Miracle-dz/UniFAR-FTUs huggingface dataset:
Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble

Recent research has shown that language models have a tendency to memorize rare or unique token sequences in the training corpus. After deploying a model, practitioners might be asked to delete any personal information from the model by individuals' requests. Re-training the underlying model every time individuals would like to practice their rights to be forgotten is computationally expensive. We employ a teacher-student framework and propose a novel leave-one-out ensemble method to unlearn the targeted textual sequences that need to be forgotten from the model. In our approach, multiple teachers are trained on disjoint sets; for each targeted sequence to be removed, we exclude the teacher trained on the set containing this sequence and aggregate the predictions from remaining teachers to provide supervision during fine-tuning. Experiments on LibriSpeech and WikiText-103 datasets show that the proposed method achieves superior privacy-utility trade-offs than other counterparts.
Orientations and aspect ratios of falling snow

Photographs of nearly 73,000 snowflakes in free fall are used to determine the aspect ratio and orientation of aggregates, moderately rimed particles, and graupel. Observations indicate that there can be a much broader range of orientation angles, with a larger median value, than has been indicated by previous observational and theoretical studies. The data show that aspect ratio depends on riming extent but that orientation is only weakly dependent on the degree of riming and on particle size. Instead, more vertical orientations for frozen particles become increasingly common with higher turbulence. The results suggest that distributions of size, fall speed, orientation, and aspect ratio may each need to be considered in order to optimize the accuracy of precipitation retrievals using microwave sensors.
Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata for Sentiment Analysis

Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for people to express themselves, including their opinions on various political figures. The resulting sentiment analysis can be useful for political executives to gain an understanding of public sentiment and develop appropriate and effective political strategies. This study aimed to build a sentiment analysis system leveraging YouTube videos metadata. The sentiment analysis system was built using Apache Kafka, Apache PySpark, and Hadoop for big data handling; TensorFlow for deep learning handling; and FastAPI for deployment on the server. The YouTube videos metadata used in this study is the video description. The sentiment analysis model was built using LSTM algorithm and produces two types of sentiments: positive and negative sentiments. The sentiment analysis results are then visualized in the form a simple web-based dashboard.
Asset Bundling for Wind Power Forecasting

The growing penetration of intermittent, renewable generation in US power grids, especially wind and solar generation, results in increased operational uncertainty. In that context, accurate forecasts are critical, especially for wind generation, which exhibits large variability and is historically harder to predict. To overcome this challenge, this work proposes a novel Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling, machine learning, and forecast reconciliation techniques. The BPR framework first learns an intermediate hierarchy level (the bundles), then predicts wind power at the asset, bundle, and fleet level, and finally reconciles all forecasts to ensure consistency. This approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks. The paper also introduces new asset-bundling criteria that capture the spatio-temporal dynamics of wind power time series. Extensive numerical experiments are conducted on an industry-size dataset of 283 wind farms in the MISO footprint. The experiments consider short-term and day-ahead forecasts, and evaluates a large variety of forecasting models that include weather predictions as covariates. The results demonstrate the benefits of BPR, which consistently and significantly improves forecast accuracy over baselines, especially at the fleet level.
Novel pyrazole compounds for pharmacological discrimination between receptor-operated and store-operated Ca2+ entry pathways

Pyrazole derivatives have recently been suggested as selective blockers of transient receptor potential cation (TRPC) channels but their ability to distinguish between the TRPC and Orai pore complexes is ill‐defined. This study was designed to characterize a series of pyrazole derivatives in terms of TRPC/Orai selectivity and to delineate consequences of selective suppression of these pathways for mast cell activation.
Natural Production Monitoring and Evaluation; Idaho Department of Fish and Game, 2000-2001 Annual Report.

This report covers efforts to monitor age composition of wild adult spring/summer chinook salmon returning to the Snake River Basin. Accurately determining the ocean age proportions of wild adult spring/summer chinook salmon is important information for monitoring the status and trends of these species. During this report period, project personnel selected the preferred structure for aging, set up a database to track all samples collected, developed procedures and ordered equipment for structure preparation and reading, and aged the adults that were sampled in 1999. Chinook salmon carcasses were sampled from representative spawning areas throughout the Snake River Basin. Ocean age proportions were determined for each 5 centimeter fork length group for wild adult spring/summer chinook salmon returning to the Snake River. These ocean age proportions were applied to the number and estimated length frequency distribution of wild chinook salmon adults passing Lower Granite Dam to estimate the number of adult returns for each ocean age group.
S PATIAL D ISTRIBUTION P ATTERNS AND S TRESS P OTENTIAL S IGNS OF C LOWNFISH IN R ECIRCULATING A QUACULTURE S YSTEMS

Monitoring and detecting ﬁsh behaviors provide essential information on ﬁsh welfare and contribute to achieving intelligent production in global aquaculture. This work proposes an efﬁcient approach to analyze the spatial distribution status and motion patterns of juvenile clownﬁsh (Amphiprion bicinctus) maintained in aquaria at three stocking densities (1, 5, and 10 individuals/aquarium). The estimated displacement is the key factor in assessing the dispersion and velocity to express the clownﬁsh’s spatial distribution and movement behavior in a recirculating aquaculture system. Indeed, we aim at computing the velocity, magnitude, and turning angle using an optical ﬂow method to assist aquaculturists in efﬁciently monitoring and identifying ﬁsh behavior. We test the system design on a database containing two days of video streams of juvenile clownﬁsh maintained in aquaria. The proposed displacement estimation reveals good performance in measuring clownﬁsh’s motion and dispersion characteristics leading to assess the potential signs of stress behaviors. Furthermore, we demonstrate the effectiveness of the proposed technique for quantifying variation in clownﬁsh activity levels between recordings taken in the morning and afternoon at different stocking densities.
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints

The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment. Reinforcement Learning from Human Feedback (RLHF) has emerged as a promising pathway towards AI alignment but brings forth challenges due to its complexity and dependence on a separate reward model. Direct Preference Optimization (DPO) has been proposed as an alternative, and it remains equivalent to RLHF under the reverse KL regularization constraint. This paper presents $f$-DPO, a generalized approach to DPO by incorporating diverse divergence constraints. We show that under certain $f$-divergences, including Jensen-Shannon divergence, forward KL divergences and $\alpha$-divergences, the complex relationship between the reward and optimal policy can also be simplified by addressing the Karush-Kuhn-Tucker conditions. This eliminates the need for estimating the normalizing constant in the Bradley-Terry model and enables a tractable mapping between the reward function and the optimal policy. Our approach optimizes LLMs to align with human preferences in a more efficient and supervised manner under a broad set of divergence constraints. Empirically, adopting these divergences ensures a balance between alignment performance and generation diversity. Importantly, $f$-DPO outperforms PPO-based methods in divergence efficiency, and divergence constraints directly influence expected calibration error (ECE).
Hematologic setpoints are a stable and patient-specific deep phenotype

The complete blood count is an important screening tool for healthy adults and is the most commonly ordered test at periodic physical exams. However, results are usually interpreted relative to one-size-fits-all reference intervals, undermining the goal of precision medicine to tailor medical care to the needs of individual patients based on their unique characteristics. Here we show that standard complete blood count indices in healthy adults have robust homeostatic setpoints that are patient-specific and stable, with the typical healthy adult's set of 9 blood count setpoints distinguishable from 98% of others, and with these differences persisting for decades. These setpoints reflect a deep physiologic phenotype, enabling improved detection of both acquired and genetic determinants of hematologic regulation, including discovery of multiple novel loci via GWAS analyses. Patient-specific reference intervals derived from setpoints enable more accurate personalized risk assessment, and the setpoints themselves are significantly correlated with mortality risk, providing new opportunities to enhance patient-specific screening and early intervention. This study shows complete blood count setpoints are sufficiently stable and patient-specific to help realize the promise of precision medicine for healthy adults.
Neotectonics of the Yakutat Collision: Changes in Deformation Driven by Mass Redistribution

The most recent period of orogenesis in southern Alaska began in the late Neogene with the collision of the Yakutat microplate, which is partially accreted to and partially subducted beneath the Alaskan margin at the easternmost extent of the Aleutian Trench. Neotectonic studies suggest significant spatial and kinematic variation in active deformation during the collision of the Yakutat microplate. The Saint Elias orogen experienced a widespread structural reorganization in the Quaternary with oblique convergence partitioned onto an en echelon thrust array. The new tectonic configuration also includes the continuing development of an incipient indentor comer, significant retrothrust motion, and shifting deformation fronts. Reorganization is temporally linked to intense glacial erosion in the core of orogen and rapid sedimentation in offshore depocenters during the Pleistocene. We propose that mass redistribution and modification of orogenic topography played an integral role in the structural and tectonic evolution of the present system. Currently, the spatial deformation front (outboard limit of deformation) and active deformation front are not the same, suggesting that deformation swept through the landscape through time, presumably as a result of glaciation, tectonic adjustment, or both. A more complete picture of the complex response of near-surface deformation to topographic disruption should improve seismic hazard assessments.
Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks

Current reinforcement learning algorithms struggle in sparse and complex environments, most notably in long-horizon manipulation tasks entailing a plethora of different sequences. In this work, we propose the Intrinsically Guided Exploration from Large Language Models (IGE-LLMs) framework. By leveraging LLMs as an assistive intrinsic reward, IGE-LLMs guides the exploratory process in reinforcement learning to address intricate long-horizon with sparse rewards robotic manipulation tasks. We evaluate our framework and related intrinsic learning methods in an environment challenged with exploration, and a complex robotic manipulation task challenged by both exploration and long-horizons. Results show IGE-LLMs (i) exhibit notably higher performance over related intrinsic methods and the direct use of LLMs in decision-making, (ii) can be combined and complement existing learning methods highlighting its modularity, (iii) are fairly insensitive to different intrinsic scaling parameters, and (iv) maintain robustness against increased levels of uncertainty and horizons.
Overview of the NLP-TEA 2015 Shared Task for Chinese Grammatical Error Diagnosis

This paper introduces the NLP-TEA 2015 shared task for Chinese grammatical error diagnosis. We describe the task, data preparation, performance metrics, and evaluation results. The hope is that such an evaluation campaign may produce more advanced Chinese grammatical error diagnosis techniques. All data sets with gold standards and evaluation tools are publicly available for research purposes.
Generation of regional climate ensembles using Atmospheric Forcing Shifting

Ensembles of high‐resolution regional climate model (RCM) simulations are crucial for assessing regional climate change and the associated uncertainties. This article presents an RCM ensemble generation technique which explores uncertainties arising from the positioning of synoptic systems in the large‐scale atmospheric forcing by shifting the atmospheric fields from global climate model (GCM) runs horizontally. Here, we discuss how the so‐called Atmospheric Forcing Shifting (AFS) affects temperature and precipitation over Europe for the period 1980–1984. We use ERA‐40 reanalysis data in which the atmospheric fields are shifted to each direction by 25 and 50 km, respectively, to run RCM simulations with COSMO‐CLM at 50‐km resolution. The analysis of the AFS ensemble includes comparisons with E‐OBS observations and COSMO‐CLM runs driven by different GCMs. AFS has an evident effect on the spatiotemporal distributions of temperature and particularly precipitation, which is most pronounced during hydrological summer (May to October) when spatial weather patterns are more variable. Furthermore, AFS produces realistic changes in the likelihood and intensity of extreme precipitation. The changes induced by AFS depend strongly on orography, i.e. precipitation increases are likely to occur where moist air masses are shifted towards the windward mountain side and vice versa. Thus, increasing the RCM ensemble spread by means of AFS is a simple and useful method for sampling observed climate statistics and assessing the variability and changes in mean and extreme climate.
Impressions of San Francisco

V IS U A L P R O C E E D IN G S 268 “Inherited Memories” explores the intertwining and transformation of photographic 2D compositing extended into animation. Based on photographs from Prague, Warsaw, and Krakow, it is a documentation of an imagined cultural past, built from layers of memories of times beyond personal experience. Producer: Cynthia Beth Rubin Contributors: Cynthia Beth Rubin, Bob Gluck Inherited Memories
Investigating sources of stream chloride near Kejimkujik National Park, southwestern Nova Scotia: A chlorine stable isotope approach

Chlorine stable isotope analysis (δ37Cl ) means of stream water (- 0.95 ‰, n = 22), rainwater (- 1.51 ‰, n = 12), fog water (- 1.08 ‰, n = 7) and silicate mineral bound chloride (+ 0.13 ‰, n = 3) are used in an isotope mass balance approach to estimate sources of stream chloride. During summer-baseflow conditions, the chloride budget of two catchments in southwestern Nova Scotia is approximately 39 % from rainfall, 37 % from fog water and 24 % from rock/water interactions. The results of a significant source of geological chloride suggest the use of chloride in stream water as a proxy for marine derived sulphate may not be valid. This conclusion implies that anthropogenic sources of sulphate to acid sensitive ecosystems of southwestern Nova Scotia have been underestimated when chloride is assumed to be a conservative ion in the hydrological cycle.
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned

We prove a fundamental limitation on the efficiency of a wide class of Reinforcement Learning (RL) algorithms. This limitation applies to model-free RL methods as well as a broad range of model-based methods, such as planning with tree search. Under an abstract definition of this class, we provide a family of RL problems for which these methods suffer a lower bound exponential in the horizon for their interactions with the environment to find an optimal behavior. However, there exists a method, not tailored to this specific family of problems, which can efficiently solve the problems in the family. In contrast, our limitation does not apply to several types of methods proposed in the literature, for instance, goal-conditioned methods or other algorithms that construct an inverse dynamics model.
Further results on the Hunters and Rabbit game through monotonicity

Hunters and Rabbit game is played on a graph $G$ where the Hunter player shoots at $k$ vertices in every round while the Rabbit player occupies an unknown vertex and, if not shot, must move to a neighbouring vertex after each round. The Rabbit player wins if it can ensure that its position is never shot. The Hunter player wins otherwise. The hunter number $h(G)$ of a graph $G$ is the minimum integer $k$ such that the Hunter player has a winning strategy (i.e., allowing him to win whatever be the strategy of the Rabbit player). This game has been studied in several graph classes, in particular in bipartite graphs (grids, trees, hypercubes.), but the computational complexity of computing $h(G)$ remains open in general graphs and even in trees. To progress further, we propose a notion of monotonicity for the Hunters and Rabbit game imposing that, roughly, a vertex that has already been shot ``must not host the rabbit anymore''. This allows us to obtain new results in various graph classes. Let the monotone hunter number be denoted by $mh(G)$. We show that $pw(G) \leq mh(G) \leq pw(G)+1$ for any graph $G$ with pathwidth $pw(G)$, implying that computing $mh(G)$, or even approximating $mh(G)$ up to an additive constant, is NP-hard. Then, we show that $mh(G)$ can be computed in polynomial time in split graphs, interval graphs, cographs and trees. These results go through structural characterisations which allow us to relate the monotone hunter number with the pathwidth in some of these graph classes. In all cases, this allows us to specify the hunter number or to show that there may be an arbitrary gap between $h$ and $mh$, i.e., that monotonicity does not help. In particular, we show that, for every $k\geq 3$, there exists a tree $T$ with $h(T)=2$ and $mh(T)=k$. We conclude by proving that computing $h$ (resp., $mh$) is FPT parameterised by the minimum size of a vertex cover.
Generative AI Mitigates Representation Bias Using Synthetic Health Data

Representation bias in health data can lead to unfair decisions, compromising the generalisability of research findings and impeding underrepresented subpopulations from benefiting from clinical discoveries. Several approaches have been developed to mitigate representation bias, ranging from simple resampling methods, such as SMOTE, to recent approaches based on generative adversarial networks (GAN). However, generating high-dimensional time-series synthetic health data remains challenging for both resampling and GAN-based approaches. In this work, we propose a novel CA-GAN architecture able to synthesise authentic, high-dimensional time series data. CA-GAN outperforms state-of-the-art methods in qualitative and quantitative evaluation while avoiding mode collapse, a significant GAN failure. We evaluate CA-GAN's generalisability in mitigating representation bias for Black patients in two diverse, clinically relevant datasets: acute hypotension and sepsis. Finally, we show that CA-GAN generates authentic data of the minority class while faithfully maintaining the original distribution of both datasets.
Application of Single-Cell RNA Sequencing in Ovarian Development

The ovary is a female reproductive organ that plays a key role in fertility and the maintenance of endocrine homeostasis, which is of great importance to women’s health. It is characterized by a high heterogeneity, with different cellular subpopulations primarily containing oocytes, granulosa cells, stromal cells, endothelial cells, vascular smooth muscle cells, and diverse immune cell types. Each has unique and important functions. From the fetal period to old age, the ovary experiences continuous structural and functional changes, with the gene expression of each cell type undergoing dramatic changes. In addition, ovarian development strongly relies on the communication between germ and somatic cells. Compared to traditional bulk RNA sequencing techniques, the single-cell RNA sequencing (scRNA-seq) approach has substantial advantages in analyzing individual cells within an ever-changing and complicated tissue, classifying them into cell types, characterizing single cells, delineating the cellular developmental trajectory, and studying cell-to-cell interactions. In this review, we present single-cell transcriptome mapping of the ovary, summarize the characteristics of the important constituent cells of the ovary and the critical cellular developmental processes, and describe key signaling pathways for cell-to-cell communication in the ovary, as revealed by scRNA-seq. This review will undoubtedly improve our understanding of the characteristics of ovarian cells and development, thus enabling the identification of novel therapeutic targets for ovarian-related diseases.
Mobility Prediction: A review of Techniques used in MANETS

Mobility prediction for individual path is a fascinating topic. Mobility prediction in mobile ad-hoc networks is a simple and effective scheme to predict when the user will leave from the current location and where he will move to next future position. The prediction of the mobility of nodes in mobile ad hoc networks becomes a key solution for the delivery of data in real time operations; which enables the reformation of the routes just prior to the modify of the topology which occurs a seamless and well-organized data delivery that meet the expectations of the real time constraints. The mobility prediction is very significant for equally homogeneous networks and heterogeneous networks, determined by applications in mobility management, smooth handoffs, call admission control, and resource reservation for improved quality of service. One way to address the truth of node mobility is devising proactive solutions that make use of mobility prediction algorithms to estimate the movement of the nodes. At the application level, user mobility prediction in combination with user’s profile may provide the user with enhanced location-based wireless services, such as route guidance, local traffic information and on-line advertising. In this paper we present the most significant mobility prediction techniques for MANETs focusing on their major design principles and description.