Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Cascade Model for Analyzing Spread in Evolving Networks

Current approaches for modeling propagation in networks (e.g., of diseases, computer viruses, rumors) cannot adequately capture temporal properties such as order/duration of evolving connections or dynamic likelihoods of propagation along connections. Temporal models on evolving networks are crucial in applications that need to analyze dynamic spread. For example, a disease spreading virus has varying transmissibility based on interactions between individuals occurring with different frequency, proximity, and venue population density. Similarly, propagation of information having a limited active period, such as rumors, depends on the temporal dynamics of social interactions. To capture such behaviors, we first develop the Temporal Independent Cascade (T-IC) model with a spread function that efficiently utilizes a hypergraph-based sampling strategy and dynamic propagation probabilities. We prove this function to be submodular, with guarantees of approximation quality. This enables scalable analysis on highly granular temporal networks where other models struggle, such as when the spread across connections exhibits arbitrary temporally evolving patterns. We then introduce the notion of “reverse spread” using the proposed T-IC processes, and develop novel solutions to identify both sentinel/detector nodes and highly susceptible nodes. Extensive analysis on real-world datasets shows that the proposed approach significantly outperforms the alternatives in modeling both if and how spread occurs, by considering evolving network topology alongside granular contact/interaction information. Our approach has numerous applications, such as virus/rumor/influence tracking. Utilizing T-IC, we explore vital challenges of monitoring the impact of various intervention strategies over real spatio-temporal contact networks where we show our approach to be highly effective.
Meta-modelling the climate of dry tide-locked rocky planets

Rocky planets hosted by close-in extrasolar systems are likely to be tidally locked in 1:1 spin-orbit resonance, a configuration where they exhibit permanent dayside and nightside. Because of the resulting day-night temperature gradient, the climate and large-scale circulation of these planets are strongly determined by their atmospheric stability against collapse, which designates the runaway condensation of greenhouse gases. To better constrain the surface conditions of rocky planets located in the habitable zone of their host star, it is therefore crucial to elucidate the mechanisms that govern the day-night heat redistribution. As a first attempt to bridge the gap between multiple modelling approaches ranging from idealised models to 3-D General Circulation Models (GCM), we developed a General Circulation Meta-Model (GCMM) able to reproduce both the closed-form solutions provided by analytical models and the numerical solutions obtained from GCM simulations. We used this approach to characterise the atmospheric stability of Earth-sized rocky planets with dry atmospheres containing CO2, and we benchmarked it against 3-D GCM simulations using THOR GCM. We observe that the collapse pressure below which collapse occurs can vary by ~40% around the value predicted by analytical scaling laws depending on the mechanisms taken into account among radiative transfer, atmospheric dynamics, and turbulent diffusion. Particularly, we find (i) that the turbulent diffusion taking place in the dayside planetary boundary layer (PBL) globally tends to warm up the nightside surface hemisphere except in the transition zone between optically thin and optically thick regimes, (ii) that the PBL also significantly affects the day-night advection timescale, and (iii) that the slow rotator approximation holds from the moment that the normalised equatorial Rossby deformation radius is greater than 2.
3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow

Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image. So far, most of the previous methods still struggle to extract semantic attributes for 3D reconstruction task. Since the semantic attributes of a single image are usually implicit and entangled with each other, it is still challenging to reconstruct 3D shape with detailed semantic structures represented by the input image. To address this problem, we propose 3DAttriFlow to disentangle and extract semantic attributes through different semantic levels in the input images. These disentangled semantic attributes will be integrated into the 3D shape reconstruction process, which can provide definite guidance to the reconstruction of specific attribute on 3D shape. As a result, the 3D decoder can explicitly capture high-level semantic features at the bottom of the network, and utilize low-level features at the top of the network, which allows to reconstruct more accurate 3D shapes. Note that the explicit disentangling is learned without extra labels, where the only supervision used in our training is the input image and its corresponding 3D shape. Our comprehensive experiments on ShapeNet dataset demonstrate that 3DAttriFlow outperforms the state-of-the-art shape reconstruction methods, and we also validate its generalization ability on shape completion task. Code is available at https://github.com/junshengzhou/3DAttriFlow.
Evaluating Prompts Across Multiple Choice Tasks In a Zero-Shot Setting

Large language models have shown that impressive zero-shot performance can be achieved through natural language prompts (Radford et al., 2019; Brown et al., 2020; Sanh et al., 2021). Creating an effective prompt, however, requires significant trial and error. That \textit{prompts} the question: how do the qualities of a prompt effects its performance? To this end, we collect and standardize prompts from a diverse range of tasks for use with tasks they were not designed for. We then evaluate these prompts across fixed multiple choice datasets for a quantitative analysis of how certain attributes of a prompt affect performance. We find that including the choices and using prompts not used during pre-training provide significant improvements. All experiments and code can be found https://github.com/gabeorlanski/zero-shot-cross-task.
Proximal-like algorithms for equilibrium seeking in mixed-integer Nash equilibrium problems

We consider potential games with mixed-integer variables, for which we propose two distributed, proximallike equilibrium seeking algorithms. Specifically, we focus on two scenarios: i) the underlying game is generalized ordinal and the agents update through iterations by choosing an exact optimal strategy; ii) the game admits an exact potential and the agents adopt approximated optimal responses. By exploiting the properties of integer-compatible regularization functions used as penalty terms, we show that both algorithms converge to either an exact or an ϵ-approximate equilibrium. We corroborate our findings on a numerical instance of a Cournot oligopoly model.
Constructing Effective Customer Feedback Systems - A Design Science Study Leveraging Blockchain Technology

Organizations have to adjust to changes in the ecosystem, and customer feedback systems (CFS) provide important information to adapt products and services to changing customer preferences. However, current systems are limited to single-dimensional rating scales and are subject to self-selection biases. This work contributes design principles for CFS and implements a CFS that advances current systems by means of contextualized feedback according to specific organizational objectives. We apply Design Science Research (DSR) methodology and report on a longitudinal DSR journey considering multiple stakeholder values by utilizing value-sensitive design methods. We conducted expert interviews, design workshops, demonstrations, and a four-day experiment in an organizational setup, involving 132 customers of a major Swiss library. In the process, we validated the identified design principles and the implemented software artifact both qualitatively and quantitatively and drew conclusions for their efficient instantiation. In particular, we found that i) blockchain technology can afford three design principles of effective CFS. Also, ii) combining DSR with value-sensitive design methods explicitly provides rationale for design principles in the form of identified important values. Moreover, iii) utilizing this methodology makes the construction of software artifacts more efficient it terms of design time by restricting the design space of a software artefact to those options that align with stakeholder values. Hence, the findings of this work advance the knowledge on the design of CFS and provides both, for researchers a theoretical contribution to reason about design principles and a guideline to managers and decision makers for designing software artefacts efficiently.
ReIL: A Framework for Reinforced Intervention-based Imitation Learning

—Compared to traditional imitation learning methods such as DAgger and DART, intervention-based imitation offers a more convenient and sample efﬁcient data collection process to users. In this paper, we introduce Reinforced Intervention-based Learning (ReIL), a framework consisting of a general intervention-based learning algorithm and a multi-task imitation learning model aimed at enabling non-expert users to train agents in real environments with little supervision or ﬁne tuning. ReIL achieves this with an algorithm that combines the advantages of imitation learning and reinforcement learning and a model capable of concurrently processing demonstrations, past experience, and current observations. Experimental results from real world mobile robot navigation challenges indicate that ReIL learns rapidly from sparse supervisor corrections without suffering deterioration in performance that is characteristic of supervised learning-based methods such as HG-Dagger and IWR. The results also demonstrate that in contrast to other intervention- based methods such as IARL and EGPO, ReIL can utilize an arbitrary reward function for training without any additional heuristics.
Efficient Reflectance Capture with a Deep Gated Mixture-of-Experts

We present a novel framework to efficiently acquire anisotropic reflectance in a pixel-independent fashion, using a deep gated mixture-of-experts. While existing work employs a unified network to handle all possible input, our network automatically learns to condition on the input for enhanced reconstruction. We train a gating module that takes photometric measurements as input and selects one out of a number of specialized decoders for reflectance reconstruction, essentially trading generality for quality. A common pre-trained latent-transform module is also appended to each decoder, to offset the burden of the increased number of decoders. In addition, the illumination conditions during acquisition can be jointly optimized. The effectiveness of our framework is validated on a wide variety of challenging near-planar samples with a lightstage. Compared with the state-of-the-art technique, our quality is improved with the same number of input images, and our input image number can be reduced to about 1/3 for equal-quality results. We further generalize the framework to enhance a state-of-the-art technique on non-planar reflectance scanning.
Adaptive Hermite Spectral Methods in Unbounded Domains

Recently, new adaptive techniques were developed that greatly improved the efficiency of solving PDEs using spectral methods. These adaptive spectral techniques are especially suited for accurately solving problems in unbounded domains and require the monitoring and dynamic adjustment of three key tunable parameters: the scaling factor, the displacement of the basis functions, and the spectral expansion order. There have been few analyses of numerical methods for unbounded domain problems. Specifically, there is no analysis of adaptive spectral methods to provide insight into how to increase efficiency and accuracy through dynamical adjustment of parameters. In this paper, we perform the first numerical analysis of the adaptive spectral method using generalized Hermite functions in both one- and multi-dimensional problems. Our analysis reveals why adaptive spectral methods work well when a"frequency indicator"of the numerical solution is controlled. We then investigate how the implementation of the adaptive spectral methods affects numerical results, thereby providing guidelines for the proper tuning of parameters. Finally, we further improve performance by extending the adaptive methods to allow bidirectional basis function translation, and the prospect of carrying out similar numerical analysis to solving PDEs arising from realistic difficult-to-solve unbounded models with adaptive spectral methods is also briefly discussed.
Sparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots

Traditional simultaneous localization and mapping (SLAM) methods focus on improvement in the robot's localization under environment and sensor uncertainty. This paper, however, focuses on mitigating the need for exact localization of a mobile robot to pursue autonomous navigation using a sparse set of images. The proposed method consists of a model architecture - RoomNet, for unsupervised learning resulting in a coarse identification of the environment and a separate local navigation policy for local identification and navigation. The former learns and predicts the scene based on the short term image sequences seen by the robot along with the transition image scenarios using long term image sequences. The latter uses sparse image matching to characterise the similarity of frames achieved vis-a-vis the frames viewed by the robot during the mapping and training stage. A sparse graph of the image sequence is created which is then used to carry out robust navigation purely on the basis of visual goals. The proposed approach is evaluated on two robots in a test environment and demonstrates the ability to navigate in dynamic environments where landmarks are obscured and classical localization methods fail.
FaasKeeper: a Blueprint for Serverless Services

FaaS (Function-as-a-Service) brought a fundamental shift into cloud computing: (persistent) virtual machines have been replaced with dynamically allocated resources, trading locality and statefulness for a pay-as-you-go model more suitable for varying and infrequent workloads. However, adapting services to function within the serverless paradigm while still fulfilling requirements is challenging. In this work, we introduce a design blueprint for creating complex serverless services and contribute a set of requirements for efficient and scalable FaaS computing. To showcase our approach, we focus on ZooKeeper, a centralized coordination service that offers a safe and wait-free consensus mechanism but requires a persistent allocation of computing resources that does not offer the flexibility needed to handle variable workloads. We design FaaS-Keeper, the first coordination service built on serverless functions and cloud-native services. FaaSKeeper provides the same consistency guarantees and interface as ZooKeeper with a price model proportional to the activity in the system. In addition, we define synchronization primitives to extend the capabilities of scalable cloud storage services with consensus semantics needed for strong data consistency.
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning

Reinforcement learning (RL) has emerged as a powerful approach for locomotion control of highly articulated robotic systems. However, one major challenge is the tedious process of tuning the reward function to achieve the desired motion style. To address this issue, imitation learning approaches such as adversarial motion priors have been proposed, which encourage a pre-defined motion style. In this work, we present an approach to enhance the concept of adversarial motion prior-based RL, allowing for multiple, discretely switchable motion styles. Our approach demonstrates that multiple styles and skills can be learned simultaneously without significant performance differences, even in combination with motion data-free skills. We conducted several real-world experiments using a wheeled-legged robot to validate our approach. The experiments involved learning skills from existing RL controllers and trajectory optimization, such as ducking and walking, as well as novel skills, such as switching between a quadrupedal and humanoid configuration. For the latter skill, the robot was required to stand up, navigate on two wheels, and sit down. Instead of manually tuning the sit-down motion, we found that a reverse playback of the stand-up movement helped the robot discover feasible sit-down behaviors and avoided the need for tedious reward function tuning.
AWAPart: Adaptive Workload-Aware Partitioning of Knowledge Graphs

Large-scale knowledge graphs are increasingly common in many domains. Their large sizes often exceed the limits of systems storing the graphs in a centralized data store, especially if placed in main memory. To overcome this, large knowledge graphs need to be partitioned into multiple subgraphs and placed in nodes in a distributed system. But querying these fragmented sub-graphs poses new challenges, such as increased communication costs, due to distributed joins involving cut edges. To combat these problems, a good partitioning should reduce the edge cuts while considering a given query workload. However, a partitioned graph needs to be continually re-partitioned to accommodate changes in the query workload and maintain a good average processing time. In this paper, an adaptive partitioning method for large-scale knowledge graphs is introduced, which adapts the partitioning in response to changes in the query workload. Our evaluation demonstrates that the performance of processing time for queries is improved after dynamically adapting the partitioning of knowledge graph triples. Keywords-knowledge graphs; adaptive graph partition; query workload.
Understanding out-of-distribution accuracies through quantifying difficulty of test samples

Existing works show that although modern neural networks achieve remarkable generalization performance on the in-distribution (ID) dataset, the accuracy drops significantly on the out-of-distribution (OOD) datasets \cite{recht2018cifar, recht2019imagenet}. To understand why a variety of models consistently make more mistakes in the OOD datasets, we propose a new metric to quantify the difficulty of the test images (either ID or OOD) that depends on the interaction of the training dataset and the model. In particular, we introduce \textit{confusion score} as a label-free measure of image difficulty which quantifies the amount of disagreement on a given test image based on the class conditional probabilities estimated by an ensemble of trained models. Using the confusion score, we investigate CIFAR-10 and its OOD derivatives. Next, by partitioning test and OOD datasets via their confusion scores, we predict the relationship between ID and OOD accuracies for various architectures. This allows us to obtain an estimator of the OOD accuracy of a given model only using ID test labels. Our observations indicate that the biggest contribution to the accuracy drop comes from images with high confusion scores. Upon further inspection, we report on the nature of the misclassified images grouped by their confusion scores: \textit{(i)} images with high confusion scores contain \textit{weak spurious correlations} that appear in multiple classes in the training data and lack clear \textit{class-specific features}, and \textit{(ii)} images with low confusion scores exhibit spurious correlations that belong to another class, namely \textit{class-specific spurious correlations}.
A Study of Reinforcement Learning Algorithms for Aggregates of Minimalistic Robots

—The aim of this paper is to study how to apply deep reinforcement learning for the control of aggregates of minimalistic robots. We deﬁne aggregates as groups of robots with a physical connection that compels them to form a speciﬁed shape. In our case, the robots are pre-attached to an object that must be collectively transported to a known location. Minimalism, in our setting, stems from the barebone capabilities we assume: The robots can sense the target location and the immediate obstacles, but lack the means to communicate explicitly through, e.g., message-passing. In our setting, communication is implicit, i.e., mediated by aggregated push-and-pull on the object exerted by each robot. We analyze the ability to reach coordinated behavior of four well-known algorithms for deep reinforcement learning (DQN, DDQN, DDPG, and TD3). Our experiments include robot failures and different types of environmental obstacles. We compare the performance of the best control strategies found, highlighting strengths and weaknesses of each of the considered training algorithms.
Using dynamic mode decomposition to predict the dynamics of a two-time non-equilibrium Green's function

Computing the numerical solution of the Kadanoff-Baym equations, a set of nonlinear integral differential equations satisfied by two-time Green's functions derived from many-body perturbation theory for a quantum many-body system away from equilibrium, is a challenging task. Recently, we have successfully applied dynamic mode decomposition (DMD) to construct a data driven reduced order model that can be used to extrapolate the time-diagonal of a two-time Green's function from numerical solution of the KBE within a small time window. In this paper, we extend the previous work and use DMD to predict off-diagonal elements of the two-time Green's function. We partition the two-time Green's function into a number of one-time functions along the diagonal and subdiagonls of the two-time window as well as in horizontal and vertical directions. We use DMD to construct separate reduced order models to predict the dynamics of these one-time functions in a two-step procedure. We extrapolate along diagonal and several subdiagonals within a subdiagonal band of a two-time window in the first step. In the second step, we use DMD to extrapolate the Green's function outside of the sub-diagonal band. We demonstrate the efficiency and accuracy of this approach by applying it to a two-band Hubbard model problem.
Spectra of Weighted Uniform Hypertrees

Let $T$ be a $k$-tree equipped with a weighting function $w: V(T)\cup E(T)\rightarrow C$, where $k\geq 3$. The weighted matching polynomial of the weighted $k$-tree $(T,w)$ is defined to be $$ \mu(T,w,x)= \sum_{M \in \mathcal{M}(T)}(-1)^{|M|}\prod_{e \in E(M)}\mathbf{w}(e)^k \prod_{v \in V(T)\backslash V(M)}(x-w(v)),$$ where $\mathcal{M}(T)$ denotes the set of matchings (including empty set) of $T$. In this paper, we investigate the eigenvalues of the adjacency tensor $\mathcal{A}(T,w)$ of the weighted $k$-tree $(T,w)$. The main result provides that $w(v)$ is an eigenvalue of $\mathcal{A}(T,w)$ for every $v\in V(T)$, and if $\lambda\neq w(v)$ for every $v\in V(T)$, then $\lambda$ is an eigenvalue of $\mathcal{A}(T,w)$ if and only if there exists a subtree $T'$ of $T$ such that $\lambda$ is a root of $\mu(T',w,x)$. Moreover, the spectral radius of $\mathcal{A}(T,w)$ is equal to the largest root of $\mu(T,w,x)$ when $w$ is real and nonnegative. The result extends a work by Clark and Cooper (On the adjacency spectra of hypertrees, Electron. J. Combin., 25 (2)(2018) $\#$P2.48) to weighted $k$-trees. As applications, two analogues of the above work for the Laplacian and the signless Laplacian tensors of $k$-trees are obtained.