Miracle-dz/UniFAR-FTUs huggingface dataset:
CaII Absorbers in the Sloan Digital Sky Survey: Element Abundances and Dust

We present measurements of element abundance ratios and dust in CaII~absorbers identified in SDSS DR7+DR9. In an earlier paper we formed a statistical sample of 435 CaII absorbers and postulated that their statistical properties might be representative of at least two populations of absorbers. Here we show that if the absorbers are roughly divided into two subsamples with CaII rest equivalent widths larger and smaller than $W_0^{\lambda 3934} = 0.7$ \AA, they are then representative of two physically different populations. Comparisons of abundance ratios between the two CaII absorber populations indicate that the weaker $W_0^{\lambda 3934}$ absorbers have properties consistent with halo-type gas, while the stronger absorbers have properties intermediate between halo- and disk-type gas. We also show that, on average, the dust extinction properties of the overall sample is consistent with a LMC or SMC dust law, and the stronger absorbers are nearly 6 times more reddened than their weaker counterparts. The absorbed-to-unabsorbed composite flux ratio at $\lambda_{rest} = 2200$ \AA\ is $\mathcal{R} \approx 0.73$ and $E(B-V) \approx 0.046$ for the stronger CaII absorbers ($W_0^{\lambda 3934} \ge 0.7$ \AA), and $\mathcal{R} \approx 0.95$ and $E(B-V) \approx 0.011$ for the weaker CaII absorbers ($W_0^{\lambda 3934} < 0.7$ \AA).
Mixed axion/neutralino cold dark matter in supersymmetric models

We consider supersymmetric (SUSY) models wherein the strong CP problem is solved by the Peccei-Quinn (PQ) mechanism with a concommitant axion/axino supermultiplet. We examine R-parity conserving models where the neutralino is the lightest SUSY particle, so that a mixture of neutralinos and axions serve as cold dark matter (a1 CDM). The mixed a1 CDM scenario can match the measured dark matter abundance for SUSY models which typically give too low a value of the usual thermal neutralino abundance, such as models with wino-like or higgsino-like dark matter. The usual thermal neutralino abundance can be greatly enhanced by the decay of thermally-produced axinos (a) to neutralinos, followed by neutralino re-annihilation at temperatures much lower than freeze-out. In this case, the relic density is usually neutralino dominated, and goes as ~ (fa/N)/ma3/2. If axino decay occurs before neutralino freeze-out, then instead the neutralino abundance can be augmented by relic axions to match the measured abundance. Entropy production from late-time axino decays can diminish the axion abundance, but ultimately not the neutralino abundance. In a1 CDM models, it may be possible to detect both a WIMP and an axion as dark matter relics. We also discuss possible modifications of our results due to production and decay of saxions. In the appendices, we present expressions for the Hubble expansion rate and the axion and neutralino relic densities in radiation, matter and decaying-particle dominated universes.
THE LOW-MASS STELLAR POPULATION IN L1641: EVIDENCE FOR ENVIRONMENTAL DEPENDENCE OF THE STELLAR INITIAL MASS FUNCTION

We present results from an optical photometric and spectroscopic survey of the young stellar population in L1641, the low-density star-forming region of the Orion A cloud south of the Orion Nebula Cluster (ONC). Our goal is to determine whether L1641 has a large enough low-mass population to make the known lack of high-mass stars a statistically significant demonstration of environmental dependence of the upper mass stellar initial mass function (IMF). Our spectroscopic sample consists of IR-excess objects selected from the Spitzer/IRAC survey and non-excess objects selected from optical photometry. We have spectral confirmation of 864 members, with another 98 probable members; of the confirmed members, 406 have infrared excesses and 458 do not. Assuming the same ratio of stars with and without IR excesses in the highly extincted regions, L1641 may contain as many as ∼1600 stars down to ∼0.1 M☉, comparable within a factor of two to the ONC. Compared to the standard models of the IMF, L1641 is deficient in O and early B stars to a 3σ–4σ significance level, assuming that we know of all the massive stars in L1641. With a forthcoming survey of the intermediate-mass stars, we will be in a better position to make a direct comparison with the neighboring, dense ONC, which should yield a stronger test of the dependence of the high-mass end of the stellar IMF on environment.
3D Body Reconstruction Revisited: Exploring the Test-time 3D Body Mesh Refinement Strategy via Surrogate Adaptation

Recent 3D body reconstruction works are achieving state-of-the-art performances. Each of the prior works applied specific modifications to their respective modules, allowing the ability to show plausible predicted 3D body poses and shapes to human eyes. Unfortunately, those outputs may sometimes be far from the correct position. In contrast to these works, we took a different perspective on how to re-improve this limitation. Without any addition or modification at the module level, we propose a test-time adaptation strategy that fine-tunes the module directly. Our approach is inspired by the science of vaccination that leverages surrogate information, which is helpful and not harmful in improving the human immune system. This notion is translated to our adaptation strategy by fine-tuning the surrogate 3D body module using reliable virtual data. In doing so, the proposed work can revisit the prior state-of-the-art works and improve their performances directly in the test phase. The experimental results demonstrate our strategy's ability to straightforwardly improve the prior works, even with fast adaptation capability.
Broadband dielectric and conductivity spectroscopy of inhomogeneous and composite conductors

Present facilities in the Department of Dielectrics, Institute of Physics ASCR, enable us to study the complex dielectric/conductivity response of various dielectric, semiconducting and poor‐metallic conducting materials and their composites in the frequency range of 10−5–1014 Hz (19 orders of magnitude). Here we summarize the phenomenological models used for fitting and basic understanding of such spectra, particularly of weak inhomogeneous and disordered conductors and composite conductor–dielectric materials within the effective medium approach. For the latter type of materials the electrical percolation phenomena are discussed. The obtained experimental results on several conductor–dielectric composites, particularly around their percolation threshold (PET‐CNT, alumina‐CNF, PZT‐PRO), porous conducting pellets (MoSI, WS2), nanofiber matrix (PVDF‐WS2), and variously conducting polyaniline pellets are briefly discussed.
Rethinking the Vulnerability of DNN Watermarking: Are Watermarks Robust against Naturalness-aware Perturbations?

Training Deep Neural Networks (DNN) is a time-consuming process and requires a large amount of training data, which motivates studies working on protecting the intellectual property (IP) of DNN models by employing various watermarking techniques. Unfortunately, in recent years, adversaries have been exploiting the vulnerabilities of the employed watermarking techniques to remove the embedded watermarks. In this paper, we investigate and introduce a novel watermark removal attack, called AdvNP, against all the existing four different types of DNN watermarking schemes via input preprocessing by injecting Adversarial Naturalness-aware Perturbations. In contrast to the prior studies, our proposed method is the first work that generalizes all the existing four watermarking schemes well without involving any model modification, which preserves the fidelity of the target model. We conduct the experiments against four state-of-the-art (SOTA) watermarking schemes on two real tasks (e.g., image classification on ImageNet, face recognition on CelebA) across multiple DNN models. Overall, our proposed AdvNP significantly invalidates the watermarks against the four watermarking schemes on two real-world datasets, i.e., 60.9% on the average attack success rate and up to 97% in the worse case. Moreover, our AdvNP could well survive the image denoising techniques and outperforms the baseline in both the fidelity preserving and watermark removal. Furthermore, we introduce two defense methods to enhance the robustness of DNN watermarking against our AdvNP. Our experimental results pose real threats to the existing watermarking schemes and call for more practical and robust watermarking techniques to protect the copyright of pre-trained DNN models. The source code and models are available at ttps://github.com/GitKJ123/AdvNP.
EASE: Robust Facial Expression Recognition via Emotion Ambiguity-SEnsitive Cooperative Networks

Facial Expression Recognition (FER) plays a crucial role in the real-world applications. However, large-scale FER datasets collected in the wild usually contain noises. More importantly, due to the ambiguity of emotion, facial images with multiple emotions are hard to be distinguished from the ones with noisy labels. Therefore, it is challenging to train a robust model for FER. To address this, we propose Emotion Ambiguity-SEnsitive cooperative networks (EASE) which contain two components. First, the ambiguity-sensitive learning module divides the training samples into three groups. The samples with small-losses in both networks are considered as clean samples, and the ones with large-losses are noisy. Note for the conflict samples that one network disagrees with the other, we distinguish the samples conveying ambiguous emotions from the ones with noises, using the polarity cues of emotions. Here, we utilize KL divergence to optimize the networks, enabling them to pay attention to the non-dominant emotions. The second part of EASE aims to enhance the diversity of the cooperative networks. With the training epochs increasing, the cooperative networks would converge to a consensus. We construct a penalty term according to the correlation between the features, which helps the networks learn diverse representations from the images. Extensive experiments on 6 popular facial expression datasets demonstrate that EASE outperforms the state-of-the-art approaches.
UNDERFLIGHT CALIBRATION OF SOHO/CDS AND HINODE/EIS WITH EUNIS-07

Flights of Goddard Space Flight Center's Extreme Ultraviolet Normal Incidence Spectrograph (EUNIS) sounding rocket in 2006 and 2007 provided updated radiometric calibrations for Solar and Heliospheric Observatory/Coronal Diagnostic Spectrometer (SOHO/CDS) and Hinode/Extreme Ultraviolet Imaging Spectrometer (Hinode/EIS). EUNIS carried two independent imaging spectrographs covering wavebands of 300–370 Å in first order and 170–205 Å in second order. After each flight, end-to-end radiometric calibrations of the rocket payload were carried out in the same facility used for pre-launch calibrations of CDS and EIS. During the 2007 flight, EUNIS, SOHO/CDS, and Hinode/EIS observed the same solar locations, allowing the EUNIS calibrations to be directly applied to both CDS and EIS. The measured CDS NIS 1 line intensities calibrated with the standard (version 4) responsivities with the standard long-term corrections are found to be too low by a factor of 1.5 due to the decrease in responsivity. The EIS calibration update is performed in two ways. One uses the direct calibration transfer of the calibrated EUNIS-07 short wavelength (SW) channel. The other uses the insensitive line pairs, in which one member was observed by the EUNIS-07 long wavelength (LW) channel and the other by EIS in either the LW or SW waveband. Measurements from both methods are in good agreement, and confirm (within the measurement uncertainties) the EIS responsivity measured directly before the instrument's launch. The measurements also suggest that the EIS responsivity decreased by a factor of about 1.2 after the first year of operation (although the size of the measurement uncertainties is comparable to this decrease). The shape of the EIS SW response curve obtained by EUNIS-07 is consistent with the one measured in laboratory prior to launch. The absolute value of the quiet-Sun He ii 304 Å intensity measured by EUNIS-07 is consistent with the radiance measured by CDS NIS in quiet regions near the disk center and the solar minimum irradiance recently obtained by CDS NIS and the Solar Dynamics Observatory/Extreme Ultraviolet Variability Experiment.
Genuinely multipartite entangled states and orthogonal arrays

A pure quantum state of N subsystems with d levels each is called k-multipartite maximally entangled state, written k-uniform, if all its reductions to k qudits are maximally mixed. These states form a natural generalization of N-qudits GHZ states which belong to the class 1-uniform states. We establish a link between the combinatorial notion of orthogonal arrays and k-uniform states and prove the existence of several new classes of such states for N-qudit systems. In particular, known Hadamard matrices allow us to explicitly construct 2-uniform states for an arbitrary number of N>5 qubits. We show that finding a different class of 2-uniform states would imply the Hadamard conjecture, so the full classification of 2-uniform states seems to be currently out of reach. Additionally, single vectors of another class of 2-uniform states are one-to-one related to maximal sets of mutually unbiased bases. Furthermore, we establish links between existence of k-uniform states, classical and quantum error correction codes and provide a novel graph representation for such states.
Continuum-Discretized Coupled-Channels calculations with core excitation

The effect of core excitation in the elastic scattering and breakup of a two-body halo nucleus on a stable target nucleus is studied. The structure of the weakly-bound projectile is described in the weak-coupling limit, assuming a particle-rotor model. The eigenfunctions and the associated eigenvalues are obtained by diagonalizing this Hamiltonian in a square-integrable basis (pseudo-states). For the radial coordinate between the particle and the core, a transformed harmonic oscillator (THO) basis is used. For the reaction dynamics, an extension of the Continuum-Discretized Coupled-Channels (CDCC) method, which takes into account dynamic core excitation and deexcitation due to the presence of non-central parts in the core-target interaction, is adapted to be used along with a pseudo-states (PS) basis.
Double outlet right ventricle in adults: Anatomic variability, surgical treatment, and late postoperative complications

Double outlet right ventricle (DORV) is a highly complex congenital heart disease (CHD) entity, gaining increasing interest due to the rapid progress of cardiac surgery. The number of patients operated for this congenital defect has been growing since 1980s and over following decades with active transitioning of this cohort into the adult medicine. However, the diversity of initial anomaly and performed interventions makes challenging the management of these patients. This is particularly important in the regions where specialized adult CHD cardiology still remains underdeveloped. In this review, we observe the basic principles of DORV nomenclature, main types of the operations and possible late complications. The article focuses on adult patients and offers illustrations from clinical practice.
Energy-Based Domain Generalization for Face Anti-Spoofing

With various unforeseeable face presentation attacks (PA) springing up, face anti-spoofing (FAS) urgently needs to generalize to unseen scenarios. Research on generalizable FAS has lately attracted growing attention. Existing methods cast FAS as a vanilla binary classification problem and address it by a standard discriminative classifier p(y|x) under a domain generalization framework. However, discriminative models are unreliable for samples far away from the training distribution. In this paper, we resort to an energy-based model (EBM) to tackle FAS in a generative perspective. Our motivation is to model the joint density p(x,y), which allows to compute not only p(y|x) but also p(x). Due to the intractability of direct modeling, we use EBMs as an alternative to probabilistic estimation. With energy-based training, real faces are encouraged to get low free energy associated with the marginal probability p(x) of real faces, and all samples with high free energy are regarded as fake faces, thus rejecting any kind of PA out of the distribution of real faces. To learn to generalize to unseen domains, we generate diverse and novel populations in feature space under the guidance of energy model. Our model is updated in a meta-learning schema, where the original source samples are utilized for meta-training and the generated ones for meta-testing. We validate our method on four widely used FAS datasets. Comprehensive experimental results demonstrate the effectiveness of our method compared with state-of-the-arts.
When True Becomes False: Few-Shot Link Prediction beyond Binary Relations through Mining False Positive Entities

Recently, the link prediction task on Hyper-relational Knowledge Graphs (HKGs) has been a hot spot, which aims to predict new facts beyond binary relations. Although previous models have accomplished considerable achievements, there remain three challenges: i) the previous models neglect the existence of False Positive Entities (FPEs), which are true entities in the binary triples, yet becomes false when encountering the query statements of HKGs; ii) Due to the sparse interactions, the models are not capable of coping with long-tail hyper-relations, which are ubiquitous in the real-world; iii) The models are generally transductive learning processes, and have difficulty in adapting new hyper-relations. To tackle the above issues, we firstly propose the task of few-shot link prediction on HKGs and devise hyper-relation-aware attention networks with a contrastive loss, which are empowered to encode all entities including FPEs effectively and increase the distance between the true entities and FPEs through contrastive learning. With few-shot references available, the proposed model then learns the representations of their long-tail hyper-relations and predicts new links by calculating the likelihood between queries and references. Furthermore, our model is inductive and can be scalable to any new hyper-relation effortlessly. Since it is the first trial on few-shot link prediction for HKGs, we also modify the existing few-shot learning approaches on binary relational data to work with HKGs as baselines. Experimental results on three real-world datasets show the superiority of our model over various state-of-the-art baselines.
Adaptive Hierarchical Pooling for Weakly-supervised Sound Event Detection

In Weakly-supervised Sound Event Detection (WSED), the ground truth of training data contains the presence or absence of each sound event only at the clip-level (i.e., no frame-level annotations). Recently, WSED has been formulated under the multi-instance learning framework, and a critical component within this formulation is the design of the temporal pooling function. In this paper, we propose an adaptive hierarchical pooling (HiPool) for WSED, which combines the advantages of max pooling in audio tagging and weighted average pooling in audio localization through a novel hierarchical structure and learns event-wise optimal pooling functions through continuous relaxation-based joint optimization. Extensive experiments on benchmark datasets show that HiPool outperforms the current pooling methods and greatly improves the performance of WSED. HiPool also has great generality - ready to be plugged into any WSED models.
ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning

Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-Shot Learning (FSL) problem across different domains has attracted rising attention. The core challenge of CD-FSL lies in the domain gap between the source and novel target datasets. Though many attempts have been made for CD-FSL without any target data during model training, the huge domain gap makes it still hard for existing CD-FSL methods to achieve very satisfactory results. Alternatively, learning CD-FSL models with few labeled target domain data which is more realistic and promising is advocated in previous work. Thus, in this paper, we stick to this setting and technically contribute a novel Multi-Expert Domain Decompositional Network (ME-D2N). Concretely, to solve the data imbalance problem between the source data with sufficient examples and the auxiliary target data with limited examples, we build our model under the umbrella of multi-expert learning. Two teacher models which can be considered to be experts in their corresponding domain are first trained on the source and the auxiliary target sets, respectively. Then, the knowledge distillation technique is introduced to transfer the knowledge from two teachers to a unified student model. Taking a step further, to help our student model learn knowledge from different domain teachers simultaneously, we further present a novel domain decomposition module that learns to decompose the student model into two domain-related sub-parts. This is achieved by a novel domain-specific gate that learns to assign each filter to only one specific domain in a learnable way. Extensive experiments demonstrate the effectiveness of our method. Codes and models are available at https://github.com/lovelyqian/ME-D2N\_for\_CDFSL.
Optimal Algorithms for Right-sizing Data Centers

Electricity cost is a dominant and rapidly growing expense in data centers. Unfortunately, much of the consumed energy is wasted, because servers are idle for extended periods of time. We study a capacity management problem that dynamically right-sizes a data center, matching the number of active servers with the varying demand for computing capacity. We resort to a data-center optimization problem introduced by Lin, Wierman, Andrew, and Thereska [25, 27] that, over a time horizon, minimizes a combined objective function consisting of operating cost, modeled by a sequence of convex functions, and server switching cost. All prior work addresses a continuous setting in which the number of active servers, at any time, may take a fractional value. In this article, we investigate for the first time the discrete data-center optimization problem where the number of active servers, at any time, must be integer valued. Thereby, we seek truly feasible solutions. First, we show that the offline problem can be solved in polynomial time. Our algorithm relies on a new, yet intuitive graph theoretic model of the optimization problem and performs binary search in a layered graph. Second, we study the online problem and extend the algorithm Lazy Capacity Provisioning (LCP) by Lin et al. [25, 27] to the discrete setting. We prove that LCP is 3-competitive. Moreover, we show that no deterministic online algorithm can achieve a competitive ratio smaller than 3. Hence, while LCP does not attain an optimal competitiveness in the continuous setting, it does so in the discrete problem examined here. We prove that the lower bound of 3 also holds in a problem variant with more restricted operating cost functions, introduced by Lin et al. [25]. In addition, we develop a randomized online algorithm that is 2-competitive against an oblivious adversary. It is based on the algorithm of Bansal et al. [7] (a deterministic, 2-competitive algorithm for the continuous setting) and uses randomized rounding to obtain an integral solution. Moreover, we prove that 2 is a lower bound for the competitive ratio of randomized online algorithms, so our algorithm is optimal. We prove that the lower bound still holds for the more restricted model. Finally, we address the continuous setting and give a lower bound of 2 on the best competitiveness of online algorithms. This matches an upper bound by Bansal et al. [7]. A lower bound of 2 was also shown by Antoniadis and Schewior [4]. We develop an independent proof that extends to the scenario with more restricted operating cost.