Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Frank-Wolfe with Unknown Delays

The online Frank-Wolfe (OFW) method has gained much popularity for online convex optimization due to its projection-free property. Previous studies showed that for convex losses, OFW attains O ( T 3 / 4 ) regret over general sets and O ( T 2 / 3 ) regret over strongly convex sets, and if losses are strongly convex, these bounds can be improved to O ( T 2 / 3 ) and O ( √ T ), respectively. However, they assumed that each gradient queried by OFW is revealed immediately, which may not hold in practice. In this paper, we consider a more practical setting where gradients arrive with arbitrary and unknown delays, and propose delayed OFW which generalizes OFW to this setting. The main idea is to perform an update similar to OFW after receiving any gradient, and play the latest decision for each round. We ﬁrst show that for convex losses, delayed OFW achieves O ( T 3 / 4 + dT 1 / 4 ) regret over general sets and O ( T 2 / 3 + dT 1 / 3 ) regret over strongly convex sets, where d is the maximum delay. Furthermore, we prove that for strongly convex losses, delayed OFW attains O ( T 2 / 3 + d log T ) regret over general sets and O ( √ T + d log T ) regret over strongly convex sets. Compared with regret bounds in the non-delayed setting, our results imply that the proposed method is robust to a relatively large amount of delay.
On the link between conscious function and general intelligence in humans and machines

In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this work, we explore the validity and potential application of this seemingly intuitive link between consciousness and intelligence. We do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST). We find that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Having identified this trend, we use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a single unified and implementable model. Given that it is made possible by cognitive abilities underlying each of the three functional theories, artificial agents capable of mental time travel would not only possess greater general intelligence than current approaches, but also be more consistent with our current understanding of the functional role of consciousness in humans, thus making it a promising near-term goal for AI research.
Size-dependent static bending of a micro-beam with a surface-mounted 0–1 polarized PbLaZrTi actuator under various boundary conditions

This article presents modified couple stress theory–based size-dependent deflection curve equations for a micro-beam with a surface-mounted 0–1 polarized PbLaZrTi patch under various boundary conditions. In these models, only an internal material length scale parameter is used to capture the size-dependent mechanical behaviors. When this parameter is set to zero, these models are simplified to the available solution based on the classical Euler–Bernoulli theory. The effect of the actuator stiffness on the deflection of the beam is thoroughly discussed. It is observed that whether the deformation of the beam is sensitive to the added actuator or not depending on the stiffness of the host beam. The larger the stiffness of the host beam, the less the effect of the added actuator. It is also observed that the less the ratio of the beam thickness to the actuator stiffness, the larger the ratio of the size-dependent solution to the local one.
From CNNs to Vision Transformers – A Comprehensive Evaluation of Deep Learning Models for Histopathology

—While machine learning is currently transforming the ﬁeld of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classiﬁcation accuracy. In order to ﬁll this gap, we conducted an extensive evaluation by benchmarking a wide range of classiﬁcation models, including recent vision transformers, convolutional neural networks and hybrid models comprising transformer and convolutional models. We thoroughly tested the models on ﬁve widely used histopathology datasets containing whole slide images of breast, gastric, and colorectal cancer and developed a novel approach using an image-to-image translation model to assess the robustness of a cancer classiﬁcation model against stain variations. Further, we extended existing interpretability methods to previously unstudied models and systematically reveal insights of the models’ classiﬁcation strategies that allow for plausibility checks and systematic comparisons. The study resulted in speciﬁc model recommendations for practitioners as well as putting forward a general methodology to quantify a model’s quality according to complementary requirements that can be transferred to future model architectures.
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks

Multiplication-less neural networks significantly reduce the time and energy cost on the hardware platform, as the compute-intensive multiplications are replaced with lightweight bit-shift operations. However, existing bit-shift networks are all directly transferred from state-of-the-art convolutional neural networks (CNNs), which lead to non-negligible accuracy drop or even failure of model convergence. To combat this, we propose ShiftNAS, the first framework tailoring Neural Architecture Search (NAS) to substantially reduce the accuracy gap between bit-shift neural networks and their real-valued counterparts. Specifically, we pioneer dragging NAS into a shift-oriented search space and endow it with the robust topology-related search strategy and custom regularization and stabilization. As a result, our ShiftNAS breaks through the incompatibility of traditional NAS methods for bit-shift neural networks and achieves more desirable performance in terms of accuracy and convergence. Extensive experiments demonstrate that ShiftNAS sets a new state-of-the-art for bit-shift neural networks, where the accuracy increases (1.69-8.07)% on CIFAR10, (5.71-18.09)% on CIFAR100 and (4.36-67.07)% on ImageNet, especially when many conventional CNNs fail to converge on ImageNet with bit-shift weights.
MedDistant19: A Challenging Benchmark for Distantly Supervised Biomedical Relation Extraction

Relation Extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts. Distant supervision is commonly used as a way to tackle the scarcity of annotated data by automatically pairing knowledge graph relationships with raw texts. Dis-tantly Supervised Biomedical Relation Extraction (Bio-DSRE) models can seemingly produce very accurate results in several benchmarks. However, given the challenging nature of the task, we set out to investigate the validity of such impressive results. We probed the datasets used by Amin et al. (2020) and Hogan et al. (2021) and found a signiﬁcant overlap between training and evaluation relationships that, once resolved, reduced the accuracy of the models by up to 71%. Furthermore, we noticed several inconsistencies with the data construction process, such as creating negative samples and improper handling of redundant relationships. We mitigate these issues and present M ED D ISTANT 19, a new benchmark dataset obtained by aligning the MEDLINE abstracts with the widely used SNOMED Clinical Terms (SNOMED CT) knowledge base. We experimented with several state-of-the-art models achieving an AUC of 55.4% and 49.8% at sentence-and bag-level, showing that there is still plenty of room for improvement.
Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation

This paper presents Video K-Net, a simple, strong, and unified framework for fully end-to-end video panoptic seg-mentation. The method is built upon K-Net, a method that unifies image segmentation via a group of learnable ker-nels. We observe that these learnable kernels from K-Net, which encode object appearances and contexts, can naturally associate identical instances across video frames. Motivated by this observation, Video K-Net learns to simultaneously segment and track “things” and “stuff” in a video with simple kernel-based appearance modeling and cross-temporal kernel interaction. Despite the simplicity, it achieves state-of-the-art video panoptic segmentation results on Citscapes-VPS and KITTI-STEP without bells and whistles. In particular on KITTI-STEP, the simple method can boost almost 12% relative improvements over previous methods. We also validate its generalization on video semantic segmentation, where we boost various baselines by 2% on the VSPW dataset. Moreover, we extend K-Net into clip-level video framework for video instance segmentation where we obtain 40.5% for ResNet50 backbone and 51.5% mAP for Swin-base on YouTube-2019 validation set. We hope this simple yet effective method can serve as a new flexible baseline in video segmentation.11Both code and models are released at here.
Learning Object-Centered Autotelic Behaviors with Graph Neural Networks

Although humans live in an open-ended world and endlessly face new challenges, they do not have to learn from scratch each time they face the next one. Rather, they have access to a handful of previously learned skills, which they rapidly adapt to new situations. In artificial intelligence, autotelic agents, which are intrinsically motivated to represent and set their own goals, exhibit promising skill adaptation capabilities. However, these capabilities are highly constrained by their policy and goal space representations. In this paper, we propose to investigate the impact of these representations on the learning and transfer capabilities of autotelic agents. We study different implementations of autotelic agents using four types of Graph Neural Networks policy representations and two types of goal spaces, either geometric or predicate-based. By testing agents on unseen goals, we show that combining object-centered architectures that are expressive enough with semantic relational goals helps learning to reach more difficult goals. We also release our graph-based implementations to encourage further research in this direction.
Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis

Abstract Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.
Unbiased Multilevel Monte Carlo methods for intractable distributions: MLMC meets MCMC

Constructing unbiased estimators from Markov chain Monte Carlo (MCMC) outputs is a difficult problem that has recently received a lot of attention in the statistics and machine learning communities. However, the current unbiased MCMC framework only works when the quantity of interest is an expectation, which excludes many practical applications. In this paper, we propose a general method for constructing unbiased estimators for functions of expectations and extend it to construct unbiased estimators for nested expectations. Our approach combines and generalizes the unbiased MCMC and Multilevel Monte Carlo (MLMC) methods. In contrast to traditional sequential methods, our estimator can be implemented on parallel processors. We show that our estimator has a finite variance and computational complexity and can achieve $\varepsilon$-accuracy within the optimal $O(1/\varepsilon^2)$ computational cost under mild conditions. Our numerical experiments confirm our theoretical findings and demonstrate the benefits of unbiased estimators in the massively parallel regime.
A comparative analysis on water quality in an urban micro watershed

Purpose This study presents a comparative analysis of water quality data in an urban micro watershed to study the magnitude of impacts on the water quality parameters over the last decade. The purpose of this paper is to evaluate the degree of deterioration using the water quality index. Design/methodology/approach Rapid urban growth without proper land use and occupation planning results in the overload of urban water resources. Therefore, a literature review was conducted on the research subject published in the dissertation databases of the Engineering Faculty of Ilha Solteira, which resulted in the selection of two dissertations on water quality in the Ipe Stream, Ilha Solteira – SP, Brazil. The results will be evaluated according to the Brazilian laws and regulations in force. Findings This study shows that pollution and degradation in the stream intensified during the study period, with the most impacted areas within the urban perimeter. Practical implications The increasing impacts underscore the need for efficient measures such as implementation of retention reservoirs, elimination of clandestine sewage connections and restoration of riparian forests. Originality/value This study highlights the need to monitor the water quality of streams in order to establish preventive and mitigating measures to avert the growing environmental impacts and to ensure quality water for future generations.
Active Learning with Label Comparisons

Supervised learning typically relies on manual annotation of the true labels. When there are many potential classes, searching for the best one can be prohibitive for a human annotator. On the other hand, comparing two candidate labels is often much easier. We focus on this type of pairwise supervision and ask how it can be used effectively in learning, and in particular in active learning. We obtain several insightful results in this context. In principle, finding the best of $k$ labels can be done with $k-1$ active queries. We show that there is a natural class where this approach is sub-optimal, and that there is a more comparison-efficient active learning scheme. A key element in our analysis is the"label neighborhood graph"of the true distribution, which has an edge between two classes if they share a decision boundary. We also show that in the PAC setting, pairwise comparisons cannot provide improved sample complexity in the worst case. We complement our theoretical results with experiments, clearly demonstrating the effect of the neighborhood graph on sample complexity.
Quasistatic Evolution with Unstable Forces

We consider load controlled quasistatic evolution. Well posedness results for the nonlocal continuum model related to peridynamics are established. We show local existence and uniqueness of quasistatic evolution for load paths originating at stable critical points. These points can be associated with local energy minima among the convex set of deformations belonging to the strength domain of the material. The evolution of the displacements however is not constrained to lie inside the strength domain of the material. The load-controlled evolution is shown to exhibit energy balance.
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

We show that deep networks trained to satisfy demographic parity often do so through a form of race or gender awareness, and that the more we force a network to be fair, the more accurately we can recover race or gender from the internal state of the network. Based on this observation, we investigate an alternative fairness approach: we add a second classification head to the network to explicitly predict the protected attribute (such as race or gender) alongside the original task. After training the two-headed network, we enforce demographic parity by merging the two heads, creating a network with the same architecture as the original network. We establish a close relationship between existing approaches and our approach by showing (1) that the decisions of a fair classifier are well-approximated by our approach, and (2) that an unfair and optimally accurate classifier can be recovered from a fair classifier and our second head predicting the protected attribute. We use our explicit formulation to argue that the existing fairness approaches, just as ours, demonstrate disparate treatment and that they are likely to be unlawful in a wide range of scenarios under US law.
S4OD: Semi-Supervised learning for Single-Stage Object Detection

Single-stage detectors suffer from extreme foreground-background class imbalance, while two-stage detectors do not. Therefore, in semisupervised object detection, two-stage detectors can deliver remarkable performance by only selecting high-quality pseudo labels based on classification scores. However, directly applying this strategy to single-stage detectors would aggravate the class imbalance with fewer positive samples. Thus, single-stage detectors have to consider both quality and quantity of pseudo labels simultaneously. In this paper, we design a dynamic self-adaptive threshold (DSAT) strategy in classification branch, which can automatically select pseudo labels to achieve an optimal trade-off between quality and quantity. Besides, to assess the regression quality of pseudo labels in single-stage detectors, we propose a module to compute the regression uncertainty of boxes based on Non-Maximum Suppression. By leveraging only 10% labeled data from COCO, our method achieves 35.0% AP on anchor-free detector (FCOS) and 32.9% on anchor-based detector (RetinaNet).
Distributed Evolution Strategies for Black-box Stochastic Optimization

This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms. We propose a distributed evolution strategy (DES) algorithm grounded on a proper modification to evolution strategies, a family of classic evolutionary algorithms, as well as a careful combination with existing distributed frameworks. On smooth and nonconvex landscapes, DES has a convergence rate competitive to existing zeroth-order methods, and can exploit the sparsity, if applicable, to match the rate of first-order methods. The DES method uses a Gaussian probability model to guide the search and avoids the numerical issue resulted from finite-difference techniques in existing zeroth-order methods. The DES method is also fully adaptive to the problem landscape, as its convergence is guaranteed with any parameter setting. We further propose two alternative sampling schemes which significantly improve the sampling efficiency while leading to similar performance. Simulation studies on several machine learning problems suggest that the proposed methods show much promise in reducing the convergence time and improving the robustness to parameter settings.
Correcting Robot Plans with Natural Language Feedback

When humans design cost or goal specifications for robots, they often produce specifications that are ambiguous, underspecified, or beyond planners' ability to solve. In these cases, corrections provide a valuable tool for human-in-the-loop robot control. Corrections might take the form of new goal specifications, new constraints (e.g. to avoid specific objects), or hints for planning algorithms (e.g. to visit specific waypoints). Existing correction methods (e.g. using a joystick or direct manipulation of an end effector) require full teleoperation or real-time interaction. In this paper, we explore natural language as an expressive and flexible tool for robot correction. We describe how to map from natural language sentences to transformations of cost functions. We show that these transformations enable users to correct goals, update robot motions to accommodate additional user preferences, and recover from planning errors. These corrections can be leveraged to get 81% and 93% success rates on tasks where the original planner failed, with either one or two language corrections. Our method makes it possible to compose multiple constraints and generalizes to unseen scenes, objects, and sentences in simulated environments and real-world environments.
Path-Tree Optimization in Partially Observable Environments using Rapidly-Exploring Belief-Space Graphs

—Robots often need to solve path planning problems where essential and discrete aspects of the environment are partially observable. This introduces a multi-modality, where the robot must be able to observe and infer the state of its environment. To tackle this problem, we introduce the Path-Tree Optimization (PTO) algorithm which plans a path-tree in belief-space. A path-tree is a tree-like motion with branching points where the robot receives an observation leading to a belief-state update. The robot takes different branches depending on the observation received. The algorithm has three main steps. First, a rapidly-exploring random graph (RRG) on the state space is grown. Second, the RRG is expanded to a belief-space graph by querying the observation model. In a third step, dynamic programming is performed on the belief-space graph to extract a path-tree. The resulting path-tree combines exploration with exploitation i.e. it balances the need for gaining knowledge about the environment with the need for reaching the goal. We demonstrate the algorithm capabilities on navigation and mobile manipulation tasks, and show its advantage over a baseline using a task and motion planning approach (TAMP) both in terms of optimality and runtime.