Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning aided k-t SENSE for fast reconstruction of highly accelerated PCMR data

Purpose: We implemented the Machine Learning (ML) aided k-t SENSE reconstruction to enable high resolution quantitative real-time phase contrast MR (PCMR). Our approach uses a U-net to create a high quality prior for the k-t SENSE reconstruction. We additionally tested a new version of U-net (U-net M) for improved restoration of magnitude images corrupted due to the very high data undersampling. Methods: A residual U-net and our U-net M (that additionally uses the time averaged data as the spatial signal distribution map) were used to generate the high resolution x-f space estimate for k-t SENSE regularisation prior. The networks were trained on 816 retrospectively gated PCMR magnitudes and were judged on their ability to generalise to real undersampled data. The in-vivo validation was done on 20 real-time 18x prospectively undersmapled GASperturbed PCMR data. The ML aided k-t SENSE reconstruction results were compared against the free-breathing Cartesian retrospectively gated sequence and the compressed sensing (CS) reconstruction of the same data. Results: The standard U-net results were inadequate for k-t SENSE priors with significant distortions to imaged signal. Consequently, only U-net M was used in the in-vivo study. In general, the ML aided k-t SENSE generated flow curves that were visually sharper than those produced using CS. In two exceptional cases, U-net M predictions exhibited blurring which propagated to the extracted velocity curves. However, there were no statistical differences in the measured peak velocities and stroke volumes between the tested methods. The ML aided k-t SENSE was estimated to be ~3.6x faster in processing than CS. Conclusion: The ML aided k-t SENSE reconstruction enables artefact suppression on a par with CS with no significant differences in quantitative measures. The timing results suggest the on-line implementation could deliver a substantial increase in clinical throughput.
Targeting Bright Metal-poor Stars in the Disk and Halo Systems of the Galaxy

We present the results of spectroscopic follow-up for 1897 low-metallicity star candidates, selected from the Best & Brightest (B&B) Survey, carried out with the GMOS-N/S (Gemini North/South telescopes) and Goodman (SOAR Telescope) spectrographs. From these low-resolution (R ∼ 2000) spectra, we estimate stellar atmospheric parameters, as well as carbon and magnesium abundance ratios. We confirm that 56% of our program stars are metal-poor ([Fe/H] < − 1.0), 30% are very metal-poor (VMP; [Fe/H] < − 2.0), and 2% are extremely metal-poor (EMP; [Fe/H] < − 3.0). There are 191 carbon-enhanced metal-poor (CEMP) stars, resulting in CEMP fractions of 19% and 43% for the VMP and EMP regimes, respectively. A total of 94 confirmed CEMP stars belong to Group I (A(C) ≳ 7.25) and 97 to Group II (A(C) ≲ 7.25) in the Yoon–Beers A(C)−[Fe/H] diagram. Moreover, we combine these data with Gaia EDR3 astrometric information to delineate new target-selection criteria, which have been applied to the Goodman/SOAR candidates, to more than double the efficiency for identification of bona fide VMP and EMP stars in comparison to random draws from the B&B catalog. We demonstrate that this target-selection approach can achieve success rates of 96%, 76%, 28%, and 4% for [Fe/H] ≤ − 1.5, ≤ − 2.0, ≤ − 2.5 and ≤ − 3.0, respectively. Finally, we investigate the presence of dynamically interesting stars in our sample. We find that several VMP/EMP ([Fe/H] ≤ − 2.5) stars can be associated with either the disk system or halo substructures like Gaia-Sausage/Enceladus and Sequoia.
The VLA Frontier Fields Survey: Deep, High-resolution Radio Imaging of the MACS Lensing Clusters at 3 and 6 GHz

The Frontier Fields project is an observational campaign targeting six galaxy clusters, with the intention of using the magnification provided by gravitational lensing to study galaxies that are extremely faint or distant. We used the Karl G. Jansky Very Large Array (VLA) at 3 and 6 GHz to observe three Frontier Fields: MACS J0416.1−2403 (z = 0.396), MACS J0717.5+3745 (z = 0.545), and MACS J1149.5+2223 (z = 0.543). The images reach noise levels of ∼1 μJy beam−1 with subarcsecond resolution (∼2.5 kpc at z = 3), providing a high-resolution view of high-z star-forming galaxies that is unbiased by dust obscuration. We generate dual-frequency continuum images at two different resolutions per band, per cluster, and derive catalogs totaling 1966 compact radio sources. Components within the areas of Hubble Space Telescope and Subaru observations are cross-matched, providing host galaxy identifications for 1296 of them. We detect 13 moderately lensed (2.1 < μ < 6.5) sources, one of which has a demagnified peak brightness of 0.9 μJy beam−1, making it a candidate for the faintest radio source ever detected. There are 66 radio sources exhibiting complex morphologies, and 58 of these have host galaxy identifications. We reveal that MACS J1149.5+2223 is not a cluster with a double relic, as the western candidate relic is resolved as a double-lobed radio galaxy associated with a foreground elliptical at z = 0.24. The VLA Frontier Fields project is a public legacy survey. The image and catalog products from this work are freely available.
Take a Bite of the Reality Sandwich: Revisiting the Security of Progressive Message Authentication Codes

Message authentication guarantees the integrity of messages exchanged over untrusted channels. However, to achieve this goal, message authentication considerably expands packet sizes, which is especially problematic in constrained wireless environments. To address this issue, progressive message authentication provides initially reduced integrity protection that is often sufficient to process messages upon reception. This reduced security is then successively improved with subsequent messages to uphold the strong guarantees of traditional integrity protection. However, contrary to previous claims, we show in this paper that existing progressive message authentication schemes are highly susceptible to packet loss induced by poor channel conditions or jamming attacks. Thus, we consider it imperative to rethink how authentication tags depend on the successful reception of surrounding packets. To this end, we propose R2-D2, which uses randomized dependencies with parameterized security guarantees to increase the resilience of progressive authentication against packet loss. To deploy our approach to resource-constrained devices, we introduce SP-MAC, which implements R2-D2 using efficient XOR operations. Our evaluation shows that SP-MAC is resilient to sophisticated network-level attacks and operates as resources-conscious and fast as existing, yet insecure, progressive message authentication schemes.
On linear relations between cohomological invariants of compact complex manifolds

Roughly speaking, the answer for all three questions is: ‘Only the expected ones’. The third question was famously asked by Hirzebruch [16]. The version of this question concerning the Chern numbers only had previously been solved by Kotschick [21], [22], using the complex bordism ring. A key new ingredient in [23] which allowed the systematic treatment of Hodge numbers was the consideration of the Hodge ring of Kähler manifolds. That is, the graded ring of Z-linear combinations of compact complex Kähler manifolds, where two manifolds are identified if they have the same Hodge numbers. Somewhat surprisingly, it is finitely generated.
Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence

We study text representation methods using deep models. Current methods, such as word-level embedding and character-level embedding schemes, treat texts as either a sequence of atomic words or a sequence of characters. These methods either ignore word morphologies or word boundaries. To overcome these limitations, we propose to convert texts into 2-D representations and develop the Sent2Matrix method. Our method allows for the explicit incorporation of both word morphologies and boundaries. When coupled with a novel serpentine padding method, our Sent2Matrix method leads to an interesting visualization in which 1-D character sequences are folded into 2-D serpentine manifolds. Notably, our method is the first attempt to represent texts in 2-D formats. Experimental results on text classification tasks shown that our method consistently outperforms prior embedding methods.
Understanding invariance via feedforward inversion of discriminatively trained classifiers

A discriminatively trained neural net classifier can fit the training data perfectly if all information about its input other than class membership has been discarded prior to the output layer. Surprisingly, past research has discovered that some extraneous visual detail remains in the logit vector. This finding is based on inversion techniques that map deep embeddings back to images. We explore this phenomenon further using a novel synthesis of methods, yielding a feedforward inversion model that produces remarkably high fidelity reconstructions, qualitatively superior to those of past efforts. When applied to an adversarially robust classifier model, the reconstructions contain sufficient local detail and global structure that they might be confused with the original image in a quick glance, and the object category can clearly be gleaned from the reconstruction. Our approach is based on BigGAN (Brock, 2019), with conditioning on logits instead of one-hot class labels. We use our reconstruction model as a tool for exploring the nature of representations, including: the influence of model architecture and training objectives (specifically robust losses), the forms of invariance that networks achieve, representational differences between correctly and incorrectly classified images, and the effects of manipulating logits and images. We believe that our method can inspire future investigations into the nature of information flow in a neural net and can provide diagnostics for improving discriminative models.
Learning a Proposal Classifier for Multiple Object Tracking

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. This framework is similar to the two-stage object detector Faster RCNN, and can solve the MOT problem in a data-driven way. For proposal generation, we propose an iterative graph clustering method to reduce the computational cost while maintaining the quality of the generated proposals. For proposal scoring, we deploy a trainable graph-convolutional-network (GCN) to learn the structural patterns of the generated proposals and rank them according to the estimated quality scores. For trajectory inference, a simple deoverlapping strategy is adopted to generate tracking output while complying with the constraints that no detection can be assigned to more than one track. We experimentally demonstrate that the proposed method achieves a clear performance improvement in both MOTA and IDF1 with respect to previous state-of-the-art on two public benchmarks. Our code is available at https://github.com/daip13/LPC_MOT. git.
Position measurement of a dipolar scatterer via self-homodyne detection

We describe a technique to measure the position of a dipolar scatterer based on self-homodyne detection of the scattered light. The method can theoretically reach the Heisenberg limit, at which information gained about the position is constrained only by the back-action of the scattered light. The technique has applications in the fields of levitated optomechanics and trapped ions and is generally applicable to the position determination of confined light scatterers.
Offline Reinforcement Learning with Fisher Divergence Critic Regularization

Many modern approaches to offline Reinforcement Learning (RL) utilize behavior regularization, typically augmenting a model-free actor critic algorithm with a penalty measuring divergence of the policy from the offline data. In this work, we propose an alternative approach to encouraging the learned policy to stay close to the data, namely parameterizing the critic as the log-behavior-policy, which generated the offline data, plus a state-action value offset term, which can be learned using a neural network. Behavior regularization then corresponds to an appropriate regularizer on the offset term. We propose using a gradient penalty regularizer for the offset term and demonstrate its equivalence to Fisher divergence regularization, suggesting connections to the score matching and generative energy-based model literature. We thus term our resulting algorithm Fisher-BRC (Behavior Regularized Critic). On standard offline RL benchmarks, Fisher-BRC achieves both improved performance and faster convergence over existing state-of-the-art methods.
Membership Inference Attacks on Machine Learning: A Survey

Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that ML models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. MIAs on ML models can directly lead to a privacy breach. For example, via identifying the fact that a clinical record that has been used to train a model associated with a certain disease, an attacker can infer that the owner of the clinical record has the disease with a high chance. In recent years, MIAs have been shown to be effective on various ML models, e.g., classification models and generative models. Meanwhile, many defense methods have been proposed to mitigate MIAs. Although MIAs on ML models form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this article, we conduct the first comprehensive survey on membership inference attacks and defenses. We provide the taxonomies for both attacks and defenses, based on their characterizations, and discuss their pros and cons. Based on the limitations and gaps identified in this survey, we point out several promising future research directions to inspire the researchers who wish to follow this area. This survey not only serves as a reference for the research community but also provides a clear description for researchers outside this research domain. To further help the researchers, we have created an online resource repository, which we will keep updated with future relevant work. Interested readers can find the repository at https://github.com/HongshengHu/membership-inference-machine-learning-literature.
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events

We present a case study of solar flare forecasting by means of metadata feature time series, by treating it as a prominent class-imbalance and temporally coherent problem. Taking full advantage of pre-flare time series in solar active regions is made possible via the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, a partitioned collection of multivariate time series of active region properties comprising 4075 regions and spanning over 9 yr of the Solar Dynamics Observatory period of operations. We showcase the general concept of temporal coherence triggered by the demand of continuity in time series forecasting and show that lack of proper understanding of this effect may spuriously enhance models’ performance. We further address another well-known challenge in rare-event prediction, namely, the class-imbalance issue. The SWAN-SF is an appropriate data set for this, with a 60:1 imbalance ratio for GOES M- and X-class flares and an 800:1 imbalance ratio for X-class flares against flare-quiet instances. We revisit the main remedies for these challenges and present several experiments to illustrate the exact impact that each of these remedies may have on performance. Moreover, we acknowledge that some basic data manipulation tasks such as data normalization and cross validation may also impact the performance; we discuss these problems as well. In this framework we also review the primary advantages and disadvantages of using true skill statistic and Heidke skill score, two widely used performance verification metrics for the flare-forecasting task. In conclusion, we show and advocate for the benefits of time series versus point-in-time forecasting, provided that the above challenges are measurably and quantitatively addressed.
Inflationary and dark energy epochs unification via quadratic gravity and nonideal fluids

Saikat Chakraborty†, Daniele Gregoris‡ †Center for Gravitation and Cosmology, College of Physical Science and Technology, Yangzhou University 180 Siwangting Road, Yangzhou City, Jiangsu Province 225002, China †School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China †International Center for Cosmology, Charusat University, Anand 388421, Gujrat, India ‡School of Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China †saikatnilch@gmail.com, †snilch@yzu.edu. cn, ‡danielegregoris@libero.it
Electronic statistics on demand: Bunching, antibunching, positive, and negative correlations in a molecular spin valve

One of the long-standing goals of quantum transport is to use the noise, rather than the average current, for information processing. However, achieving this requires on-demand control of quantum fluctuations in the electric current. In this paper, we demonstrate theoretically that transport through a molecular spin valve provides access to many different statistics of electron tunneling events. Simply by changing highly tunable parameters, such as electrode spin polarization, magnetization angle, and voltage, one is able to switch between Poisson behavior, bunching and antibunching of electron tunnelings, and positive and negative temporal correlations. The molecular spin valve is modeled by a single spin-degenerate molecular orbital with local electronic repulsion coupled to two ferromagnetic leads with magnetization orientations allowed to rotate relative to each other. The electron transport is described via Born-Markov master equation and fluctuations are studied with higher-order waiting time distributions. For highly magnetized parallel-aligned electrodes, we find that strong positive temporal correlations emerge in the voltage range where the second transport channel is partially open. These are caused by a spin-induced electron-bunching, which does not manifest in the stationary current alone.
Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers

The abolitionist movement of the nineteenth-century United States remains among the most significant social and political movements in US history. Abolitionist newspapers played a crucial role in spreading information and shaping public opinion around a range of issues relating to the abolition of slavery. These newspapers also serve as a primary source of information about the movement for scholars today, resulting in powerful new accounts of the movement and its leaders. This paper supplements recent qualitative work on the role of women in abolition’s vanguard, as well as the role of the Black press, with a quantitative text modeling approach. Using diachronic word embeddings, we identify which newspapers tended to lead lexical semantic innovations — the introduction of new usages of specific words — and which newspapers tended to follow. We then aggregate the evidence across hundreds of changes into a weighted network with the newspapers as nodes; directed edge weights represent the frequency with which each newspaper led the other in the adoption of a lexical semantic change. Analysis of this network reveals pathways of lexical semantic influence, distinguishing leaders from followers, as well as others who stood apart from the semantic changes that swept through this period. More specifically, we find that two newspapers edited by women -- The Provincial Freeman and The Lily -- led a large number of semantic changes in our corpus, lending additional credence to the argument that a multiracial coalition of women led the abolitionist movement in terms of both thought and action. It also contributes additional complexity to the scholarship that has sought to tease apart the relation of the abolitionist movement to the women’s suffrage movement, and the vexed racial politics that characterized their relation.
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation—a technique particularly suitable for training with limited data—for this few-shot, highly-multiclass text classification setting. On four diverse text classification tasks, we find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average. To further boost performance, we present a simple training strategy called curriculum data augmentation, which leverages curriculum learning by first training on only original examples and then introducing augmented data as training progresses. We explore a two-stage and a gradual schedule, and find that, compared with standard single-stage training, curriculum data augmentation trains faster, improves performance, and remains robust to high amounts of noising from augmentation.
Lithium in nigerite-group minerals

Nigerite-group minerals (polysomatic tin-bearing oxides) from the type locality Egbe District, Kabba Province (Nigeria) and from the abandoned tin mine of Tsomtsaub, between Omaruru and Uis in Namibia have been reinvestigated chemically and by single-crystal X-ray diffraction. At both localities nigerite occurs in quartz-sillimanite rocks associated with pegmatites. Both nigerites can be classified as ‘zinconigerite-6 N 6 S ’, space group R 3M m , a = 5.71, c = 55.4 A. In both structures the octahedral M2 site in the T1-layer of the nolanite module is completely occupied by Sn with minor Ti. Charge balance for the tetravalent cations is achieved by partial Li substitution for Al in adjacent octahedral sites (O-layer) sandwiched between two Trlayers. For the first time the presence of essential Li in nigerite-group minerals has been demonstrated by Secondary Ion Mass Spectrometry (SIMS) yielding 0.61 (6) and 0.70(7) wt. % Li2O for nigerite from Nigeria and Namibia, respectively. Associated gahnite in the Nigerian sample is free of Li2O. Both investigated nigerite-group minerals have tetrahedral Zn: Fe > 1 in the spinel module and are therefore assigned to the zinconigerite subgroup. It is suggested that Sn-rich nigerite-group minerals (6 N 6 S and 2 N 1 S polysomes) from rare-element pegmatites or skarns possess essential Li for charge balance whereas low Sn, Ti-bearing nigerites, displaying an extended solid solution with hogbomite, do not require Li for charge balance.