Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the effect of aerosol and greenhouse gas forcing on the South Asian and East Asian monsoons with an intermediate-complexity climate model

Abstract. The South Asian and East Asian summer monsoons are globally significant meteorological features, creating a strongly seasonal pattern of precipitation, with the majority of the annual precipitation falling between June and September. The stability the monsoons is of extreme importance for a vast range of ecosystems and for the livelihoods of a large share of the world's population. Simulations are performed with an intermediate-complexity climate model in order to assess the future response of the South Asian and East Asian monsoons to changing concentrations of aerosols and greenhouse gases. The radiative forcing associated with absorbing aerosol loading consists of a mid-tropospheric warming and a compensating surface cooling, which is applied to India, Southeast Asia, and eastern China both concurrently and independently. The primary effect of increased absorbing aerosol loading is a decrease in summer precipitation in the vicinity of the applied forcing, although the regional responses vary significantly. The decrease in precipitation is not ascribable to a decrease in the precipitable water and instead derives from a reduction in the precipitation efficiency due to changes in the stratification of the atmosphere. When the absorbing aerosol loading is added in all regions simultaneously, precipitation in eastern China is most strongly affected, with a quite distinct transition to a low precipitation regime as the radiative forcing increases beyond 60 W m−2. The response is less abrupt as we move westward, with precipitation in southern India being least affected. By applying the absorbing aerosol loading to each region individually, we are able to explain the mechanism behind the lower sensitivity observed in India and attribute it to remote absorbing aerosol forcing applied over eastern China. Additionally, we note that the effect on precipitation is approximately linear with the forcing. The impact of doubling carbon dioxide levels is to increase precipitation over the region while simultaneously weakening the circulation. When the carbon dioxide and absorbing aerosol forcings are applied at the same time, the carbon dioxide forcing partially offsets the surface cooling and reduction in precipitation associated with the absorbing aerosol response. Assessing the relative contributions of greenhouse gases and aerosols is important for future climate scenarios, as changes in the concentrations of these species has the potential to impact monsoonal precipitation.
Improved uniform error bounds on a Lawson-type exponential integrator for the long-time dynamics of sine-Gordon equation

We establish the improved uniform error bounds on a Lawson-type exponential integrator Fourier pseudospectral (LEI-FP) method for the long-time dynamics of sine-Gordon equation where the amplitude of the initial data is $O(\varepsilon)$ with $0<\varepsilon \ll 1$ a dimensionless parameter up to the time at $O(1/\varepsilon^2)$. The numerical scheme combines a Lawson-type exponential integrator in time with a Fourier pseudospectral method for spatial discretization, which is fully explicit and efficient in practical computation thanks to the fast Fourier transform. By separating the linear part from the sine function and employing the regularity compensation oscillation (RCO) technique which is introduced to deal with the polynomial nonlinearity by phase cancellation, we carry out the improved error bounds for the semi-discreization at $O(\varepsilon^2\tau)$ instead of $O(\tau)$ according to classical error estimates and at $O(h^m+\varepsilon^2\tau)$ for the full-discretization up to the time $T_{\varepsilon} = T/\varepsilon^2$ with $T>0$ fixed. This is the first work to establish the improved uniform error bound for the long-time dynamics of the NKGE with non-polynomial nonlinearity. The improved error bound is extended to an oscillatory sine-Gordon equation with $O(\varepsilon^2)$ wavelength in time and $O(\varepsilon^{-2})$ wave speed, which indicates that the temporal error is independent of $\varepsilon$ when the time step size is chosen as $O(\varepsilon^2)$. Finally, numerical examples are shown to confirm the improved error bounds and to demonstrate that they are sharp.
Hubble Space Telescope Observations of Tadpole Galaxies Kiso3867, SBS0, SBS1, and UM461

Tadpole galaxies are metal-poor dwarfs with typically one dominant star-forming region, giving them a head–tail structure when inclined. A metallicity drop in the head suggests that gas accretion with even lower metallicity stimulated the star formation. Here we present multiband Hubble Space Telescope WFC3 and Advanced Camera for Surveys images of four nearby (<25 Mpc) tadpoles, SBS0, SBS1, Kiso3867, and UM461, selected for their clear metallicity drops shown in previous spectroscopic studies. The properties of the star complexes and compact clusters are measured. Each galaxy contains from three to 10 young stellar complexes with 103–105 M ⊙ of stars ∼3–10 Myr old. Between the complexes, the disk has a typical age of ∼3 Gyr. Numerous star clusters cover the galaxies, both inside and outside the complexes. The combined cluster mass function, made by normalizing the masses and counts before stacking, is a power law with a slope of −1.12 ± 0.14 on a log–log plot and the combined distribution function of cluster lifetime decays with age as t −0.65±0.24. A comparison between the summed theoretical Lyman continuum (LyC) emission from all the clusters, given their masses and ages, is comparable to or exceeds the LyC needed to excite the observed Hα in some galaxies, suggesting LyC absorption by dust or undetected gas in the halo, or perhaps galaxy escape.
Switching the function of the quantum Otto cycle in non-Markovian dynamics: Heat engine, heater, and heat pump

Quantum thermodynamics explores novel thermodynamic phenomena that emerge when interactions between macroscopic systems and microscopic quantum ones go into action. Among various issues, quantum heat engines, in particular, have attracted much attention as a critical step in theoretical formulation of quantum thermodynamics and investigation of efficient use of heat by means of quantum resources. In the present paper, we focus on heat absorption and emission processes as well as work extraction processes of a quantum Otto cycle. We describe the former as non-Markovian dynamics, and thereby find that the interaction energy between a macroscopic heat bath and a microscopic qubit is not negligible. Specifically, we reveal that the interaction energy is divided into the system and the bath in a region of the short interaction time and remains negative in the region of the long interaction time. In addition, a counterintuitive energy flow from the system and the interaction energy to the hot bath occurs in another region of the short interaction time. We quantify these effects by defining an index of non-Markovianity in terms of the interaction energy. With this behavior of the interaction energy, we show that a non-Markovian quantum Otto cycle can switch functions such as an engine as well as a heater or a heat pump by controlling the interaction time with the heat bath. In particular, the qubit itself loses its energy if we shorten the interaction time, and in this sense, the qubit is cooled through the cycle. This property has a possibility of being utilized for cooling the qubits in quantum computing. We also describe the work extraction from the microscopic system to a macroscopic system like us humans as an indirect measurement process by introducing a work storage as a new reservoir.
Optimal Constrained Task Planning as Mixed Integer Programming

For robots to successfully execute tasks as-signed to them, they must be capable of planning the right sequence of actions. These actions must be both optimal with respect to a specified objective and satisfy whatever constraints exist in their world. We propose an approach for robot task planning that is capable of planning the optimal sequence of grounded actions to accomplish a task given a specific objective function while satisfying all specified numerical constraints. Our approach accomplishes this by encoding the entire task planning problem as a single mixed integer convex program, which it then solves using an off-the-shelf Mixed Integer Program-ming solver. We evaluate our approach on several mobile manipulation tasks in both simulation and on a physical humanoid robot. Our approach is able to consistently produce optimal plans while accounting for all specified numerical constraints in the mobile manipulation tasks. Open-source implementations of the components of our approach as well as videos of robots executing planned grounded actions in both simulation and the physical world can be found at this url: https://adubredu.github. io/gtpmip
Non-Markovian quantum control as coherent stochastic trajectories

We develop a notion of stochastic quantum trajectories. First, we construct a basis set of trajectories, called elementary trajectories, and go on to show that any quantum dynamical process, including those that are non-Markovian, can be expressed as a linear combination of this set. We then show that the set of processes divide into two natural classes: those that can be expressed as a convex mixture of elementary trajectories and those that cannot be. The former are shown to be entanglement breaking processes (in each step), while the latter are dubbed coherent processes. This division of processes is analogous to separable and entangled states. In the second half of the paper, we show, with an information theoretic game, that when a process is non-Markovian, coherent trajectories allow for decoupling from the environment while preserving arbitrary quantum information encoded into the system. We give explicit expressions for the temporal correlations (quantifying non-Markovianity) and show that, in general, there are more quantum correlations than classical ones. This shows that non-Markovian quantum processes are indeed fundamentally different from their classical counterparts. Furthermore, we demonstrate how coherent trajectories (with the aid of coherent control) could turn non-Markovianity into a resource. In the final section of the paper we explore this phenomenon in a geometric picture with a convenient set of basis trajectories.
An efficient combination of quantum error correction and authentication

When sending quantum information over a channel, we want to ensure that the message remains intact. Quantum error correction and quantum authentication both aim to protect (quantum) information, but approach this task from two very different directions: error-correcting codes protect against probabilistic channel noise and are meant to be very robust against small errors, while authentication codes prevent adversarial attacks and are designed to be very sensitive against any error, including small ones. In practice, when sending an authenticated state over a noisy channel, one would have to wrap it in an error-correcting code to counterbalance the sensitivity of the underlying authentication scheme. We study the question of whether this can be done more efficiently by combining the two functionalities in a single code. To illustrate the potential of such a combination, we design the threshold code, a modification of the trap authentication code which preserves that code's authentication properties, but which is naturally robust against depolarizing channel noise. We show that the threshold code needs polylogarithmically fewer qubits to achieve the same level of security and robustness, compared to the naive composition of the trap code with any concatenated CSS code. We believe our analysis opens the door to combining more general error-correction and authentication codes, which could improve the practicality of the resulting scheme.
A Review of Intelligent Music Generation Systems

With the introduction of ChatGPT, the public's perception of AI-generated content (AIGC) has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects.
Zero-Shot Dynamic Quantization for Transformer Inference

We introduce a novel run-time method for significantly reducing the accuracy loss associated with quantizing BERT-like models to 8-bit integers. Existing methods for quantizing models either modify the training procedure,or they require an additional calibration step to adjust parameters that also requires a selected held-out dataset. Our method permits taking advantage of quantization without the need for these adjustments. We present results on several NLP tasks demonstrating the usefulness of this technique.
Probing Left-handed Heavy Neutral Leptons on the Massive Vector Doublet Model

In the first part of this thesis, we consider an extension to the Standard Model composed by a Massive Vector Doublet under SU(2)L and a Left-handed Heavy Neutral Lepton. We study the production of these exotic leptons with a Same Flavor Opposite Sign standard lepton pair, and jets. We establish limits on the parameter space considering previous searches in this final state. After the limit definition, we study a modification on the event selection criteria in order to increase the possibility of probing these new particles at the Large Hadron Collider. Finally, we perform a parameter scan considering the new lepton as a dark matter candidate, identifying relevant regions on the parameter space for future studies.
Single Transits and Eclipses Observed by K2

Photometric survey data from the Kepler mission have been used to discover and characterize thousands of transiting exoplanet and eclipsing binary (EB) systems. These discoveries have enabled empirical studies of occurrence rates which reveal that exoplanets are ubiquitous and found in a wide variety of system architectures and physical compositions. Because the detection strategy of these missions is most sensitive to short orbital periods, the vast majority of these objects reside within 1 AU of their host star. Although other detection techniques have successfully identified exoplanets at wider orbits beyond the snow lines of their respective host stars (e.g., radial velocity, microlensing, direct imaging), occurrence rates within this population remain poorly constrained. As such, identifying long period objects (LPOs) from archival Kepler and K2 data is valuable from both a statistical and theoretical standpoint, particularly for massive gas giants which are thought to heavily influence the formation and evolution dynamics of their respective systems. Here we present a catalog of 164 single transit and eclipse candidates detected during a comprehensive survey of all currently available K2 data.
SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms

—Pattern recognition from audio signals is an active research topic encompassing audio tagging, acoustic scene classiﬁcation, music classiﬁcation, and other areas. Spectrogram and mel-frequency cepstral coefﬁcients (MFCC) are among the most commonly used features for audio signal analysis and classi- ﬁcation. Recently, deep convolutional neural networks (CNN) have been successfully used for audio classiﬁcation problems using spectrogram-based 2D features. In this paper, we present SpectNet, an integrated front-end layer that extracts spectrogram features within a CNN architecture that can be used for audio pattern recognition tasks. The front-end layer utilizes learnable gammatone ﬁlters that are initialized using mel-scale ﬁlters. The proposed layer outputs a 2D spectrogram image which can be fed into a 2D CNN for classiﬁcation. The parameters of the entire network, including the front-end ﬁlterbank, can be updated via back-propagation. This training scheme allows for ﬁne-tuning the spectrogram-image features according to the target audio dataset. The proposed method is evaluated in two different audio signal classiﬁcation tasks: heart sound anomaly detection and acoustic scene classiﬁcation. The proposed method shows a signiﬁcant 1.02% improvement in MACC for the heart sound classiﬁcation task and 2.11% improvement in accuracy for the acoustic scene classiﬁcation task compared to the classical spectrogram image features. The source code of our experiments can be found at
A New Stellar Mass Proxy for Subhalo Abundance Matching

Subhalo abundance matching (SHAM) has played an important role in improving our understanding of how galaxies populate their host dark matter halos. In essence, the SHAM framework is to find a dark matter halo property that best correlates with an attribute of galaxies, such as stellar mass. The peak value of the maximum circular velocity ( Vmax ) a halo/subhalo has ever attained throughout its lifetime, V peak, has been a popular choice for SHAM. A recent study by Tonnesen & Ostriker suggested that quantity ϕ, which combines the present-day Vmax and the peak value of halo dark matter mass, performs better in predicting stellar mass than V peak. Inspired by their approach, in this work, we find that further improvement can be achieved by a quantity ψ 5 that combines the 90th percentile of Vmax a halo/subhalo has ever achieved with the 60th percentile of the dark matter halo time variation rate. Tests based on the simulation TNG300 of the IllustrisTNG project show that our new SHAM scheme, with just three free parameters, can improve the stellar mass prediction and mass-dependent clustering by 15% and 18% from ϕ, respectively, over the redshift range z = 0–2.
Robust peak-to-peak gain analysis using integral quadratic constraints

This work provides a framework to compute an upper bound on the robust peak-to-peak gain of discrete-time uncertain linear systems using integral quadratic constraints (IQCs). Such bounds are of particular interest in the computation of reachable sets and the $\ell_1$-norm, as well as when safety-critical constraints need to be satisfied pointwise in time. The use of $\rho$-hard IQCs with a terminal cost enables us to deal with a wide variety of uncertainty classes, for example, we provide $\rho$-hard IQCs with a terminal cost for the class of parametric uncertainties. This approach unifies, generalizes, and significantly improves state-of-the-art methods, which is also demonstrated in a numerical example.
Outracing Human Racers with Model-based Planning and Control for Time-trial Racing

Autonomous racing has become a popular sub-topic of autonomous driving in recent years. The goal of autonomous racing research is to develop software to control the vehicle at its limit of handling and achieve human-level racing performance. In this work, we investigate how to approach human expert-level racing performance with model-based planning and control methods using the high-fidelity racing simulator Gran Turismo Sport (GTS). GTS enables a unique opportunity for autonomous racing research, as many recordings of racing from highly skilled human players can served as expert emonstrations. By comparing the performance of the autonomous racing software with human experts, we better understand the performance gap of existing software and explore new methodologies in a principled manner. In particular, we focus on the commonly adopted model-based racing framework, consisting of an offline trajectory planner and an online Model Predictive Control-based (MPC) tracking controller. We thoroughly investigate the design challenges from three perspective, namely vehicle model, planning algorithm, and controller design, and propose novel solutions to improve the baseline approach toward human expert-level performance. We showed that the proposed control framework can achieve top 0.95% lap time among human-expert players in GTS. Furthermore, we conducted comprehensive ablation studies to validate the necessity of proposed modules, and pointed out potential future directions to reach human-best performance.
I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance Cameras

The development of autonomous vehicles arises new challenges in urban traffic scenarios where vehicle-pedestrian interactions are frequent e.g. vehicle yields to pedestrians, pedestrian slows down due approaching to the vehicle. Over the last years, several datasets have been developed to model these interactions. However, available datasets do not cover near-accident scenarios that our dataset covers. We introduce I see you, a new vehicle-pedestrian interaction dataset that tackles the lack of trajectory data in near-accident scenarios using YOLOv5 and camera calibration methods. I see you consist of 170 near-accident occurrences in seven intersections in Cusco-Peru. This new dataset and pipeline code are available on GitHub.
Planning Irregular Object Packing via Hierarchical Reinforcement Learning

Object packing by autonomous robots is an important challenge in warehouses and logistics industry. Most conventional data-driven packing planning approaches focus on regular cuboid packing, which are usually heuristic and limit the practical use in realistic applications with everyday objects. In this paper, we propose a deep hierarchical reinforcement learning approach to simultaneously plan packing sequence and placement for irregular object packing. Specifically, the top manager network infers packing sequence from six principal view heightmaps of all objects, and then the bottom worker network receives heightmaps of the next object to predict the placement position and orientation. The two networks are trained hierarchically in a self-supervised Q-Learning framework, where the rewards are provided by the packing results based on the top height, object volume and placement stability in the box. The framework repeats sequence and placement planning iteratively until all objects have been packed into the box or no space is remained for unpacked items. We compare our approach with existing robotic packing methods for irregular objects in a physics simulator. Experiments show that our approach can pack more objects with less time cost than the state-of-the-art packing methods of irregular objects. We also implement our packing plan with a robotic manipulator to show the generalization ability in the real world.
Achieving Social Optimality for Energy Communities via Dynamic NEM Pricing

We propose a social welfare maximizing mechanism for an energy community that aggregates individual and shared community resources under a general net energy metering (NEM) policy. Referred to as Dynamic NEM, the proposed mechanism adopts the standard NEM tariff model and sets NEM prices dynamically based on the total shared renewables within the community. We show that Dynamic NEM aligns the individual member’s incentive with that of the overall community; each member optimizing individual surplus under Dynamic NEM results in maximum community’s social welfare. We further show that Dynamic NEM guarantees a higher benefit to each community member than possible outside the community. Dynamic NEM is also shown to satisfy the cost-causation principle. Empirical studies using real data on a hypothetical energy community demonstrate the benefits to community members and grid operators.