Miracle-dz/UniFAR-FTUs huggingface dataset:
On the bias, risk and consistency of sample means in multi-armed bandits

The sample mean is among the most well studied estimators in statistics, having many desirable properties such as unbiasedness and consistency. However, when analyzing data collected using a multi-armed bandit (MAB) experiment, the sample mean is biased and much remains to be understood about its properties. For example, when is it consistent, how large is its bias, and can we bound its mean squared error? This paper delivers a thorough and systematic treatment of the bias, risk and consistency of MAB sample means. Specifically, we identify four distinct sources of selection bias (sampling, stopping, choosing and rewinding) and analyze them both separately and together. We further demonstrate that a new notion of \emph{effective sample size} can be used to bound the risk of the sample mean under suitable loss functions. We present several carefully designed examples to provide intuition on the different sources of selection bias we study. Our treatment is nonparametric and algorithm-agnostic, meaning that it is not tied to a specific algorithm or goal. In a nutshell, our proofs combine variational representations of information theoretic divergences with new martingale concentration inequalities.
Desikan-Killiany-Tourville Atlas Compatible Version of M-CRIB Neonatal Parcellated Whole Brain Atlas: The M-CRIB 2.0

Our recently published M-CRIB atlas comprises 100 neonatal brain regions including 68 compatible with the widely-used Desikan-Killiany adult cortical atlas. A successor to the Desikan-Killiany atlas is the Desikan-Killiany-Tourville atlas, in which some regions with unclear boundaries were removed, and many existing boundaries were revised to conform to clearer landmarks in sulcal fundi. Our first aim here was to modify cortical M-CRIB regions to comply with the Desikan-Killiany-Tourville protocol, in order to offer: a) compatibility with this adult cortical atlas, b) greater labelling accuracy due to clearer landmarks, and c) optimisation of cortical regions for integration with surface-based infant parcellation pipelines. Secondly, we aimed to update subcortical regions in order to offer greater compatibility with subcortical segmentations produced in FreeSurfer. Data utilized were the T2-weighted MRI scans in our M-CRIB atlas, for ten healthy neonates (postmenstrual age at MRI 40-43 weeks, 4 female), and corresponding parcellated images. Edits were performed on the parcellated images in volume space using ITK-SNAP. Cortical updates included deletion of frontal and temporal poles and ‘Banks STS’, and modification of boundaries of many other regions. Changes to subcortical regions included the addition of ‘ventral diencephalon’, and deletion of ‘subcortical matter’ labels. A detailed updated parcellation protocol was produced. The resulting whole-brain M-CRIB 2.0 atlas comprises 94 regions altogether. This atlas provides comparability with adult Desikan-Killiany-Tourville-labelled cortical data and FreeSurfer-labelled subcortical data, and is more readily adaptable for incorporation into surface-based neonatal parcellation pipelines. As such, it offers the ability to help facilitate a broad range of investigations into brain structure and function both at the neonatal time point and developmentally across the lifespan.
Cdrom Archive: A Gateway to Study Camel Phenotypes

Camels are livestock that exhibit unique morphological, biochemical, and behavioral traits, which arose by natural and artificial selection. Investigating the molecular basis of camel traits has been limited by: (1) the absence of a comprehensive record of morphological trait variation (e.g., diseases) and the associated mode of inheritance, (2) the lack of extended pedigrees of specific trait(s), and (3) the long reproductive cycle of the camel, which makes the cost of establishing and maintaining a breeding colony (i.e., monitoring crosses) prohibitively high. Overcoming these challenges requires (1) detailed documentation of phenotypes/genetic diseases and their likely mode of inheritance (and collection of related DNA samples), (2) conducting association studies to identify phenotypes/genetic diseases causing genetic variants (instead of classical linkage analysis, which requires extended pedigrees), and (3) validating likely causative variants by screening a large number of camel samples from different populations. We attempt to address these issues by establishing a systematic way of collecting camel DNA samples, and associated phenotypic information, which we call the “Cdrom Archive. ” Here, we outline the process of building this archive to introduce it to other camel researchers (as an example). Additionally, we discuss the use of this archive to study the phenotypic traits of Arabian Peninsula camel breeds (the “Mezayen” camels). Using the Cdrom Archive, we report variable phenotypic traits related to the coat (color, length, and texture), ear and tail lengths, along with other morphological measurements.
Serum Lipoprotein Profile Is Associated With Protective Effects of Oral Contraceptive Use on Multiple Sclerosis Severity: A Cross-Sectional Study

Background: The mechanisms underlying the influence of sex hormones in multiple sclerosis (MS) are uncertain. Sex steroids interact with cholesterol metabolism and the serum lipid profile has been associated with the severity of the disease. We hypothesized that the putative associations between lipoprotein metabolism and MS could be modulated by sex steroids exposure. The aim of this study was to investigate whether oral contraceptives (OC) use changes the lipoprotein profile associated with disability in patients with multiple sclerosis. Methods: Clinical data was collected from 133 relapsing-remitting multiple sclerosis (RRMS) women with a mean of 6.5 years of disease duration and prior to the start of disease-modifying therapies. Patients who were using OC after disease onset (DO) (OC+, n = 57) were compared to those who never used OC or discontinued its intake before DO (OC–, n = 76). In both cohorts of subjects, the associations between the apolipoprotein E (ApoE) polymorphism, and plasma lipid levels, and the annualized relapse rate (RR), the Expanded Disability Status Score (EDSS), and the Multiple Sclerosis Severity Score (MSSS) were evaluated using a hierarchic multiple regression analysis after adjustment for confounders. Results: Low density lipoprotein (LDL) levels were associated with higher EDSS (p = 0.010) and MSSS (p = 0.024) in the whole studied cohort. In E3/E3 phenotype carriers (73.7%), EDSS and MSSS were lower in OC+ in comparison with OC– subgroup of patients (p < 0.01). LDL and total cholesterol were associated with EDSS (p = 0.005 and p = 0.043, respectively), and LDL and the triglyceride/high density lipoprotein ratio with MSSS (p = 0.011 and p = 0.048, respectively) in OC+ patients. In OC– subgroup of patients, ApoE levels were associated with EDSS (p = 0.012) and MSSS (p = 0.031). No significant interactions between the lipid variables or OC use and RR were observed. Conclusions: Serum lipid profile is associated with protective effects of OC use on disability of RRMS patients. Lipoprotein metabolism may be involved in the modulatory effects of sex steroids on the severity of the disease.
Identification of Loci and Candidate Genes Responsible for Fiber Length in Upland Cotton (Gossypium hirsutum L.) via Association Mapping and Linkage Analyses

Fiber length (FL) is an important fiber quality trait in cotton. Although many fiber quality quantitative trait loci (QTL) responsible for FL have been identified, most cannot be applied to breeding programs, mainly due to unstable environments or large confidence intervals. In this study, we combined a genome-wide association study (GWAS) and linkage mapping to identify and validate high-quality QTLs responsible for FL. For the GWAS, we developed 93,250 high-quality single-nucleotide polymorphism (SNP) markers based on 355 accessions, and the FL was measured in eight different environments. For the linkage mapping, we constructed an F2 population from two extreme accessions. The high-density linkage maps spanned 3,848.29 cM, with an average marker interval of 1.41 cM. In total, 14 and 13 QTLs were identified in the association and linkage mapping analyses, respectively. Most importantly, a major QTL on chromosome D03 identified in both populations explained more than 10% of the phenotypic variation (PV). Furthermore, we found that a sucrose synthesis-related gene (Gh_D03G1338) was associated with FL in this QTL region. The RNA-seq data showed that Gh_D03G1338 was highly expressed during the fiber development stage, and the qRT-PCR analysis showed significant expression differences between the long fiber and short fiber varieties. These results suggest that Gh_D03G1338 may determine cotton fiber elongation by regulating the synthesis of sucrose. Favorable QTLs and candidate genes should be useful for increasing fiber quality in cotton breeding.
CapStore: Energy-Efficient Design and Management of the On-Chip Memory for CapsuleNet Inference Accelerators

Deep Neural Networks (DNNs) have been established as the state-of-the-art algorithm for advanced machine learning applications. Recently, CapsuleNets have improved the generalization ability, as compared to DNNs, due to their multi-dimensional capsules. However, they pose high computational and memory requirements, which makes energy-efficient inference a challenging task. In this paper, we perform an extensive analysis to demonstrate their key limitations due to intense memory accesses and large on-chip memory requirements. To enable efficient CaspuleNet inference accelerators, we propose a specialized on-chip memory hierarchy which minimizes the off-chip memory accesses, while efficiently feeding the data to the accelerator. We analyze the on-chip memory requirements for each memory component of the architecture. By leveraging this analysis, we propose a methodology to explore different on-chip memory designs and a power-gating technique to further reduce the energy consumption, depending upon the utilization across different operations of a CapsuleNet. Our memory designs can significantly reduce the energy consumption of the on-chip memory by up to 86%, when compared to a state-of-the-art memory design. Since the power consumption of the memory elements is the major contributor in the power breakdown of the CapsuleNet accelerator, as we will also show in our analyses, the proposed memory design can effectively reduce the overall energy consumption of the complete CapsuleNet accelerator architecture.
New Algorithms for Multiplayer Bandits when Arm Means Vary Among Players

We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. Moreover, we assume each arm has a different mean for each player. Let T denote the number of rounds. An algorithm with regret O ((log T ) 2+ κ ) for any constant κ was recently presented by Bistritz and Leshem (NeurIPS 2018), who left the existence of an algorithm with O (log T ) regret as an open question. In this paper, we provide an afﬁrmative answer to this question in the case when there is a unique optimal assignment of players to arms. For the general case we present an algorithm with expected regret O ((log T ) 1+ κ ) , for any κ > 0 .
Joint Cluster Unary Loss for Efficient Cross-Modal Hashing

Recently, cross-modal deep hashing has received broad attention for solving cross-modal retrieval problems efficiently. Most cross-modal hashing methods generate $O(n^2)$ data pairs and $O(n^3)$ data triplets for training, but the training procedure is less efficient because the complexity is high for large-scale dataset. In this paper, we propose a novel and efficient cross-modal hashing algorithm named Joint Cluster Cross-Modal Hashing (JCCH). First, We introduce the Cross-Modal Unary Loss (CMUL) with $O(n)$ complexity to bridge the traditional triplet loss and classification-based unary loss, and the JCCH algorithm is introduced with CMUL. Second, a more accurate bound of the triplet loss for structured multilabel data is introduced in CMUL. The resultant hashcodes form several clusters in which the hashcodes in the same cluster share similar semantic information, and the heterogeneity gap on different modalities is diminished by sharing the clusters. Experiments on large-scale datasets show that the proposed method is superior over or comparable with state-of-the-art cross-modal hashing methods, and training with the proposed method is more efficient than others.
Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodeling

Computational immunology studies the interactions between the components of the immune system that includes the interplay between regulatory and inflammatory elements. It provides a solid framework that aids the conversion of pre-clinical and clinical data into mathematical equations to enable modeling and in silico experimentation. The modeling-driven insights shed lights on some of the most pressing immunological questions and aid the design of fruitful validation experiments. A typical system of equations, mapping the interaction among various immunological entities and a pathogen, consists of a high-dimensional input parameter space that could drive the stochastic system outputs in unpredictable directions. In this paper, we perform spatio-temporal metamodel-based sensitivity analysis of immune response to Helicobacter pylori infection using the computational model developed by the ENteric Immune SImulator. We propose a two-stage procedure to obtain the estimates of the Sobol’ total and first-order indices for each input parameter, for quantifying their time-varying impacts on each output of interest. In particular, we fully reuse and exploit information from an existing simulated dataset, develop a novel sampling design for constructing the two-stage metamodels, and perform metamodel-based sensitivity analysis. The proposed procedure is scalable, easily interpretable, and adaptable to any multi-input multi-output complex systems of equations with a high-dimensional input parameter space.
Online Multiclass Classification Based on Prediction Margin for Partial Feedback

We consider the problem of online multiclass classification with partial feedback, where an algorithm predicts a class for a new instance in each round and only receives its correctness. Although several methods have been developed for this problem, recent challenging real-world applications require further performance improvement. In this paper, we propose a novel online learning algorithm inspired by recent work on learning from complementary labels, where a complementary label indicates a class to which an instance does not belong. This allows us to handle partial feedback deterministically in a margin-based way, where the prediction margin has been recognized as a key to superior empirical performance. We provide a theoretical guarantee based on a cumulative loss bound and experimentally demonstrate that our method outperforms existing methods which are non-margin-based and stochastic.
Adaptive stochastic gradient algorithms on Riemannian manifolds

Adaptive stochastic gradient algorithms in the Euclidean space have attracted much attention lately. Such explorations on Riemannian manifolds, on the other hand, are relatively new, limited, and challenging. This is because of the intrinsic non-linear structure of the underlying manifold and the absence of a canonical coordinate system. In machine learning applications, however, most manifolds of interest are represented as matrices with notions of row and column subspaces. In addition, the implicit manifold-related constraints may also lie on such subspaces. For example, the Grassmann manifold is the set of column subspaces. To this end, such a rich structure should not be lost by transforming matrices into just a stack of vectors while developing optimization algorithms on manifolds. We propose novel stochastic gradient algorithms for problems on Riemannian manifolds by adapting the row and column subspaces of gradients. Our algorithms are provably convergent and they achieve the convergence rate of order ${O}(\log (T)/\sqrt{T})$, where $T$ is the number of iterations. Our experiments illustrate that the proposed algorithms outperform existing Riemannian adaptive stochastic algorithms.
A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories

The problem of maneuvering a vehicle through a race course in minimum time requires computation of both longitudinal (brake and throttle) and lateral (steering wheel) control inputs. Unfortunately, solving the resulting nonlinear optimal control problem is typically computationally expensive and infeasible for real-time trajectory planning. This paper presents an iterative algorithm that divides the path generation task into two sequential subproblems that are significantly easier to solve. Given an initial path through the race track, the algorithm runs a forward-backward integration scheme to determine the minimum-time longitudinal speed profile, subject to tire friction constraints. With this speed profile fixed, the algorithm updates the vehicle’s path by solving a convex optimization problem that minimizes the resulting path curvature while staying within track boundaries and obeying affine, time-varying vehicle dynamics constraints. This two-step process is repeated iteratively until the predicted lap time no longer improves. While providing no guarantees of convergence or a globally optimal solution, the approach performs well when tested on the Thunderhill Raceway course in Willows, CA. The lap time reaches a minimum value after only three iterations, with each iteration over the full 5 km race course requiring only thirty seconds of computation time on a laptop computer. The resulting vehicle path and speed profile match very well with a nonlinear gradient descent solution and a path driven by a professional racecar driver, indicating that the proposed method is a viable option for online trajectory planning in the near future.Copyright © 2015 by ASME
fNIRS Responses in Professional Violinists While Playing Duets: Evidence for Distinct Leader and Follower Roles at the Brain Level

Music played in ensembles is a naturalistic model to study joint action and leader-follower relationships. Recently, the investigation of the brain underpinnings of joint musical actions has gained attention; however, the cerebral correlates underlying the roles of leader and follower in music performance remain elusive. The present study addressed this question by simultaneously measuring the hemodynamic correlates of functional neural activity elicited during naturalistic violin duet performance using fNIRS. Findings revealed distinct patterns of functional brain activation when musicians played the Violin 2 (follower) than the Violin 1 part (leader) in duets, both compared to solo performance. More specifically, results indicated that musicians playing the Violin 2 part had greater oxy-Hb activation in temporo-parietal (p = 0.02) and somatomotor (p = 0.04) regions during the duo condition in relation to the solo. On the other hand, there were no significant differences in the activation of these areas between duo/solo conditions during the execution of the Violin 1 part (p’s > 0.05). These findings suggest that ensemble cohesion during a musical performance may impose particular demands when musicians play the follower position, especially in brain areas associated with the processing of dynamic social information and motor simulation. This study is the first to use fNIRS hyperscanning technology to simultaneously measure the brain activity of two musicians during naturalistic music ensemble performance, opening new avenues for the investigation of brain correlates underlying joint musical actions with multiple subjects in a naturalistic environment.
Estimation with Fast Landmark Selection in Robot Visual Navigation

We consider the visual feature selection to improve the estimation quality required for the accurate navigation of a robot. We build upon a key property that asserts: contributions of trackable features (landmarks) appear linearly in the information matrix of the corresponding estimation problem. We utilize standard models for motion and vision system using a camera to formulate the feature selection problem over moving finite time horizons. A scalable randomized sampling algorithm is proposed to select more informative features (and ignore the rest) to achieve a superior position estimation quality. We provide probabilistic performance guarantees for our method. The time-complexity of our feature selection algorithm is linear in the number of candidate features, which is practically plausible and outperforms existing greedy methods that scale quadratically with the number of candidates features. Our numerical simulations confirm that not only the execution time of our proposed method is comparably less than that of the greedy method, but also the resulting estimation quality is very close to the greedy method.
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression

State-of-the-art deep model compression methods exploit the low-rank approximation and sparsity pruning to remove redundant parameters from a learned hidden layer. However, they process each hidden layer individually while neglecting the common components across layers, and thus are not able to fully exploit the potential redundancy space for compression. To solve the above problem and enable further compression of a model, removing the cross-layer redundancy and mining the layer-wise inheritance knowledge is necessary. In this paper, we introduce a holistic model compression framework, namely MIning Cross-layer Inherent similarity Knowledge (MICIK), to fully excavate the potential redundancy space. The proposed MICIK framework simultaneously, (1) learns the common and unique weight components across deep neural network layers to increase compression rate; (2) preserves the inherent similarity knowledge of nearby layers and distant layers to minimize the accuracy loss and (3) can be complementary to other existing compression techniques such as knowledge distillation. Extensive experiments on large-scale convolutional neural networks demonstrate that MICIK is superior over state-of-the-art model compression approaches with 16X parameter reduction on VGG-16 and 6X on GoogLeNet, all without accuracy loss.
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.