m6A modifications on ancient orthologous genes, and demonstrate less m6A divergence between orthologous gene pairs with earlier evolutionary origins. Further investigation revealed that the evolutionary divergence of m6A modifications is related to sequence variation between homologs from whole-genome duplication and gene family expansion from local-genome duplication. Unexpectedly, a significant negative correlation was found between the retention ratio of m6A modifications and the number of family members. Moreover, the divergence of m6A modifications is accompanied by variation in the expression level and translation efficiency of duplicated genes from whole- and local-genome duplication. Our work reveals new insights into evolutionary patterns of m6A methylomes in plant species and their implications, and provides a resource of plant m6A profiles for further studies of m6A regulation and function in an evolutionary context.
Sensitivity to the communicative partner's attentional state: A developmental study on mother–infant dyads in wild chimpanzees (Pan troglodytes schweinfurthii)

Gestural communication permeates all domains of chimpanzees' social life and is intentional in use. However, we still have only limited information on how young apes develop the sociocognitive skills needed for intentional communication. In this cross‐sectional study, we document the development of behavioral adjustment to the recipient's visual attention—considered a hallmark of intentional communication—in wild immature chimpanzees' gestural communication. We studied 11 immature chimpanzees (Pan troglodytes schweinfurthii): three infants, four juveniles, and four adolescents gesturing towards their mother. We quantified silent‐visual, audible, and contact gestures indexed to maternal visual attention and inattention. We investigated unimodal adjustment, defined by the capacity of young chimpanzees to deploy fewer silent‐visual signals when their mothers did not show full visual attention towards them as compared with when they did. We then examined cross‐modal adjustment, defined as the capacity of chimpanzees to deploy more audible‐or‐contact gestures than silent‐visual gestures in the condition where their mothers did not show full visual attention as compared to when they did. Our results show a gradual decline in the use of silent‐visual gestures when the mother is not visually attentive with increasing age. The absence of silent‐visual gesture production toward a visually inattentive recipient (complete unimodal adjustment) was not fully in place until adolescence. Immature chimpanzees used more audible‐or‐contact gestures than silent‐visual ones when their mothers did not show visual attention and vice‐versa when they did. This cross‐modal adjustment was expressed in juveniles and adolescents but not in infants. Overall, this study shows that infant chimpanzees were limited in their sensitivity to maternal attention when gesturing, whereas adolescent chimpanzees adjusted their communication appropriately. Juveniles present an intermediate pattern with cross‐modal adjustment preceding unimodal adjustment and with variability in the age of onset.
Different morphologies of strontium carbonate in water/ethylene glycol and their photocatalytic activity

Abstract Three-dimensional SrCO3 flower-like nanostructures self-assembly from nanocrystals have been successfully synthesized by solvothermal method at the temperature of 120 °C. In the presence of anionic surfactant sodium dodecyl sulfate, SrCO3 is crystallized in different alcohols and aqueous solutions. X-ray diffraction (XRD) pattern reveals that needles and flakes flower-like SrCO3 are orthorhombic phases. The product also has a high specific surface area of about 60.52 m2 g−1. Based on the nucleation and self-assembly process, a possible formation mechanism is proposed. The effects of SDS and alcohol on the product are also studied. Results suggest that –OH groups of alcohol play a key role in morphological controlling of SrCO3. Moreover, the FCNS exhibits excellent photocatalytic activity for the photodegradation of RhB under visible light irradiation and the degradation rate approached to 90.32%.
Vector-quantized Image Modeling with Improved VQGAN

Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks. Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at \(256\times256\) resolution, we achieve Inception Score (IS) of 175.1 and Fr'echet Inception Distance (FID) of 4.17, a dramatic improvement over the vanilla VQGAN, which obtains 70.6 and 17.04 for IS and FID, respectively. Based on ViT-VQGAN and unsupervised pretraining, we further evaluate the pretrained Transformer by averaging intermediate features, similar to Image GPT (iGPT). This ImageNet-pretrained VIM-L significantly beats iGPT-L on linear-probe accuracy from 60.3% to 73.2% for a similar model size. VIM-L also outperforms iGPT-XL which is trained with extra web image data and larger model size.
Dynamic Process Isolation

In the quest for efficiency and performance, edge-computing providers eliminate isolation boundaries between tenants, such as strict process isolation, and instead let them compute in a more lightweight multi-threaded single-process design. Edge-computing providers support a high number of tenants per machine to reduce the physical distance to customers without requiring a large number of machines. Isolation is provided by sandboxing mechanisms, e.g., tenants can only run sandboxed V8 JavaScript code. While this is as secure as a sandbox for software vulnerabilities, microarchitectural attacks can bypass these sandboxes. In this paper, we show that it is possible to mount a Spectre attack on such a restricted environment, leaking secrets from co-located tenants. Cloudflare Workers is one of the top three edge-computing solutions and handles millions of HTTP requests per second worldwide across tens of thousands of web sites every day. We demonstrate a remote Spectre attack using amplification techniques in combination with a remote timing server, which is capable of leaking 120 bit/h. This motivates our main contribution, Dynamic Process Isolation, a process isolation mechanism that only isolates suspicious worker scripts following a detection mechanism. In the worst case of only false positives, Dynamic Process Isolation simply degrades to process isolation. Our proof-of-concept implementation augments a real-world cloud infrastructure framework, Cloudflare Workers, which is used in production at large scale. With a false-positive rate of only 0.61%, we demonstrate that our solution vastly outperforms strict process isolation in terms of performance. In our security evaluation, we show that Dynamic Process Isolation statistically provides the same security guarantees as strict process isolation, fully mitigating Spectre attacks between multiple tenants.
Walking into the complex plane to 'order' better time integrators

Most numerical methods for time integration use real time steps. Complex time steps provide an additional degree of freedom, as we can select the magnitude of the step in both the real and imaginary directions. By time stepping along speciﬁc paths in the complex plane, integrators can gain higher orders of accuracy or achieve expanded stability regions. We show how to derive these paths for explicit and implicit methods, discuss computational costs and storage beneﬁts, and demonstrate clear advantages for complex-valued systems like the Schrodinger equation. We also explore how complex time stepping also allows us to break the Runge-Kutta order barrier, enabling 5th order accuracy using only ﬁve function evaluations for real-valued diﬀerential equations.
Multi-parameter analysis of the obstacle scattering problem

We consider the acoustic field scattered by a bounded impenetrable obstacle and we study its dependence upon a certain set of parameters. As usual, the problem is modeled by an exterior Dirichlet problem for the Helmholtz equation Δu + k 2 u = 0. We show that the solution u and its far field pattern u ∞ depend real analytically on the shape of the obstacle, the wave number k, and the Dirichlet datum. We also prove a similar result for the corresponding Dirichlet-to-Neumann map.
Universal Paralinguistic Speech Representations Using self-Supervised Conformers

Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishing real from synthetic speech. In this work, we introduce a new state-of-the-art paralinguistic representation derived from large-scale, fully self-supervised training of a 600M+ parameter Conformer-based architecture. We benchmark on a diverse set of speech tasks and demonstrate that simple linear classifiers trained on top of our time-averaged representation outperform nearly all previous results, in some cases by large margins. Our analyses of context-window size demonstrate that, surprisingly, 2 second context-windows achieve 96% the performance of the Conformers that use the full long-term context on 7 out of 9 tasks. Furthermore, while the best per-task representations are extracted internally in the network, stable performance across several layers allows a single universal representation to reach near optimal performance on all tasks.
Streaming on-Device Detection of Device Directed Speech from Voice and Touch-Based Invocation

When interacting with smart devices such as mobile-phones or wearables, the user typically invokes a virtual assistant (VA) by saying a keyword or by pressing a button on the device. However, in many cases, the VA can accidentally be invoked by the keyword-like speech or accidental button press, which may have implications on user experience and privacy. To this end, we propose an acoustic false-trigger-mitigation (FTM) approach for on-device device-directed speech detection that simultaneously handles the voice-trigger and touch-based invocation. To facilitate the model deployment on-device, we introduce a new streaming decision layer, derived using the notion of temporal convolutional networks (TCN) [1], known for their computational efficiency. To the best of our knowledge, this is the first approach that can detect device-directed speech from more than one invocation type in a streaming fashion. We compare this approach with streaming alternatives based on vanilla Average layer, and canonical LSTMs, and show: (i) that all the models show only a small degradation in accuracy compared with the invocation-specific models, and (ii) that the newly introduced streaming TCN consistently performs better or comparable with the alternatives, while mitigating device-undirected speech faster in time, and with (relative) reduction in runtime peak-memory over the LSTM-based approach of 33% vs. 7%, when compared to a non-streaming counterpart.
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits

Embedding learning has found widespread applications in recommendation systems and natural language modeling, among other domains. To learn quality embeddings efficiently, adaptive learning rate algorithms have demonstrated superior empirical performance over SGD, largely accredited to their token-dependent learning rate. However, the underlying mechanism for the efficiency of token-dependent learning rate remains underexplored. We show that incorporating frequency information of tokens in the embedding learning problems leads to provably efficient algorithms, and demonstrate that common adaptive algorithms implicitly exploit the frequency information to a large extent. Specifically, we propose (Counter-based) Frequency-aware Stochastic Gradient Descent, which applies a frequency-dependent learning rate for each token, and exhibits provable speed-up compared to SGD when the token distribution is imbalanced. Empirically, we show the proposed algorithms are able to improve or match adaptive algorithms on benchmark recommendation tasks and a large-scale industrial recommendation system, closing the performance gap between SGD and adaptive algorithms. Our results are the first to show token-dependent learning rate provably improves convergence for non-convex embedding learning problems.
Fat-Shattering Dimension of $k$-fold Aggregations

We provide estimates on the fat-shattering dimension of aggregation rules of real-valued function classes. The latter consists of all ways of choosing $k$ functions, one from each of the $k$ classes, and computing a pointwise function of them, such as the median, mean, and maximum. The bound is stated in terms of the fat-shattering dimensions of the component classes. For linear and affine function classes, we provide a considerably sharper upper bound and a matching lower bound, achieving, in particular, an optimal dependence on $k$. Along the way, we improve several known results in addition to pointing out and correcting a number of erroneous claims in the literature.
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning

Due to the limited and even imbalanced data, semi-supervised semantic segmentation tends to have poor performance on some certain categories, e.g., tailed categories in Cityscapes dataset which exhibits a long-tailed label distribution. Existing approaches almost all neglect this problem, and treat categories equally. Some popular approaches such as consistency regularization or pseudo-labeling may even harm the learning of under-performing categories, that the predictions or pseudo labels of these categories could be too inaccurate to guide the learning on the unlabeled data. In this paper, we look into this problem, and propose a novel framework for semi-supervised semantic segmentation, named adaptive equalization learning (AEL). AEL adaptively balances the training of well and badly performed categories, with a confidence bank to dynamically track category-wise performance during training. The confidence bank is leveraged as an indicator to tilt training towards under-performing categories, instantiated in three strategies: 1) adaptive Copy-Paste and CutMix data augmentation approaches which give more chance for under-performing categories to be copied or cut; 2) an adaptive data sampling approach to encourage pixels from under-performing category to be sampled; 3) a simple yet effective re-weighting method to alleviate the training noise raised by pseudo-labeling. Experimentally, AEL outperforms the state-of-the-art methods by a large margin on the Cityscapes and Pascal VOC benchmarks under various data partition protocols. Code is available at https://github.com/hzhupku/SemiSeg-AEL
Point Cloud Augmentation with Weighted Local Transformations

Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature. In this paper, we propose a simple and effective augmentation method called PointWOLF for point cloud augmentation. The proposed method produces smoothly varying non-rigid deformations by locally weighted transformations centered at multiple anchor points. The smooth deformations allow diverse and realistic augmentations. Furthermore, in order to minimize the manual efforts to search the optimal hyperparameters for augmentation, we present AugTune, which generates augmented samples of desired difficulties producing targeted confidence scores. Our experiments show our framework consistently improves the performance for both shape classification and part segmentation tasks. Particularly, with PointNet++, PointWOLF achieves the state-of-the-art 89.7 accuracy on shape classification with the real-world ScanObjectNN dataset. The code is available at https://github.com/mlvlab/PointWOLF.
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference

Performing event and entity coreference resolution across documents vastly increases the number of candidate mentions, making it intractable to do the full n^2 pairwise comparisons. Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference, common in many applications. As a result cross-document coreference algorithms are rarely applied to downstream tasks. We draw on an insight from discourse coherence theory: potential coreferences are constrained by the reader’s discourse focus. We model the entities/events in a reader’s focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters. We then use these neighborhoods to sample only hard negatives to train a fine-grained classifier on mention pairs and their local discourse features. Our approach achieves state-of-the-art results for both events and entities on the ECB+, Gun Violence, Football Coreference, and Cross-Domain Cross-Document Coreference corpora. Furthermore, training on multiple corpora improves average performance across all datasets by 17.2 F1 points, leading to a robust coreference resolution model that is now feasible to apply to downstream tasks.
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks. However, CLIP is quite data-hungry and requires 400M image-text pairs for pre-training, thereby restricting its adoption. This work proposes a novel training paradigm, Data efficient CLIP (DeCLIP), to alleviate this limitation. We demonstrate that by carefully utilizing the widespread supervision among the image-text pairs, our De-CLIP can learn generic visual features more efficiently. Instead of using the single image-text contrastive supervision, we fully exploit data potential through the use of (1) self-supervision within each modality; (2) multi-view supervision across modalities; (3) nearest-neighbor supervision from other similar pairs. Benefiting from intrinsic supervision, our DeCLIP-ResNet50 can achieve 60.4% zero-shot top1 accuracy on ImageNet, which is 0.8% above the CLIP-ResNet50 while using 7.1 x fewer data. Our DeCLIP-ResNet50 outperforms its counterpart in 8 out of 11 visual datasets when transferred to downstream tasks. Moreover, Scaling up the model and computing also works well in our framework.Our code, dataset and models are released at: https://github.com/Sense-GVT/DeCLIP
Convergence of Random Reshuffling Under The Kurdyka-Łojasiewicz Inequality

We study the random reshuffling (RR) method for smooth nonconvex optimization problems with a finite-sum structure. Though this method is widely utilized in practice such as the training of neural networks, its convergence behavior is only understood in several limited settings. In this paper, under the well-known Kurdyka-Lojasiewicz (KL) inequality, we establish strong limit-point convergence results for RR with appropriate diminishing step sizes, namely, the whole sequence of iterates generated by RR is convergent and converges to a single stationary point in an almost sure sense. In addition, we derive the corresponding rate of convergence, depending on the KL exponent and the suitably selected diminishing step sizes. When the KL exponent lies in $[0,\frac12]$, the convergence is at a rate of $\mathcal{O}(t^{-1})$ with $t$ counting the iteration number. When the KL exponent belongs to $(\frac12,1)$, our derived convergence rate is of the form $\mathcal{O}(t^{-q})$ with $q\in (0,1)$ depending on the KL exponent. The standard KL inequality-based convergence analysis framework only applies to algorithms with a certain descent property. We conduct a novel convergence analysis for the non-descent RR method with diminishing step sizes based on the KL inequality, which generalizes the standard KL framework. We summarize our main steps and core ideas in an informal analysis framework, which is of independent interest. As a direct application of this framework, we also establish similar strong limit-point convergence results for the reshuffled proximal point method.
Connectedness Principle for 3-Folds in Characteristic p>5

A conjecture, known as the Shokurov-Koll\'ar connectedness principle, predicts the following. Let $(X,B)$ be a pair, and let $f \colon X \rightarrow S$ be a contraction with $-(K_X + B)$ nef over $S$; then, for any point $s \in S$, the intersection $f^{-1} (s) \cap \mathrm{Nklt}(X,B)$ has at most two connected components, where $\mathrm{Nklt}(X,B)$ denotes the non-klt locus of $(X,B)$. This conjecture has been extensively studied in characteristic zero, and it has been recently settled in that context. In this work, we consider this conjecture in the setup of positive characteristic algebraic geometry. We prove this conjecture holds for threefolds in characteristic $p>5$, and, under the same assumptions, we characterize the cases in which $\mathrm{Nklt}(X,B)$ fails to be connected.