Miracle-dz/UniFAR-FTUs huggingface dataset:
Contributions of the North Pacific Meridional Mode to Ensemble Spread of ENSO prediction

AbstractSeasonal prediction of El Nino–Southern Oscillation (ENSO) employs the ensemble method, which samples the uncertainty in initial conditions. While much attention has been given to the ensemble mean, the ensemble spread limits the reliability of the forecast. Spatiotemporal coevolution of intermember anomalies of sea surface temperature (SST) and low-level winds over the Pacific is examined in ensemble hindcasts. Two types of evolution of intermember SST anomalies in the equatorial Pacific are identified. The first features an apparent southwestward propagation of the SST spread from the subtropical northeastern Pacific southeast of Hawaii to the central equatorial Pacific in boreal winter–spring, indicative of the precursor effect of the North Pacific meridional mode (NPMM) on ENSO variability. Extratropical atmospheric variability generates ensemble spread in ENSO through wind–evaporation–SST (WES) in the subtropical northeastern Pacific and then Bjerknes feedback on the equator. In the second ty.
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement

Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach satisfactory levels of performance, many of them are in the low-data regime, making fine-tuning challenging. To address this, we propose LLM2LLM, a targeted and iterative data augmentation strategy that uses a teacher LLM to enhance a small seed dataset by augmenting additional data that can be used for fine-tuning on a specific task. LLM2LLM (1) fine-tunes a baseline student LLM on the initial seed data, (2) evaluates and extracts data points that the model gets wrong, and (3) uses a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data. This approach amplifies the signal from incorrectly predicted data points by the LLM during training and reintegrates them into the dataset to focus on more challenging examples for the LLM. Our results show that LLM2LLM significantly enhances the performance of LLMs in the low-data regime, outperforming both traditional fine-tuning and other data augmentation baselines. LLM2LLM reduces the dependence on labor-intensive data curation and paves the way for more scalable and performant LLM solutions, allowing us to tackle data-constrained domains and tasks. We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime using a LLaMA2-7B student model.
Founders, Drift, and Infidelity: The Relationship between Y Chromosome Diversity and Patrilineal Surnames

Most heritable surnames, like Y chromosomes, are passed from father to son. These unique cultural markers of coancestry might therefore have a genetic correlate in shared Y chromosome types among men sharing surnames, although the link could be affected by mutation, multiple foundation for names, nonpaternity, and genetic drift. Here, we demonstrate through an analysis of 1,678 Y-chromosomal haplotypes within 40 British surnames a remarkably high degree of coancestry that generally increases as surnames become rarer. On average, the proportion of haplotypes lying within descent clusters is 62% but ranges from 0% to 87%. The shallow time depth of many descent clusters within names, the lack of a detectable effect of surname derivation on diversity, and simulations of surname descent suggest that genetic drift through variation in reproductive success is important in structuring haplotype diversity. Modern patterns therefore provide little reliable information about the original founders of surnames some 700 years ago. A comparative analysis of published data on Y diversity within Irish surnames demonstrates a relative lack of surname frequency dependence of coancestry, a difference probably mediated through distinct Irish and British demographic histories including even more marked genetic drift in Ireland.
A Transfer Attack to Image Watermarks

Watermark has been widely deployed by industry to detect AI-generated images. The robustness of such watermark-based detector against evasion attacks in the white-box and black-box settings is well understood in the literature. However, the robustness in the no-box setting is much less understood. In particular, multiple studies claimed that image watermark is robust in such setting. In this work, we propose a new transfer evasion attack to image watermark in the no-box setting. Our transfer attack adds a perturbation to a watermarked image to evade multiple surrogate watermarking models trained by the attacker itself, and the perturbed watermarked image also evades the target watermarking model. Our major contribution is to show that, both theoretically and empirically, watermark-based AI-generated image detector is not robust to evasion attacks even if the attacker does not have access to the watermarking model nor the detection API.
Safe and Stable Teleoperation of Quadrotor UAVs under Haptic Shared Autonomy

We present a novel approach that aims to address both safety and stability of a haptic teleoperation system within a framework of Haptic Shared Autonomy (HSA). We use Control Barrier Functions (CBFs) to generate the control input that follows the user's input as closely as possible while guaranteeing safety. In the context of stability of the human-in-the-loop system, we limit the force feedback perceived by the user via a small $L_2$-gain, which is achieved by limiting the control and the force feedback via a differential constraint. Specifically, with the property of HSA, we propose two pathways to design the control and the force feedback: Sequential Control Force (SCF) and Joint Control Force (JCF). Both designs can achieve safety and stability but with different responses to the user's commands. We conducted experimental simulations to evaluate and investigate the properties of the designed methods. We also tested the proposed method on a physical quadrotor UAV and a haptic interface.
Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection

Modern pre-trained architectures struggle to retain previous information while undergoing continuous fine-tuning on new tasks. Despite notable progress in continual classification, systems designed for complex vision tasks such as detection or segmentation still struggle to attain satisfactory performance. In this work, we introduce a memory-based detection transformer architecture to adapt a pre-trained DETR-style detector to new tasks while preserving knowledge from previous tasks. We propose a novel localized query function for efficient information retrieval from memory units, aiming to minimize forgetting. Furthermore, we identify a fundamental challenge in continual detection referred to as background relegation. This arises when object categories from earlier tasks reappear in future tasks, potentially without labels, leading them to be implicitly treated as background. This is an inevitable issue in continual detection or segmentation. The introduced continual optimization technique effectively tackles this challenge. Finally, we assess the performance of our proposed system on continual detection benchmarks and demonstrate that our approach surpasses the performance of existing state-of-the-art resulting in 5-7% improvements on MS-COCO and PASCAL-VOC on the task of continual detection.
StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text

Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video generation (typically 16 or 24 frames), ending up with hard-cuts when naively extended to the case of long video synthesis. To overcome these limitations, we introduce StreamingT2V, an autoregressive approach for long video generation of 80, 240, 600, 1200 or more frames with smooth transitions. The key components are:(i) a short-term memory block called conditional attention module (CAM), which conditions the current generation on the features extracted from the previous chunk via an attentional mechanism, leading to consistent chunk transitions, (ii) a long-term memory block called appearance preservation module, which extracts high-level scene and object features from the first video chunk to prevent the model from forgetting the initial scene, and (iii) a randomized blending approach that enables to apply a video enhancer autoregressively for infinitely long videos without inconsistencies between chunks. Experiments show that StreamingT2V generates high motion amount. In contrast, all competing image-to-video methods are prone to video stagnation when applied naively in an autoregressive manner. Thus, we propose with StreamingT2V a high-quality seamless text-to-long video generator that outperforms competitors with consistency and motion. Our code will be available at: https://github.com/Picsart-AI-Research/StreamingT2V
Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs

Event temporal relation (TempRel) is a primary subject of the event relation extraction task. However, the inherent ambiguity of TempRel increases the difficulty of the task. With the rise of prompt engineering, it is important to design effective prompt templates and verbalizers to extract relevant knowledge. The traditional manually designed templates struggle to extract precise temporal knowledge. This paper introduces a novel retrieval-augmented TempRel extraction approach, leveraging knowledge retrieved from large language models (LLMs) to enhance prompt templates and verbalizers. Our method capitalizes on the diverse capabilities of various LLMs to generate a wide array of ideas for template and verbalizer design. Our proposed method fully exploits the potential of LLMs for generation tasks and contributes more knowledge to our design. Empirical evaluations across three widely recognized datasets demonstrate the efficacy of our method in improving the performance of event temporal relation extraction tasks.
An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets

Does the training of large language models potentially infringe upon code licenses? Furthermore, are there any datasets available that can be safely used for training these models without violating such licenses? In our study, we assess the current trends in the field and the importance of incorporating code into the training of large language models. Additionally, we examine publicly available datasets to see whether these models can be trained on them without the risk of legal issues in the future. To accomplish this, we compiled a list of 53 large language models trained on file-level code. We then extracted their datasets and analyzed how much they overlap with a dataset we created, consisting exclusively of strong copyleft code. Our analysis revealed that every dataset we examined contained license inconsistencies, despite being selected based on their associated repository licenses. We analyzed a total of 514 million code files, discovering 38 million exact duplicates present in our strong copyleft dataset. Additionally, we examined 171 million file-leading comments, identifying 16 million with strong copyleft licenses and another 11 million comments that discouraged copying without explicitly mentioning a license. Based on the findings of our study, which highlights the pervasive issue of license inconsistencies in large language models trained on code, our recommendation for both researchers and the community is to prioritize the development and adoption of best practices for dataset creation and management.
Coooolll: A Deep Learning System for Twitter Sentiment Classification

In this paper, we develop a deep learning system for message-level Twitter sentiment classification. Among the 45 submitted systems including the SemEval 2013 participants, our system (Coooolll) is ranked 2nd on the Twitter2014 test set of SemEval 2014 Task 9. Coooolll is built in a supervised learning framework by concatenating the sentiment-specific word embedding (SSWE) features with the state-of-the-art hand-crafted features. We develop a neural network with hybrid loss function 1 to learn SSWE, which encodes the sentiment information of tweets in the continuous representation of words. To obtain large-scale training corpora, we train SSWE from 10M tweets collected by positive and negative emoticons, without any manual annotation. Our system can be easily re-implemented with the publicly available sentiment-specific word embedding.
The Use of Glyburide Compared With Other Sulfonylureas and the Risk of Cancer in Patients With Type 2 Diabetes

OBJECTIVE To determine whether the use of glyburide is associated with an increased risk of cancer compared with the use of other second-generation sulfonylureas among patients with type 2 diabetes. RESEARCH DESIGN AND METHODS The U.K. Clinical Practice Research Datalink was used to conduct a cohort study among 52,600 patients newly prescribed glyburide or other second-generation sulfonylureas between 1 January 1988 and 31 July 2013. A time-dependent Cox proportional hazards model was used to estimate adjusted hazard ratios (HRs) and 95% CIs of any cancer associated with the use of glyburide compared with the use of second-generation sulfonylureas. Secondary analyses were conducted to determine whether the association varied with cumulative duration of use and cumulative dose (expressed as defined daily dose [DDD]). RESULTS During 280,288 person-years of follow-up, 4,105 patients were given a new diagnosis of cancer (incidence rate 14.6 per 1,000 person-years). Overall, when compared with the use of other second-generation sulfonylureas, the use of glyburide was associated with a nonsignificant increased risk of any cancer (HR 1.09 [95% CI 0.98–1.22]). In secondary analyses, duration- and dose-response relationships were observed, with longer cumulative durations and cumulative doses associated with an increased risk of any cancer (>36 months: HR 1.21 [95% CI: 1.03–1.42]; >1,096 DDDs: HR 1.27 [95% CI 1.06–1.51]). CONCLUSIONS In this population-based cohort study, longer cumulative durations and higher cumulative doses of glyburide were associated with an increased risk of cancer.
Edge currents as probe of topology in twisted cuprate bilayers

Bilayers made of high-$T_c$ cuprate superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ assembled with a twist angle close to $45^\circ$ have been recently shown to spontaneously break time reversal symmetry $\mathcal{T}$, consistent with theoretical predictions for emergent chiral topological $d_{x^2-y^2}+id_{xy}$ phase in such twisted $d$-wave superconductors. Here we use a minimal microscopic model to estimate the size of spontaneous chiral edge currents expected to occur in the $\mathcal{T}$-broken phase. In accord with previous theoretical studies of chiral $d$-wave superconductors we find small but non-vanishing edge currents which we nevertheless predict to be above the detection threshold of the state-of-the-art magnetic scanning probe microscopy. In addition, by deriving a simple relation between the edge current and the electron spectral function we help elucidate the longstanding disparity between the size of edge currents in chiral $d$-wave and $p$-wave superconductors.
Optimal control in large open quantum systems: the case of transmon readout and reset

We present a framework that combines the adjoint state method together with reverse-time back-propagation to solve otherwise prohibitively large open-system quantum control problems. Our approach enables the optimization of arbitrary cost functions with fully general controls applied on large open quantum systems described by a Lindblad master equation. It is scalable, computationally efficient, and has a low memory footprint. We apply this framework to optimize two inherently dissipative operations in superconducting qubits which lag behind in terms of fidelity and duration compared to other unitary operations: the dispersive readout and all-microwave reset of a transmon qubit. Our results show that, given a fixed set of system parameters, shaping the control pulses can yield 2x improvements in the fidelity and duration for both of these operations compared to standard strategies. Our approach can readily be applied to optimize quantum controls in a vast range of applications such as reservoir engineering, autonomous quantum error correction, and leakage-reduction units.
Event-Triggered State Estimation Through Confidence Level

This paper considers the state estimation problem for discrete-time linear systems under event-triggered scheme. In order to improve performance, a novel event-triggered scheme based on confidence level is proposed using the chi-square distribution and mild regularity assumption. In terms of the novel event-triggered scheme, a minimum mean squared error (MMSE) state estimator is proposed using some results presented in this paper. Two algorithms for communication rate estimation of the proposed MMSE state estimator are developed where the first algorithm is based on information with one-step delay, and the second algorithm is based on information with two-step delay. The performance and effectiveness of the proposed MMSE state estimator and the two communication rate estimation algorithms are illustrated using a target tracking scenario.
Vascular Endothelial Cell Injury Is an Important Factor in the Development of Encapsulating Peritoneal Sclerosis in Long-Term Peritoneal Dialysis Patients

Background and Objectives Encapsulating peritoneal sclerosis (EPS) is a rare but serious and life-threatening complication of peritoneal dialysis (PD). However, the precise pathogenesis remains unclear; in addition, predictors and early diagnostic biomarkers for EPS have not yet to be established. Methods Eighty-three peritoneal membrane samples taken at catheter removal were examined to identify pathological characteristics of chronic peritoneal deterioration, which promotes EPS in patients undergoing long-term PD treatment with low occurrence of peritonitis. Results According to univariable logistic regression analysis of the pathological findings, thickness of the peritoneal membrane (P = 0.045), new membrane formation score (P = 0.006), ratio of luminal diameter to vessel diameter (L/V ratio, P<0.001), presence of CD31-negative vessels (P = 0.021), fibrin deposition (P<0.001), and collagen volume fraction (P = 0.018) were associated with EPS development. In analyses of samples with and without EPS matched for PD treatment period, non-diabetes, and PD solution, univariable analysis identified L/V ratio (per 0.1 increase: odds ratio (OR) 0.44, P = 0.003) and fibrin deposition (OR 6.35, P = 0.027) as the factors associated with EPS. L/V ratio was lower in patients with fibrin exudation than in patients without fibrin exudation. Conclusions These findings suggest that damage to vascular endothelial cells, as represented by low L/V ratio, could be a predictive finding for the development of EPS, particularly in long-term PD patients unaffected by peritonitis.
Intercropping in cabbage (Brassica Oleracea L.var. capitata f.) for growth, yield, quality and sustainable soil health under foothills of Eastern himalayan region

The present study was done for intercropping in cabbage (Brassica Oleracea L.var.capitata f.) for growth, yield, quality and sustainable soil health under foothills of Eastern Himalayan region. The pooled results revealed that head yield, ascorbic acid content and nutrient uptake from soil varied according to treatments combination. Sole cropping of cabbage recorded maximum values for most of the growth characters like plant height (1.30 cm), shoot weight (59.47 g), root weight (46.36 g), shoot to root ratio (1.30), root length (17.97 cm) and root volume (37.24 cc) and yield (37.11 t ha-1). But as per cabbage equivalent yield (44.39 t ha-1),economics (B:C ratio 2.96) and production efficiency (591.80 kg-1ha-1day) are concerned cabbage and garden pea intercropping system recorded significantly highest values than all other treatments. cabbage and garden pea intercropping system also recorded maximum ascorbic content(38.61mg-1 100g). Highest residual N (174.12 kg ha-1) content was obtained in sole garden pea plots whereas maximum values for available P2O5 (30.31 kg ha-1) and K2O (171.83 kg ha-1) were recorded in sole coriander plots. Cabbage and beet root intercropping system recorded minimum values for most of the growth characters like shoot weight (35.88 g), root weight (26.80 g),root length(10.83 cm), shoot and root volume (23.54 cc),cabbage equivalent yield (32.10 t ha-1) ascorbic content(29.94 mg-1 100g). Inclusion of garden pea in cabbage inter rows may be adopted by cabbage growers of foothills of eastern Himalayan region of West Bengal for additional income and soil enrichment.
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning

Mitigating catastrophic forgetting is a key hurdle in continual learning. Deep Generative Replay (GR) provides techniques focused on generating samples from prior tasks to enhance the model's memory capabilities using generative AI models ranging from Generative Adversarial Networks (GANs) to the more recent Diffusion Models (DMs). A major issue is the deterioration in the quality of generated data compared to the original, as the generator continuously self-learns from its outputs. This degradation can lead to the potential risk of catastrophic forgetting (CF) occurring in the classifier. To address this, we propose the Gradient Projection Class-Prototype Conditional Diffusion Model (GPPDM), a GR-based approach for continual learning that enhances image quality in generators and thus reduces the CF in classifiers. The cornerstone of GPPDM is a learnable class prototype that captures the core characteristics of images in a given class. This prototype, integrated into the diffusion model's denoising process, ensures the generation of high-quality images of the old tasks, hence reducing the risk of CF in classifiers. Moreover, to further mitigate the CF of diffusion models, we propose a gradient projection technique tailored for the cross-attention layer of diffusion models to maximally maintain and preserve the representations of old task data in the current task as close as possible to their representations when they first arrived. Our empirical studies on diverse datasets demonstrate that our proposed method significantly outperforms existing state-of-the-art models, highlighting its satisfactory ability to preserve image quality and enhance the model's memory retention.