In fact, the first demonstrations of QD A typical accumulation-mode QD device consists of two-qubit gates with fidelities exceeding the thresholds two sets of gates--plungers and barriers--that collectively for fault-tolerant computing were developed in 2022 [4-control the overall potential profile, the QD-specific 6]. However, because the individual charge carriers that single-particle energy detuning of individual QDs, the make up qubits have electrochemical sensitivity to minor tunnel couplings between QDs, and tunnel rates between impurities and imperfections, calibration and tuning of the outermost QDs and reservoirs. Ideally, each plunger QD devices is a nontrivial and time-consuming process, gate would affect only the electrochemical potential of with each QD requiring careful adjustment of a gate voltage a single targeted QD and each barrier gate only one intended to define charge number, and multiple gate voltages tunnel barrier. Due to the tight proximity, however, to specify tunnel coupling between QDs for two-qubit each gate capacitively couples to nearby potential gates or to reservoirs for reset and measurement.

Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress.This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline. For fair evaluation, we compare 12 representative methods on three datasets, which are roughly grouped into prompt-based and prototype-based models for detailed analysis. Experiments consistently demonstrate that prompt-based methods, including ChatGPT, still significantly trail prototype-based methods in terms of overall performance. To investigate their superior performance, we break down their design elements along several dimensions and build a unified framework on prototype-based methods. Under such unified view, each prototype-method can be viewed a combination of different modules from these design elements. We further combine all advantageous modules and propose a simple yet effective baseline, which outperforms existing methods by a large margin (e.g., 2.7% F1 gains under low-resource setting).

Aspect or query-based summarization has recently caught more attention, as it can generate differentiated summaries based on users' interests. However, the current dataset for aspect or query-based summarization either focuses on specific domains, contains relatively small-scale instances, or includes only a few aspect types. Such limitations hinder further explorations in this direction. In this work, we take advantage of crowd-sourcing knowledge on Wikipedia.org and automatically create a high-quality, large-scale open-domain aspect-based summarization dataset named OASum, which contains more than 3.7 million instances with around 1 million different aspects on 2 million Wikipedia pages. We provide benchmark results on OASum and demonstrate its ability for diverse aspect-based summarization generation. To overcome the data scarcity problem on specific domains, we also perform zero-shot, few-shot, and fine-tuning on seven downstream datasets. Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model. Our dataset and pre-trained checkpoints are publicly available.

Randomized controlled trials (RCTs) are considered the gold standard for estimating the average treatment effect (ATE) of interventions. One use of RCTs is to study the causes of global poverty -- a subject explicitly cited in the 2019 Nobel Memorial Prize awarded to Duflo, Banerjee, and Kremer "for their experimental approach to alleviating global poverty." Because the ATE is a population summary, anti-poverty experiments often seek to unpack the effect variation around the ATE by conditioning (CATE) on tabular variables such as age and ethnicity that were measured during the RCT data collection. Although such variables are key to unpacking CATE, using only such variables may fail to capture historical, geographical, or neighborhood-specific contributors to effect variation, as tabular RCT data are often only observed near the time of the experiment. In global poverty research, when the location of the experiment units is approximately known, satellite imagery can provide a window into such factors important for understanding heterogeneity. However, there is no method that specifically enables applied researchers to analyze CATE from images. In this paper, using a deep probabilistic modeling framework, we develop such a method that estimates latent clusters of images by identifying images with similar treatment effects distributions. Our interpretable image CATE model also includes a sensitivity factor that quantifies the importance of image segments contributing to the effect cluster prediction. We compare the proposed methods against alternatives in simulation; also, we show how the model works in an actual RCT, estimating the effects of an anti-poverty intervention in northern Uganda and obtaining a posterior predictive distribution over effects for the rest of the country where no experimental data was collected. We make all models available in open-source software.

Car-following is a control process in which a following vehicle (FV) adjusts its acceleration to keep a safe distance from the lead vehicle (LV). Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. In contrast, research fields such as image recognition and object detection have benchmark datasets like ImageNet, Microsoft COCO, and KITTI. To address this gap and promote the development of microscopic traffic flow modeling, we establish a public benchmark dataset for car-following behavior modeling. The benchmark consists of more than 80K car-following events extracted from five public driving datasets using the same criteria. These events cover diverse situations including different road types, various weather conditions, and mixed traffic flows with autonomous vehicles. Moreover, to give an overview of current progress in car-following modeling, we implemented and tested representative baseline models with the benchmark. Results show that the deep deterministic policy gradient (DDPG) based model performs competitively with a lower MSE for spacing compared to traditional intelligent driver model (IDM) and Gazis-Herman-Rothery (GHR) models, and a smaller collision rate compared to fully connected neural network (NN) and long short-term memory (LSTM) models in most datasets. The established benchmark will provide researchers with consistent data formats and metrics for cross-comparing different car-following models, promoting the development of more accurate models. We open-source our dataset and implementation code in https://github.com/HKUST-DRIVE-AI-LAB/FollowNet.

Abstract--Many two-sample network hypothesis testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known. In this paper, we consider the degradation of power in two-sample graph hypothesis testing when there are misaligned/label-shuffled vertices across networks. In the context of random dot product and stochastic block model networks, we theoretically explore the power loss due to shuffling for a pair of hypothesis tests based on Frobenius norm differences between estimated edge probability matrices or between adjacency matrices. The loss in testing power is further reinforced by numerous simulations and experiments, both in the stochastic block model and in the random dot product graph model, where we compare the power loss across multiple recently proposed tests in the literature. Lastly, we demonstrate the impact that shuffling can have in real-data testing in a pair of examples from neuroscience and from social network analysis.

Distributed trust is a nebulous concept that has evolved from different perspectives in recent years. While one can attribute its current prominence to blockchain and cryptocurrency, the distributed trust concept has been cultivating progress in federated learning, trustworthy and responsible AI in an ecosystem setting, data sharing, privacy issues across organizational boundaries, and zero trust cybersecurity. This paper will survey the concept of distributed trust in multiple disciplines. It will take a system/software architecture point of view to look at trust redistribution/shift and the associated tradeoffs in systems and applications enabled by distributed trust technologies.

Many Machine Learning models are vulnerable to adversarial attacks: There exist methodologies that add a small (imperceptible) perturbation to an input such that the model comes up with a wrong prediction. Better understanding of such attacks is crucial in particular for models used in security-critical domains, such as monitoring of water distribution networks, in order to devise counter-measures enhancing model robustness and trustworthiness. We propose a taxonomy for adversarial attacks against machine learning based leakage detectors in water distribution networks. Following up on this, we focus on a particular type of attack: an adversary searching the least sensitive point, that is, the location in the water network where the largest possible undetected leak could occur. Based on a mathematical formalization of the least sensitive point problem, we use three different algorithmic approaches to find a solution. Results are evaluated on two benchmark water distribution networks.

Over the past decade, machine learning-based algorithms have been deployed across a wide range of use cases and industries. From the algorithms that assess an individual's creditworthiness, to algorithms that serve up suggested movies and shows to watch on Netflix, the impact of Big Data, analytics, and automation are felt daily by nearly everyone. One area of life where algorithms have not yet been perfected is with payments made by government or relief organizations to people in the aftermath of a crisis, emergency, or natural disaster, where getting financial relief to the people who need it most is critical. Though there have been pilot programs and limited use of artificial intelligence (AI) to provide targeted aid, the practice is far from widespread. Key drivers behind the desire to incorporate more automation and data analysis into aid dispersion is the time-consuming nature of assessing who is eligible to receive aid, and then ensuring that aid is only delivered to those legitimate recipients.

Boston Dynamics recently released a video introducing Atlas, a six-foot bipedal humanoid robot capable of search and rescue missions. Part of the video contained employees apparently abusing Atlas (for example, kicking, hitting it with a hockey stick, pushing it with a heavy ball). The video quickly raised a public and academic debate regarding how humans should treat robots. A robot, in some sense, is nothing more than software embedded in hardware, much like a laptop computer. If it is your property and kicking it harms no one nor infringes on anyone's rights, it's okay to kick it, although that would be a stupid thing to do. Likewise, there seems to be no significant reason that kicking a robot should be deemed as a moral or legal wrong. However, the question--"What do we owe to robots?"--is not that simple. Philosophers and legal scholars have seriously explored and defended some significant aspects of the moral and legal status of robots--and their rights.3,6,15,16,24,29,36 In fact, various non-natural entities--for example, corporations--are treated as persons and even enjoy some constitutional rights.a In addition, humans are not the only species that get moral and legal status. In most developed societies, for example, moral and legal considerations preclude researchers from gratuitously using animals for lab experiments. The fact that corporations are treated as persons and animals are recognized as having some rights does not entail that robots should be treated analogously. These facts are instructive, however.