Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Communication About Sexual Violence May Backfire: Online Experiment with Young Men

Sexual violence harms millions of individuals each year in the United States. Survivors of sexual violence endure long-term hardships such as significant financial setbacks, physical and mental health consequences, academic challenges, and stunted career achievement. Digital feminist activism (DFA) has created space online where women can disclose experiences of sexual violence. Research thus far has been limited to documenting the existence and value of DFA for those who participate and has not addressed whether DFA can influence a key demographic for prevention, young men. We conducted an experiment to examine the impact of DFA on college-aged men’s reactance to messaging, rape myth acceptance, knowledge about severity, and susceptibility to perpetrate sexual violence. University men (n=230, 18–29 years old) were randomized to one of three conditions: (1) tweets from women challenging rape myths, (2) tweets from women providing information about sexual violence harms, or (3) no exposure control. Tweets challenging rape myths or providing information did not impact rape myth perceptions, knowledge, or susceptibility. Participants had greater reactance (unintended outcome) to tweets challenging rape myths, and subsequently higher rape myth acceptance and lower knowledge about the severity of sexual violence. Rape myth acceptance was associated with susceptibility to perpetrate sexual violence overall. Our finding that some forms of DFA have a negative, indirect influence among college-aged males highlights important unintended consequences and the need for more efficacious communication to prevent sexual violence perpetration.
RISE: RISC-V SoC for En/Decryption Acceleration on the Edge for Homomorphic Encryption

Today, edge devices commonly connect to the cloud to use its storage and computing capabilities. This leads to security and privacy concerns about user data. Homomorphic encryption (HE) is a promising solution to address the data privacy problem as it allows arbitrarily complex computations on encrypted data without ever needing to decrypt it. While there has been a lot of work on accelerating HE computations in the cloud, small attention has been paid to the message-to-ciphertext and ciphertext-to-message conversion operations on the edge. In this work, we profile the edge-side conversion operations, and our analysis shows that during conversion error sampling, encryption and decryption operations are the bottlenecks. To overcome these bottlenecks, we present RISE, an area and energy-efficient RISC-V system-on-chip (SoC). RISE leverages an efficient and lightweight pseudorandom number generator (PRNG) core and combines it with fast sampling techniques to accelerate the error sampling operations. To accelerate the encryption and decryption operations, RISE uses scalable data-level parallelism to implement the number theoretic transform (NTT) operation, the main bottleneck within the encryption and decryption operations. In addition, RISE saves area by implementing a unified en/decryption datapath, and efficiently exploits techniques like memory reuse and data reordering to utilize a minimal amount of ON-chip memory. We evaluate RISE using a complete RTL design containing a RISC-V processor interfaced with our accelerator. Our analysis reveals that for message-to-ciphertext and ciphertext-to-message conversions, using RISE leads up to $5986.99\times $ and $1164.1\times $ more energy-efficient solution, respectively, than when using just the RISC-V processor.
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking

Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical role in KB maintenance, but has not yet been fully explored. The current methods are mostly limited to the simple threshold-based approach and feature-based classification, and the datasets for evaluation are relatively rare. We propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a special NIL entity. To better utilize BERT, we propose new techniques including NIL entity representation and classification, with synonym enhancement. We also apply KB Pruning and Versioning strategies to automatically construct out-of-KB datasets from common in-KB EL datasets. Results on five datasets of clinical notes, biomedical publications, and Wikipedia articles in various domains show the advantages of BLINKout over existing methods to identify out-of-KB mentions for the medical ontologies, UMLS, SNOMED CT, and the general KB, WikiData.
Deep Reinforcement Learning for Multi-User Massive MIMO With Channel Aging

The design of beamforming for downlink multi-user massive multi-input multi-output (MIMO) relies on accurate downlink channel state information (CSI) at the transmitter (CSIT). In fact, it is difficult for the base station (BS) to obtain perfect CSIT due to user mobility, and latency/feedback delay (between downlink data transmission and CSI acquisition). Hence, robust beamforming under imperfect CSIT is needed. In this paper, considering multiple antennas at all nodes (base station and user terminals), we develop a multi-agent deep reinforcement learning (DRL) framework for massive MIMO under imperfect CSIT, where the transmit and receive beamforming are jointly designed to maximize the average information rate of all users. Leveraging this DRL-based framework, interference management is explored and three DRL-based schemes, namely the distributed-learning-distributed-processing scheme, partial-distributed-learning-distributed-processing, and central-learning-distributed-processing scheme, are proposed and analyzed. This paper 1) highlights the fact that the DRL-based strategies outperform the random action-chosen strategy and the delay-sensitive strategy named as sample-and-hold (SAH) approach, and achieved over 90% of the information rate of two selected benchmarks with lower complexity: the zero-forcing channel-inversion (ZF-CI) with perfect CSIT and the Greedy Beam Selection strategy, 2) demonstrates the inherent robustness of the proposed designs in the presence of channel aging. 3) conducts detailed convergence and scalability analysis on the proposed framework.
Non-uniform array design for robust LoS MIMO via convex optimization

The array design problem of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) transmit environment is examined. As uniform array configurations at the transmitter (Tx) and receiver (Rx) are optimal at specific transmit distances only, they lead to reduced spectral efficiency over a range of transmit distances. To that end, the joint design of non-uniform Tx and Rx arrays towards maximizing the minimum capacity of a LoS MIMO system across a range of transmit distances is investigated in this paper. By introducing convex relaxation, the joint Tx and Rx array design is cast as a convex optimization problem, which is solved in a iterative manner. In addition, we also implement a local search to obtain a refined solution that achieves an improved performance. It is shown that the non-uniform configurations designed with our proposed approach outperform uniform and non-uniform array designs of the literature in terms of capacity and/or complexity.
$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

Quadrotors that can operate safely in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present L1Quad, a control architecture for quadrotors based on the L1 adaptive control. L1Quad enables safe tubes centered around a desired trajectory that the quadrotor is always guaranteed to remain inside. Our design applies to both the rotational and the translational dynamics of the quadrotor. We lump various types of uncertainties and disturbances as unknown nonlinear (time- and state-dependent) forces and moments. Without assuming or enforcing parametric structures, L1Quad can accurately estimate and compensate for these unknown forces and moments. Extensive experimental results demonstrate that L1Quad is able to significantly outperform baseline controllers under a variety of uncertainties with consistently small tracking errors.
GRAVITY AND THE COMPLEXITY OF COORDINATES IN FISHER INFORMATION

The principle of extreme physical information is a tool for deriving various equations of physics. Physical information is defined to be the difference between the Fisher information and the bound information specific to every physical system. Using the extreme physical information, equations are obtained for the probability of finding the location of a physical entity (a system of particles or a field) in space–time. Those equations turn out to be the relevant physical equations (Dirac's, Maxwell's equations etc.). A peculiarity of the method is that this only works if some of the coordinates that specify the whereabout of the physical entity in space–time are chosen to be imaginary. I will argue that this peculiarity is a direct effect of gravity.
Text-Guided Scene Sketch-to-Photo Synthesis

We propose a method for scene-level sketch-to-photo synthesis with text guidance. Although object-level sketch-to-photo synthesis has been widely studied, whole-scene synthesis is still challenging without reference photos that adequately reflect the target style. To this end, we leverage knowledge from recent large-scale pre-trained generative models, resulting in text-guided sketch-to-photo synthesis without the need for reference images. To train our model, we use self-supervised learning from a set of photographs. Specifically, we use a pre-trained edge detector that maps both color and sketch images into a standardized edge domain, which reduces the gap between photograph-based edge images (during training) and hand-drawn sketch images (during inference). We implement our method by fine-tuning a latent diffusion model (i.e., Stable Diffusion) with sketch and text conditions. Experiments show that the proposed method translates original sketch images that are not extracted from color images into photos with compelling visual quality.
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems

Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms. However, its application in the Federated Learning setting remains relatively underexplored, and the impact of Federated Learning's inherent challenges on the convergence of bilevel algorithms remain obscure. In this work, we investigate Federated Bilevel Optimization problems and propose a communication-efficient algorithm, named FedBiOAcc. The algorithm leverages an efficient estimation of the hyper-gradient in the distributed setting and utilizes the momentum-based variance-reduction acceleration. Remarkably, FedBiOAcc achieves a communication complexity $O(\epsilon^{-1})$, a sample complexity $O(\epsilon^{-1.5})$ and the linear speed up with respect to the number of clients. We also analyze a special case of the Federated Bilevel Optimization problems, where lower level problems are locally managed by clients. We prove that FedBiOAcc-Local, a modified version of FedBiOAcc, converges at the same rate for this type of problems. Finally, we validate the proposed algorithms through two real-world tasks: Federated Data-cleaning and Federated Hyper-representation Learning. Empirical results show superior performance of our algorithms.
Self-mediated exploration in artificial intelligence inspired by cognitive psychology

Exploration of the physical environment is an indispensable precursor to data acquisition and enables knowledge generation via analytical or direct trialing. Artificial Intelligence lacks the exploratory capabilities of even the most underdeveloped organisms, hindering its autonomy and adaptability. Supported by cognitive psychology, this works links human behavior and artificial agents to endorse self-development. In accordance with reported data, paradigms of epistemic and achievement emotion are embedded to machine-learning methodology contingent on their impact when decision making. A study is subsequently designed to mirror previous human trials, which artificial agents are made to undergo repeatedly towards convergence. Results demonstrate causality, learned by the vast majority of agents, between their internal states and exploration to match those reported for human counterparts. The ramifications of these findings are pondered for both research into human cognition and betterment of artificial intelligence.
Scheduling Coflows for Minimizing the Makespan in Identical Parallel Networks

With the rapid advancement of technology, parallel computing applications have become increasingly popular and are commonly executed in large data centers. These applications involve two phases: computation and communication, which are executed repeatedly to complete the work. However, due to the ever-increasing demand for computing power, large data centers are struggling to meet the massive communication demands. To address this problem, coflow has been proposed as a networking abstraction that captures communication patterns in data-parallel computing frameworks. This paper focuses on the coflow scheduling problem in identical parallel networks, where the primary objective is to minimize the makespan, which is the maximum completion time of coflows. It is considered one of the most significant $\mathcal{NP}$-hard problems in large data centers. In this paper, we consider two problems: flow-level scheduling and coflow-level scheduling. In the flow-level scheduling problem, distinct flows can be transferred through different network cores, whereas in the coflow-level scheduling problem, all flows must be transferred through the same network core. To address the flow-level scheduling problem, this paper proposes two algorithms: a $(3-\tfrac{2}{m})$-approximation algorithm and a $(\tfrac{8}{3}-\tfrac{2}{3m})$-approximation algorithm, where $m$ represents the number of network cores. For the coflow-level scheduling problem, this paper proposes a $(2m)$-approximation algorithm. Finally, we conduct simulations on our proposed algorithm and Weaver's algorithm, as presented in Huang \textit{et al.} (2020) in the 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). We also validate the effectiveness of the proposed algorithms on heterogeneous parallel networks.
A three-dimensional generalization of Eliassen's balanced vortex equations derived from Hamilton's principle

A new set of equations is derived for nearly circular flow in gradient balance. For precisely axisymmetric motion, the system reduces to the well-known balanced vortex equations of Eliassen. The derivation is based on the assumption that the radial component of velocity is small in comparison to the azimuthal component. By applying this approximation to Hamilton's principle for a continuum of fluid parcels, while preserving the time and particle-labelling symmetries of the primitive equations, it is ensured that the resulting system possesses conservation laws for energy and potential vorticity. In potential radius coordinates, the Lagrangian equations of motion take the form of the geostrophic and hydrostatic balance conditions. The system is also presented in Eulerian form, and a practical integration scheme, based on a linear elliptic equation for geopotential tendency, is described. Finally, the set is rederived by a conventional scale analysis in order to determine constraints on the diabatic forcings which are required for consistency with the approximation.
Improved Algorithm and Lower Bound for Variable Time Quantum Search

We study variable time search, a form of quantum search where queries to different items take different time. Our first result is a new quantum algorithm that performs variable time search with complexity $O(\sqrt{T}\log n)$ where $T=\sum_{i=1}^n t_i^2$ with $t_i$ denoting the time to check the $i$-th item. Our second result is a quantum lower bound of $\Omega(\sqrt{T\log T})$. Both the algorithm and the lower bound improve over previously known results by a factor of $\sqrt{\log T}$ but the algorithm is also substantially simpler than the previously known quantum algorithms.
A Macrocolumn Architecture Implemented with Spiking Neurons

The macrocolumn is a key component of a neuromorphic computing system that interacts with an external environment under control of an agent. Environments are learned and stored in the macrocolumn as labeled directed graphs where edges connect features and labels indicate the relative displacements between them. Macrocolumn functionality is first defined with a state machine model. This model is then implemented with a neural network composed of spiking neurons. The neuron model employs active dendrites and mirrors the Hawkins/Numenta neuron model. The architecture is demonstrated with a research benchmark in which an agent employs a macrocolumn to first learn and then navigate 2-d environments containing pseudo-randomly placed features.
Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch to Portrait Generation

Creating the photo-realistic version of people’s sketched portraits is useful to various entertainment purposes. Existing studies only generate portraits in the 2D plane with fixed views, making the results less vivid. In this paper, we present Stereoscopic Simplified Sketch-to-Portrait (SSSP), which explores the possibility of creating Stereoscopic 3D-aware portraits from simple contour sketches by involving 3D generative models. Our key insight is to design sketch-aware constraints that can fully exploit the prior knowledge of a tri-plane-based 3D-aware generative model. Specifically, our designed region-aware volume rendering strategy and global consistency constraint further enhance detail correspondences during sketch encoding. Moreover, in order to facilitate the usage of layman users, we propose a Contour-to-Sketch module with vector quantized representations, so that easily drawn contours can directly guide the generation of 3D portraits. Extensive comparisons show that our method generates high-quality results that match the sketch. Our usability study verifies that our system is preferred by users.
Error estimation in IBM Quantum Computers

One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyze the main sources of errors in current (IBM) quantum computers and we present a useful tool (TED-qc) designed to facilitate the total error probability expected for any quantum circuit. We propose this total error probability as the best way to estimate the ﬁdelity in the NISQ era, mainly because we do not have to compare our quantum calculations with any classical one. In order to contrast the robustness of our tool we compute the total error probability that may occur in three diﬀerent quantum models: 1) the four-electron Ising model, 2) the Quantum-Phase Estimation (QPE) and 3) the Grover’s algorithm. For each model, we compute a statistically signiﬁcant sample size for both the expectation value of the related observable and the ﬁdelity, comparing them with the value calculated in the simulator as a function of the error probability. The analysis is satisfactory in more than the 99% of the cases. In addition, we study how the error mitigation techniques are able to eliminate the noise induced during the measurement.
The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects

To operate safely and efficiently alongside human workers, collaborative robots (cobots) require the ability to quickly understand the dynamics of manipulated objects. However, traditional methods for estimating the full set of inertial parameters rely on motions that are necessarily fast and unsafe (to achieve a sufficient signal-to-noise ratio). In this work, we take an alternative approach: by combining visual and force-torque measurements, we develop an inertial parameter identification algorithm that requires slow or “stop-and-go” motions only, and hence is ideally tailored for use around humans. Our technique, called Homogeneous Part Segmentation (HPS), leverages the observation that man-made objects are often composed of distinct, homogeneous parts. We combine a surface-based point clustering method with a volumetric shape segmentation algorithm to quickly produce a part-level segmentation of a manipulated object; the segmented representation is then used by HPS to accurately estimate the object's inertial parameters. To benchmark our algorithm, we create and utilize a novel dataset consisting of realistic meshes, segmented point clouds, and inertial parameters for 20 common workshop tools. Finally, we demonstrate the real-world performance and accuracy of HPS by performing an intricate ‘hammer balancing act’ autonomously and online with a low-cost collaborative robotic arm. Our code and dataset are open source and freely available.
Simplified Continuous High-Dimensional Belief Space Planning With Adaptive Probabilistic Belief-Dependent Constraints

Online decision making under uncertainty in partially observable domains, also known as Belief Space Planning, is a fundamental problem in Robotics and Artificial Intelligence. Due to an abundance of plausible future unravelings, calculating an optimal course of action inflicts an enormous computational burden on the agent. Moreover, in many scenarios, e.g., Information gathering, it is required to introduce a belief-dependent constraint. Prompted by this demand, in this article, we consider a recently introduced probabilistic belief-dependent constrained partially observable Markov decision process (POMDP). We present a technique to adaptively accept or discard a candidate action sequence with respect to a probabilistic belief-dependent constraint, before expanding a complete set of sampled future observations episodes and without any loss in accuracy. Moreover, using our proposed framework, we contribute an adaptive method to find a maximal feasible return (e.g., Information Gain) in terms of Value at Risk and a corresponding action sequence, given a set of candidate action sequences, with substantial acceleration. On top of that, we introduce an adaptive simplification technique for a probabilistically constrained setting. Such an approach provably returns an identical-quality solution while dramatically accelerating the online decision making. Our universal framework applies to any belief-dependent constrained continuous POMDP with parameteric beliefs, as well as nonparameteric beliefs represented by particles. In the context of an information-theoretic constraint, our presented framework stochastically quantifies if a cumulative Information Gain along the planning horizon is sufficiently significant (for e.g., Information Gathering, active simultaneous localization and mapping (SLAM)). As a case study, we apply our method to two challenging problems of high dimensional belief space planning: active SLAM and sensor deployment. Extensive realistic simulations corroborate the superiority of our proposed ideas.
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent

We propose new limiting dynamics for stochastic gradient descent in the small learning rate regime called stochastic modified flows. These SDEs are driven by a cylindrical Brownian motion and improve the so-called stochastic modified equations by having regular diffusion coefficients and by matching the multi-point statistics. As a second contribution, we introduce distribution dependent stochastic modified flows which we prove to describe the fluctuating limiting dynamics of stochastic gradient descent in the small learning rate - infinite width scaling regime.