Miracle-dz/UniFAR-FTUs huggingface dataset:
The Environments of CO Cores and Star Formation in the Dwarf Irregular Galaxy WLM

The low metallicities of dwarf irregular galaxies (dIrr) greatly influence the formation and structure of molecular clouds. These clouds, which consist primarily of H2, are typically traced by CO, but low-metallicity galaxies are found to have little CO despite ongoing star formation. In order to probe the conditions necessary for CO core formation in dwarf galaxies, we have used the catalog of Rubio et al. for CO cores in WLM, a Local Group dwarf with an oxygen abundance that is 13% of solar. Here we aim to characterize the galactic environments in which these 57 CO cores formed. We grouped the cores together based on proximity to each other and strong FUV emission, examining properties of the star-forming region enveloping the cores and the surrounding environment where the cores formed. We find that high H i surface density does not necessarily correspond to higher total CO mass, but regions with higher CO mass have higher H i surface densities. We also find the cores in star-forming regions spanning a wide range of ages show no correlation between age and CO core mass, suggesting that the small size of the cores is not due to fragmentation of the clouds with age. The presence of CO cores in a variety of different local environments, along with the similar properties between star-forming regions with and without CO cores, leads us to conclude that there are no obvious environmental characteristics that drive the formation of these CO cores.
A computational study of steady and stagnating positive streamers in N2–O2 mixtures

In this paper, we address two main topics: steady propagation fields for positive streamers in air and streamer deceleration in fields below the steady propagation field. We generate constant-velocity positive streamers in air with an axisymmetric fluid model, by initially adjusting the applied voltage based on the streamer velocity. After an initial transient, we observe steady propagation for velocities of 3 × 104 m s−1 to 1.2 × 105 m s−1, during which streamer properties and the background field do not change. This propagation mode is not fully stable, in the sense that a small change in streamer properties or background field eventually leads to acceleration or deceleration. An important finding is that faster streamers are able to propagate in significantly lower background fields than slower ones, indicating that there is no unique stability field. We relate the streamer radius, velocity, maximal electric field and background electric field to a characteristic time scale for the loss of conductivity. This relation is qualitatively confirmed by studying streamers in N2–O2 mixtures with less oxygen than air. In such mixtures, steady streamers require lower background fields, due to a reduction in the attachment and recombination rates. We also study the deceleration of streamers, which is important to predict how far they can propagate in a low field. Stagnating streamers are simulated by applying a constant applied voltage. We show how the properties of these streamers relate to the steady cases, and present a phenomenological model with fitted coefficients that describes the evolution of the velocity and radius. Finally, we compare the lengths of the stagnated streamers with predictions based on the conventional stability field.
Accreting on the Edge: A Luminosity-dependent Cyclotron Line in the Be/X-Ray Binary 2S 1553-542 Accompanied by Accretion Regimes Transition

Accreting X-ray pulsars undergo luminous X-ray outbursts during which the luminosity-dependent spectral and timing features of the neutron star’s emission can be analyzed in detail, thus shedding light on the accretion regime at work. We took advantage of a monitoring campaign that was performed with NuSTAR, Swift/XRT, AstroSat and NICER to follow the Be/X-ray Binary 2S 1553-542 along one of its rare outbursts, and thus trace its spectral and timing evolution. We report the discovery of a luminosity-dependent cyclotron line energy for the first time in this source. The pulse profiles and pulsed fraction also show variability along the outburst, which is consistent with the interpretation that the source transitions from the subcritical to the supercritical accretion regime, separated by a critical luminosity of L crit ≈ 4 × 1037 erg s−1.
FairMod: Fair Link Prediction and Recommendation via Graph Modification

As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about the inherent biases in the data that may be perpetuated by the model. Recently, many studies have shown that such biases are also imbibed in Graph Neural Network (GNN) models if the input graph is biased. In this work, we aim to mitigate the bias learned by GNNs through modifying the input graph. To that end, we propose FairMod , a Fair Graph Mod ification methodology with three formulations: the Global Fairness Optimization (GFO), Community Fairness Optimization (CFO), and Fair Edge Weighting (FEW) models. Our proposed models perform either microscopic or macroscopic edits to the input graph while training GNNs and learn node embeddings that are both accurate and fair under the context of link recommendations. We demonstrate the effectiveness of our approach on four real world datasets and show that we can improve the recommendation fairness by several factors at negligible cost to link prediction accuracy.
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of a multilingual benchmark, however, vision-and-language research has mostly focused on English language tasks. To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together - by both aggregating pre-existing datasets and creating new ones - visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups. Based on the evaluation of the available state-of-the-art models, we find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks. Moreover, downstream performance is partially explained by the amount of available unlabelled textual data for pretraining, and only weakly by the typological distance of target-source languages. We hope to encourage future research efforts in this area by releasing the benchmark to the community.
Capacity and Achievable Rates of Fading Few-mode MIMO IM/DD Optical Fiber Channels

The optical fiber multiple-input multiple-output (MIMO) channel with intensity modulation and direct detection (IM/DD) per spatial path is treated. The spatial dimensions represent the multiple modes employed for transmission and the cross-talk between them originates in the multiplexers and demultiplexers, which are polarization dependent and thus time-varying. The upper bounds from free-space IM/DD MIMO channels are adapted to the fiber case, and the constellation constrained capacity is constructively estimated using the Blahut-Arimoto algorithm. An autoencoder is then proposed to optimize a practical MIMO transmission in terms of pre-coder and detector assuming channel distribution knowledge at the transmitter. The pre-coders are shown to be robust to changes in the channel.
SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders

Self-supervised learning is an emerging machine learning (ML) paradigm. Compared to supervised learning which leverages high-quality labeled datasets, self-supervised learning relies on unlabeled datasets to pre-train powerful encoders which can then be treated as feature extractors for various downstream tasks. The huge amount of data and computational resources consumption makes the encoders themselves become the valuable intellectual property of the model owner. Recent research has shown that the ML model's copyright is threatened by model stealing attacks, which aim to train a surrogate model to mimic the behavior of a given model. We empirically show that pre-trained encoders are highly vulnerable to model stealing attacks. However, most of the current efforts of copyright protection algorithms such as watermarking concentrate on classifiers. Meanwhile, the intrinsic challenges of pre-trained encoder's copyright protection remain largely unstudied. We fill the gap by proposing SSLGuard, the first watermarking scheme for pre-trained encoders. Given a clean pre-trained encoder, SSLGuard injects a watermark into it and outputs a watermarked version. The shadow training technique is also applied to preserve the watermark under potential model stealing attacks. Our extensive evaluation shows that SSLGuard is effective in watermark injection and verification, and it is robust against model stealing and other watermark removal attacks such as input noising, output perturbing, overwriting, model pruning, and fine-tuning.
Self‐Certifying Classification by Linearized Deep Assignment

We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC‐Bayes risk certification paradigm. Classifiers are realized as linearly parametrized deep assignment flows with random initial conditions. Building on the recent PAC‐Bayes literature and data‐dependent priors, this approach enables (i) to use risk bounds as training objectives for learning posterior distributions on the hypothesis space and (ii) to compute tight out‐of‐sample risk certificates of randomized classifiers more efficiently than related work. Comparison with empirical test set errors illustrates the performance and practicality of this self‐certifying classification method.
The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes

The theory of instability of accretion disks about black holes, neutron stars, or protoplanets is revisited by means of the recent method of the Spectral Web. The cylindrical accretion disk differential equation is shown to be governed by the forward and backward Doppler-shifted continuous Alfvén spectra ΩA±≡mΩ±ωA , where ω A is the static Alfvén frequency. It is crucial to take nonaxisymmetry (m ≠ 0) and super-Alfvénic rotation of the Doppler frames (∣mΩ∣ ≫ ∣ω A∣) into account. The continua ΩA+ and ΩA− then overlap, ejecting a plethora of super-Alfvénic rotational instabilities (SARIs). In-depth analysis for small inhomogeneity shows that the two Alfvén singularities reduce the extent of the modes to sizes much smaller than the width of the accretion disk. Generalization for large inhomogeneity leads to the completely unprecedented result that, for mode numbers ∣k∣ ≫ ∣m∣, any complex ω in a wide neighborhood of the real axis is an approximate “eigenvalue. ” The difference with genuine eigenmodes is that the amount of complementary energy to excite the modes is tiny, ∣W com∣ ≤ c, with c the machine accuracy of the computation. This yields a multitude of two-dimensional continua of quasi-discrete modes: quasi-continuum SARIs. We conjecture that the onset of 3D turbulence in magnetized accretion disks is governed not by the excitation of discrete axisymmetric magnetorotational instabilities but by the excitation of modes from these two-dimensional continua of quasi-discrete nonaxisymmetric SARIs.
Deterministic Focused Ion Beam Implantation with 99.6% Yield for High-Fidelity Shallow Donor Arrays in Silicon

S. G. Robson,1, ∗ P. Räcke,2, 3 A. M. Jakob,1 N. Collins,1 H. R. Firgau,4 V. Schmitt,4 V. Mourik,4 A. Morello,4 E. Mayes,5 D. Spemann,2, 3 and D. N. Jamieson1 1Centre for Quantum Computation and Communication Technology, School of Physics, University of Melbourne, Parkville VIC 3010, Australia 2Leibniz Institute of Surface Engineering (IOM), Permoserstr. 15, D-04318 Leipzig, Germany 3Leibniz Joint Lab ‘Single Ion Implantation’, Permoserstr. 15, D-04318 Leipzig, Germany 4Centre for Quantum Computation and Communication Technology, School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney NSW 2052, Australia 5RMIT Microscopy and Microanalysis Facility, RMIT University, Melbourne VIC 3001, Australia (Dated: January 28, 2022)
Acceleration of spin-polarized proton beams via two parallel laser pulses

We present a setup for highly polarized proton beams using two parallel propagating laser pulses that have a carrier envelope phase diﬀerence of π . This mechanism is examined utilizing particle-in-cell simulations and compared to a single-pulse setup commonly used for magnetic vortex acceleration. We ﬁnd that the use of the dual-pulse setup allows for peak energies of 124 MeV and good angular spread for two pulses with normalized laser vector potential a 0 = 100. Compared to a single pulse, we further observe higher polarization of the accelerated bunch.
Chronic iEEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states

Background and Objectives: Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Methods: Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterised the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states (SNSs). We then compared SNS occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. Results: In most patients, the occurrence or duration of at least one SNS was associated with the time since implantation. Some patients had one or more SNSs that were associated with phases of circadian and/or multidien spike rate cycles. A given SNS's occurrence and duration were usually not associated with the same timescale. Discussion: Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a SNS's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures, and similar principles likely apply to other neurological conditions.
Height bounds for certain exceptional points in some variations of Hodge structures

We consider smooth projective morphisms f : X → S of Kvarieties with S an open curve and K a number field. We establish upper bounds of the Weil height h(s) by [K(s) : K] at certain points s ∈ S(K̄) that are “exceptional” with respect to the variation of Hodge structures R(f)∗(QXan C ), where n = dimX−1. We work under the assumption that the generic special Mumford-Tate group of this variation is Sp(μ,Q), the variation degenerates in a strong fashion over some fixed point s0 of a proper curve that contains S, the Hodge conjecture holds, and that what we define as a “good arithmetic model” exists for the morphism f over the ring OK .
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities

Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and other CNN-based models like VGG and Resnet. The high accuracy of these models on benchmark tasks has translated into their practical use across many domains including safety-critical applications like autonomous driving and medical diagnostics. Despite their widespread use, image models have been shown to be fragile to changes in the operating environment, bringing their robustness into question. There is an urgent need for methods that systematically characterise and quantify the capabilities of these models to help designers understand and provide guarantees about their safety and robustness. In this paper, we propose Vision Checklist, a framework aimed at interrogating the capabilities of a model in order to produce a report that can be used by a system designer for robustness evaluations. This framework proposes a set of perturbation operations that can be applied on the underlying data to generate test samples of different types. The perturbations reflect potential changes in operating environments, and interrogate various properties ranging from the strictly quantitative to more qualitative. Our framework is evaluated on multiple datasets like Tinyimagenet, CIFAR10, CIFAR100 and Camelyon17 and for models like ViT and Resnet. Our Vision Checklist proposes a specific set of evaluations that can be integrated into the previously proposed concept of a model card. Robustness evaluations like our checklist will be crucial in future safety evaluations of visual perception modules, and be useful for a wide range of stakeholders including designers, deployers, and regulators involved in the certification of these systems. Source code of Vision Checklist would be open for public use.
Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification (short paper)

In recent years, social media has enabled users to get exposed to a myriad of misinformation and disinformation; thus, misinformation has attracted a great deal of attention in research fields and as a social issue. To address the problem, we propose a framework, Pre-CoFact, composed of two pre-trained models for extracting features from text and images, and multiple co-attention networks for fusing the same modality but different sources and different modalities. Besides, we adopt the ensemble method by using different pre-trained models in Pre-CoFact to achieve better performance. We further illustrate the effectiveness from the ablation study and examine different pre-trained models for comparison. Our team, Yao, won the fifth prize (F1-score: 74.585\%) in the Factify challenge hosted by De-Factify @ AAAI 2022, which demonstrates that our model achieved competitive performance without using auxiliary tasks or extra information. The source code of our work is publicly available at https://github.com/wywyWang/Multi-Modal-Fact-Verification-2021
From Motion to Muscle

Voluntary human motion is the product of muscle activity that results from upstream motion planning of the motor cortical areas. We show that muscle activity can be artificially generated based on motion features such as position, velocity, and acceleration. For this purpose, we specifically develop an approach based on a recurrent neural network trained in a supervised learning session; additional neural network architectures are considered and evaluated. The performance is evaluated by a new score called the zero-line score. The latter adaptively rescales the loss function of the generated signal for all channels by comparing the overall range of muscle activity and thus dynamically evaluates similarities between both signals. The model achieves a remarkable precision for previously trained motion while new motions that were not trained before still have high accuracy. Further, these models are trained on multiple subjects and thus are able to generalize across individuals. In addition, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific pre-trained model that uses the general model as a basis and is adapted to a specific subject afterward. The subject-specific generation of muscle activity can be further exploited to improve the rehabilitation of neuromuscular diseases with myoelectric prostheses and functional electric stimulation.
Direct First Parker Solar Probe Observation of the Interaction of Two Successive Interplanetary Coronal Mass Ejections in 2020 November

We investigate the effects of the evolutionary processes in the internal magnetic structure of two interplanetary coronal mass ejections (ICMEs) detected in situ between 2020 November 29 and December 1 by the Parker Solar Probe (PSP). The sources of the ICMEs were observed remotely at the Sun in EUV and subsequently tracked to their coronal counterparts in white light. This period is of particular interest to the community as it has been identified as the first widespread solar energetic particle event of solar cycle 25. The distribution of various solar and heliospheric-dedicated spacecraft throughout the inner heliosphere during PSP observations of these large-scale magnetic structures enables a comprehensive analysis of the internal evolution and topology of such structures. By assembling different models and techniques, we identify the signatures of interaction between the two consecutive ICMEs and the implications for their internal structure. We use multispacecraft observations in combination with a remote-sensing forward modeling technique, numerical propagation models, and in situ reconstruction techniques. The outcome, from the full reconciliations, demonstrates that the two coronal mass ejections (CMEs) are interacting in the vicinity of the PSP. Thus, we identify the in situ observations based on the physical processes that are associated with the interaction and collision of both CMEs. We also expand the flux rope modeling and in situ reconstruction technique to incorporate the aging and expansion effects in a distorted internal magnetic structure and explore the implications of both effects in the magnetic configuration of the ICMEs.
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass

Supervised learning in artificial neural networks typically relies on backpropagation, where the weights are updated based on the error-function gradients and sequentially propagated from the output layer to the input layer. Although this approach has proven effective in a wide domain of applications, it lacks biological plausibility in many regards, including the weight symmetry problem, the dependence of learning on non-local signals, the freezing of neural activity during error propagation, and the update locking problem. Alternative training schemes have been introduced, including sign symmetry, feedback alignment, and direct feedback alignment, but they invariably rely on a backward pass that hinders the possibility of solving all the issues simultaneously. Here, we propose to replace the backward pass with a second forward pass in which the input signal is modulated based on the error of the network. We show that this novel learning rule comprehensively addresses all the above-mentioned issues and can be applied to both fully connected and convolutional models. We test this learning rule on MNIST, CIFAR-10, and CIFAR-100. These results help incorporate biological principles into machine learning.
DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators

The design of DNN accelerators includes two key parts: HW resource configuration and mapping strategy. Intensive research has been conducted to optimize each of them independently. Unfortunately, optimizing for both together is extremely challenging due to the extremely large cross-coupled search space. To address this, in this paper, we propose a HW-Mapping co-optimization framework, an efficient encoding of the immense design space constructed by HW and Mapping, and a domain-aware genetic algorithm, named DiGamma, with specialized operators for improving search efficiency. We evaluate DiGamma with seven popular DNNs models with different properties. Our evaluations show DiGamma can achieve (geomean) 3.0x and 10.0x speedup, comparing to the best-performing baseline optimization algorithms, in edge and cloud settings.