Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-Preserving Matrix Factorization for Recommendation Systems using Gaussian Mechanism

Building a recommendation system involves analyzing user data, which can potentially leak sensitive information about users. Anonymizing user data is often not sufficient for preserving user privacy. Motivated by this, we propose a privacy-preserving recommendation system based on the differential privacy framework and matrix factorization, which is one of the most popular algorithms for recommendation systems. As differential privacy is a powerful and robust mathematical framework for designing privacy-preserving machine learning algorithms, it is possible to prevent adversaries from extracting sensitive user information even if the adversary possesses their publicly available (auxiliary) information. We implement differential privacy via the Gaussian mechanism in the form of output perturbation and release user profiles that satisfy privacy definitions. We employ R\'enyi Differential Privacy for a tight characterization of the overall privacy loss. We perform extensive experiments on real data to demonstrate that our proposed algorithm can offer excellent utility for some parameter choices, while guaranteeing strict privacy.
Revisiting the Design Agenda for Privacy Notices and Security Warnings

System-generated user-facing notices, dialogs, and warnings in privacy and security interventions present the opportunity to support users in making informed decisions about identified risks. However, too often, they are bypassed, ignored, and mindlessly clicked through, mainly in connection to the well-studied effect of user fatigue and habituation. The contribution of this position paper is to provide a summarized review of established and emergent design dimensions and principles to limit such risk-prone behavior, and to identify three emergent research and design directions for privacy-enhancing dialogs.
Optimal moral-hazard-free reinsurance under extended distortion premium principles

We study an optimal reinsurance problem under a diffusion risk model for an insurer who aims to minimize the probability of lifetime ruin. To rule out moral hazard issues, we only consider moral-hazard-free reinsurance contracts by imposing the incentive compatibility constraint on indemnity functions. The reinsurance premium is calculated under an extended distortion premium principle, in which the distortion function is not necessarily concave. We first show that an optimal reinsurance contract always exists and then derive two sufficient and necessary conditions to characterize it. Due to the presence of the incentive compatibility constraint and the nonconcavity of the distortion, the optimal contract is obtained as a solution to a double obstacle problem. At last, we apply the general result to study three examples and obtain the optimal contract in (semi)closed form.
Addressing Variable Dependency in GNN-based SAT Solving

Boolean satisfiability problem (SAT) is fundamental to many applications. Existing works have used graph neural networks (GNNs) for (approximate) SAT solving. Typical GNN-based end-to-end SAT solvers predict SAT solutions concurrently. We show that for a group of symmetric SAT problems, the concurrent prediction is guaranteed to produce a wrong answer because it neglects the dependency among Boolean variables in SAT problems. % We propose AsymSAT, a GNN-based architecture which integrates recurrent neural networks to generate dependent predictions for variable assignments. The experiment results show that dependent variable prediction extends the solving capability of the GNN-based method as it improves the number of solved SAT instances on large test sets.
Effects of Clutter on Egocentric Distance Perception in Virtual Reality

To assess the impact of clutter on egocentric distance perception, we performed a mixed-design study with 60 participants in four different virtual environments (VEs) with three levels of clutter. Additionally, we compared the indoor/outdoor VE characteristics and the HMD's FOV. The participants wore a backpack computer and a wide FOV head-mounted display (HMD) as they blind-walked towards three distinct targets at distances of 3m, 4.5m, and 6m. The HMD's field of view (FOV) was programmatically limited to 165{\deg}$\times$110{\deg}, 110{\deg}$\times$110{\deg}, or 45{\deg}$\times$35{\deg}. The results showed that increased clutter in the environment led to more precise distance judgment and less underestimation, independent of the FOV. In comparison to outdoor VEs, indoor VEs showed more accurate distance judgment. Additionally, participants made more accurate judgements while looking at the VEs through wider FOVs.
Parallel Longest Increasing Subsequence and van Emde Boas Trees

This paper studies parallel algorithms for the longest increasing subsequence (LIS) problem. Let n be the input size and k be the LIS length of the input. Sequentially, LIS is a simple problem that can be solved using dynamic programming (DP) in O(n log n) work. However, parallelizing LIS is a long-standing challenge. We are unaware of any parallel LIS algorithm that has optimal O(n log n) work and non-trivial parallelism (i.e., Õ(k) or o(n) span). This paper proposes a parallel LIS algorithm that costs O(n log k) work, Õ(k) span, and O(n) space, and is much simpler than the previous parallel LIS algorithms. We also generalize the algorithm to a weighted version of LIS, which maximizes the weighted sum for all objects in an increasing subsequence. To achieve a better work bound for the weighted LIS algorithm, we designed parallel algorithms for the van Emde Boas (vEB tree, which has the same structure as the sequential vEB tree, and supports work-efficient parallel batch insertion, deletion, and range queries. We also implemented our parallel LIS algorithms. Our implementation is light-weighted, efficient, and scalable. On input size 109, our LIS algorithm outperforms a highly-optimized sequential algorithm (with O(n log k)cost) on inputs with k ≤ 3 x 105. Our algorithm is also much faster than the best existing parallel implementation by Shen et al. (2022) on all input instances.
Parameterized Neural Networks for Finance

We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specific data sample. In this sense, it may help to reduce the overfitting problem, since, after learning the model class over a larger data sample consisting of such different data sets, just a few parameters need to be adjusted for modeling a new, specific problem. After analyzing the method theoretically and by regression examples for different one-dimensional problems, we finally apply the approach to one of the standard problems asset managers and banks are facing: the calibration of spread curves. The presented results clearly show the potential that lies within this method. Furthermore, this application is of particular interest to financial practitioners, since nearly all asset managers and banks which are having solutions in place may need to adapt or even change their current methodologies when ESG ratings additionally affect the bond spreads.
CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.
DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables

A lot of recent progress has been made in ultra low-bit quantization, promising significant improvements in latency, memory footprint and energy consumption on edge devices. Quantization methods such as Learned Step Size Quantization can achieve model accuracy that is comparable to full-precision floating-point baselines even with sub-byte quantization. However, it is extremely challenging to deploy these ultra low-bit quantized models on mainstream CPU devices because commodity SIMD (Single Instruction, Multiple Data) hardware typically supports no less than 8-bit precision. To overcome this limitation, we propose DeepGEMM, a lookup table based approach for the execution of ultra low-precision convolutional neural networks on SIMD hardware. The proposed method precomputes all possible products of weights and activations, stores them in a lookup table, and efficiently accesses them at inference time to avoid costly multiply-accumulate operations. Our 2-bit implementation outperforms corresponding 8-bit integer kernels in the QNNPACK framework by up to 1.74× on x86 platforms.
Decoding neural activity to assess individual latent state in ecologically valid contexts

Objective. Currently, there exists very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make accurate inferences about latent states, associated cognitive processes, or proximal behavior. Improving our understanding of when and how specific patterns of neural activity manifest in ecologically valid scenarios would provide validation for laboratory-based approaches that study similar neural phenomena in isolation and meaningful insight into the latent states that occur during complex tasks. Approach. Domain generalization methods, borrowed from the work of the brain-computer interface community, have the potential to capture high-dimensional patterns of neural activity in a way that can be reliably applied across experimental datasets in order to address this specific challenge. We previously used such an approach to decode phasic neural responses associated with visual target discrimination. Here, we extend that work to more tonic phenomena such as internal latent states. We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We apply the trained models to an ecologically valid paradigm in which participants performed multiple, concurrent driving-related tasks while perched atop a six-degrees-of-freedom ride-motion simulator. Main Results. Using the pretrained models, we estimate latent state and the associated patterns of neural activity. As the patterns of neural activity become more similar to those patterns observed in the training data, we find changes in behavior and task performance that are consistent with the observations from the original, laboratory-based paradigms. Significance. These results lend ecological validity to the original, highly controlled, experimental designs and provide a methodology for understanding the relationship between neural activity and behavior during complex tasks.
Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and {promote trustworthy data dissemination} in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions{. To defend against free-riding attacks,} we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, {and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game} to identify malicious AVs {in disseminating fake information}. {Furthermore, for} lack of sufficient knowledge on parameters of {the} network model and user cost model {in dynamic game scenarios}, a two-tier reinforcement learning based algorithm with hotbooting is developed to obtain the optimal {strategies of subscriber AVs and publisher AVs with free-rider prevention}. Extensive simulations are conducted, and the results validate that our SPAD can effectively {prevent free-riders and enhance the dependability of disseminated contents,} compared with conventional schemes.
A Multiobjective Model For Locating Solid Waste Transfer Facilities Using An Empirical Opposition Function

AbstractSolid waste transfer stations (SWTS) are facilities where municipal refuse is transferred from collection trucks to long-haul trucks for more economical shipping to distant landfills. In this paper, a multiobjective model for locating SWTS examines the tradeoffs between minimizing costs and public opposition. The cost objective combines the transshipment and the fixed-charge problems, while expected public opposition is modeled as a decreasing function of distance from the facility. We believe this is the first location model for any type of undesirable facility to use an opposition function derived empirically from opinion survey data (Rahman, et al., 1992). A case study of Phoenix, Arizona uses actual data on sites, zones, tonnages, costs, and local residential attitudes. The model is calibrated to historical budget data for accuracy and also to explore the model’s sensitivity to various parameters. Six sets of multiobjective analyses generate noninferior tradeoff curves under various assumption.
Smallworldness in hypergraphs

Most real-world networks are endowed with the small-world property, by means of which the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. The evidence sparkled a wealth of studies trying to reveal possible mechanisms through which the pairwise interactions amongst the units of a network are structured in a way to determine such observed regularity. Here we show that smallworldness occurs also when interactions are of higher order. Namely, by considering Q-uniform hypergraphs and a process through which connections can be randomly rewired with given probability p, we find that such systems may exhibit prominent clustering properties in connection with small average path lengths for a wide range of p values, in analogy to the case of dyadic interactions. The nature of small-world transition remains the same at different orders Q ( =2,3,4,5, and 6) of the interactions, however, the increase in the hyperedge order reduces the range of rewiring probability for which smallworldness emerge.
Socially Assistive Robots as Decision Makers in the Wild: Insights from a Participatory Design Workshop

Socially Assistive Robots (SARs) are becoming very popular every day because of their effectiveness in handling social situations. However, social robots are perceived as intelligent, and thus their decision-making process might have a significant effect on how they are perceived and how effective they are. In this paper, we present the findings from a participatory design study consisting of 5 design workshops with 30 participants, focusing on several decision-making scenarios of SARs in the wild. Through the findings of the PD study, we have discussed 5 directions that could aid the design of decision-making systems of SARs in the wild.
Physical cool-core condensation radius in massive galaxy clusters

Aims. We investigate the properties of cool cores in an optimally selected sample of 37 massive and X-ray-bright galaxy clusters, with regular morphologies, observed with Chandra. We started by measuring the density, temperature, and abundance radial profiles of their intracluster medium (ICM). From these independent quantities, we computed the cooling (tcool), free-fall (tff), and turbulence (teddy) timescales as a function of radius. Methods. By requiring the profile-crossing condition, tcool/teddy = 1, we measured the cool-core condensation radius, Rccc, within which the balancing feeding and feedback processes generate the turbulent condensation rain and related chaotic cold accretion (CCA). We also constrained the complementary (quenched) cooling flow radius, Rqcf, obtained via the condition tcool = 25 × tff, that encompasses the region of thermally unstable cooling. Results. We find that in our our massive cluster sample and in the limited redshift range considered (1.3 × 1014 < M500 < 16.6 × 1014 M⊙, 0.03 < z < 0.29), the distribution of Rccc peaks at ∼0.01 r500 and the entire range remains below ∼0.07 r500, with a very weak increase with redshift and no dependence on the cluster mass. We find that Rqcf is typically three times larger than Rccc, with a wider distribution, and growing more slowly along Rccc, according to an average relation Rqcf∝ Rccc0.46, with a large intrinsic scatter. Conclusions. We suggest that this sublinear relation can be understood as an effect of the micro rain of pockets of cooled gas flickering in the turbulent ICM, whose dynamical and thermodynamical properties are referred to as “macro weather”. Substituting the classical ad hoc cool-core radius R7.7 Gyr, we propose that Rqcf is an indicator of the size of global cool cores tied to the long-term macro weather, with the inner Rccc closely tracing the effective condensation rain and chaotic cold accretion (CCA) zone that feeds the central supermassive black hole (SMBH).
Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning

We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data. Our primary goal is to identify important variables for distinguishing between the classes; existing low-dimensional methods can then be applied for final class assignment. Motivated by a generalized Rayleigh quotient, we score projections according to the traces of the estimated whitened between-class covariance matrices on the projected data. This enables us to assign an importance weight to each variable for a given projection, and to select our signal variables by aggregating these weights over high-scoring projections. Our theory shows that the resulting Sharp-SSL algorithm is able to recover the signal coordinates with high probability when we aggregate over sufficiently many random projections and when the base procedure estimates the whitened between-class covariance matrix sufficiently well. The Gaussian EM algorithm is a natural choice as a base procedure, and we provide a new analysis of its performance in semi-supervised settings that controls the parameter estimation error in terms of the proportion of labeled data in the sample. Numerical results on both simulated data and a real colon tumor dataset support the excellent empirical performance of the method.
AutoTaskFormer: Searching Vision Transformers for Multi-task Learning

Vision Transformers have shown great performance in single tasks such as classification and segmentation. However, real-world problems are not isolated, which calls for vision transformers that can perform multiple tasks concurrently. Existing multi-task vision transformers are handcrafted and heavily rely on human expertise. In this work, we propose a novel one-shot neural architecture search framework, dubbed AutoTaskFormer (Automated Multi-Task Vision TransFormer), to automate this process. AutoTaskFormer not only identifies the weights to share across multiple tasks automatically, but also provides thousands of well-trained vision transformers with a wide range of parameters (e.g., number of heads and network depth) for deployment under various resource constraints. Experiments on both small-scale (2-task Cityscapes and 3-task NYUv2) and large-scale (16-task Taskonomy) datasets show that AutoTaskFormer outperforms state-of-the-art handcrafted vision transformers in multi-task learning. The entire code and models will be open-sourced.
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain-specific. To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4's reasoning ability by introducing varying levels of inference difficulty. Our results show that 1) GPT-4 outperforms ChatGPT in the radiology NLI task; 2) other specifically fine-tuned models require significant amounts of data samples to achieve comparable performance to ChatGPT/GPT-4. These findings demonstrate that constructing a generic model that is capable of solving various tasks across different domains is feasible.
Probing vortical structures in heavy-ion collisions at RHIC-BES energies through helicity polarization

We investigate the hydrodynamic helicity polarization of $\Lambda$ hyperons, defined as the projection of the spin polarization vector along the directions of particle momenta, at RHIC-BES energies by utilizing the relativistic (3+1)D CLVisc hydrodynamics framework with SMASH initial conditions. As opposed to local spin polarization at high energy collisions, our hydrodynamic simulations demonstrate that the helicity polarization induced by the kinetic vorticity dominates over other contributions at intermediate and low collision energies. Our findings provide an opportunity to probe the fine structure of local kinetic vorticity as a function of azimuthal angle at intermediate and low collision energies by mapping our predictions to the future measurements in experiments.