Miracle-dz/UniFAR-FTUs huggingface dataset:
MimicTouch: Learning Human's Control Strategy with Multi-Modal Tactile Feedback

In robotics and artificial intelligence, the integration of tactile processing is becoming increasingly pivotal, especially in learning to execute intricate tasks like alignment and insertion. However, existing works focusing on tactile methods for insertion tasks predominantly rely on robot teleoperation data and reinforcement learning, which do not utilize the rich insights provided by human's control strategy guided by tactile feedback. For utilizing human sensations, methodologies related to learning from humans predominantly leverage visual feedback, often overlooking the invaluable tactile feedback that humans inherently employ to finish complex manipulations. Addressing this gap, we introduce"MimicTouch", a novel framework that mimics human's tactile-guided control strategy. In this framework, we initially collect multi-modal tactile datasets from human demonstrators, incorporating human tactile-guided control strategies for task completion. The subsequent step involves instructing robots through imitation learning using multi-modal sensor data and retargeted human motions. To further mitigate the embodiment gap between humans and robots, we employ online residual reinforcement learning on the physical robot. Through comprehensive experiments, we validate the safety of MimicTouch in transferring a latent policy learned through imitation learning from human to robot. This ongoing work will pave the way for a broader spectrum of tactile-guided robotic applications.
CQM: Curriculum Reinforcement Learning with a Quantized World Model

Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often face challenges when they generate curriculum goals in a high-dimensional space. Thus, they usually rely on manually specified goal spaces. To alleviate this limitation and improve the scalability of the curriculum, we propose a novel curriculum method that automatically defines the semantic goal space which contains vital information for the curriculum process, and suggests curriculum goals over it. To define the semantic goal space, our method discretizes continuous observations via vector quantized-variational autoencoders (VQ-VAE) and restores the temporal relations between the discretized observations by a graph. Concurrently, ours suggests uncertainty and temporal distance-aware curriculum goals that converges to the final goals over the automatically composed goal space. We demonstrate that the proposed method allows efficient explorations in an uninformed environment with raw goal examples only. Also, ours outperforms the state-of-the-art curriculum RL methods on data efficiency and performance, in various goal-reaching tasks even with ego-centric visual inputs.
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult

Large learning rates, when applied to gradient descent for nonconvex optimization, yield various implicit biases including the edge of stability (Cohen et al., 2021), balancing (Wang et al., 2022), and catapult (Lewkowycz et al., 2020). These phenomena cannot be well explained by classical optimization theory. Though significant theoretical progress has been made in understanding these implicit biases, it remains unclear for which objective functions would they be more likely. This paper provides an initial step in answering this question and also shows that these implicit biases are in fact various tips of the same iceberg. To establish these results, we develop a global convergence theory under large learning rates, for a family of nonconvex functions without globally Lipschitz continuous gradient, which was typically assumed in existing convergence analysis. Specifically, these phenomena are more likely to occur when the optimization objective function has good regularity. This regularity, together with gradient descent using a large learning rate that favors flatter regions, results in these nontrivial dynamical behaviors. Another corollary is the first non-asymptotic convergence rate bound for large-learning-rate gradient descent optimization of nonconvex functions. Although our theory only applies to specific functions so far, the possibility of extrapolating it to neural networks is also experimentally validated, for which different choices of loss, activation functions, and other techniques such as batch normalization can all affect regularity significantly and lead to very different training dynamics.
Max-min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness

A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convexsolver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput.
Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users

Transliteration is very important in the Indian language context due to the usage of multiple scripts and the widespread use of romanized inputs. However, few training and evaluation sets are publicly available. We introduce Aksharantar, the largest publicly available transliteration dataset for Indian languages created by mining from monolingual and parallel corpora, as well as collecting data from human annotators. The dataset contains 26 million transliteration pairs for 21 Indic languages from 3 language families using 12 scripts. Aksharantar is 21 times larger than existing datasets and is the first publicly available dataset for 7 languages and 1 language family. We also introduce the Aksharantar testset comprising 103k word pairs spanning 19 languages that enables a fine-grained analysis of transliteration models on native origin words, foreign words, frequent words, and rare words. Using the training set, we trained IndicXlit, a multilingual transliteration model that improves accuracy by 15% on the Dakshina test set, and establishes strong baselines on the Aksharantar testset introduced in this work. The models, mining scripts, transliteration guidelines, and datasets are available at https://github.com/AI4Bharat/IndicXlit under open-source licenses. We hope the availability of these large-scale, open resources will spur innovation for Indic language transliteration and downstream applications. We hope the availability of these large-scale, open resources will spur innovation for Indic language transliteration and downstream applications.
Divide et Impera: Multi-Transformer Architectures for Complex NLP-Tasks

The growing capabilities of transformer models pave the way for solving increasingly complex NLP tasks. A key to supporting application-specific requirements is the ability to fine-tune. However, compiling a fine-tuning dataset tailored to complex tasks is tedious and results in large datasets, limiting the ability to control transformer output. We present an approach in which complex tasks are divided into simpler subtasks. Multiple transformer models are fine-tuned to one subtask each, and lined up to accomplish the complex task. This simplifies the compilation of fine-tuning datasets and increases overall controllability. Using the example of reducing gender bias as a complex task, we demonstrate our approach and show that it performs better than using a single model.
Genetic Analysis and Natural History of Parkinson's Disease Due to the LRRK2 G2019S Variant

The LRRK2 G2019S variant is the most common cause of monogenic Parkinson's Disease (PD); however, questions remain regarding the penetrance, clinical phenotype, and natural history of carriers. We performed a 3.5 year prospective longitudinal online study in a large number of 1,286 genotyped LRRK2 G2019S carriers and 109,154 controls, with and without Parkinson's disease (PD) recruited from the 23andMe Research Cohort. We collected self-reported motor and non-motor symptoms every six months, as well as demographics, family histories, and environmental risk factors. Incident cases of PD (phenoconverters) were identified at follow-up. We determined lifetime risk of PD using accelerated failure time modeling and explored the impact of polygenic risk on penetrance. We also computed the genetic ancestry of all LRRK2 G2019S carriers in the 23andMe database and identified regions of the world where carrier frequencies are highest. We observed that despite a one year longer disease duration (p=0.016), LRRK2 G2019S carriers with PD had similar burden of motor symptoms, yet significantly fewer non-motor symptoms including cognitive difficulties, REM sleep behavior disorder (RBD), and hyposmia (all p-values<0.0002). The cumulative incidence of PD in G2019S carriers by age 60 was 8.66%. G2019S carriers had a 10-fold risk of developing PD versus non-carriers. This rose to a 27-fold risk in G2019S carriers with a PD polygenic risk score in the top 25% versus non-carriers in the bottom 25%. In addition to identifying ancient founding events in people of North African and Ashkenazi descent, our genetic ancestry analyses infer that the G2019S variant was later introduced to Spanish colonial territories in the Americas. Our results suggest LRRK2 G2019S PD appears to be a slowly progressive predominantly motor subtype of PD with a lower prevalence of hyposmia, RBD, and cognitive impairment. This suggests that the current prodromal criteria, which are based on idiopathic PD, may lack sensitivity to detect the early phases of LRRK2 PD in G2019S carriers. We show that polygenic burden may contribute to the development of PD in the LRRK2 G2019S carrier population. Collectively, the results should help support screening programs and candidate enrichment strategies for upcoming trials of LRRK2 inhibitors in early-stage disease.
Determining Stream Origin of Four Purported Walleye Stocks in Lake Erie Using Otolith Elemental Analysis

Understanding stock structure and movement in aquatic organisms is necessary for successful population management. This is particularly important in exploited fishes, where stocks likely contribute differentially to the lake-wide population and the fishery. In Lake Erie, the walleye population is important, contributing both ecologically and economically to the lake and surrounding region. Although managed as a single unit, this population likely contains multiple stocks for which anecdotal evidence reveals that these stocks may exhibit different productivities, contributing differentially to the United States recreational and the Canadian commercial fishery. In this study, we sought to quantify homing behavior of walleye stocks and evaluate their contribution to the recreational fishery. By collecting water and spawning adult walleye (N=332) during 2003-2005 from Maumee, Sandusky, and Grand rivers and Toussaint Reef located in the western and central basins of Lake Erie, we could identify otolith chemical characteristics and compare them to the water from these locations. To assess their contribution, we collected three samples (N=300) from fish cleaning houses in Ottawa, Ashtabula, and Erie counties in Ohio during 2005. Again, otolith microchemistry identified element signatures, walleye were grouped, and the relative contribution of each purported stock was assessed. Extent of homing differed among the four spawning stocks. The Sandusky River had the highest site fidelity (92%), followed by the Maumee (60%) and Grand rivers (40%), and finally Toussaint Reef (37%). Toussaint Reef contributed >50% of the fish sampled from the recreational fishery, whereas Maumee, Sandusky, and Grand rivers contributed only 46% combined. By identifying stocks that exhibit high fidelity, we could protect them, thereby maintaining their genetic integrity. Armed with this stock-specific information, managers can protect or exploit populations in proportion to their contribution to the fishery.
REGULATION OF CARBON SEQUESTRATION AND WATER USE IN A OZARK FOREST: PROPOSING A NEW STRATEGICALLY LOCATED AMERIFLUX TOWER SITE IN MISSOURI

by June 14, 2004, the MOFLUX site was fully instrumented and data streams started to flow. A primary accomplished deliverable for the project period was the data streams of CO{sub 2} and water vapor fluxes and numerous meteorological variables (from which prepared datasets have been submitted to the AmeriFlux data archive for 2004-2006, Additionally, measurements of leaf biochemistry and physiology, biomass inventory, tree allometry, successional trends other variables were obtained.
Selective Cancellation of Reactive and Anticipated Movement: Differences in Speed of Action Reprogramming, but not Stopping

The ability to inhibit movements is an essential component of a healthy executive control system. Two distinct but commonly used tasks to assess motor inhibition are the stop signal task (SST) and the anticipated response inhibition (ARI) task. The SST and ARI tasks are similar in that they both require cancelation of a prepotent movement; however, the SST involves cancelation of a speeded reaction to a temporally unpredictable signal, while the ARI task involves cancelation of an anticipated response that the participant has prepared to enact at a wholly predictable time. 33 participants (mean age = 33.3 years, range = 18-55 years) completed variants of the SST and ARI task. In each task, the majority of trials required bimanual button presses, while on a subset of trials a stop signal indicated that one of the presses should be cancelled (i.e., motor selective inhibition). Additional variants of the tasks also included trials featuring signals which were to be ignored, allowing for insights into the attentional component of the inhibitory response. Electromyographic (EMG) recordings allowed detailed comparison of the characteristics of voluntary action and cancellation. The speed of the inhibitory process was not influenced by whether the enacted movement was reactive (SST) or anticipated (ARI task). However, the ongoing (non-cancelled) component of anticipated movements was more efficient than reactive movements, as a result of faster action reprogramming (i.e., faster ongoing actions following successful selective inhibition). Older age was associated with both slower inhibition and slower action reprogramming across all reactive and anticipated tasks.
Evidence for a role of synchrony but not common fate in the perception of biological group movements

Extensive research has shown that observers are able to efficiently extract summary information from groups of people. However, little is known about the cues that determine whether multiple people are represented as a social group or as independent individuals. Initial research on this topic has primarily focused on the role of static cues. Here, we instead investigate the role of dynamic cues. In two experiments with male and female human participants, we use EEG frequency tagging to investigate the influence of two fundamental Gestalt principles - synchrony and common fate - on the grouping of biological movements. In Experiment 1, we find that brain responses coupled to four point-light figures walking together are enhanced when they move in sync vs. out of sync, but only when they are presented upright. In contrast, we found no effect of movement direction (i.e., common fate). In Experiment 2, we rule out that synchrony takes precedence over common fate by replicating the null effect of movement direction while keeping synchrony. These results put forward synchrony as an important driver of social grouping, consistent with the fact that it is an important feature of social interaction and an indicator of social cohesion. In contrast, the influence of common fate on social grouping is less clear and will require further research.
The worst North American drought year of the last millennium: 1934

During the summer of 1934, over 70% of western North America experienced extreme drought, placing this summer far outside the normal range of drought variability and making 1934 the single worst drought year of the last millennium. Strong atmospheric ridging along the West Coast suppressed cold season precipitation across the Northwest, Southwest, and California, a circulation pattern similar to the winters of 1976–1977 and 2013–2014. In the spring and summer, the drying spread to the Midwest and Central Plains, driven by severe precipitation deficits downwind from regions of major dust storm activity, consistent with previous work linking drying during the Dust Bowl to anthropogenic dust aerosol forcing. Despite a moderate La Niña, contributions from sea surface temperature forcing were small, suggesting that the anomalous 1934 drought was primarily a consequence of atmospheric variability, possibly amplified by dust forcing that intensified and spread the drought across nearly all of western North America.
A high-fat and fructose diet in dogs mirrors insulin resistance and β-cell dysfunction characteristic of impaired glucose tolerance in humans

This study examined the impact of a hypercaloric high-fat high-fructose diet (HFFD) in dogs as a potential model for human impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM). The HFFD not only led to weight gain but also triggered metabolic alterations akin to the precursors of human T2DM, notably insulin resistance and β-cell dysfunction. Following the HFFD intervention, the dogs exhibited a 50% decrease in insulin sensitivity within the first four weeks, paralleling observations in the progression from normal to IGT in humans. Calculations of the insulinogenic index using both insulin and C-peptide revealed a significant and sustained decrease in early-phase insulin release, with partial compensation in the later phase, predominantly stemming from reduced hepatic insulin clearance. In addition, the Disposition Index, representing the β-cell’s capacity to compensate for diminished insulin sensitivity, fell dramatically. These results confirm that a HFFD can instigate metabolic changes in dogs akin to the early stages of progression to T2DM in humans. The study underscores the potential of using dogs subjected to a HFFD as a model organism for studying human IGT and T2DM.
Cysteine hyperoxidation rewires communication pathways in the nucleosome and destabilizes the dyad

Gene activity is tightly controlled by reversible chemical modifications called epigenetic marks, which are of various types and modulate gene accessibility without affecting the DNA sequence. Despite an increasing body of evidence demonstrating the role of oxidative-type modifications of histones in gene expression regulation, there remains a complete absence of structural data at the atomistic level to understand the molecular mechanisms behind their regulatory action. Owing to μs time-scale MD simulations and protein communication networks analysis, we describe the impact of histone H3 hyperoxidation (i.e., S-sulfonylation) on the nucleosome dynamics. Our results reveal the atomic-scale details of the intrinsic structural networks within the canonical histone core and their perturbation by hyperoxidation of the histone H3 C110. We show that this modification involves local rearrangement of the communication networks and destabilizes the dyad, which could be important for nucleosome disassembly.
Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data

High-resolution molecular characterization of intra-tumoral clonal structure defined by genomic and epigenomic alterations is crucial in understanding the natural history of tumors and advancing cancer treatment strategies. Copy number alterations (CNA) are of notable importance as both drivers and markers of clonal structure that can now be assayed at individual cell resolution. However, specific computational methods are needed for accurate inference of clonal profiles and cell states from sparse and noisy single-cell ’omics data. Here, we develop a new Bayesian model to utilize single-cell RNA sequencing (scRNA-seq) data for automatic analysis of intra-tumoral clonal structure with respect to CNAs, without reliance on prior knowledge. The model clusters cells into sub-tumoral clones while simultaneously identifying CNA events in each clone, jointly modelling input from gene expression and germline single-nucleotide polymorphisms. Unlike previous methods, our approach automatically infers the number of clones present in the tumor. In detailed simulation studies our model frequently achieves very high (>90%) cell clustering accuracy and high (>80%) CN state inference accuracy, even in settings of high variance and sparsity. Overall, our method compares strongly against existing software tools. Application to human metastatic melanoma tumor data demonstrates accurate clustering of tumor and non-tumor cells, and reveals clonal CNA profiles that highlight functional gene expression differences between clones from the same tumor. Our method is implemented in a publicly-available, open-source R package, Chloris.
Insect Pollinators: The Time is Now for Identifying Species of Greatest Conservation Need

Severe declines in the abundance of insects, including economically and ecologically important pollinators are alarming conservationists and the public. Yet despite these increasingly well documented declines, relatively few pollinating insects other than some butterflies and bumble bees have appeared as Species of Greatest Conservation Need (SGCN) in State Wildlife Action Plans, decadal-scale blueprints for wildlife conservation efforts across the United States that require updating in 2025. Species absent from SGCN lists are ineligible for congressionally appropriated State Wildlife Grants that direct millions of dollars annually for their conservation. In the past, knowledge about the distribution and abundance of many insect pollinators was too poor to identify those meeting state guidelines for inclusion as SGCN. Using case studies from 4 northeastern states, we demonstrate that sufficient conservation status data now exists for many pollinators, including bees, butterflies, moths, beetles, and flower flies, to identify at-risk species meriting inclusion on SGCN lists in many states. Doing so will increase funding available for surveys, habitat protection and enhancement, and other conservation activities that will benefit this vitally important guild.