In this article, we propose using network-based sampling strategies to estimate the number of unsheltered people experiencing homelessness within a given administrative service unit, known as a Continuum of Care. Further, we specifically advocate for the network sampling method known as Respondent Driven Sampling (RDS), which has been shown to provide unbiased or low-biased estimates of totals and proportions for hard-to-reach populations in contexts where a sampling frame (e.g., housing addresses) not available. To make the RDS estimator work for estimating the total number of unsheltered people, we introduce a new method that leverages administrative data from the HUD-mandated Homeless Management Information System (HMIS). The HMIS provides high-quality counts and demographics for people experiencing homelessness who sleep in emergency shelters. We then demonstrate this method using network data collected in Nashville, TN, combined with simulation methods to illustrate the efficacy of this approach. Finally, we end with discussing how this could be used in practice.
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Language Models (LMs) often must integrate facts they memorized in pretraining with new information that appears in a given context. These two sources can disagree, causing competition within the model, and it is unclear how an LM will resolve the conflict. On a dataset that queries for knowledge of world capitals, we investigate both distributional and mechanistic determinants of LM behavior in such situations. Specifically, we measure the proportion of the time an LM will use a counterfactual prefix (e.g., "The capital of Poland is London") to overwrite what it learned in pretraining ("Warsaw"). On Pythia and GPT2, the training frequency of both the query country ("Poland") and the in-context city ("London") highly affect the models' likelihood of using the counterfactual. We then use head attribution to identify individual attention heads that either promote the memorized answer or the in-context answer in the logits. By scaling up or down the value vector of these heads, we can control the likelihood of using the in-context answer on new data. This method can increase the rate of generating the in-context answer to 88\% of the time simply by scaling a single head at runtime. Our work contributes to a body of evidence showing that we can often localize model behaviors to specific components and provides a proof of concept for how future methods might control model behavior dynamically at runtime.
With the ongoing efforts to empower people with mobility impairments and the increase in technological acceptance by the general public, assistive technologies, such as collaborative robotic arms, are gaining popularity. Yet, their widespread success is limited by usability issues, specifically the disparity between user input and software control along the autonomy continuum. To address this, shared control concepts provide opportunities to combine the targeted increase of user autonomy with a certain level of computer assistance. This paper presents the free and open-source AdaptiX XR framework for developing and evaluating shared control applications in a high-resolution simulation environment. The initial framework consists of a simulated robotic arm with an example scenario in Virtual Reality (VR), multiple standard control interfaces, and a specialized recording/replay system. AdaptiX can easily be extended for specific research needs, allowing Human-Robot Interaction (HRI) researchers to rapidly design and test novel interaction methods, intervention strategies, and multi-modal feedback techniques, without requiring an actual physical robotic arm during the early phases of ideation, prototyping, and evaluation. Also, a Robot Operating System (ROS) integration enables the controlling of a real robotic arm in a PhysicalTwin approach without any simulation-reality gap. Here, we review the capabilities and limitations of AdaptiX in detail and present three bodies of research based on the framework. AdaptiX can be accessed at //adaptix.robot-research.de.
Hate speech on social media threatens the mental and physical well-being of individuals and is further responsible for real-world violence. An important driver behind the spread of hate speech and thus why hateful posts can go viral are reshares, yet little is known about why users reshare hate speech. In this paper, we present a comprehensive, causal analysis of the user attributes that make users reshare hate speech. However, causal inference from observational social media data is challenging, because such data likely suffer from selection bias, and there is further confounding due to differences in the vulnerability of users to hate speech. We develop a novel, three-step causal framework: (1) We debias the observational social media data by applying inverse propensity scoring. (2) We use the debiased propensity scores to model the latent vulnerability of users to hate speech as a latent embedding. (3) We model the causal effects of user attributes on users' probability of sharing hate speech, while controlling for the latent vulnerability of users to hate speech. Compared to existing baselines, a particular strength of our framework is that it models causal effects that are non-linear, yet still explainable. We find that users with fewer followers, fewer friends, and fewer posts share more hate speech. Younger accounts, in return, share less hate speech. Overall, understanding the factors that drive users to share hate speech is crucial for detecting individuals at risk of engaging in harmful behavior and for designing effective mitigation strategies.
With the advent of 5G era, factories are transitioning towards wireless networks to break free from the limitations of wired networks. In 5G-enabled factories, unmanned automatic devices such as automated guided vehicles and robotic arms complete production tasks cooperatively through the periodic control loops. In such loops, the sensing data is generated by sensors, and transmitted to the control center through uplink wireless communications. The corresponding control commands are generated and sent back to the devices through downlink wireless communications. Since wireless communications, sensing and control are tightly coupled, there are big challenges on the modeling and design of such closed-loop systems. In particular, existing theoretical tools of these functionalities have different modelings and underlying assumptions, which make it difficult for them to collaborate with each other. Therefore, in this paper, an analytical closed-loop model is proposed, where the performances and resources of communication, sensing and control are deeply related. To achieve the optimal control performance, a co-design of communication resource allocation and control method is proposed, inspired by the model predictive control algorithm. Numerical results are provided to demonstrate the relationships between the resources and control performances.
Backpropagation within neural networks leverages a fundamental element of automatic differentiation, which is referred to as the reverse mode differentiation, or vector Jacobian Product (VJP) or, in the context of differential geometry, known as the pull-back process. The computation of gradient is important as update of neural network parameters is performed using gradient descent method. In this study, we present a genric randomized method, which updates the parameters of neural networks by using directional derivatives of loss functions computed efficiently by using forward mode AD or Jacobian vector Product (JVP). These JVP are computed along the random directions sampled from different probability distributions e.g., Bernoulli, Normal, Wigner, Laplace and Uniform distributions. The computation of gradient is performed during the forward pass of the neural network. We also present a rigorous analysis of the presented methods providing the rate of convergence along with the computational experiments deployed in scientific Machine learning in particular physics-informed neural networks and Deep Operator Networks.
In this paper, we give an algorithm to publish the number of paths and Katz centrality under the local differential privacy (LDP), providing a thorough theoretical analysis. Although various works have already introduced subgraph counting algorithms under LDP, they have primarily concentrated on subgraphs of up to five nodes. The challenge in extending this to larger subgraphs is the cumulative and exponential growth of noise as the subgraph size increases in any publication under LDP. We address this issue by proposing an algorithm to publish the number of paths that start at every node in the graph, leading to an algorithm that publishes the Katz centrality of all nodes. This algorithm employs multiple rounds of communication and the clipping technique. Both our theoretical and experimental assessments indicate that our algorithm exhibits acceptable bias and variance, considerably less than an algorithm that bypasses clipping. Furthermore, our Katz centrality estimation is able to recall up to 90% of the nodes with the highest Katz centrality.
In this paper, we study two well known methods of Ising structure learning, namely the pseudolikelihood approach and the interaction screening approach, in the context of tensor recovery in $k$-spin Ising models. We show that both these approaches, with proper regularization, retrieve the underlying hypernetwork structure using a sample size logarithmic in the number of network nodes, and exponential in the maximum interaction strength and maximum node-degree. We also track down the exact dependence of the rate of tensor recovery on the interaction order $k$, that is allowed to grow with the number of samples and nodes, for both the approaches. Finally, we provide a comparative discussion of the performance of the two approaches based on simulation studies, which also demonstrate the exponential dependence of the tensor recovery rate on the maximum coupling strength.
Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to interpret. In that regard, many efforts have been made to explain the prediction mechanisms of these models from perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works present systematic frameworks to interpret GNNs, a holistic review for explainable GNNs is unavailable. In this survey, we present a comprehensive review of explainability techniques developed for GNNs. We focus on explainable graph neural networks and categorize them based on the use of explainable methods. We further provide the common performance metrics for GNNs explanations and point out several future research directions.
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.
Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain shift. The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions. Given that annotating the medical data is especially expensive, the supervised transfer learning approaches are not quite optimal. In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations. Specifically, our model is based on a dilated fully convolutional network for pixel-wise prediction. Moreover, we build a plug-and-play domain adaptation module (DAM) to map the target input to features which are aligned with source domain feature space. A domain critic module (DCM) is set up for discriminating the feature space of both domains. We optimize the DAM and DCM via an adversarial loss without using any target domain label. Our proposed method is validated by adapting a ConvNet trained with MRI images to unpaired CT data for cardiac structures segmentations, and achieved very promising results.
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