Miracle-dz/UniFAR-FTUs huggingface dataset:
Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding

To bridge the gap between supervised semantic segmentation and real-world applications that acquires one model to recognize arbitrary new concepts, recent zero-shot segmentation attracts a lot of attention by exploring the relationships between unseen and seen object categories, yet requiring large amounts of densely-annotated data with diverse base classes. In this paper, we propose a new open-world semantic segmentation pipeline that makes the first attempt to learn to segment semantic objects of various open-world categories without any efforts on dense annotations, by purely exploiting the image-caption data that naturally exist on the Internet. Our method, Vision-language-driven Semantic Segmentation (ViL-Seg), employs an image and a text encoder to generate visual and text embeddings for the image-caption data, with two core components that endow its segmentation ability: First, the image encoder is jointly trained with a vision-based contrasting and a cross-modal contrasting, which encourage the visual embeddings to preserve both fine-grained semantics and high-level category information that are crucial for the segmentation task. Furthermore, an online clustering head is devised over the image encoder, which allows to dynamically segment the visual embeddings into distinct semantic groups such that they can be classified by comparing with various text embeddings to complete our segmentation pipeline. Experiments show that without using any data with dense annotations, our method can directly segment objects of arbitrary categories, outperforming zero-shot segmentation methods that require data labeling on three benchmark datasets.
Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update

Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize client-variance reduction techniques to facilitate server aggregation and global adaptive update techniques to accelerate learning. Our experiments on the Cifar-10 classification task show that ComFed can improve state-of-the-art algorithms dedicated to Non-IID data.
FLOWGEN: Fast and slow graph generation

Machine learning systems typically apply the same model to both easy and tough cases. This is in stark contrast with humans, who tend to evoke either fast (instinctive) or slow (analytical) thinking depending on the problem difficulty, a property called the dual-process theory of mind. We present FLOWGEN, a graph-generation model inspired by the dual-process theory of mind that generates large graphs incrementally. Depending on the difficulty of completing the graph at the current step, graph generation is routed to either a fast (weaker) or a slow (stronger) model. These modules have identical architectures, but vary in the number of parameters and consequently differ in generative power. Experiments on real-world graphs show that ours can successfully generate graphs similar to those generated by a single large model, while being up to 2x faster.
Optimizing Data Collection in Deep Reinforcement Learning

Reinforcement learning (RL) workloads take a notoriously long time to train due to the large number of samples collected at run-time from simulators. Unfortunately, cluster scale-up approaches remain expensive, and commonly used CPU implementations of simulators induce high overhead when switching back and forth between GPU computations. We explore two optimizations that increase RL data collection efficiency by increasing GPU utilization: (1) GPU vectorization: parallelizing simulation on the GPU for increased hardware parallelism, and (2) simulator kernel fusion: fusing multiple simulation steps to run in a single GPU kernel launch to reduce global memory bandwidth requirements. We find that GPU vectorization can achieve up to $1024\times$ speedup over commonly used CPU simulators. We profile the performance of different implementations and show that for a simple simulator, ML compiler implementations (XLA) of GPU vectorization outperform a DNN framework (PyTorch) by $13.4\times$ by reducing CPU overhead from repeated Python to DL backend API calls. We show that simulator kernel fusion speedups with a simple simulator are $11.3\times$ and increase by up to $1024\times$ as simulator complexity increases in terms of memory bandwidth requirements. We show that the speedups from simulator kernel fusion are orthogonal and combinable with GPU vectorization, leading to a multiplicative speedup.
Aspectos epidemiológicos da caprinocultura cearense

The development of the goat raising in the Northeastern region of Brazil is hardly affected by several factors, among them, the high incidence of illness. The aim of this study was to assess the adopted sanitary management and the healthy problems according to Cearas goat breeders. The work was carried out in many regions of dairy and native goat raising. There were selected 127 properties where a questionnaire was applied. Mean percentage of animals mortality was 22.8 and 4.6% for young and adult, respectively. Reported clinical signs were: anemia and dewlap edema (81.9% of the properties), diarrhea (78.7%), abortion (75.6%), foot rot (67.7%), lymphadenitis (66.9%), external parasites (63.8%), mastitis (51.2%), pneumonia (44.9%), goat pox (35.4%), keratoconjunctivitis (29.1%), nervous disorders (26.8%), fetal malformation (15%), cryptorchidism (11%), uterus/vagina prolapsed (11%), arthritis (8.7%), foot and mouth disease (2.4%) and rabies (0.8%). It could be concluded that the goats sanitary management of those properties is precarious, independently either the exploitation way or the raising regimen; the animals mortality, especially the youngest ones, is considered high; and even in dairy properties there is a lack of rigid worry concerning about hygiene and milk quality.
Detection of Poisoning Attacks with Anomaly Detection in Federated Learning for Healthcare Applications: A Machine Learning Approach

—The application of Federated Learning (FL) is steadily increasing, especially in privacy-aware applications, such as healthcare. However, its applications have been limited by security concerns due to various adversarial attacks, such as poisoning attacks (model and data poisoning). Such attacks attempt to poison the local models and data to manipulate the global models in order to obtain undue beneﬁts and malicious use. Traditional methods of data auditing to mitigate poisoning attacks ﬁnd their limited applications in FL because the edge devices never share their raw data directly due to privacy concerns, and are globally distributed with no insight into their training data. Thereafter, it is challenging to develop appropriate strategies to address such attacks and minimize their impact on the global model in federated learning. In order to address such challenges in FL, we proposed a novel framework to detect poisoning attacks using deep neural networks and support vector machines, in the form of anomaly without acquiring any direct access or information about the underlying training data of local edge devices. We illustrate and evaluate the proposed framework using different state of art poisoning attacks for two different healthcare applications: Electrocardiograph classiﬁcation and human activity recognition. Our experimental analysis shows that the proposed method can efﬁciently detect poisoning attacks and can remove the identiﬁed poisoned updated from the global aggregation. Thereafter can increase the performance of the federated global.
A Scintillation Arc Survey of 22 Pulsars with Low to Moderate Dispersion Measures

Context. By providing information about the location of scattering material along the line of sight (LoS) to pulsars, scintillation arcs are a powerful tool for exploring the distribution of ionized material in the interstellar medium (ISM). Here, we present observations that probe the ionized ISM on scales of ∼0.001–30 au. Aims. We have surveyed pulsars for scintillation arcs in a relatively unbiased sample with DM < 100 pc cm−3. We present multifrequency observations of 22 low to moderate DM pulsars. Many of the 54 observations were also observed at another frequency within a few days. Methods. For all observations, we present dynamic spectra, autocorrelation functions, and secondary spectra. We analyze these data products to obtain scintillation bandwidths, pulse broadening times, and arc curvatures. Results. We detect definite or probable scintillation arcs in 19 of the 22 pulsars and 34 of the 54 observations, showing that scintillation arcs are a prevalent phenomenon. The arcs are better defined in low DM pulsars. We show that well-defined arcs do not directly imply anisotropy of scattering. Only the presence of reverse arclets and a deep valley along the delay axis, which occurs in about 20% of the pulsars in the sample, indicates substantial anisotropy of scattering. Conclusions. The survey demonstrates substantial patchiness of the ionized ISM on both astronomical-unit-size scales transverse to the LoS and on ∼100 pc scales along it. We see little evidence for distributed scattering along most lines of sight in the survey.
Collaborative Best Arm Identification with Limited Communication on Non-IID Data

In this paper, we study the tradeoffs between time-speedup and the number of communication rounds of the learning process in the collaborative learning model on non-IID data, where multiple agents inter-act with possibly different environments and they want to learn an objective in the aggregated environ-ment. We use a basic problem in bandit theory called best arm identiﬁcation in multi-armed bandits as a vehicle to deliver the following conceptual message: we show that implicit of when with complex hard algorithms.
Real-time End-to-End Video Text Spotter with Contrastive Representation Learning

Video text spotting(VTS) is the task that requires simultaneously detecting, tracking and recognizing text in the video. Existing video text spotting methods typically develop sophisticated pipelines and multiple models, which is not friend for real-time applications. Here we propose a real-time end-to-end video text spotter with Contrastive Representation learning (CoText). Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e.g., text detection, tracking, recognition) in a real-time end-to-end trainable framework. 2) With contrastive learning, CoText models long-range dependencies and learning temporal information across multiple frames. 3) A simple, lightweight architecture is designed for effective and accurate performance, including GPU-parallel detection post-processing, CTC-based recognition head with Masked RoI. Extensive experiments show the superiority of our method. Especially, CoText achieves an video text spotting IDF1 of 72.0% at 41.0 FPS on ICDAR2015video, with 10.5% and 32.0 FPS improvement the previous best method. The code can be found at github.com/weijiawu/CoText.
Kernel-based Federated Learning with Personalization

We consider federated learning with personalization, where in addition to a global objective, each client is also interested in maximizing a personalized local objective. We consider this problem under a general continuous action space setting where the objective functions belong to a reproducing kernel Hilbert space. We propose algorithms based on surrogate Gaussian process (GP) models that achieve the optimal regret order (up to polylogarithmic factors). Furthermore, we show that the sparse approximations of the GP models signiﬁcantly reduce the communication cost across clients
Learning inducing points and uncertainty on molecular data

Uncertainty control and scalability to large datasets are the two main issues for the deployment of Gaussian process (GP) models within the autonomous machine learning-based prediction pipelines in material science and chemistry. One way to address both of these issues is by introducing the latent inducing point variables and choosing the right approximation for the marginal log-likelihood objective. Here, we empirically show that variational learning of the inducing points in a molecular descriptor space improves the prediction of energies and atomic forces on two molecular dynamics datasets. First, we show that variational GPs can learn to represent the configurations of the molecules of different types that were not present within the initialization set of configurations. We provide a comparison of alternative log-likelihood training objectives and variational distributions. Among several evaluated approximate marginal log-likelihood objectives, we show that predictive log-likelihood provides excellent uncertainty estimates at the slight expense of predictive quality. Furthermore, we extend our study to a large molecular crystal system, showing that variational GP models perform well for predicting atomic forces by efficiently learning a sparse representation of the dataset.
EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders

There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states as well as generate synthetic EEG data that are participant-and/or emotion-specific. We propose a conditional variational autoencoder based framework, EEG2Vec, to learn generative-discriminative representations from EEG data. Experimental results on affective EEG recording datasets demonstrate that our model is suitable for unsupervised EEG modeling, classification of three distinct emotion categories (positive, neutral, negative) based on the latent representation achieves a robust performance of 68.49%, and generated synthetic EEG sequences resemble real EEG data inputs to particularly reconstruct low-frequency signal components. Our work advances areas where affective EEG representations can be useful in e.g., generating artificial (labeled) training data or alleviating manual feature extraction, and provide efficiency for memory constrained edge computing applications.
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation

Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS). Most of the previous methods directly extract and fuse the motion and appearance features for segmenting target objects in the UVOS setting. However, optical flow is intrinsically an instantaneous velocity of all pixels among consecutive frames, thus making the motion features not aligned well with the primary objects among the corresponding frames. To solve the above challenge, we propose a concise, practical, and efficient architecture for appearance and motion feature alignment, dubbed hierarchical feature alignment network (HFAN). Specifically, the key merits in HFAN are the sequential Feature AlignMent (FAM) module and the Feature AdaptaTion (FAT) module, which are leveraged for processing the appearance and motion features hierarchically. FAM is capable of aligning both appearance and motion features with the primary object semantic representations, respectively. Further, FAT is explicitly designed for the adaptive fusion of appearance and motion features to achieve a desirable trade-off between cross-modal features. Extensive experiments demonstrate the effectiveness of the proposed HFAN, which reaches a new state-of-the-art performance on DAVIS-16, achieving 88.7 $\mathcal{J}\&\mathcal{F}$ Mean, i.e., a relative improvement of 3.5% over the best published result.
Asymptotic stability of depths of localizations of modules

Let R be a commutative noetherian ring, I an ideal of R , and M a ﬁnitely generated R - module. The asymptotic behavior of the quotient modules M/I n M of M is an actively studied subject in commutative algebra. The main result of this paper asserts that the depth of the localization of M/I n M at any prime ideal of R is stable for large integers n that do not depend on the prime ideal, if the module M or M/I n M is Cohen–Macaulay for some n > 0, or the ring R is one of the following: a homomorphic image of a Cohen–Macaulay ring, a semi-local ring, an excellent ring, a quasi-excellent and catenary ring, and an acceptable ring.
Nutational switching in ferromagnets and antiferromagnets

It was demonstrated recently that on ultrashort timescales magnetization dynamics not only exhibits precession but also nutation. Here, we investigate how nutation can contribute to spin switching, leading towards ultrafast data writing. We use analytic theory and numerical spin simulations to discuss the behavior of ferromagnets and antiferromagnets in high-frequency magnetic ﬁelds. In ferromagnets, linearly polarized ﬁelds align the magnetization perpendicular to the external ﬁeld, enabling 90 ◦ switching. For circularly polarized ﬁelds in the xy plane, the magnetization tilts to the z direction. During this tilting it rotates around the z axis, allowing 180 ◦ switching. In antiferromagnets, external ﬁelds with frequencies higher than the nutation frequency align the order parameter parallel to the ﬁeld direction, while for lower frequencies it is oriented perpendicular to the ﬁeld. The switching frequency increases with the magnetic ﬁeld strength, and it deviates from the Larmor frequency, making it possible to outpace precessional switching in high magnetic ﬁelds.
Sampling of the wiener process for remote estimation over a channel with unknown delay statistics

In this paper, we study an online sampling problem of the Wiener process. The goal is to minimize the mean squared error (MSE) of the remote estimator under a sampling frequency constraint when the transmission delay distribution is unknown. The sampling problem is reformulated into a renewal reward optimization problem, and we propose an online sampling algorithm that can adaptively learn the optimal sampling policy through stochastic approximation. We show that the cumulative MSE regret grows with rate O(ln k), where k is the number of samples. Through Le Cam's two point method, we show that the worst-case cumulative MSE regret of any online sampling algorithm is lower bounded by Ω (ln k). Hence, the proposed online sampling algorithm is minimax order-optimal. Finally, we validate the performance of the proposed algorithm via numerical simulations.
SEMANTIC SIMILARITY MODELING BASED ON MULTI-GRANULARITY INTERACTION MATCHING

Determining whether two sentences are semantically equivalent is complicated by the ambiguity and variability of natural language expression. The most approaches have used classiﬁers employing hand engineered features derived from complex natural language processing pipelines to automatically recognize equivalence relations; thus, the performances of the models heavily rely on the features designing. To avoid speciﬁc assumptions about the underlying language, we propose a recurrent neural network model for semantic similarity. Interaction features and text representations on multiple levels of granularity are automatically learned using a conditional bidirectional long short-term memory encoder. We extend this model with a soft-alignment attention mechanism that encourages ﬁne-grained reasoning over equivalence or contradiction of pairs of words and phrases. The sentence-pair encoding is input to an output layer to determine the clas-siﬁcation. The eﬀectiveness of our model is demonstrated using two tasks: paraphrase identiﬁcation and semantic relatedness measurement. The results on MRPC and SICK datasets show that our model leads to signiﬁcant quality improvement on tasks, exceeding the previous state-of-the-art without using any hand-crafted features.
Class-incremental Novel Class Discovery

We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories. Apart from discovering novel classes, we also aim at preserving the ability of the model to recognize previously seen base categories. Inspired by rehearsal-based incremental learning methods, in this paper we propose a novel approach for class-iNCD which prevents forgetting of past information about the base classes by jointly exploiting base class feature prototypes and feature-level knowledge distillation. We also propose a self-training clustering strategy that simultaneously clusters novel categories and trains a joint classifier for both the base and novel classes. This makes our method able to operate in a class-incremental setting. Our experiments, conducted on three common benchmarks, demonstrate that our method significantly outperforms state-of-the-art approaches. Code is available at https://github.com/OatmealLiu/class-iNCD
Luminous Optical and X-Ray Flaring of the Putative Redback Millisecond Pulsar 1FGL J0523.5–2529

Several redback and black widow millisecond pulsar binaries have episodes of flaring in X-rays and optical. We initially detected such behavior from the Fermi selected redback candidate 1FGL J0523.5–2529 during optical time-series monitoring. Triggered observations with the Neil Gehrels Swift Observatory over the next ≈100 days showed episodic flaring in X-rays with luminosity up to 8 × 1033 erg s−1 (∼100 times the minimum), and a comparable luminosity in the optical/UV, with similar power-law spectra of f ν ∝ ν −0.7. These are the most luminous flares seen in any nonaccreting “spider” pulsar system, which may be related to the large size of the companion through the fraction of the pulsar wind that it or its ablated wind intercepts. Simultaneously with an optical flare, we see Balmer line and He i emission, not previously known in this object, which is evidence of a stellar wind that may also inhibit detection of radio pulsations. The quiescent optical light curves, while dominated by ellipsoidal modulation, show evidence of variable nonuniform temperature that could be due either to large starspots or asymmetric heating of the companion by the pulsar. This may explain a previous measurement of unusual nonzero orbital eccentricity as, alternatively, distortion of the radial-velocity curve by the surface temperature distribution of the large companion.