Miracle-dz/UniFAR-FTUs huggingface dataset:
CRTFASTGEEPWR: A SAS Macro for Power of Generalized Estimating Equations Analysis of Multi-Period Cluster Randomized Trials with Application to Stepped Wedge Designs

Multi-period cluster randomized trials (CRTs) are increasingly used for the evaluation of interventions delivered at the group level. While generalized estimating equations (GEE) are commonly used to provide population-averaged inference in CRTs, there is a gap of general methods and statistical software tools for power calculation based on multi-parameter, within-cluster correlation structures suitable for multi-period CRTs that can accommodate both complete and incomplete designs. A computationally fast, non-simulation procedure for determining statistical power is described for the GEE analysis of complete and incomplete multi-period cluster randomized trials. The procedure is implemented via a SAS macro, \%CRTFASTGEEPWR, which is applicable to binary, count and continuous responses and several correlation structures in multi-period CRTs. The SAS macro is illustrated in the power calculation of two complete and two incomplete stepped wedge cluster randomized trial scenarios under different specifications of marginal mean model and within-cluster correlation structure. The proposed GEE power method is quite general as demonstrated in the SAS macro with numerous input options. The power procedure and macro can also be used in the planning of parallel and crossover CRTs in addition to cross-sectional and closed cohort stepped wedge trials.
Generalizing Hierarchical Bayesian Bandits

A contextual bandit is a popular and practical framework for online learning to act under uncertainty. In many problems, the number of actions is huge and their mean rewards are correlated. In this work, we introduce a general framework for capturing such correlations through a two-level graphical model where actions are related through multiple shared latent parameters . We propose a Thompson sampling algorithm G-HierTS that uses this structure to explore efﬁciently and bound its Bayes regret. The regret has two terms, one for learning action parameters and the other for learning the shared latent parameters. The terms reﬂect the structure of our model as well as the quality of priors. Our theoretical ﬁndings are validated empirically using both synthetic and real-world problems. We also experiment with G-HierTS that maintains a factored posterior over latent parameters. While this approximation does not come with guarantees, it improves computational efﬁciency with a minimal impact on empirical regret.
Explicit temperature coupling in phase-field crystal models of solidification

We present a phase-field crystal model for solidification that accounts for thermal transport and a temperature-dependent lattice parameter. Elasticity effects are characterized through the continuous elastic field computed from the microscopic density field. We showcase the model capabilities via selected numerical investigations which focus on the prototypical growth of two-dimensional crystals from the melt, resulting in faceted shapes and dendrites. This work sets the grounds for a comprehensive mesoscale model of solidification including thermal expansion.
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts

Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions. The current research is mainly developed for problems with a 'regular' triangle-like Pareto-optimal front (PF), whereas the performance can significantly deteriorate when the PF consists of disconnected segments. Furthermore, the offspring reproduction in the current data-driven EMO does not fully leverage the latent information of the surrogate model. Bearing these considerations in mind, this paper proposes a data-driven EMO algorithm based on multiple-gradient descent. By leveraging the regularity information provided by the up-to-date surrogate model, it is able to progressively probe a set of well distributed candidate solutions with a convergence guarantee. In addition, its infill criterion recommends a batch of promising candidate solutions to conduct expensive objective function evaluations. Experiments on $33$ benchmark test problem instances with disconnected PFs fully demonstrate the effectiveness of our proposed method against four selected peer algorithms.
Adversarial Bandits Robust to S-Switch Regret

We study the adversarial bandit problem under S number of switching best arms for unknown S . For handling this problem, we adopt the master-base framework using the online mirror descent method (OMD). We ﬁrst provide a master-base algorithm with basic OMD, achieving ˜ O ( S 1 / 2 K 1 / 3 T 2 / 3 ) . For improving the regret bound with respect to T , we propose to use adaptive learning rates for OMD to control variance of loss estimators, and achieve ˜ O (min { E [ p SKTρ T ( h † )] , S √ KT } ) , where ρ T ( h † ) is a variance term for loss estimators.
Percolation in Random Graphs of Unbounded Rank

Bootstrap percolation in (random) graphs is a contagion dynamics among a set of vertices with certain threshold levels. The process is started by a set of initially infected vertices, and an initially uninfected vertex with threshold $k$ gets infected as soon as the number of its infected neighbors exceeds $k$. This process has been studied extensively in so called \textit{rank one} models. These models can generate random graphs with heavy-tailed degree sequences but they are not capable of clustering. In this paper, we treat a class of random graphs of unbounded rank which allow for extensive clustering. Our main result determines the final fraction of infected vertices as the fixed point of a non-linear operator defined on a suitable function space. We propose an algorithm that facilitates neural networks to calculate this fixed point efficiently. We further derive criteria based on the Fr\'echet derivative of the operator that allows one to determine whether small infections spread through the entire graph or rather stay local.
Accelerating spiking neural network training

Spiking neural networks (SNN) are a type of artiﬁcial network inspired by the use of action potentials in the brain. There is a growing interest in emulating these networks on neuromorphic computers due to their improved energy consumption and speed, which are the main scaling issues of their counterpart the artiﬁcial neural network (ANN). Signiﬁcant progress has been made in directly training SNNs to perform on par with ANNs in terms of accuracy. These methods are however slow due to their sequential nature, leading to long training times. We propose a new technique for directly training single-spike-per-neuron SNNs which eliminates all sequential computation and relies exclusively on vectorised operations. We demonstrate over a × 10 speedup in training with robust classiﬁcation performance on real datasets of low to medium spatio-temporal complexity (Fashion-MNIST and Neuromophic-MNIST). Our proposed solution manages to solve certain tasks with over a 95 . 68% reduction in spike counts relative to a conventionally trained SNN, which could signiﬁcantly reduce energy requirements when deployed on neuromorphic computers.
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection

To achieve autonomous driving, developing 3D detection fusion methods, which aim to fuse the camera and LiDAR information, has draw great research interest in recent years. As a common practice, people rely on large-scale datasets to fairly compare the performance of different methods. While these datasets have been carefully cleaned to ideally minimize any potential noise, we observe that they cannot truly reflect the data seen on a real autonomous vehicle, whose data tends to be noisy due to various reasons. This hinders the ability to simply estimate the robust performance under realistic noisy settings. To this end, we collect a series of real-world cases with noisy data distribution, and systematically formulate a robustness benchmark toolkit. It can simulate these cases on any clean dataset, which has the camera and LiDAR input modality. We showcase the effectiveness of our toolkit by establishing two novel robustness benchmarks on widely-adopted datasets, nuScenes and Waymo, then holistically evaluate the state-of-the-art fusion methods. We discover that: i) most fusion methods, when solely developed on these data, tend to fail inevitably when there is a disruption to the LiDAR input; ii) the improvement of the camera input is significantly inferior to the LiDAR one. We publish the robust fusion dataset, benchmark, detailed documents and instructions on https://anonymous-benchmark.github. io/robust-benchmark-website.
Network Vector Autoregressive Model for Dyadic Response Variables

For general panel data, by introducing network structure, network vector autoregressive (NVAR) model captured the linear inter dependencies among multiple time series. In this paper, we propose network vector autoregressive model for dyadic response variables (NVARD), which describes the dynamic process of dyadic data in the case of the dependencies among diﬀerent pairs are taken into conside-ration. Besides, due to the existence of heterogeneity between time and individual, we propose time-varying coeﬃcient network vector autoregressive model for dyadic response variables (VCNVARD). Finally, we apply these models to predict world bilateral trade ﬂows.
Do Deep Neural Networks Always Perform Better When Eating More Data?

Data has now become a shortcoming of deep learning. Researchers in their own fields share the thinking that"deep neural networks might not always perform better when they eat more data,"which still lacks experimental validation and a convincing guiding theory. Here to fill this lack, we design experiments from Identically Independent Distribution(IID) and Out of Distribution(OOD), which give powerful answers. For the purpose of guidance, based on the discussion of results, two theories are proposed: under IID condition, the amount of information determines the effectivity of each sample, the contribution of samples and difference between classes determine the amount of sample information and the amount of class information; under OOD condition, the cross-domain degree of samples determine the contributions, and the bias-fitting caused by irrelevant elements is a significant factor of cross-domain. The above theories provide guidance from the perspective of data, which can promote a wide range of practical applications of artificial intelligence.
Environmental Monitoring for Smart Cities

This work presents an innovative, multidisciplinary and cost-effective ecosystem of ICT solutions able to collect, process and distribute geo-referenced information about the influence of pollution and micro-climatic conditions on the quality of life in Smart Cities. The system has been developed and experimentally evaluated in the framework of the research project SHE, co-funded by the Tuscany Region (Italy). Specifically, an innovative monitoring network has been developed, constituted by fixed and mobile sensor nodes, which provided comparable measurements in stationary and mobile conditions. In addition, sensor data have been enriched with those generated by citizens through the use of a dedicated mobile application, exploiting participatory sensing and MSN paradigms.
So3krates - Self-attention for higher-order geometric interactions on arbitrary length-scales

The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difﬁculty of modeling them efﬁciently. This work proposes a modiﬁed attention mechanism adapted to the underlying physics, which allows to recover the relevant non-local effects. Namely, we introduce spherical harmonic coordinates (SPHCs) to reﬂect higher-order geometric information for each atom in a molecule, enabling a non-local formulation of attention in the SPHC space. Our proposed model S O 3 KRATES – a self-attention based message passing neural network – uncouples geometric information from atomic features, making them independently amenable to attention mechanisms. We show that in contrast to other published methods, S O 3 KRATES is able to describe non-local quantum mechanical effects over arbitrary length scales. Further, we ﬁnd evidence that the inclusion of higher-order geometric correlations increases data efﬁciency and improves generalization. S O 3 KRATES matches or exceeds state-of-the-art performance on popular benchmarks, notably, requiring a signiﬁcantly lower number of parameters (0.25–0.4x) while at the same time giving a substantial speedup (6–14x for training and 2–11x for inference) compared to other models.
DNA Storage Error Simulator: A Tool for Simulating Errors in Synthesis, Storage, PCR and Sequencing

Summary: DNA has many valuable characteristics that make it suitable for a long-term storage medium, in particular its durability and high information density. DNA can be stored safely for hundreds of years with virtually no degradation, in contrast to hard disk drives which typically last for about 5 years. Furthermore, the duration of DNA-Storage can be extended to potentially up to thousands of years if it is desiccated and cooled in storage. Advances in DNA technologies have made it possible to store the entirety of Wikipedia in a test tube and read that information using a handheld sequencing device, although imperfections in writing (synthesis) and reading (sequencing) need to be mitigated for it to be viable as a mainstream storage medium. New sequencing technologies, such as nanopore sequencing, aim to penetrate the consumer world, thanks to their affordability and size. However, the error characteristics of nanopore sequencing are not yet well characterised. DNA Storage Error Simulator models errors that can be introduced in all the phases of DNA storage workflow, including synthesis, storage, PCR for amplification and finally sequencing. The error characteristics for sequencing and synthesis can be configured in all necessary detail or can be chosen from a predefined set of values based on available literature and our own analysis. Availability: DNA Storage Error Simulator can be accessed online from https://master.dbahb2jho41s4. amplifyapp.com/ (https://dnastorage.doc.ic.ac.uk/DNA-error-simulator) Contact: t.heinis@imperial.ac. uk, j.alnasir@imperial.ac. uk
A Computational and Experimental Analysis of Higher Order Modes in a Strongly Focusing Optical Cavity

Optical cavities operating in the near-concentric regime are the fundamental tools to perform high precision experiments like cavity QED applications. A strong focusing regime unfortunately is prone to excite higher-order modes. Higher-order mode excitation is challenging to avoid for the realistic strong focusing cavities, and if these modes are closely spaced, overall cavity linewidth gets significantly broadened. In this study, a computational method alongside the experiment is provided for the optical mode decomposition into cavity eigenmodes with justified approximations. It is shown that it is possible to recreate the intensity and spectral profile of the cavity transmission, with the provided model. As a result, a more complete treatment of the realistic near-concentric cavities can be done.
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning

We study reward poisoning attacks on online deep reinforcement learning (DRL), where the attacker is oblivious to the learning algorithm used by the agent and the dynamics of the environment. We demonstrate the intrinsic vulnerability of state-of-the-art DRL algorithms by designing a general, black-box reward poisoning framework called adversarial MDP attacks. We instantiate our framework to construct two new attacks which only corrupt the rewards for a small fraction of the total training timesteps and make the agent learn a low-performing policy. We provide a theoretical analysis of the efficiency of our attack and perform an extensive empirical evaluation. Our results show that our attacks efficiently poison agents learning in several popular classical control and MuJoCo environments with a variety of state-of-the-art DRL algorithms, such as DQN, PPO, SAC, etc.
Interactive and Robust Mesh Booleans

Boolean operations are among the most used paradigms to create and edit digital shapes. Despite being conceptually simple, the computation of mesh Booleans is notoriously challenging. Main issues come from numerical approximations that make the detection and processing of intersection points inconsistent and unreliable, exposing implementations based on floating point arithmetic to many kinds of degeneracy and failure. Numerical methods based on rational numbers or exact geometric predicates have the needed robustness guarantees, that are achieved at the cost of increased computation times that, as of today, has always restricted the use of robust mesh Booleans to offline applications. We introduce an algorithm for Boolean operations with robustness guarantees that is capable of operating at interactive frame rates on meshes with up to 200K triangles. We evaluate our tool thoroughly, considering not only interactive applications but also batch processing of large collections of meshes, processing of huge meshes containing millions of elements and variadic Booleans of hundreds of shapes altogether. In all these experiments, we consistently outperform prior robust floating point methods by at least one order of magnitude.
Disconnecting the Dots: Re-examining the Nature of Stellar “Strings” in the Milky Way

Recent analyses of Gaia data have resulted in the identification of new stellar structures, including a new class of extended stellar filaments called stellar “strings,” first proposed by Kounkel & Covey. We explore the spatial, kinematic, and chemical composition of strings to demonstrate that these newfound structures are largely inconsistent with being physical objects whose members share a common origin. Examining the 3D spatial distribution of string members, we find that the spatial dispersion around the claimed string spine does not improve in the latest Gaia DR3 data release—despite tangible gains in the signal-to-noise ratio of the parallax measurements—counter to expectations of a bona fide structure. Using the radial velocity dispersion of the strings (averaging σVr=16kms−1 ) to estimate their virial masses, we find that all strings are gravitationally unbound. Given the finding that the strings are dispersing, the reported stellar ages of the strings are typically 120× larger than their measured dispersal times. Finally, we validate prior work that stellar strings are more chemically homogeneous than their local field stars but show it is possible to obtain the same signatures of chemical homogeneity by drawing random samples of stars from spatially, temporally, and kinematically unrelated open clusters. Our results show that while some strings may be composed of real substructures, there is no consistent evidence for larger string-like connections over the sample. These results underscore the need for caution in over-interpreting the significance of these strings and their role in understanding the star formation history of the Milky Way.
Cupid is not Doomed Yet: On the Stability of the Inner Moons of Uranus

Some of the small inner moons of Uranus have very closely-spaced orbits. Multiple numerical studies have found that the moons Cressida and Desdemona, within the Portia subgroup, are likely to collide in less than 100 Myr. The subsequent discovery of three new moons (Cupid, Perdita, and Mab) made the system even more crowded. In particular, it has been suggested that the Belinda group (Cupid, Belinda, and Perdita) will become unstable in as little as 105 yr. Here we revisit the issue of the stability of the inner moons of Uranus using updated orbital elements and considering tidal dissipation. We find that the Belinda group can be stable on 108 yr timescales due to an orbital resonance between Belinda and Perdita. We find that tidal evolution cannot form the Belinda–Perdita resonance, but convergent migration could contribute to the long-term instability of the Portia group. We propose that Belinda captured Perdita into the resonance during the last episode of disruption and reaccretion among the inner moons, possibly hundreds of Myr ago.
Methodologies, Workloads, and Tools for Processing-in-Memory: Enabling the Adoption of Data-Centric Architectures

The increasing prevalence and growing size of data in modern applications have led to high costs for computation in tra-ditional processor-centric computing systems. Moving large volumes of data between memory devices (e.g., DRAM) and computing elements (e.g., CPUs, GPUs) across bandwidth-limited memory channels can consume more than 60% of the total energy in modern systems [1], [2]. To mitigate these costs, the processing-in-memory (PIM) [1, 3–9] paradigm moves computation closer to where the data resides, reducing (and in some cases eliminating) the need to move data between memory and the processor.
Exploiting Partial FDD Reciprocity for Beam-Based Pilot Precoding and CSI Feedback in Deep Learning

Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed learning-based DL CSI estimation that lowers uplink feedback overhead. One often overlooked problem is the limited number of DL pilots available for CSI estimation. One proposed solution leverages temporal CSI coherence by utilizing past CSI estimates and only sending channel state information-reference symbols (CSI-RS) for partial arrays to preserve CSI recovery performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to base stations with direct access to uplink CSI. In this work, we propose a new learning-based feedback architecture and a reconfigurable CSI-RS placement scheme to reduce DL CSI training overhead and to improve encoding efficiency of CSI feedback. Our results demonstrate superior performance in both indoor and outdoor scenarios by the proposed framework for CSI recovery at substantial reduction of computation power and storage requirements at UEs.
Estimation Of 3d Body Shape And Clothing Measurements From Frontal-And Side-View Images

The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several conditions, such as lack of publicly available realistic datasets, ambiguity in multiple camera resolutions, and the undefinable human shape space. Existing works proposed various solutions to these problems but could not succeed in the industry adaptation because of complexity and restrictions. To solve the complexity and challenges, in this paper, we propose a simple yet effective architecture to estimate both shape and measures from frontal- and side-view images. We utilize silhouette segmentation from the two multi-view images and implement an auto-encoder network to learn low-dimensional features from segmented silhouettes. Then, we adopt a kernel-based regularized regression module to estimate the body shape and measurements. The experimental results show that the proposed method provides competitive results on the synthetic dataset, NOMO-3d-400-scans Dataset, and RGB Images of humans captured in different cameras.