Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum cooling activated by coherently-controlled thermalisation

It has been shown in a recent letter[1] that thermalising a quantum system with two identical baths of temperature T in an indefinite causal order (ICO) can output a system with temperature different to T , allowing for novel non-classical thermodynamic cooling cycle. However, it is in general hard to extract heat from extremely cold system, and for the existing ICO fridge with qubit working system and two thermal reservoirs, the amount of heat the working system can extract decreases rapidly for very low reservoirs temperatures. In this paper, we show that it is possible to significantly boost the heat extraction ability of the ICO fridge by applying N identical thermalising channels in a superposition of N cyclic causal orders[2], and that this can be further boosted in the ultracold regime by replacing the working qubit with a quDit working substance. Moreover, we show that for the alternative controlled-SWAPs scheme presented in [1] where one additionally has access to the reservoir qubits which are quantum correlated with the control-target system, the performance can be greatly enhanced in general (tripled for all N ’s and temperatures). Then inspired by [3, 4], we show that quantum coherent control between thermalising a working system with one ofN identical thermalising channels (where causal indefiniteness plays no role) yield same advantages in controlled-SWAPs scheme compared to the generalisedN -SWITCH protocol for the thermodynamic task described in [1]. We also provide an experimental simulatable quantum cooling protocol with coherently-controlled thermalising channels and notice that it can outperform ICO refrigerator with some specific implementations of the thermalising channel in the case when we only have access to the control-target system. These 2 quantum cooling protocols bear much lower circuit complexity compared to the one with indefinite causal order which makes it more accessible for implementation of this type of nonclassical refrigerator with cutting edge quantum technologies.
Human-Level Control Through Directly Trained Deep Spiking Q-Networks

As the third-generation neural networks, spiking neural networks (SNNs) have great potential on neuromorphic hardware because of their high energy efficiency. However, deep spiking reinforcement learning (DSRL), that is, the reinforcement learning (RL) based on SNNs, is still in its preliminary stage due to the binary output and the nondifferentiable property of the spiking function. To address these issues, we propose a deep spiking <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-network (DSQN) in this article. Specifically, we propose a directly trained DSRL architecture based on the leaky integrate-and-fire (LIF) neurons and deep <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-network (DQN). Then, we adapt a direct spiking learning algorithm for the DSQN. We further demonstrate the advantages of using LIF neurons in DSQN theoretically. Comprehensive experiments have been conducted on 17 top-performing Atari games to compare our method with the state-of-the-art conversion method. The experimental results demonstrate the superiority of our method in terms of performance, stability, generalization and energy efficiency. To the best of our knowledge, our work is the first one to achieve state-of-the-art performance on multiple Atari games with the directly trained SNN.
On the Convergence Rates of Policy Gradient Methods

We consider infinite-horizon discounted Markov decision problems with finite state and action spaces and study the convergence rates of the projected policy gradient method and a general class of policy mirror descent methods, all with direct parametrization in the policy space. First, we develop a theory of weak gradient-mapping dominance and use it to prove sharper sublinear convergence rate of the projected policy gradient method. Then we show that with geometrically increasing step sizes, a general class of policy mirror descent methods, including the natural policy gradient method and a projected Q-descent method, all enjoy a linear rate of convergence without relying on entropy or other strongly convex regularization. Finally, we also analyze the convergence rate of an inexact policy mirror descent method and estimate its sample complexity under a simple generative model.
Determining the Timescale over Which Stellar Feedback Drives Turbulence in the Interstellar Medium: A Study of Four Nearby Dwarf Irregular Galaxies

Stellar feedback is fundamental to the modeling of galaxy evolution, as it drives turbulence and outflows in galaxies. Understanding the timescales involved are critical for constraining the impact of stellar feedback on the interstellar medium. We analyzed the resolved star formation histories along with the spatial distribution and kinematics of the atomic and ionized gas of four nearby star-forming dwarf galaxies (NGC 4068, NGC 4163, NGC 6789, and UGC 9128) to determine the timescales over which stellar feedback drives turbulence. The four galaxies are within 5 Mpc and have a range of properties including current star formation rates of 0.0005–0.01 M ⊙ yr−1, log(M */M ⊙) between 7.2 and 8.2, and log(M H i /M ⊙) between 7.2 and 8.3. Their color–magnitude diagram derived star formation histories over the past 500 Myr were compared to their atomic and ionized gas velocity dispersion and H i energy surface densities as indicators of turbulence. The Spearman’s rank correlation coefficient was used to identify any correlations between their current turbulence and their past star formation activity on local scales (∼400 pc). The strongest correlation found was between the H i turbulence measures and the star formation rate 100–200 Myr ago. This suggests a coupling between the star formation activity and atomic gas on this timescale. No strong correlation between the ionized gas velocity dispersion and the star formation activity between 5 and 500 Myr ago was found. The sample and analysis are the foundation of a larger program aimed at understanding the timescales over which stellar feedback drives turbulence.
"Learn the Facts About COVID-19": Analyzing the Use of Warning Labels on TikTok Videos

During the COVID-19 pandemic, health-related misinformation and harmful content shared online had a significant adverse effect on society. In an attempt to mitigate this adverse effect, mainstream social media platforms like Facebook, Twitter, and TikTok employed soft moderation interventions (i.e., warning labels) on potentially harmful posts. Such interventions aim to inform users about the post's content without removing it, hence easing the public's concerns about censorship and freedom of speech. Despite the recent popularity of these moderation interventions, as a research community, we lack empirical analyses aiming to uncover how these warning labels are used in the wild, particularly during challenging times like the COVID-19 pandemic. In this work, we analyze the use of warning labels on TikTok, focusing on COVID-19 videos. First, we construct a set of 26 COVID-19 related hashtags, and then we collect 41K videos that include those hashtags in their description. Second, we perform a quantitative analysis on the entire dataset to understand the use of warning labels on TikTok. Then, we perform an in-depth qualitative study, using thematic analysis, on 222 COVID-19 related videos to assess the content and the connection between the content and the warning labels. Our analysis shows that TikTok broadly applies warning labels on TikTok videos, likely based on hashtags included in the description (e.g., 99% of the videos that contain #coronavirus have warning labels). More worrying is the addition of COVID-19 warning labels on videos where their actual content is not related to COVID-19 (23% of the cases in a sample of 143 English videos that are not related to COVID-19). Finally, our qualitative analysis on a sample of 222 videos shows that 7.7% of the videos share misinformation/harmful content and do not include warning labels, 37.3% share benign information and include warning labels, and that 35% of the videos that share misinformation/harmful content (and need a warning label) are made for fun. Our study demonstrates the need to develop more accurate and precise soft moderation systems, especially on a platform like TikTok that is extremely popular among people of younger age.
Near-optimal sparse allreduce for distributed deep learning

Communication overhead is one of the major obstacles to train large deep learning models at scale. Gradient sparsification is a promising technique to reduce the communication volume. However, it is very challenging to obtain real performance improvement because of (1) the difficulty of achieving an scalable and efficient sparse allreduce algorithm and (2) the sparsification overhead. This paper proposes Ok-Topk, a scheme for distributed training with sparse gradients. Ok-Topk integrates a novel sparse allreduce algorithm (less than 6k communication volume which is asymptotically optimal) with the decentralized parallel Stochastic Gradient Descent (SGD) optimizer, and its convergence is proved. To reduce the sparsification overhead, Ok-Topk efficiently selects the top-k gradient values according to an estimated threshold. Evaluations are conducted on the Piz Daint supercomputer with neural network models from different deep learning domains. Empirical results show that Ok-Topk achieves similar model accuracy to dense allreduce. Compared with the optimized dense and the state-of-the-art sparse allreduces, Ok-Topk is more scalable and significantly improves training throughput (e.g., 3.29x-12.95x improvement for BERT on 256 GPUs).
Radio Spectra of Luminous, Heavily Obscured WISE-NVSS Selected Quasars

We present radio spectra spanning 0.1–10 GHz for the sample of heavily obscured luminous quasars with extremely red mid-infrared-optical colors and compact radio emission. The spectra are constructed from targeted 10 GHz observations and archival radio survey data that together yield 6–11 flux-density measurements for each object. Our primary result is that most (62%) of the sample have peaked or curved radio spectra and many (37%) could be classified as Gigahertz-Peaked Spectrum (GPS) sources. This indicates compact emission regions likely arising from recently triggered radio jets. Assuming synchrotron self-absorption (SSA) generates the peaks, we infer compact source sizes (3–100 pc) with strong magnetic fields (6–100 mG) and young ages (30–104 yr). Conversely, free-free absorption (FFA) could also create peaks due to the high column densities associated with the deeply embedded nature of the sample. However, we find no correlations between the existence or frequency of the peaks and any parameters of the MIR emission. The high-frequency spectral indices are steep (α ≈ −1) and correlate, weakly, with the ratio of MIR photon energy density to magnetic energy density, suggesting that the spectral steepening could arise from inverse Compton scattering off the intense MIR photon field. This study provides a foundation for combining multifrequency and mixed-resolution radio survey data for understanding the impact of young radio jets on the ISM and star-formation rates of their host galaxies. faGithub
Efficient training of spiking neural networks with temporally-truncated local backpropagation through time

Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on event-based datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs' models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT. Thus, the proposed method has shown high potential to enable fast and energy-efficient on-chip training for real-time learning at the edge.
MUPHOTEN: A MUlti-band PHOtometry Tool for TElescope Network

The early and complete temporal characterization of optical, fast, transient sources requires continuous and multiband observations over different timescales (hours to months). For time-domain astronomy, using several telescopes to analyze single objects is the usual method, allowing the acquisition of highly sampled light curves. Taking a series of images each night helps to construct an uninterrupted chain of observations with a high cadence and low duty cycle. Speed is paramount, especially at early times, in order to capture early features in the light curve that help determine the nature of the observed transients and assess their astrophysical properties. However, the problem of rapidly extracting source properties (temporal and color evolution) with a heterogeneous data set remains. Consequently, we present Muphoten, a general and fast-computation photometric pipeline able to address these constraints. It is suitable for extracting transient brightness over multitelescope and multiband networks to create a single homogeneous photometric time series. We show the performance of Muphoten with observations of the optical transient SN 2018cow (from 2018 June to 2018 July), monitored by the GRANDMA network and with the publicly available data of the Liverpool Telescope.
Exploring Bosonic and Fermionic Link Models on $(3+1)-$d tubes

Quantum link models (QLMs) have attracted a lot of attention in recent times as a generalization of Wilson's lattice gauge theories (LGT), and are particularly suitable for realization on quantum simulators and computers. These models are known to host new phases of matter and act as a bridge between particle and condensed matter physics. In this article, we study the Abelian $U(1)$ lattice gauge theory in $(3+1)$-d tubes using large-scale exact diagonalization (ED). We are then able to motivate the phase diagram of the model with finite size scaling techniques (FSS), and in particular propose the existence of a Coulomb phase. Furthermore, we introduce the first models involving fermionic quantum links, which generalize the gauge degrees of freedom to be of fermionic nature. We prove that while the spectra remain identical between the bosonic and the fermionic versions of the $U(1)$-symmetric quantum link models in $(2+1)$-d, they are different in $(3+1)$-d. We discuss the prospects of realizing the magnetic field interactions as correlated hopping in quantum simulator experiments.
Real-Time Gaze Tracking with Event-Driven Eye Segmentation

Gaze tracking is increasingly becoming an essential component in Augmented and Virtual Reality. Modern gaze tracking algorithms are heavyweight; they operate at most 5 Hz on mobile processors despite that near-eye cameras comfortably operate at a real-time rate (> 30 Hz). This paper presents a real-time eye tracking algorithm that, on average, operates at 30 Hz on a mobile processor, achieves 0.1°–0.5° gaze accuracies, all the while requiring only 30K parameters, one to two orders of magnitude smaller than state-of-the-art eye tracking algorithms. The crux of our algorithm is an Auto ROI mode, which continuously predicts the Regions of Interest (ROIs) of near-eye images and judiciously processes only the ROIs for gaze estimation. To that end, we introduce a novel, lightweight ROI prediction algorithm by emulating an event camera. We discuss how a software emulation of events enables accurate ROI prediction without requiring special hardware. The code of our paper is available at https://github.com/horizon-research/edgaze.
Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment

We provide new insights regarding the headline result that Medicaid increased emergency department (ED) use from the Oregon experiment. We find meaningful heterogeneous impacts of Medicaid on ED use using causal machine learning methods. The individualized treatment effect distribution includes a wide range of negative and positive values, suggesting the average effect masks substantial heterogeneity. A small group-about 14% of participants-in the right tail of the distribution drives the overall effect. We identify priority groups with economically significant increases in ED usage based on demographics and previous utilization. Intensive margin effects are an important driver of increases in ED utilization.
New Constraints on the Spin of the Black Hole Cygnus X-1 and the Physical Properties of its Accretion Disk Corona

We present a new analysis of NuSTAR and Suzaku observations of the black hole Cygnus X-1 in the intermediate state. The analysis is performed using kerrC, a new model for analyzing spectral and spectropolarimetric X-ray observations of black holes. kerrC builds on a large library of simulated black holes in X-ray binaries. The model accounts for the X-ray emission from a geometrically thin, optically thick accretion disk, the propagation of the X-rays through the curved black hole spacetime, the reflection off the accretion disk, and the Comptonization of photons in coronae of different 3D shapes and physical properties before and after the reflection. We present the results from using kerrC for the analysis of archival NuSTAR and Suzaku observations taken on 2015 May 27–28. The best wedge-shaped corona gives a better fit than the cone-shaped corona. Although we included cone-shaped coronae in the funnel regions above and below the black hole to resemble to some degree the common assumption of a compact lamppost corona hovering above and/or below the black hole, the fit chooses a very large version of this corona that makes it possible to Comptonize a sufficiently large fraction of the accretion disk photons to explain the observed power-law emission. The analysis indicates a black hole spin parameter a (−1 ≤ a ≤ 1) between 0.861 and 0.921. The kerrC model provides new insights into the radial distribution of the energy flux of returning and coronal emission irradiating the accretion disk. kerrC furthermore predicts small polarization fractions around 1% in the 2–8 keV energy range of the recently launched Imaging X-ray Polarimetry Explorer.
Emergent Instabilities in Algorithmic Feedback Loops

Algorithms that aid human tasks, such as recommendation systems, are ubiquitous. They appear in everything from social media to streaming videos to online shopping. However, the feedback loop between people and algorithms is poorly understood and can amplify cognitive and social biases (algorithmic confounding), leading to unexpected outcomes. In this work, we explore algorithmic confounding in collaborative filtering-based recommendation algorithms through teacher-student learning simulations. Namely, a student collaborative filtering-based model, trained on simulated choices, is used by the recommendation algorithm to recommend items to agents. Agents might choose some of these items, according to an underlying teacher model, with new choices then fed back into the student model as new training data (approximating online machine learning). These simulations demonstrate how algorithmic confounding produces erroneous recommendations which in turn lead to instability, i.e., wide variations in an item’s popularity between each simulation realization. We use the simulations to demonstrate a novel approach to training collaborative filtering models that can create more stable and accurate recommendations. Our methodology is general enough that it can be extended to other socio-technical systems in order to better quantify and improve the stability of algorithms. These results highlight the need to account for emergent behaviors from interactions between people and algorithms.
Object Detection in Autonomous Vehicles: Status and Open Challenges

Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans. Object detection is also one of the critical components to support autonomous driving. Autonomous vehicles rely on the perception of their surroundings to ensure safe and robust driving performance. This perception system uses object detection algorithms to accurately determine objects such as pedestrians, vehicles, traffic signs, and barriers in the vehicle's vicinity. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.
05 Kotrajaras

Foreign direct investment (FDI) has increased rapidly in East Asian countries over the past two decades, and many studies find that it has significant linkages with economic growth. This paper examines the impacts of FDI in groups of 15 East Asian countries classified by level of economic development, using panel data analysis together with cointegration methods. The results show that favourable impacts of FDI on East Asian countries depend on complementary factors, particularly each host country’s economic conditions such as levels of financial market development, institutional development, better governance, and appropriate macroeconomic policies. Furthermore, East Asian countries need to increase their investment in fundamental infrastructures, human capital development, and facilities for enhancing international trade and investment climate.
05 Chan Sok Gee

The main objective of this study is to examine the impact of ownership structure on the corporate performance of the Malaysian public listed companies (PLC). Empirical evidence suggests that insider and institutional shareholding do not influence the corporate performance of the Malaysian PLC. The results seem to suggest that institutional shareholders have failed in their monitoring role and principal agent problem will not be solved by increasing the director’s shareholding in the company. Despite segmenting the company according to market capitalization, results do not differ significantly. The relationship between corporate performance and insider and institutional shareholding is only significant in big market capitalization companies.
04 Thu Thi Hoang

This study examines the effects of the foreign direct investment (FDI) on economic growth in Vietnam by using the panel data model across Vietnam’s sixty-one provinces in 1995–2006. The study shows that there is a strong and positive effect of FDI on economic growth in Vietnam as a channel of increasing the stock of capital. Human capital and trade in Vietnam are not yet the channels that give access to advance technology and knowledge transfers from FDI inflows to increase Vietnam’s economic growth.