Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Nearest Neighbor Classification for Crowdsourcing

In machine learning, crowdsourcing is an economical way to label a large amount of data. However, the noise in the produced labels may deteriorate the accuracy of any classification method applied to the labelled data. We propose an enhanced nearest neighbor classifier (ENN) to overcome this issue. Two algorithms are developed to estimate the worker quality (which is often unknown in practice): one is to construct the estimate based on the denoised worker labels by applying the $k$NN classifier to the expert data; the other is an iterative algorithm that works even without access to the expert data. Other than strong numerical evidence, our proposed methods are proven to achieve the same regret as its oracle version based on high-quality expert data. As a technical by-product, a lower bound on the sample size assigned to each worker to reach the optimal convergence rate of regret is derived.
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation

We present the first distributed optimization al-gorithm with lazy communication for collaborative geometric estimation, the backbone of modern collaborative simultaneous localization and mapping (SLAM) and structure-from-motion (SfM) applications. Our method allows agents to cooperatively reconstruct a shared geometric model on a central server by fusing individual observations, but without the need to transmit potentially sensitive information about the agents themselves (such as their locations). Furthermore, to alleviate the burden of communication during iterative optimization, we design a set of communication triggering conditions that enable agents to selectively upload a targeted subset of local information that is useful to global optimization. Our approach thus achieves significant communication reduction with minimal impact on optimization performance. As our main theoretical contribution, we prove that our method converges to first-order critical points with a global sublinear convergence rate. Numerical evaluations on bundle adjustment problems from collaborative SLAM and SfM datasets show that our method performs competitively against existing distributed techniques, while achieving up to 78% total communication reduction.
Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante

State-of-the-art reinforcement learning is now able to learn versatile locomotion, balancing and push-recovery capabilities for bipedal robots in simulation. Yet, the reality gap has mostly been overlooked and the simulated results hardly transfer to real hardware. Either it is unsuccessful in practice because the physics is over-simplified and hardware limitations are ignored, or regularity is not guaranteed, and unexpected hazardous motions can occur. This paper presents a reinforcement learning framework capable of learning ro-bust standing push recovery for bipedal robots that smoothly transfer to reality, providing only instantaneous proprioceptive observations. By combining original termination conditions and policy smoothness conditioning, we achieve stable learning, sim-to-real transfer and safety using a policy without memory nor explicit history. Reward engineering is then used to give insights into how to keep balance. We demonstrate its performance in reality on the lower-limb medical exoskeleton Atalante.
Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution

Clustering aims to group unlabeled objects based on similarity inherent among them into clusters. It is important for many tasks such as anomaly detection, database sharding, record linkage, and others. Some clustering methods are taken as batch algorithms that incur a high overhead as they cluster all the objects in the database from scratch or assume an incremental workload. In practice, database objects are updated, added, and removed from databases continuously which makes previous results stale. Running batch algorithms is infeasible in such scenarios as it would incur a significant overhead if performed continuously. This is particularly the case for high-velocity scenarios such as ones in Internet of Things applications. In this paper, we tackle the problem of clustering in high-velocity dynamic scenarios, where the objects are continuously updated, inserted, and deleted. Specifically, we propose a generally dynamic approach to clustering that utilizes previous clustering results. Our system, DynamicC, uses a machine learning model that is augmented with an existing batch algorithm. The DynamicC model trains by observing the clustering decisions made by the batch algorithm. After training, the DynamicC model is usedin cooperation with the batch algorithm to achieve both accurate and fast clustering decisions. The experimental results on four real-world and one synthetic datasets show that our approach has a better performance compared to the state-of-the-art method while achieving similarly accurate clustering results to the baseline batch algorithm.
HighMMT: Towards Modality and Task Generalization for High-Modality Representation Learning

Learning multimodal representations involves discovering correspondences and integrating information from multiple heterogeneous sources of data. While recent research has begun to explore the design of more general-purpose multimodal models (contrary to prior focus on domain and modality-speciﬁc architectures), these methods are still largely focused on a small set of modalities in the language, vision, and audio space. In order to accelerate generalization towards diverse and understudied modalities, we investigate methods for high-modality (a large set of diverse modalities) and partially-observable (each task only deﬁned on a small subset of modalities) scenarios. To tackle these challenges, we design a general multimodal model that enables multitask and transfer learning: multitask learning with shared parameters enables stable parameter counts (addressing scalability), and cross-modal transfer learning enables information sharing across modalities and tasks (addressing partial observability). Our resulting model generalizes across text, image, video, audio, time-series, sensors, tables, and set modalities from different research areas, improves the tradeoff between performance and efﬁciency, transfers to new modalities and tasks, and reveals surprising insights on the nature of information sharing in multitask models. We release our code and benchmarks which we hope will present a uniﬁed platform for subsequent theoretical and empirical analysis: https://github.com/pliang279/HighMMT .
Empowering Elegant Cloud Services Owing To Remote Signification

We create cloud-helped remote detecting systems for empowering dispersed agreement estimation of obscure parameters in a given geographic range. We first propose an appropriated sensor system virtualization calculation that looks for, chooses, and directions Internet-available sensors to perform a detecting undertaking in a particular locale. The calculation focalizes in linearithmic time for expansive scale organizes, and obliges trading various messages that is at most direct in the quantity of sensors. Second, we outline an awkward, appropriated calculation that depends on the chose sensors to gauge an arrangement of parameters without obliging synchronization among the sensors. Our reproduction results demonstrate that the proposed calculation, when contrasted with traditional ADMM (Alternating Direction Method of Multipliers), diminishes correspondence overhead essentially Without trading off the estimation mistake. Furthermore, the joining time, however builds somewhat, is Still straight as on account of ordinary ADMM.
REAL-X -- Robot open-Ended Autonomous Learning Architectures: Achieving Truly End-to-End Sensorimotor Autonomous Learning Systems

Open-ended learning is a core research field of developmental robotics and AI aiming to build learning machines and robots that can autonomously acquire knowledge and skills incrementally as infants and children. The first contribution of this work is to study the challenges posed by the previously proposed benchmark `REAL competition' aiming to foster the development of truly open-ended learning robot architectures. The competition involves a simulated camera-arm robot that: (a) in a first `intrinsic phase' acquires sensorimotor competence by autonomously interacting with objects; (b) in a second `extrinsic phase' is tested with tasks unknown in the intrinsic phase to measure the quality of knowledge previously acquired. This benchmark requires the solution of multiple challenges usually tackled in isolation, in particular exploration, sparse-rewards, object learning, generalisation, task/goal self-generation, and autonomous skill learning. As a second contribution, we present a set of `REAL-X' robot architectures that are able to solve different versions of the benchmark, where we progressively release initial simplifications. The architectures are based on a planning approach that dynamically increases abstraction, and intrinsic motivations to foster exploration. REAL-X achieves a good performance level in very demanding conditions. We argue that the REAL benchmark represents a valuable tool for studying open-ended learning in its hardest form.
Mitigating statistical bias within differentially private synthetic data

Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data. However, mechanisms of privacy preservation can significantly reduce the utility of synthetic data, which in turn impacts downstream tasks such as learning predictive models or inference. We propose several re-weighting strategies using privatised likelihood ratios that not only mitigate statistical bias of downstream estimators but also have general applicability to differentially private generative models. Through large-scale empirical evaluation, we show that private importance weighting provides simple and effective privacy-compliant augmentation for general applications of synthetic data.
Differentiable IFS Fractals

I present my explorations in rendering Iterated Function System (IFS) fractals using a differentiable rendering pipeline. Differentiable rendering is a recent innovation at the intersection of graphics and machine learning. This opens up many possibilities for generating fractals that meet particular criteria. In this paper I show how my method can be used to generate an IFS fractal that resembles a target image.
Systematically assessing microbiome–disease associations identifies drivers of inconsistency in metagenomic research

Evaluating the relationship between the human gut microbiome and disease requires computing reliable statistical associations. Here, using millions of different association modeling strategies, we evaluated the consistency—or robustness—of microbiome-based disease indicators for 6 prevalent and well-studied phenotypes (across 15 public cohorts and 2,343 individuals). We were able to discriminate between analytically robust versus nonrobust results. In many cases, different models yielded contradictory associations for the same taxon–disease pairing, some showing positive correlations and others negative. When querying a subset of 581 microbe–disease associations that have been previously reported in the literature, 1 out of 3 taxa demonstrated substantial inconsistency in association sign. Notably, >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were particularly nonrobust in this regard. We additionally quantified how potential confounders—sequencing depth, glucose levels, cholesterol, and body mass index, for example—influenced associations, analyzing how these variables affect the ostensible correlation between Faecalibacterium prausnitzii abundance and a healthy gut. Overall, we propose our approach as a method to maximize confidence when prioritizing findings that emerge from microbiome association studies.
A Comparison of Behavioral and Psychological Symptoms of Dementia (BPSD) and BPSD Sub-Syndromes in Early-Onset and Late-Onset Alzheimer’s Disease

Background: Behavioral and psychological symptoms of dementia (BPSD) have a large impact on the quality of life of patients with Alzheimer’s disease (AD). Few studies have compared BPSD between early-onset (EOAD) and late-onset (LOAD) patients, finding conflicting results. Objective: The aims of this study were to: 1) characterize the presence, overall prevalence, and time of occurrence of BPSD in EOAD versus LOAD; 2) estimate the prevalence over time and severity of each BPSD in EOAD versus LOAD in three stages: pre-T0 (before the onset of the disease), T0 (from onset to 5 years), and T1 (from 5 years onwards); 3) track the manifestation of BPSD sub-syndromes (i.e., hyperactivity, psychosis, affective, and apathy) in EOAD versus LOAD at T0 and T1. Methods: The sample includes 1,538 LOAD and 387 EOAD diagnosed from 1996 to 2018. Comprehensive assessment batteries, including the Neuropsychiatric Inventory (NPI), were administered at the first medical assessment and at different follow-up period. Results: The overall prevalence for the most of BPSD was significantly higher in EOAD compared to LOAD whereas most BPSD appeared significantly later in EOAD patients. Between the two groups, from pre-T0 to T1 we recorded a different pattern of BPSD prevalence over time as well as for BPSD sub-syndromes at T0 and T1. Results on severity of BPSD did not show significant differences. Conclusion: EOAD and LOAD represent two different forms of a single entity not only from a neuropathological, cognitive, and functional level but also from a psychiatric point of view.
Integrated Analysis of DNA Repair Genes Identifies SLC6A1 as a New Marker for the Clinical Outcome of Patients with Colorectal Cancer

Colorectal cancer (CRC) remains an important malignancy worldwide with poor prognosis. It has been known that DNA repair genes are involved in the development and progression of various tumors. Therefore, the purpose of this study was to explore DNA repair gene-based prognostic biomarkers for CRC. In this study, the expressing pattern and prognostic values of DNA repair genes in CRC patients were analyzed using TCGA database. GO and KEGG enrichment analyses were conducted to clarify the functional roles of dysregulated genes. We observed 358 differentially expressed DNA repair genes in CRC specimens, including 84 downregulated genes and 275 upregulated genes. 36 survival-related DNA repair genes were correlated with CRC patients' five-year survival, including 6 low-risk genes and 30 high-risk genes. Among the 10 overlapping genes, we focused on SLC6A1 which was highly expressed in CRC, and multivariate analysis confirmed that SLC6A1 expression as well as age and clinical stage could be regarded as an independent predicting factor for CRC prognosis. KEGG assays revealed that SLC6A1 may influence the clinical progression via regulating TGF-beta and PI3K-Akt signaling pathways. In addition, we observed that SLC6A1 was negatively regulated by SLC6A1 methylation, leading to its low expression in CRC specimens. Overall, SLC6A1 is upexpressed in CRC and can be used as a marker of poor prognosis in CRC patients.
From Cave Dragons to Genomics: Advancements in the Study of Subterranean Tetrapods

Abstract Throughout most of the kingdom Animalia, evolutionary transitions from surface life to a life permanently bound to caves and other subterranean habitats have occurred innumerous times. Not so in tetrapods, where a mere 14 cave-obligate species—all plethodontid and proteid salamanders—are known. We discuss why cave tetrapods are so exceptional and why only salamanders have made the transition. Their evolution follows predictable and convergent, albeit independent pathways. Among the many known changes associated with transitions to subterranean life, eye degeneration, starvation resistance, and longevity are especially relevant to human biomedical research. Recently, sequences of salamander genomes have become available opening up genomic research for cave tetrapods. We discuss new genomic methods that can spur our understanding of the evolutionary mechanisms behind convergent phenotypic change, the relative roles of selective and neutral evolution, cryptic species diversity, and data relevant for conservation such as effective population size and demography.
Hard-templated engineering of versatile 2D amorphous metal oxide nanosheets

Two-dimensional (2D) nanomaterials have received ever-increasing attention and in-depth exploration in multifarious fields on account of their superior mass transfer ability and abundant catalytic-active sites. Especially, the amorphous 2D nanomaterials feature unique properties distinct from atomic crystalline materials. However, the synthesis of high-quality and large-sized amorphous 2D nanomaterials encounters a big challenge. Here, a general and facile synthetic strategy for a series of 2D amorphous metal and nonmetallic oxides nanosheets, including SiO2, AlOOH, ZrO2 and TiO2 nanosheets, is reported. The versatile 2D amorphous nanomaterials are fabricated via manipulating the surface energy of relevant metal alkoxide precursors with liquid feature and controlling the related synthesis parameters to form solid 2D amorphous nanosheets by in situ hydrolysis and condensation of precursors. Density functional theory (DFT) calculations reveal the molecular adsorption mechanism of wetting process of precursor infiltrated on solid NaCl substrate, which attributes to the strong interaction between Na–O atom pairs from NaCl and metal alkoxides respectively. Furthermore, taking the 2D Fe–ZrO2 nanomaterials as the catalyst, the excellent catalytic performance for Rhodamine B (RhB) degradation illustrates that these 2D nanomaterials prepared by this method have the characteristics of easy functionalization. This work provides an efficient strategy for nanomaterials functionalization during 2D nanosheets synthetic process and further being applied in catalysis-related field and beyond.
Association between healthy lifestyle pattern and early onset of puberty: based on a longitudinal follow-up study

Abstract The present study aimed to explore the association between healthy lifestyle pattern and childhood early onset of puberty. Based on a cohort study in Xiamen of China, a total of 1294 children was followed for three and a half years. Children’s lifestyles, including dietary behaviour, physical activity, sleep duration, smoking and drinking behaviour and sedentary behaviour, were collected by questionnaires. Healthy lifestyle pattern was determined mainly according to the recommendations by the Dietary Guidelines for Chinese school-age children and Canadian Guidelines for children and youth. The pubertal development was assessed by clinical examination according to Tanner stages. The association between pre-pubertal lifestyle and early onset of puberty was estimated using linear regression and log-binomial regression. We found that children who adhered to a healthy lifestyle had a 0·36-year delay of the age of puberty onset (coef = 0·36, 95 % CI (0·08, 0·65)) and 53 % lower risk of early onset of puberty (risk ratio = 0·47, 95 % CI (0·27, 0·80)), compared with those who had a poor lifestyle. However, the beneficial effect of favourable lifestyles on the early onset of puberty was found only in boys with normal weight. Boys who adhered to active physical activity and low sedentary behaviour had a relatively delayed age of puberty onset (coef = 0·49, 95 % CI (0·26, 0·72)). This is the first time to find that healthy lifestyle pattern was associated with a substantially lower risk of early onset of puberty, especially in boys with normal weight. Advocating an integrated healthy lifestyle is essential for the development of children.
Pregnant and early parenting Indigenous women who use substances in Canada: A scoping review of health and social issues, supports, and strategies

Abstract This study reviews and synthesizes the literature on Indigenous women who are pregnant/early parenting and using substances in Canada to understand the scope and state of knowledge to inform research with the Aboriginal Health and Wellness Centre of Winnipeg in Manitoba and the development of a pilot Indigenous doula program. A scoping review was performed searching ten relevant databases, including one for gray literature. We analyzed 56 articles/documents. Themes include: (1) cyclical repercussions of state removal of Indigenous children from their families; (2) compounding barriers and inequities; (3) prevalence and different types of substance use; and (4) intervention strategies. Recommendations for future research are identified and discussed.
A gecko-inspired robot with CPG-based neural control for locomotion and body height adaptation

Today’s gecko-inspired robots have shown the ability of omnidirectional climbing on slopes with a low centre of mass. However, such an ability cannot efficiently cope with bumpy terrains or terrains with obstacles. In this study, we developed a gecko-inspired robot (Nyxbot) with an adaptable body height to overcome this limitation. Based on an analysis of the skeletal system and kinematics of real geckos, the adhesive mechanism and leg structure design of the robot were designed to endow it with adhesion and adjustable body height capabilities. Neural control with exteroceptive sensory feedback is utilised to realise body height adaptability while climbing on a slope. The locomotion performance and body adaptability of the robot were tested by conducting slope climbing and obstacle crossing experiments. The gecko robot can climb a 30° slope with spontaneous obstacle crossing (maximum obstacle height of 38% of the body height) and can climb even steeper slopes (up to 60°) without an obstacle or bump. Using 3D force measuring platforms for ground reaction force analysis of geckos and the robot, we show that the motions of the developed robot driven by neural control and the motions of geckos are dynamically comparable. To this end, this study provides a basis for developing climbing robots with adaptive bump/obstacle crossing on slopes towards more agile and versatile gecko-like locomotion.
A System for Secured Data Retrieval in Hostile Regions using CP-ABE Based in Ad Hoc Disruption- Tolerant Networks

Generally Networks operated in ad hoc mode suffer isolated network connectivity in the Hostile Military environments like battlefield. Deployment of Disruption-tolerant networks (DTN) enhances the connectivity between wireless devices carried by soldiers in battle field, this provides them to communicate effectively and share the information confidently. Cipertext -policy attribute based encryption (CP-ABE) is effective cryptographic technique to access control issues. Ad hoc network are decentralized and resource constrained networks, applying CP-ABE to such networks is a challenging issue, in turn it introduces new security and privacy issues related to attribute revocation, coordination of attributes, and key escrow. This paper mainly focuses on a secure data collection mechanism using CP-ABE for ad hoc DTNs where more than one key authority manages their attributes dynamically and independently. We analyzed the proposed mechanism and applied to the disruption-tolerant military network to access the information securely.