Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Lockdown Policies driven by Socioeconomic Costs

In this research paper we modify a classical SIR model to better adapt to the dynamics of COVID-19, that is we propose the heterogeneous SQAIRD model where COVID-19 spreads over a population of economic agents, namely: the elderly, adults and young people. We then compute and simulate an optimal control problem faced by a Government, where its objective is to minimize the costs generated by the pandemics using as control a compulsory quarantine measure (that is, a lockdown). We first analyze the problem from a theoretical perspective, claiming that different lockdown policies (total lockdown, no lockdown or partial lockdown) may justified by different cost (concave or convex) structures of the economies. We then focus on a particular cost structure (convex costs) and we simulate a targeted optimal policy vs. a uniform optimal policy, by dividing the whole population in three demographic groups (young, adults and old). We also simulate the dynamic of the pandemic with no policy implemented. Simulations highlighted the fact that: a) a policy of lockdown is always better than the \emph{laissez faire} policy, because it limits the costs that the pandemic generates in an uncontrolled situation;b) a targeted policy based on age of the individuals outperforms a uniform policy in terms of costs that it generates, being a targeted policy less costly and equally effective in the control of the pandemic.
Univariate Long-Term Municipal Water Demand Forecasting

This study describes an investigation into the modelling of citywide water consumption in London, Canada. Multiple modelling techniques were evaluated for the task of univariate time series forecasting with water consumption, including linear regression, Facebook's Prophet method, recurrent neural networks, and convolutional neural networks. Prophet was identified as the model of choice, having achieved a mean absolute percentage error of 2.51%, averaged across a 5-fold cross validation. Prophet was also found to have other advantages deemed valuable to water demand management stakeholders, including inherent interpretability and graceful handling of missing data. The implementation for the methods described in this paper has been open sourced, as they may be adaptable by other municipalities.
Perrin numbers that are concatenations of two distinct repdigits

Let (Pn)n≥0 be the sequence of Perrin numbers defined by ternary relation P0 = 3, P1 = 0, P2 = 2, and Pn+3 = Pn+1 + Pn for all n ≥ 0. In this paper, we use Baker’s theory for nonzero linear forms in logarithms of algebraic numbers and the reduction procedure involving the theory of continued fractions, to explicitly determine all Perrin numbers that are concatenations of two distinct repeated digit numbers.
Strong suppression of heat conduction in a laboratory replica of galaxy-cluster turbulent plasmas

In conventional gases and plasmas, it is known that heat fluxes are proportional to temperature gradients, with collisions between particles mediating energy flow from hotter to colder regions and the coefficient of thermal conduction given by Spitzer’s theory. However, this theory breaks down in magnetized, turbulent, weakly collisional plasmas, although modifications are difficult to predict from first principles due to the complex, multiscale nature of the problem. Understanding heat transport is important in astrophysical plasmas such as those in galaxy clusters, where observed temperature profiles are explicable only in the presence of a strong suppression of heat conduction compared to Spitzer’s theory. To address this problem, we have created a replica of such a system in a laser laboratory experiment. Our data show a reduction of heat transport by two orders of magnitude or more, leading to large temperature variations on small spatial scales (as is seen in cluster plasmas).
Self-interpretable Convolutional Neural Networks for Text Classification

Deep learning models for natural language processing (NLP) are inherently complex and often viewed as black box in nature. This paper develops an approach for interpreting convolutional neural networks for text classification problems by exploiting the local-linear models inherent in ReLU-DNNs. The CNN model combines the word embedding through convolutional layers, filters them using max-pooling, and optimizes using a ReLU-DNN for classification. To get an overall self-interpretable model, the system of local linear models from the ReLU DNN are mapped back through the max-pool filter to the appropriate n-grams. Our results on experimental datasets demonstrate that our proposed technique produce parsimonious models that are self-interpretable and have comparable performance with respect to a more complex CNN model. We also study the impact of the complexity of the convolutional layers and the classification layers on the model performance.
SHARE: a System for Hierarchical Assistive Recipe Editing

The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset—83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints—demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.
Unsupervised Compound Domain Adaptation for Face Anti-Spoofing

We address the problem of face anti-spoofing which aims to make the face verification systems robust in the real world settings. The context of detecting live vs. spoofed face images may differ significantly in the target domain, when compared to that of labeled source domain where the model is trained. Such difference may be caused due to new and unknown spoof types, illumination conditions, scene backgrounds, among many others. These varieties of differences make the target a compound domain, thus calling for the problem of the unsupervised compound domain adaptation. We demonstrate the effectiveness of the compound domain assumption for the task of face anti-spoofing, for the first time in this work. To this end, we propose a memory augmentation method for adapting the source model to the target domain in a domain aware manner, inspired by [29]. The adaptation process is further improved by using the curriculum learning and the domain agnostic source network training approaches. The proposed method successfully adapts to the compound target domain consisting multiple new spoof types. Our experiments on multiple benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art. Our source code will be made publicly available.
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons

We study statistical and algorithmic aspects of using hypergraphons, that are limits of large hypergraphs, for modeling higher-order interactions. Although hypergraphons are extremely powerful from a modeling perspective, we consider a restricted class of Simple Lipschitz Hypergraphons (SLH), that are amenable to practically efficient estimation. We also provide rates of convergence for our estimator that are optimal for the class of SLH. Simulation results are provided to corroborate the theory.
IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture

Understanding pedestrian crossing behavior is an essential goal in intelligent vehicle development, leading to an improvement in their security and traffic flow. In this paper, we developed a method called IntFormer. It is based on transformer architecture and a novel convolutional video classification model called RubiksNet. Following the evaluation procedure in a recent benchmark, we show that our model reaches state-of-the-art results with good performance ($\approx 40$ seq. per second) and size ($8\times $smaller than the best performing model), making it suitable for real-time usage. We also explore each of the input features, finding that ego-vehicle speed is the most important variable, possibly due to the similarity in crossing cases in PIE dataset.
Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach

While markdowns in retail have been studied for decades in traditional business, nowadays e-commerce fresh retail brings much more challenges. Due to the limited shelf life of perishable products and the limited opportunity of price changes, it is difficult to predict sales of a product at a counterfactual price, and therefore it is hard to determine the optimal discount price to control inventory and to maximize future revenue. Traditional machine learning-based methods have high predictability but they can not reveal the relationship between sales and price properly. Traditional economic models have high interpretability but their prediction accuracy is low. In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization. Firstly, we build a semi-parametric structural model to learn individual price elasticity and predict counterfactual demand. This semi-parametric model takes advantage of both the predictability of nonparametric machine learning model and the interpretability of economic model. Secondly, we propose a multi-period dynamic pricing algorithm to maximize the overall profit of a perishable product over its finite selling horizon. Different with the traditional approaches that use the deterministic demand, we model the uncertainty of counterfactual demand since it inevitably has randomness in the prediction process. Based on the stochastic model, we derive a sequential pricing strategy by Markov decision process, and design a two-stage algorithm to solve it. The proposed algorithm is very efficient. It reduces the time complexity from exponential to polynomial. Experimental results show the advantages of our pricing algorithm, and the proposed framework has been successfully deployed to the well-known e-commerce fresh retail scenario - Freshippo.
MetaSys: A Practical Open-source Metadata Management System to Implement and Evaluate Cross-layer Optimizations

This article introduces the first open-source FPGA-based infrastructure, MetaSys, with a prototype in a RISC-V system, to enable the rapid implementation and evaluation of a wide range of cross-layer techniques in real hardware. Hardware-software cooperative techniques are powerful approaches to improving the performance, quality of service, and security of general-purpose processors. They are, however, typically challenging to rapidly implement and evaluate in real hardware as they require full-stack changes to the hardware, system software, and instruction-set architecture (ISA). MetaSys implements a rich hardware-software interface and lightweight metadata support that can be used as a common basis to rapidly implement and evaluate new cross-layer techniques. We demonstrate MetaSys’s versatility and ease-of-use by implementing and evaluating three cross-layer techniques for: (i) prefetching in graph analytics; (ii) bounds checking in memory unsafe languages, and (iii) return address protection in stack frames; each technique requiring only ~100 lines of Chisel code over MetaSys. Using MetaSys, we perform the first detailed experimental study to quantify the performance overheads of using a single metadata management system to enable multiple cross-layer optimizations in CPUs. We identify the key sources of bottlenecks and system inefficiency of a general metadata management system. We design MetaSys to minimize these inefficiencies and provide increased versatility compared to previously proposed metadata systems. Using three use cases and a detailed characterization, we demonstrate that a common metadata management system can be used to efficiently support diverse cross-layer techniques in CPUs. MetaSys is completely and freely available at https://github.com/CMU-SAFARI/MetaSys.
Linear Tracking MPC for Nonlinear Systems—Part I: The Model-Based Case

In this article, we develop a tracking model predictive control (MPC) scheme for nonlinear systems using the linearized dynamics at the current state as a prediction model. Under reasonable assumptions on the linearized dynamics, we prove that the proposed MPC scheme exponentially stabilizes the optimal reachable equilibrium w.r. t. a desired target setpoint. Our theoretical results rely on the fact that, close to the steady-state manifold, the prediction error of the linearization is small, and hence, we can slide along the steady-state manifold toward the optimal reachable equilibrium. The closed-loop stability properties mainly depend on a cost matrix, which allows us to trade off performance, robustness, and the size of the region of attraction. In an application to a nonlinear continuous stirred tank reactor, we show that the scheme, which only requires solving a convex quadratic program online, has comparable performance to a nonlinear MPC scheme while being computationally significantly more efficient. Furthermore, our results provide the basis for controlling nonlinear systems based on data-dependent linear prediction models, which we explore in our companion paper.
Toward Designing Social Human-Robot Interactions for Deep Space Exploration

In planning for future human space exploration, it is important to consider how to design for uplifting interpersonal communications and social dynamics among crew members. What if embodied social robots could help to improve the overall team interaction experience in space? On Earth, social robots have been shown effective in providing companionship, relieving stress and anxiety, fostering connection among people, enhancing team performance, and mediating conflicts in human groups. In this paper, we introduce a set of novel research questions exploring social human-robot interactions in long-duration space exploration missions.
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator

Recent years have witnessed great progress on building emotional chatbots. Tremendous methods have been proposed for chatbots to generate responses with given emotions. However, the emotion changes of the user during the conversation has not been fully explored. In this work, we study the problem of positive emotion elicitation, which aims to generate responses that can elicit positive emotion of the user, in human-machine conversation. We propose a weakly supervised Emotion Eliciting Machine (EEM) to address this problem. Specifically, we first collect weak labels of user emotion status changes in a conversion based on a pre-trained emotion classifier. Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation. An emotion eliciting factor is introduced on top of the dual structure to balance the positive and negative emotional impacts on the generated response during emotion elicitation. The factor also provides a fine-grained controlling manner for emotion elicitation. Experimental results on a large real-world dataset show that EEM outperforms the existing models in generating responses with positive emotion elicitation.
ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale

AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background. In particular, altering these DNN models in the deployment stage posits a tremendous challenge. In this research, we propose and develop a low-code solution, ModelPS (an acronym for"Model Photoshop"), to enable and empower collaborative DNN model editing and intelligent model serving. The ModelPS solution embodies two transformative features: 1) a user-friendly web interface for a developer team to share and edit DNN models pictorially, in a low-code fashion, and 2) a model genie engine in the backend to aid developers in customizing model editing configurations for given deployment requirements or constraints. Our case studies with a wide range of deep learning (DL) models show that the system can tremendously reduce both development and communication overheads with improved productivity.
Durable Queues: The Second Amendment

We consider durable data structures for non-volatile main memory, such as the new Intel Optane memory architecture. Substantial recent work has concentrated on making concurrent data structures durable with low overhead, by adding a minimal number of blocking persist operations (i.e., flushes and fences). In this work we show that focusing on minimizing the number of persist instructions is important, but not enough. We show that access to flushed content is of high cost due to cache invalidation in current architectures. Given this finding, we present a design of the queue data structure that properly takes care of minimizing blocking persist operations as well as minimizing access to flushed content. The proposed design outperforms state-of-the-art durable queues. We start by providing a durable version of the Michael Scott queue (MSQ ). We amend MSQ by adding a minimal number of persist instructions, fewer than in available durable queues, and meeting the theoretical lower bound on the number of blocking persist operations. We then proceed with a second amendment to this design, that eliminates accesses to flushed data. Evaluation shows that the second amendment yields substantial performance improvement, outperforming the state of the art and demonstrating the importance of reduced accesses to flushed content. The presented queues are durably linearizable and lock-free. Finally, we discuss the theoretical optimal number of accesses to flushed content.
EMISSOR: A platform for capturing multimodal interactions as Episodic Memories and Interpretations with Situated Scenario-based Ontological References

We present EMISSOR: a platform to capture multimodal interactions as recordings of episodic experiences with explicit referential interpretations that also yield an episodic Knowledge Graph (eKG). The platform stores streams of multiple modalities as parallel signals. Each signal is segmented and annotated independently with interpretation. Annotations are eventually mapped to explicit identities and relations in the eKG. As we ground signal segments from different modalities to the same instance representations, we also ground different modalities across each other. Unique to our eKG is that it accepts different interpretations across modalities, sources and experiences and supports reasoning over conflicting information and uncertainties that may result from multimodal experiences. EMISSOR can record and annotate experiments in virtual and real-world, combine data, evaluate system behavior and their performance for preset goals but also model the accumulation of knowledge and interpretations in the Knowledge Graph as a result of these episodic experiences.
Zero Dynamics, Pendulum Models, and Angular Momentum in Feedback Control of Bipedal Locomotion

Low-dimensional models are ubiquitous in the bipedal robotics literature. On the one hand is the community of researchers that bases feedback control design on pendulum models selected to capture the center of mass dynamics of the robot during walking. On the other hand is the community that bases feedback control design on virtual constraints, which induce an exact low-dimensional model in the closed-loop system. In the first case, the low-dimensional model is valued for its physical insight and analytical tractability. In the second case, the low-dimensional model is integral to a rigorous analysis of the stability of walking gaits in the full-dimensional model of the robot. This paper seeks to clarify the commonalities and differences in the two perspectives for using low-dimensional models. In the process of doing so, we argue that angular momentum about the contact point is a better indicator of robot state than linear velocity. Concretely, we show that an approximate (pendulum and zero dynamics) model parameterized by angular momentum provides better predictions for foot placement on a physical robot (e.g., legs with mass) than does a related approximate model parameterized in terms of linear velocity. We implement an associated angular-momentum-based controller on Cassie, a 3D robot, and demonstrate high agility and robustness in experiments.
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations

Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning ( DL ) to solve complex real-world problems. A DL program encodes the network structure of a desirable DL model and the process by which the model learns from the training dataset. Like any software, a DL program can be faulty, which implies substantial challenges of software quality assurance, especially in safety-critical domains. It is therefore crucial to equip DL development teams with efficient fault detection techniques and tools. In this article, we propose NeuraLint , a model-based fault detection approach for DL programs, using meta-modeling and graph transformations. First, we design a meta-model for DL programs that includes their base skeleton and fundamental properties. Then, we construct a graph-based verification process that covers 23 rules defined on top of the meta-model and implemented as graph transformations to detect faults and design inefficiencies in the generated models (i.e., instances of the meta-model). First, the proposed approach is evaluated by finding faults and design inefficiencies in 28 synthesized examples built from common problems reported in the literature. Then NeuraLint successfully finds 64 faults and design inefficiencies in 34 real-world DL programs extracted from Stack Overflow posts and GitHub repositories. The results show that NeuraLint effectively detects faults and design issues in both synthesized and real-world examples with a recall of 70.5% and a precision of 100%. Although the proposed meta-model is designed for feedforward neural networks, it can be extended to support other neural network architectures such as recurrent neural networks. Researchers can also expand our set of verification rules to cover more types of issues in DL programs.