Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Deep Reinforcement Learning for Holistic Smart Building Control

In this paper, we take a holistic approach to deal with the tradeoffs between energy use and comfort in commercial buildings. We developed a system called OCTOPUS, which employs a novel deep reinforcement learning (DRL) framework that uses a data-driven approach to find the optimal control sequences of all building's subsystems, including HVAC, lighting, blind and window systems. The DRL architecture includes a novel reward function that allows the framework to explore the tradeoffs between energy use and users' comfort, while at the same time enabling the solution of the high-dimensional control problem due to the interactions of four different building subsystems. In order to cope with OCTOPUS's data training requirements, we argue that calibrated simulations that match the target building operational points are the vehicle to generate enough data to be able to train our DRL framework to find the control solution for the target building. In our work, we trained OCTOPUS with 10-year weather data and a building model that is implemented in the EnergyPlus building simulator, which was calibrated using data from a real production building. Through extensive simulations, we demonstrate that OCTOPUS can achieve 14.26% and 8.1% energy savings compared with the state-of-the-art rule-based method in a LEED Gold Certified building and the latest DRL-based method available in the literature respectively, while maintaining human comfort within a desired range.
Just Another Day on Twitter: A Complete 24 Hours of Twitter Data

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected all 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.
Improving Behavioural Cloning with Positive Unlabeled Learning

Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed quality, with a limited number of the trajectories that we would consider as positive examples; i.e., high-quality demonstrations. Therefore, we propose a novel iterative learning algorithm for identifying expert trajectories in unlabeled mixed-quality robotics datasets given a minimal set of positive examples, surpassing existing algorithms in terms of accuracy. We show that applying behavioral cloning to the resulting filtered dataset outperforms several competitive offline reinforcement learning and imitation learning baselines. We perform experiments on a range of simulated locomotion tasks and on two challenging manipulation tasks on a real robotic system; in these experiments, our method showcases state-of-the-art performance. Our website: \url{https://sites.google. com/view/offline-policy-learning-pubc}.
Meta-Learning Mini-Batch Risk Functionals

Supervised learning typically optimizes the expected value risk functional of the loss, but in many cases, we want to optimize for other risk functionals. In full-batch gradient descent, this is done by taking gradients of a risk functional of interest, such as the Conditional Value at Risk (CVaR) which ignores some quantile of extreme losses. However, deep learning must almost always use mini-batch gradient descent, and lack of unbiased estimators of various risk functionals make the right optimization procedure unclear. In this work, we introduce a meta-learning-based method of learning an interpretable mini-batch risk functional during model training, in a single shot. When optimizing for various risk functionals, the learned mini-batch risk functions lead to risk reduction of up to 10% over hand-engineered mini-batch risk functionals. Then in a setting where the right risk functional is unknown a priori, our method improves over baseline by 14% relative (~9% absolute). We analyze the learned mini-batch risk functionals at different points through training, and find that they learn a curriculum (including warm-up periods), and that their final form can be surprisingly different from the underlying risk functional that they optimize for.
fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling

The package fnets for the R language implements the suite of methodologies proposed by Barigozzi et al. (2022) for the network estimation and forecasting of high-dimensional time series under a factor-adjusted vector autoregressive model, which permits strong spatial and temporal correlations in the data. Additionally, we provide tools for visualising the networks underlying the time series data after adjusting for the presence of factors. The package also offers data-driven methods for selecting tuning parameters including the number of factors, vector autoregressive order and thresholds for estimating the edge sets of the networks of interest in time series analysis. We demonstrate various features of fnets on simulated datasets as well as real data on electricity prices.
Thermal curvature perturbations in thermal inflation

We compute the power spectrum of super-horizon curvature perturbations generated during a late period of thermal inflation, taking into account fluctuation-dissipation effects resulting from the scalar flaton field's interactions with the ambient radiation bath. We find that, at the onset of thermal inflation, the flaton field may reach an equilibrium with the radiation bath even for relatively small coupling constants, maintaining a spectrum of thermal fluctuations until the critical temperature $T_c$, below which thermal effects stop holding the field at the false potential minimum. This enhances the field variance compared to purely quantum fluctuations, therefore increasing the average energy density during thermal inflation and damping the induced curvature perturbations. In particular, we find that this inhibits the later formation of primordial black holes, at least on scales that leave the horizon for $T>T_c$. The larger thermal field variance also reduces the duration of a period of fast-roll inflation below $T_c$, as the field rolls to the true potential minimum, which should also affect the generation of (large) curvature perturbations on even smaller scales.
Detecting Pump&Dump Stock Market Manipulation from Online Forums

The intersection of social media, low-cost trading platforms, and naive investors has created an ideal situation for information-based market manipulations, especially pump&dumps. Manipulators accumulate small-cap stocks, disseminate false information on social media to inflate their price, and sell at the peak. We collect a dataset of stocks whose price and volume profiles have the characteristic shape of a pump&dump, and social media posts for those same stocks that match the timing of the initial price rises. From these we build predictive models for pump&dump events based on the language used in the social media posts. There are multiple difficulties: not every post will cause the intended market reaction, some pump&dump events may be triggered by posts in other forums, and there may be accidental confluences of post timing and market movements. Nevertheless, our best model achieves a prediction accuracy of 85% and an F1-score of 62%. Such a tool can provide early warning to investors and regulators that a pump&dump may be underway.
Sub-Standards and Mal-Practices: Misinformation's Role in Insular, Polarized, and Toxic Interactions

How do users and communities respond to news from unreliable sources? How does news from these sources change online conversations? In this work, we examine the role of misinformation in sparking political incivility and toxicity on the social media platform Reddit. Utilizing the Google Jigsaw Perspective API to identify toxicity, hate speech, and other forms of incivility, we find that Reddit comments posted in response to articles on websites known to spread misinformation are 71.4% more likely to be toxic than comments responding to authentic news articles. Identifying specific instances of incivility and utilizing an exponential random graph model, we then show that when reacting to a misinformation story, Reddit users are more likely to be toxic to users of different political beliefs. Finally, utilizing a zero-inflated negative binomial regression, we identify that as the toxicity of subreddits increases, users are more likely to comment on misinformation-related submissions.
Enabling Multi-threading in Heterogeneous Quantum-Classical Programming Models

While quantum computers enable significant performance improvements for certain classes of applications, building a well-defined programming model has been a pressing issue. In this paper, we address some of the key limitations to realizing a generic heterogeneous parallel programming model for quantum-classical heterogeneous platforms. We discuss our experience in enabling user-level multi-threading in QCOR [1] as well as challenges that need to be addressed for programming future quantum-classical systems. Specifically, we discuss our design and implementation of introducing C++-based parallel constructs to enable 1) parallel execution of a quantum kernel with std::thread and 2) asynchronous execution with std::async. To do so, we provide a detailed overview of the current implementation of the QCOR programming model and runtime, and discuss how we add 1) thread-safety to some of its user-facing API routines, and 2) increase parallelism in QCOR by removing data races that inhibit multi-threading so as to better utilize available computing resources. We also present preliminary performance results with the Quantum++ [2] back end on a single-node Ryzen9 3900X machine that has 12 physical cores (24 hardware threads) with 128GB of RAM. The results show that running two Bell kernels with 12 threads per kernel in parallel outperforms running the kernels one after the other each with 24 threads (1.63× improvement). In addition, we observe the same trend when running two Shor’s algorthm kernels in parallel (1.22× faster than executing the kernels one after the other). Furthermore, the parallel version is better in terms of strong scalability. We believe that our design, implementation, and results will open up an opportunity not only for 1) enabling quicker prototyping of parallel-aware quantum-classical algorithms on quantum circuit simulators in the short-term, but also for 2) realizing a generic parallel programming model for quantum-classical heterogeneous platforms in the long-term.
An Interior Penalty Finite Element Method with Discontinuous Elements

A new semidiscrete finite element method for the solution of second order nonlinear parabolic boundary value problems is formulated and analyzed. The test and trial spaces consist of discontinuous piecewise polynomial functions over quite general meshes with interelement continuity enforced approximately by means of penalties. Optimal order error estimates in energy and $L^2$-norms are stated in terms of locally expressed quantities. They are proved first for a model problem and then in general.
Ultrafast superconducting single-photon detectors for infrared wavelength quantum communications

We have developed a new class of superconducting single-photon detectors (SSPDs) for ultrafast counting of infrared (IR) photons for secure quantum communications. The devices are operated on the quantum detection mechanism, based on the photon-induced hotspot formation and subsequent appearance of a transient resistive barrier across an ultrathin and submicron-wide superconducting stripe. The detectors are fabricated from 3.5-nm-thick NbN films and they operate at 4.2 K inside a closed-cycle refrigerator or liquid helium cryostat. Various continuous and pulsed laser sources have been used in our experiments, enabling us to determine the detector experimental quantum efficiency (QE) in the photon-counting mode, response time, time jitter, and dark counts. Our 3.5-nm-thick SSPDs reached QE above 15% for visible light photons and 5% at 1.3 - 1.5 μm infrared range. The measured real-time counting rate was above 2 GHz and was limited by the read-out electronics (intrinsic response time is <30 ps). The measured jitter was <18 ps, and the dark counting rate was <0.01 per second. The measured noise equivalent power (NEP) is 2 x 10-18 W/Hz1/2 at λ = 1.3 μm. In near-infrared range, in terms of the counting rate, jitter, dark counts, and overall sensitivity, the NbN SSPDs significantly outperform their semiconductor counterparts. An ultrafast quantum cryptography communication technology based on SSPDs is proposed and discussed.
A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

Various kinds of applications take advantage of GPUs through automation tools that attempt to automatically exploit the available performance of the GPU's parallel architecture. Directive-based programming models, such as OpenACC, are one such method that easily enables parallel computing by just adhering code annotations to code loops. Such abstract models, however, often prevent programmers from making additional low-level optimizations to take advantage of the advanced architectural features of GPUs because the actual generated computation is hidden from the application developer. This paper describes and implements a novel flexible optimization technique that operates by inserting a code emulator phase to the tail-end of the compilation pipeline. Our tool emulates the generated code using symbolic analysis by substituting dynamic information and thus allowing for further low-level code optimizations to be applied. We implement our tool to support both CUDA and OpenACC directives as the frontend of the compilation pipeline, thus enabling low-level GPU optimizations for OpenACC that were not previously possible. We demonstrate the capabilities of our tool by automating warp-level shuffle instructions that are difficult to use by even advanced GPU programmers. Lastly, evaluating our tool with a benchmark suite and complex application code, we provide a detailed study to assess the benefits of shuffle instructions across four generations of GPU architectures.
Mixed Attention Network for Hyperspectral Image Denoising

Hyperspectral image denoising is unique for the highly similar and correlated spectral information that should be properly considered. However, existing methods show lim-itations in exploring the spectral correlations across different bands and feature interactions within each band. Besides, the low- and high-level features usually exhibit different importance for different spatial-spectral regions, which is not fully explored for current algorithms as well. In this paper, we present a Mixed Attention Network (MAN) that simultaneously considers the inter- and intra-spectral correlations as well as the interactions between low- and high-level spatial-spectral meaningful features. Speciﬁcally, we introduce a multi-head recurrent spectral attention that ef-ﬁciently integrates the inter-spectral features across all the spectral bands. These features are further enhanced with a progressive spectral channel attention by exploring the intra-spectral relationships. Moreover, we propose an attentive skip-connection that adaptively controls the propor-tion of the low- and high-level spatial-spectral features from the encoder and decoder to better enhance the aggregated features. Extensive experiments show that our MAN outperforms existing state-of-the-art methods on simulated and real noise settings while maintaining a low cost of parameters and running time. Code is available at https: //github.com/Zeqiang-Lai/MAN .
Call for Papers - The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

We present the call for papers for the BabyLM Challenge: Sample-efﬁcient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an interest in small scale language modeling, human language acquisition, low-resource NLP, and cognitive modeling. In partnership with CoNLL and CMCL, we provide a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children. The task has three tracks, two of which restrict the training data to pre-released datasets of 10M and 100M words and are ded-icated to explorations of approaches such as architectural variations, self-supervised objec-tives, or curriculum learning. The ﬁnal track only restricts the amount of text used, allowing innovation in the choice of the data, its domain, and even its modality (i.e., data from sources other than text is welcome). We will release a shared evaluation pipeline which scores models on a variety of benchmarks and tasks, in-cluding targeted syntactic evaluations and natural language understanding.
An Adaptive-Discard-Graph for online error control

In recent years, graphical multiple testing procedures have gained popularity due to their generality and ease of interpretation. In contemporary research, online error control is often required, where an error criterion, such as familywise error rate (FWER) or false discovery rate (FDR), shall remain under control while testing an a priori unbounded sequence of hypotheses. Although the classical graphical procedure can be extended to the online setting, previous work has shown that it leads to low power, and other approaches, such as Adaptive-Discard (ADDIS) procedures, are preferred instead. In this paper, we introduce an ADDIS-Graph with FWER control and its extension for the FDR setting. These graphical ADDIS procedures combine the good interpretability of graphical procedures with the high online power of ADDIS procedures. Moreover, they can be adapted to a local dependence structure and an asynchronous testing setup, leading to power improvements over the current state-of-art methods. Consequently, the proposed methods are useful for a wide range of applications, including innovative complex trial designs, such as platform trials, and large-scale test designs, such as in the evaluation of A/B tests for marketing research.
Adaptive Least-Squares Methods for Convection-Dominated Diffusion-Reaction Problems

This paper studies adaptive least-squares finite element methods for convection-dominated diffusion-reaction problems. The least-squares methods are based on the first-order system of the primal and dual variables with various ways of imposing outflow boundary conditions. The coercivity of the homogeneous least-squares functionals are established, and the a priori error estimates of the least-squares methods are obtained in a norm that incorporates the streamline derivative. All methods have the same convergence rate provided that meshes in the layer regions are fine enough. To increase computational accuracy and reduce computational cost, adaptive least-squares methods are implemented and numerical results are presented for some test problems.
Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection

We study statistical inference on the similarity/distance between two time-series under uncertain environment by considering a statistical hypothesis test on the distance obtained from Dynamic Time Warping (DTW) algorithm. The sampling distribution of the DTW distance is too difficult to derive because it is obtained based on the solution of the DTW algorithm, which is complicated. To circumvent this difficulty, we propose to employ the conditional selective inference framework, which enables us to derive a valid inference method on the DTW distance. To our knowledge, this is the first method that can provide a valid p-value to quantify the statistical significance of the DTW distance, which is helpful for high-stake decision making such as abnormal time-series detection problems. We evaluate the performance of the proposed inference method on both synthetic and real-world datasets.
Stellar Turbulent Convection: A New Model and Applications

Improvements of the mixing-length theory (MLT) of turbulent convection in stellar atmospheres are developed theoretically. It is pointed out that inaccuracies are introduced into MLT by the approximating assumptions of a single large eddy (rather than many eddies of different sizes) and of incompressibility. In the proposed new model, the full spectrum of turbulent eddies is determined using more recent turbulence models (e.g., the eddy-damped quasi-normal Markovian model of Orszag, 1977), and a new formula for the convective flux is obtained which gives values up to 10 times greater than those of the MLT at high convective efficiencies. The problem of compressibility is addressed by adding one of two new expressions (one with no free parameters) for the mixing length. Numerical results from simulations of a solar-type star and a 0.8-solar-mass globular-cluster star are presented in tables and graphs and discussed in detail; the agreement with observations is found to be better than with the MLT. 65 refs.
On Some Compressible Fluid Models: Korteweg, Lubrication, and Shallow Water Systems

Abstract In this article, we give some mathematical results for an isothermal model of capillary compressible fluids derived by Dunn and Serrin in [1]Dunn JE, Serrin J. On the thermodynamics of interstitial working. Arch Rational Mech Anal. 1985; 88(2):95–133), which can be used as a phase transition model. We consider a periodic domain Ω = T d (d = 2 ou 3) or a strip domain Ω = (0,1) × T d −1. We look at the dependence of the viscosity μ and the capillarity coefficient κwith respect to the density ρ. Depending on the cases we consider, different results are obtained. We prove for instance for a viscosity μ(ρ) = νρ and a surface tension the global existence of weak solutions of the Korteweg system without smallness assumption on the data. This model includes a shallow water model and a lubrication model. We discuss the validity of the result for the shallow water equations since the density is less regular than in the Korteweg case.
Precision and accuracy of sward height distributions

Double normal distributions can be used to resolve many sward height frequency distributions into two components representing the 'short' (patches) and 'tall' (non∼patches) areas in the sward. The effect of sample size on the precision and accuracy of parameters of sward height distributions was examined by drawing sub-samples (n=10) of increasing sample size (50 to 1 000) from simulated height data (n=10 000) from three different, typical height distributions, viz. normal (ungrazed), bimodal (leniently grazed) and positively skewed (intensely grazed). The coefficient of variation of components of all three distributions decreased sharply with increasing sample size and CVs for all means were <15% with 200 height measures, and <10% for all means, with the exception of the 'tall' mean in the bimodal distribution, at a sample size of 100. At a given sample size, proportions in the two components were less-precisely measured than the means, especially when the components are equally represented in the popula.