Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal divergences of quantum measurement engine

A quantum engine fueled by quantum measurement is proposed. Under the finite-time unitary transformation, the conversion of heat to work is realized without the compression and expansion of the resonance frequency. The work output, quantum heat, and efficiency are derived, highlighting the important role of the thermal divergence recently reappearing in open quantum systems. The key problem of how the measurement basis can be optimized to enhance the performance is solved by connecting the thermal divergence to the nonequilibrium free energy and entropy. The spin-engine architecture offers a comprehensive platform for future investigations of extracting work from quantum measurement.
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach

The challenge in the widely applicable online matching problem lies in making irrevocable assignments while there is uncertainty about future inputs. Most theoretically-grounded policies are myopic or greedy in nature. In real-world applications where the matching process is repeated on a regular basis, the underlying data distribution can be leveraged for better decision-making. We present an end-to-end Reinforcement Learning framework for deriving better matching policies based on trial-and-error on historical data. We devise a set of neural network architectures, design feature representations, and empirically evaluate them across two online matching problems: Edge-Weighted Online Bipartite Matching and Online Submodular Bipartite Matching. We show that most of the learning approaches perform consistently better than classical baseline algorithms on four synthetic and real-world datasets. On average, our proposed models improve the matching quality by 3--10\% on a variety of synthetic and real-world datasets. Our code is publicly available at https://github.com/lyeskhalil/CORL.
Software-Defined Elastic Provisioning of IoT Edge Computing Virtual Resources

The fast growth of Internet-connected embedded devices demands for new capabilities at the network edge. These new capabilities are local processing, efficient communications, and resource virtualization. The current work aims to address these capabilities by designing and deploying a new management proposal, which offers on-demand activation of offline Internet of Things (IoT) fog computing assets via a Software Defined Networking (SDN) based solution combined with containerization and sensor virtualization. We propose a testbed as a proof of concept for the main functionalities of this novel solution. The obtained results evidence that the current SDN-based solution can deploy with success activation policies on computational edge containers, which are located within the same network domain of the SDN controller. In addition, different application-level scenarios are also investigated.
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images

Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large-scale variation, complex background interference, and nonuniform density distribution greatly limit the counting accuracy, particularly striking in remote-sensing imagery. To mitigate the above issues, this article proposes a novel framework for dense object counting in remote-sensing images, which incorporates a pyramidal scale module (PSM) and a global context module (GCM), dubbed PSGCNet, where PSM is used to adaptively capture multi-scale information and GCM is to guide the model to select suitable scales generated from PSM. Moreover, a reliable supervision manner improved from Bayesian and counting loss (BCL) is utilized to learn the density probability and then compute the count expectation at each annotation. It can relieve nonuniform density distribution to a certain extent. Extensive experiments on four remote-sensing counting datasets demonstrate the effectiveness of the proposed method and its superiority compared with state of the arts. Additionally, experiments extended on four commonly used crowd counting datasets further validate the generalization ability of the model. Code is available at https://github.com/gaoguangshuai/psgcnet.
Generalized Fractional Repetition Codes for Binary Coded Computations

This paper addresses the gradient coding and coded matrix multiplication problems in distributed optimization and coded computing. We present a numerically stable binary coding method which overcomes the drawbacks of the \textit{Fractional Repetition Coding} gradient coding method proposed by Tandon et al., and can also be leveraged by coded computing networks whose servers are of heterogeneous nature. Specifically, we propose a construction for fractional repetition gradient coding; while ensuring that the generator matrix remains close to perfectly balanced for any set of coded parameters, as well as a low complexity decoding step. The proposed binary encoding avoids operations over the real and complex numbers which are inherently numerically unstable, thereby enabling numerically stable distributed encodings of the partial gradients. We then make connections between gradient coding and coded matrix multiplication. Specifically, we show that any gradient coding scheme can be extended to coded matrix multiplication. Furthermore, we show how the proposed binary gradient coding scheme can be used to construct two different coded matrix multiplication schemes, each achieving different trade-offs.
Personalized Online Machine Learning

In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degrees of personalization. Namely, POSL optimizes predictions with respect to baseline covariates, so personalization can vary from completely individualized (i.e., optimization with respect to baseline covariate subject ID) to many individuals (i.e., optimization with respect to common baseline covariates). As an online algorithm, POSL learns in real-time. POSL can leverage a diversity of candidate algorithms, including online algorithms with different training and update times, fixed algorithms that are never updated during the procedure, pooled algorithms that learn from many individuals' time-series, and individualized algorithms that learn from within a single time-series. POSL's ensembling of this hybrid of base learning strategies depends on the amount of data collected, the stationarity of the time-series, and the mutual characteristics of a group of time-series. In essence, POSL decides whether to learn across samples, through time, or both, based on the underlying (unknown) structure in the data. For a wide range of simulations that reflect realistic forecasting scenarios, and in a medical data application, we examine the performance of POSL relative to other current ensembling and online learning methods. We show that POSL is able to provide reliable predictions for time-series data and adjust to changing data-generating environments. We further cultivate POSL's practicality by extending it to settings where time-series enter/exit dynamically over chronological time.
ES-ENAS: BLACKBOX OPTIMIZATION

We consider the problem of efficient blackbox optimization over a large hybrid search space, consisting of a mixture of a high dimensional continuous space and a complex combinatorial space. Such examples arise commonly in evolutionary computation, but also more recently, neuroevolution and architecture search for Reinforcement Learning (RL) policies. Unfortunately however, previous mutation-based approaches suffer in high dimensional continuous spaces both theoretically and practically. We thus instead propose ES-ENAS, a simple joint optimization procedure by combining Evolutionary Strategies (ES) and combinatorial optimization techniques in a highly scalable and intuitive way, inspired by the one-shot or supernet paradigm introduced in Efficient Neural Architecture Search (ENAS). Through this relatively simple marriage between two different lines of research, we are able to gain the best of both worlds, and empirically demonstrate our approach by optimizing BBOB functions over hybrid spaces as well as combinatorial neural network architectures via edge pruning and quantization on popular RL benchmarks. Due to the modularity of the algorithm, we also are able incorporate a wide variety of popular techniques ranging from use of different continuous and combinatorial optimizers, as well as constrained optimization.
Towards Cyber–Physical Systems Robust to Communication Delays: A Differential Game Approach

Collaboration between interconnected cyber-physical systems is becoming increasingly pervasive. Time-delays in communication channels between such systems are known to induce catastrophic failure modes, like high frequency oscillations in robotic manipulators in bilateral teleoperation or string instability in platoons of autonomous vehicles. This letter considers nonlinear time-delay systems representing coupled robotic agents, and proposes controllers that are robust to time-varying communication delays. We introduce approximations that allow the delays to be considered as implicit control inputs themselves, and formulate the problem as a zero-sum differential game between the stabilizing controllers and the delays acting adversarially. The ensuing optimal control law is finally compared to known results from Lyapunov-Krasovskii based approaches via numerical experiments.
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning

Due to difficulties in acquiring ground truth depth of equirectangular (360) images, the quality and quantity of equirectangular depth data today is insufficient to represent the various scenes in the world. Therefore, 360 depth estimation studies, which relied solely on supervised learning, are destined to produce unsatisfactory results. Although self-supervised learning methods focusing on equirectangular images (EIs) are introduced, they often have incorrect or non-unique solutions, causing unstable performance. In this paper, we propose 360 monocular depth estimation methods which improve on the areas that limited previous studies. First, we introduce a self-supervised 360 depth learning method that only utilizes gravity-aligned videos, which has the potential to eliminate the needs for depth data during the training procedure. Second, we propose a joint learning scheme realized by combining supervised and self-supervised learning. The weakness of each learning is compensated, thus leading to more accurate depth estimation. Third, we propose a non-local fusion block, which can further retain the global information encoded by vision transformer when reconstructing the depths. With the proposed methods, we successfully apply the transformer to 360 depth estimations, to the best of our knowledge, which has not been tried before. On several benchmarks, our approach achieves significant improvements over previous works and establishes a state of the art.
Quantum optical analysis of squeezed state of light through dispersive non-Hermitian optical bilayers

We investigate the propagation of a normally incident squeezed coherent state of light through dispersive non-Hermitian optical bilayers, particularly at a frequency that the bilayers hold parity-time (𝒫𝒯) symmetry. To check the realization of 𝒫𝒯-symmetry in quantum optics, we reveal how dispersion and loss/gain-induced noises and thermal effects in such bilayers can affect quantum features of the incident light, such as squeezing and sub-Poissonian statistics. The numerical results show thermally induced noise at room temperature has an insignificant effect on the propagation properties in these non-Hermitian bilayers. Moreover, tuning the bilayers’ loss/gain strength, we show that the transmitted squeezed coherent states through the structure can retain to some extent their nonclassical characteristics, specifically for the frequencies far from the emission frequency of the gain layer. Furthermore, we demonstrate, only below a critical value of gain, quantum optical effective medium theory can correctly predict the propagation of quantized waves in non-Hermitian and 𝒫𝒯-symmetric bilayers.
Physical relevance of the fiducial cell in loop quantum cosmology

A common way to avoid divergent integrals in homogeneous spatially non-compact gravitational systems is to introduce a fiducial cell by cutting-off the spatial slice at a finite region $V_o$. This is usually considered as an auxiliary regulator to be removed after computations by sending $V_o\to\infty$. In this paper, we analyse the dependence of the classical and quantum theory of homogeneous, isotropic and spatially flat cosmology on $V_o$. We show that each fixed $V_o$ regularisation leads to a different canonically independent theory. At the classical level, the dynamics of observables is not affected by the regularisation on-shell. For the quantum theory, however, this leads to a family of regulator dependent quantum representations and the limit $V_o\to\infty$ becomes then more subtle. First, we construct a novel isomorphism between different $V_o$-regularisations, which allows us to identify states in the different $V_o$-labelled Hilbert spaces to ensure equivalent dynamics for any value of $V_o$. The $V_o\to\infty$ limit would then correspond to choosing a state for which the volume assigned to the fiducial cell becomes infinite as appropriate in the late-time regime. As second main result of our analysis, quantum fluctuations of observables smeared over subregions $V\subset V_o$, unlike those smeared over the full $V_o$, explicitly depend on the size of the fiducial cell through the ratio $V/V_o$ interpreted as the (inverse) number of subcells $V$ homogeneously patched together into $V_o$. Physically relevant fluctuations for a finite region, as e.g. in the early-time regime, which would be unreasonably suppressed in a na\"ive $V_o\to\infty$ limit, become appreciable at small volumes. Our results suggest that the fiducial cell is not playing the role of a mere regularisation but is physically relevant at the quantum level and complement previous statements in the literature.
Airborne virus transmission under different weather conditions

The COVID19 infection is known to disseminate through droplets ejected by infected individuals during coughing, sneezing, speaking, and breathing. The spread of the infection and hence its menace depend on how the virus-loaded droplets evolve in space and time with changing environmental conditions. In view of this, we investigate the evolution of the droplets within the purview of the Brownian motion of the evaporating droplets in the air with varying weather conditions under the action of gravity. We track the movement of the droplets until either they gravitationally settle on the ground or evaporate to aerosols of size 2 μm or less. Droplets with radii 2 μm or less may continue to diffuse and remain suspended in the air for a long time. The effects of relative humidity and temperature on the evaporation are found to be significant. We note that under strong flowing conditions, droplets travel large distances. It is found that the bigger droplets fall on the ground due to the dominance of gravity over the diffusive force despite the loss of mass due to evaporation. The smaller evaporating droplets may not settle on the ground but remain suspended in the air due to the dominance of the diffusive force. The fate of the intermediate size droplets depends on the weather conditions and plays crucial roles in the spread of the infection. These environment dependent effects indicate that the maintenance of physical separation to evade the virus is not corroborated, making the use of face masks indispensable.
Recursively Summarizing Books with Human Feedback

A major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate. We present progress on this problem on the task of abstractive summarization of entire fiction novels. Our method combines learning from human feedback with recursive task decomposition: we use models trained on smaller parts of the task to assist humans in giving feedback on the broader task. We collect a large volume of demonstrations and comparisons from human labelers, and fine-tune GPT-3 using behavioral cloning and reward modeling to do summarization recursively. At inference time, the model first summarizes small sections of the book and then recursively summarizes these summaries to produce a summary of the entire book. Our human labelers are able to supervise and evaluate the models quickly, despite not having read the entire books themselves. Our resulting model generates sensible summaries of entire books, even matching the quality of human-written summaries in a few cases ($\sim5\%$ of books). We achieve state-of-the-art results on the recent BookSum dataset for book-length summarization. A zero-shot question-answering model using these summaries achieves state-of-the-art results on the challenging NarrativeQA benchmark for answering questions about books and movie scripts. We release datasets of samples from our model.
Simulated Annealing for Emotional Dialogue Systems

Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions. In this study, we consider the task of expressing a specific emotion for dialogue generation. Previous approaches take the emotion as a training signal, which may be ignored during inference. Here, we propose a search-based emotional dialogue system by simulated annealing (SA). Specifically, we first define a scoring function that combines contextual coherence and emotional correctness. Then, SA iteratively edits a general response, and search for a generation with a high score. In this way, we enforce the presence of the desired emotion. We evaluate our system on the NLPCC2017 dataset. The proposed method shows about 12% improvements in emotion accuracy compared with the previous state-of-the-art method, without hurting the generation quality (measured by BLEU).
Vibration Improves Performance in Granular Jamming Grippers

Granular jamming is a popular soft robotics technology that has seen recent widespread applications including industrial gripping, surgical robotics and haptics. However, to date the field has not fully exploited the fundamental science of the jamming phase transition, which has been rigorously studied in the field of statistical and condensed matter physics. This work introduces vibration as a means to improve the properties of granular jamming grippers through vibratory fluidisation and the exploitation of resonant modes within the granular material. We show that vibration in soft jamming grippers can improve holding strength, reduce the downwards force needed for the gripping action, and lead to a simplified setup where the second air pump, generally used for unjamming, could be removed. In a series of studies, we show that frequency and amplitude of the waveforms are key determinants to performance, and that jamming performance is also dependent on temporal properties of the induced waveform. We hope to encourage further study in transitioning fundamental jamming mechanisms into a soft robotics context to improve performance and increase diversity of applications for granular jamming grippers.
The Birth Mass Function of Population III Stars

Population III stars ended the cosmic dark ages and began early cosmological reionization and chemical enrichment. However, in spite of their importance to the evolution of the early universe, their properties remain uncertain because of the limitations on previous numerical simulations and the lack of any observational constraints. Here, we investigate Population III star formation in five primordial halos using 3D radiation-hydrodynamical cosmological simulations. We find that multiple stars form in each minihalo and that their numbers increase over time, with up to 23 stars forming in one of the halos. Radiative feedback from the stars generates strong outflows, deforms the surrounding protostellar disk, and delays star formation for a few thousand years. Star formation rates vary with halo, and depend on the mass accretion onto the disk, the halo spin number, and the fraction of massive stars in the halo. The stellar masses in our models range from 0.1–37 M ⊙, and of the 55 stars that form in our models, 12 are >10 M ⊙ and most of the others are 1–10 M ⊙. Our simulations thus suggest that Population III stars have characteristic masses of 1–10 M ⊙ and top-heavy initial mass functions with dN/dM ∝M*−1.18 . Up to 70% of the stars are ejected from their disks by three-body interactions that, along with ionizing UV feedback, limit their final masses.
ALMA Imaging of a Galactic Molecular Outflow in NGC 4945

We present the ALMA detection of molecular outflowing gas in the central regions of NGC 4945, one of the nearest starbursts and also one of the nearest hosts of an active galactic nucleus (AGN). We detect four outflow plumes in CO J = 3 − 2 at ∼0. ″3 resolution that appear to correspond to molecular gas located near the edges of the known ionized outflow cone and its (unobserved) counterpart behind the disk. The fastest and brightest of these plumes has emission reaching observed line-of-sight projected velocities of over 450 km s−1 beyond systemic, equivalent to an estimated physical outflow velocity v ≳ 600 km s−1 for the fastest emission. Most of these plumes have corresponding emission in HCN or HCO+ J = 4 − 3. We discuss a kinematic model for the outflow emission where the molecular gas has the geometry of the ionized gas cone and shares the rotation velocity of the galaxy when ejected. We use this model to explain the velocities we observe, constrain the physical speed of the ejected material, and account for the fraction of outflowing gas that is not detected due to confusion with the galaxy disk. We estimate a total molecular mass outflow rate Ṁmol∼20 M ⊙ yr−1 flowing through a surface within 100 pc of the disk midplane, likely driven by a combination of the central starburst and AGN.
AMBER: A Semi-numerical Abundance Matching Box for the Epoch of Reionization

The Abundance Matching Box for the Epoch of Reionization (AMBER) is a semi-numerical code for modeling the cosmic dawn. The new algorithm is not based on the excursion set formalism for reionization, but takes the novel approach of calculating the reionization-redshift field zre(x) assuming that hydrogen gas encountering higher radiation intensity are photoionized earlier. Redshift values are assigned while matching the abundance of ionized mass according to a given mass-weighted ionization fraction x¯i(z) . The code has the unique advantage of allowing users to directly specify the reionization history through the redshift midpoint z mid, duration Δz, and asymmetry A z input parameters. The reionization process is further controlled through the minimum halo mass M min for galaxy formation and the radiation mean free path l mfp for radiative transfer. We implement improved methods for constructing density, velocity, halo, and radiation fields, which are essential components for modeling reionization observables. We compare AMBER with two other semi-numerical methods and find that our code more accurately reproduces the results from radiation-hydrodynamic simulations. The parallelized code is over four orders of magnitude faster than radiative transfer simulations and will efficiently enable large-volume models, full-sky mock observations, and parameter-space studies. AMBER will be made publicly available to facilitate and transform studies of the Epoch of Reionization.