Miracle-dz/UniFAR-FTUs huggingface dataset:
Tails of Foreign Exchange at Risk (FEaR): Exchange Rate Disasters and Dollar Liquidity Yields∗

This paper first builds a heterogeneous-financier model, with currency speculators and hedgers, to draw testable implications for exchange rate dynamics during global disaster events. Second, it develops a novel empirical framework centered on signed quantile Uncovered Interest Parity (UIP) regressions to test the model’s main predictions. Reflecting the behavior of currency speculators, I find strong evidence in support of the disaster-risk theory of exchange rates for all currencies: high-interest-rate currencies suffer large “disaster-state” depreciations against low-interest-rate currencies in the left tail of the exchange rate distribution, but appreciate mildly at the median. Reflecting the behavior of currency hedgers, I find that the currency with the greatest liquidity yield, the U.S. dollar, tends to experience a left-tail appreciation in disasters against all other currencies—a safe-haven effect. Ultimately, and different from other currencies, the behavior of the U.S. dollar during disasters reflects a balance between these two forces, which reinforce each other when the U.S. interest rate is relatively low but offset each other when the U.S. interest rate is relatively high. ∗I thank my supervisor Giancarlo Corsetti for his guidance and support, as well as Balduin Bippus, Simon Lloyd, Emile Marin and participants at the Cambridge Macroeconomics Workshop and Disaster Risk Reading Group for useful comments and discussion. †Office Address: Faculty of Economics, University of Cambridge, Sidgwick Avenue, Cambridge, CB3 9DD, UK. Email: dao33@cam.ac. uk ‡First Version: October 26, 2020
Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency

This paper considers data-driven chance-constrained stochastic optimization problems in a Bayesian framework. Bayesian posteriors afford a principled mechanism to incorporate data and prior knowledge into stochastic optimization problems. However, the computation of Bayesian posteriors is typically an intractable problem, and has spawned a large literature on approximate Bayesian computation. Here, in the context of chance-constrained optimization, we focus on the question of statistical consistency (in an appropriate sense) of the optimal value, computed using an approximate posterior distribution. To this end, we rigorously prove a frequentist consistency result demonstrating the convergence of the optimal value to the optimal value of a fixed, parameterized constrained optimization problem. We augment this by also establishing a probabilistic rate of convergence of the optimal value. We also prove the convex feasibility of the approximate Bayesian stochastic optimization problem. Finally, we demonstrate the utility of our approach on an optimal staffing problem for an M/M/c queueing model.
Mapping flows on weighted and directed networks with incomplete observations

Detecting significant community structure in networks with incomplete observations is challenging because the evidence for specific solutions fades away with missing data. For example, recent research shows that flow-based community detection methods can highlight spurious communities in sparse undirected and unweighted networks with missing links. Current Bayesian approaches developed to overcome this problem do not work for incomplete observations in weighted and directed networks that describe network flows. To address this gap, we extend the idea behind the Bayesian estimate of the map equation for unweighted and undirected networks to enable more robust community detection in weighted and directed networks. We derive a weighted and directed prior network that can incorporate metadata information and show how an efficient implementation in the community-detection method Infomap provides more reliable communities even with a significant fraction of data missing.
Spectral-Spatial Graph Reasoning Network for Hyperspectral Image Classification

—In this paper, we propose a spectral-spatial graph reasoning network (SSGRN) for hyperspectral image (HSI) classiﬁcation. Concretely, this network contains two parts that separately named spatial graph reasoning subnetwork (SAGRN) and spectral graph reasoning subnetwork (SEGRN) to capture the spatial and spectral graph contexts, respectively. Different from the previous approaches implementing superpixel segmentation on the original image or attempting to obtain the category features under the guide of label image, we perform the superpixel segmentation on intermediate features of the network to adaptively produce the homogeneous regions to get the effective descriptors. Then, we adopt a similar idea in spectral part that reasonably aggregating the channels to generate spectral descriptors for spectral graph contexts capturing. All graph reasoning procedures in SAGRN and SEGRN are achieved through graph convolution. To guarantee the global perception ability of the proposed methods, all adjacent matrices in graph reasoning are obtained with the help of non-local self-attention mechanism. At last, by combining the extracted spatial and spectral graph contexts, we obtain the SSGRN to achieve a high accuracy classiﬁcation. Extensive quantitative and qualitative experiments on three public HSI benchmarks demonstrate the competitiveness of the proposed methods compared with other state-of-the-art approaches.
FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity

Recent works on sparse neural networks have demonstrated that it is possible to train a sparse network in isolation to match the performance of the corresponding dense networks with a fraction of parameters. However, the iden-tiﬁcation of these performant sparse neural networks (winning tickets) either involves a costly iterative train-prune-retrain process (e.g., Lottery Ticket Hypothesis) or an over-extended sparse training time (e.g., Training with Dynamic Sparsity), both of which would raise ﬁnancial and environmental concerns. In this work, we attempt to address this cost-reducing problem by introducing the FreeTickets concept, as the ﬁrst solution which can boost the performance of sparse convolutional neural networks over their dense network equivalents by a large margin, while using for complete training only a fraction of the computational resources required by the latter. Concretely, we instantiate the FreeTickets concept, by proposing two novel efﬁcient ensemble methods with dynamic sparsity, which yield in one shot many diverse and accurate tickets “for free” during the sparse training process. The combination of these free tickets into an ensemble demonstrates a signiﬁcant improvement in accuracy, uncertainty estimation, robustness, and efﬁciency over the corresponding dense (ensemble) networks. Our results provide new
Unified Simultaneous Wireless Information and Power Transfer for IoT: Signaling and Architecture With Deep Learning Adaptive Control

In this article, we propose a unified simultaneous wireless information and power transfer (SWIPT) signaling and architecture to take advantage of both single tone and multitone signaling by adjusting only the power allocation ratio of a unified signal. Toward this, we design a unified and integrated receiver architecture for the proposed unified SWIPT signaling via an envelope detection with low power consumption. To lower the computational complexity of the receiver, we propose an adaptive control algorithm where the transmitter adjusts the communication mode through temporal convolutional network (TCN)-based asymmetric processing. To this end, the transmitter optimizes the modulation index and power allocation ratio in short-term scale while updating the mode switching threshold in long-term scale. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition at low-power Internet of Things (IoT) devices. Consequently we will facilitate the effective deployment of low-power IoT networks that concurrently supply both information and energy wirelessly to the devices by using the proposed unified SWIPT and adaptive control algorithm at the transmitter side.
Diffusion of gravity waves by random space inhomogeneities in pancake-ice fields. Theory and validation with wave buoys and synthetic aperture radar

We study the diffusion of ocean waves by ice bodies much smaller than a wavelength, such as pancakes and small ice floes. We argue that inhomogeneities in the ice cover at scales comparable to that of the wavelength significantly increase diffusion, producing a contribution to wave attenuation comparable to what is observed in the field and usually explained by viscous effects. The resulting attenuation spectrum is characterized by a peak at the scale of the inhomogeneities in the ice cover, which could explain the rollover of the attenuation profile at small wavelengths observed in field experiments. The proposed attenuation mechanism leads to the same behaviors that would be produced by a viscous wave model with effective viscosity linearly dependent on the ice thickness. This may explain recent findings that viscous wave models require a thickness-dependent viscosity to fit experimental attenuation data. Experimental validation is carried out using wave buoy attenuation data and synthetic aperture radar image analysis.
Disentangling the Physical Origin of Emission Line Ratio Offsets at High Redshift with Spatially Resolved Spectroscopy

We present spatially resolved Hubble Space Telescope grism spectroscopy of 15 galaxies at z ∼ 0.8 drawn from the DEEP2 survey. We analyze Hα+[N ii], [S ii], and [S iii] emission on kiloparsec scales to explore which mechanisms are powering emission lines at high redshifts, testing which processes may be responsible for the well-known offset of high-redshift galaxies from the z ∼ 0 locus in the [O iii]/Hβ versus [N ii]/Hα Baldwin—Phillips—Terlevich (BPT) excitation diagram. We study spatially resolved emission-line maps to examine evidence for active galactic nuclei (AGN), shocks, diffuse ionized gas (DIG), or escaping ionizing radiation, all of which may contribute to the BPT offsets observed in our sample. We do not find significant evidence of AGN in our sample and quantify that, on average, AGN would need to contribute ∼25% of the Hα flux in the central resolution element in order to cause the observed BPT offsets. We find weak (2σ) evidence of DIG emission at low surface brightnesses, yielding an implied total DIG emission fraction of ∼20%, which is not significant enough to be the dominant emission line driver in our sample. In general we find that the observed emission is dominated by star-forming H ii regions. We discuss trends with demographic properties and the possible role of α-enhanced abundance patterns in the emission spectra of high-redshift galaxies. Our results indicate that photoionization modeling with stellar population synthesis inputs is a valid tool to explore the specific star formation properties which may cause BPT offsets, to be explored in future work.
Deciphering the light vector meson contribution to the cross sections of 
e+e−
 annihilations into the open-strange channels through a combined analysis

Jun-Zhang Wang1,2,∗ Li-Ming Wang3,† Xiang Liu1,2,4‡,§ and Takayuki Matsuki5¶ 1School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China 2Research Center for Hadron and CSR Physics, Lanzhou University & Institute of Modern Physics of CAS, Lanzhou 730000, China 3Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, China 4Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Frontiers Science Center for Rare Isotopes, Lanzhou University, Lanzhou 730000, China 5Tokyo Kasei University, 1-18-1 Kaga, Itabashi, Tokyo 173-8602, Japan (Dated: June 29, 2021)
Asymptotic Log-Det Rank Minimization via (Alternating) Iteratively Reweighted Least Squares

The affine rank minimization (ARM) problem is well known for both its applications and the fact that it is NP-hard. One of the most successful approaches, yet arguably underrepresented, is iteratively reweighted least squares (IRLS), more specifically $\mathrm{IRLS}$-$0$. Despite comprehensive empirical evidence that it overall outperforms nuclear norm minimization and related methods, it is still not understood to a satisfying degree. In particular, the significance of a slow decrease of the therein appearing regularization parameter denoted $\gamma$ poses interesting questions. While commonly equated to matrix recovery, we here consider the ARM independently. We investigate the particular structure and global convergence property behind the asymptotic minimization of the log-det objective function on which $\mathrm{IRLS}$-$0$ is based. We expand on local convergence theorems, now with an emphasis on the decline of $\gamma$, and provide representative examples as well as counterexamples such as a diverging $\mathrm{IRLS}$-$0$ sequence that clarify theoretical limits. We present a data sparse, alternating realization $\mathrm{AIRLS}$-$p$ (related to prior work under the name $\mathrm{SALSA}$) that, along with the rest of this work, serves as basis and introduction to the more general tensor setting. In conclusion, numerical sensitivity experiments are carried out that reconfirm the success of $\mathrm{IRLS}$-$0$ and demonstrate that in surprisingly many cases, a slower decay of $\gamma$ will yet lead to a solution of the ARM problem, up to the point that the exact theoretical phase transition for generic recoverability can be observed. Likewise, this suggests that non-convexity is less substantial and problematic for the log-det approach than it might initially appear.
The Star Formation History of a Post-starburst Galaxy Determined from Its Cluster Population

The recent star formation histories (SFHs) of post-starburst galaxies have been determined almost exclusively from detailed modeling of their composite starlight. This has provided important but limited information on the number, strength, and duration of bursts of star formation. In this work, we present a direct and independent measure of the recent SFH of the post-starburst galaxy S12 (plate-mjd-fiber for SDSS 623-52051-207; designated EAS12 in Smercina et al.) from its star cluster population. We detect clusters from high-resolution, UBR optical images taken with the Hubble Space Telescope and compare their luminosities and colors with stellar population models to estimate the ages and masses of the clusters. No clusters younger than ∼70 Myr are found, indicating star formation shut off at this time. Clusters formed ∼120 Myr ago reach masses up to a ∼few × 107 M ⊙, several times higher than similar-age counterparts formed in actively merging galaxies like the Antennae and NGC 3256. We develop a new calibration based on known properties for eight nearby galaxies to estimate the star formation rate (SFR) of a galaxy from the mass of the most massive cluster, M max. The cluster population indicates that S12 experienced an extremely intense but short-lived burst ∼120 Myr ago, with an estimated peak of 500−250+500M⊙yr−1 and duration of 50 ± 25 Myr, one of the highest SFRs estimated for any galaxy in the modern universe. The cluster population also allows us to fill in more of the backstory of S12. Prior to the recent, intense burst, S12 was forming stars at a moderate rate of ∼3–5 M ⊙ yr−1, typical of spiral galaxies, but the system experienced an earlier burst at some point, approximately 1–3 Gyr ago. While fairly uncertain, we estimate that the SFR during this earlier burst was ∼20–30 M ⊙ yr−1, similar to the current SFR in the Antennae and NGC 3256.
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders

This work presents CLIPDraw, an algorithm that synthesizes novel drawings based on natural language input. CLIPDraw does not require any training; rather a pre-trained CLIP language-image encoder is used as a metric for maximizing similarity between the given description and a generated drawing. Crucially, CLIPDraw operates over vector strokes rather than pixel images, a constraint that biases drawings towards simpler human-recognizable shapes. Results compare between CLIPDraw and other synthesis-through-optimization methods, as well as highlight various interesting behaviors of CLIPDraw, such as satisfying ambiguous text in multiple ways, reliably producing drawings in diverse artistic styles, and scaling from simple to complex visual representations as stroke count is increased. Code for experimenting with the method is available at: https://colab.research. google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb
SN1987A: Tracing the flux decline and spectral evolution through a comparison of SRG/eROSITA and XMM-Newton observations

SN 1987A is the closest observed supernova in the last four centuries and provides a unique opportunity to witness the birth and evolution of a supernova remnant. The source has been monitored by XMM Newton EPIC-pn from 2007--2020. SRG/eROSITA also observed the source during its commissioning phase and the first light in Sept. and Oct. 2019. We investigated the spectral and flux evolution of SN 1987A in X-rays over the last fourteen years up to Nov. 2020 using XMM-Newton and eROSITA observations. We performed a detailed spectral analysis using a three-component plane-parallel shock model and analysed and modelled the EPIC-pn monitoring and eROSITA observations in a consistent manner. This paper reports a complete and most up to date flux evolution of SN 1987A in the soft (0.5-2 keV) and hard (3-10 keV) X-ray band. The flux in the soft band flattened around 9424 d and then displayed a turnover between 10142-10493 d after which it showed a continued decline. Around the same time, a break in the hard-band flux time evolution slope was detected. This implies that the blast wave has now passed beyond the dense structures of the equatorial ring and is expanding further into more tenuous circumstellar medium. The temporal evolution of the normalizations of the three shock components match well the results of hydrodynamical simulations predicting a blue supergiant progenitor scenario. The trend at recent epochs indicate that the emission caused by the forward shock after leaving the equatorial ring and by the reverse shock in the ejecta is becoming more dominant now. The elemental abundances in the hot plasma component are significantly higher than those in the `cooler' one, indicating its origin from the reverse shock propagating into the ejecta.
Exact Simulation of Pigment-Protein Complexes: Vibronic Renormalisation of Electronic Parameters in Ultrafast Spectroscopy

The primary steps of photosynthesis generate, transport and trap delocalised electronic excitations (excitons) in pigment-protein complexes (PPCs). Generically, PPCs possess highly structured vibrational spectra with a large number of discrete intraand quasi-continuous inter-pigment modes while exhibiting electronvibrational (vibronic) couplings that are comparable to electronic inter-pigment coupling. Consequently, establishing a quantitative connection between spectroscopic data and underlying microscopic models of PPC dynamics remains an outstanding challenge. We address this challenge with two numerically exact simulation methods that support an analytical theory of multimode vibronic effects. Vibronic coupling across the entire vibrational spectrum, including highfrequency modes, needs to be accounted for to ensure quantitatively correct description of optical spectra, where dynamic localization effects modulate the intensity of vibrational sidebands and multimode mixing shifts the absorption peaks. Furthermore, we show that high-frequency modes can support long-lived oscillations in multidimensional nonlinear spectra, which are not obtained in a coarse-grained description of the electronvibrational coupling. Light-harvesting (LH) antennas and photo-chemical reaction centers (RC) provide the elementary building blocks of the photosynthetic apparatus of plants, algae and bacteria [1]. Primarily these molecular aggregates consist of absorbing molecules (pigments) complexed with specific proteins to form a PPC. Despite its fundamental importance to biology, the dynamical characterization of these complexes to a degree that can reproduce all reported spectroscopic data in a single microscopic model remains an outstanding challenge. Reduced models of excitonic dynamics subject to purely thermal fluctuations can achieve reasonable agreement with linear optical spectra [2–9]. The quantitative explanation of all relevant aspects of multi-dimensional nonlinear spectroscopy though, requires a more detailed model of the system-environment interaction that takes into account the full complexity of the environmental
Few-Shot Domain Expansion for Face Anti-Spoofing

Face anti-spoofing (FAS) is an indispensable and widely used module in face recognition systems. Although high accuracy has been achieved, a FAS system will never be perfect due to the non-stationary applied environments and the potential emergence of new types of presentation attacks in real-world applications. In practice, given a handful of labeled samples from a new deployment scenario (target domain) and abundant labeled face images in the existing source domain, the FAS system is expected to perform well in the new scenario without sacrificing the performance on the original domain. To this end, we identify and address a more practical problem: Few-Shot Domain Expansion for Face Anti-Spoofing (FSDE-FAS). This problem is challenging since with insufficient target domain training samples, the model may suffer from both overfitting to the target domain and catastrophic forgetting of the source domain. To address the problem, this paper proposes a Style transfer-based Augmentation for Semantic Alignment (SASA) framework. We propose to augment the target data by generating auxiliary samples based on photorealistic style transfer. With the assistant of the augmented data, we further propose a carefully designed mechanism to align different domains from both instance-level and distribution-level, and then stabilize the performance on the source domain with a less-forgetting constraint. Two benchmarks are proposed to simulate the FSDE-FAS scenarios, and the experimental results show that the proposed SASA method outperforms state-of-the-art methods.
Noisy-MAPPO: Noisy Advantage Values for Cooperative Multi-agent Actor-Critic methods

Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent co-operative tasks such as robot swarms control, autonomous vehicle coordination, and computer games. Recent works have applied the Proximal Policy Optimization (PPO) to the multi-agent tasks, called Multi-agent PPO (MAPPO). However, the MAPPO in current works lacks theoretical support for its convergence; and requires artificial agent-specific features, called MAPPO-agent-specific (MAPPO-AS). In addition, the performance of MAPPO-AS is still lower than the finetuned QMIX on the popular benchmark environment StarCraft Multi-agent Challenge (SMAC). In this paper, we firstly theoretically generalize single-agent PPO to MAPPO, which can be seen as theoretical support for MAPPO. Secondly, since the sampled advantage values in vanilla MAPPO may mislead the learning of some agents, which are not related to these advantage values, called The Policies Overfitting in Multi-agent Cooperation(POMAC) problem. We propose the Noisy Advantage-Values (Noisy-MAPPO and Advantage-Noisy-MAPPO) to solve it. The experimental results show that the average performance of Noisy-MAPPO is better than that of finetuned QMIX; Noisy-MAPPO is the first algorithm that achieves more than 90% winning rates in all SMAC scenarios. We open-source the code at https://github.com/hijkzzz/noisy-mappo.