Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining primordial black holes as dark matter using the global 21-cm signal with X-ray heating and excess radio background

Using the global 21-cm signal measurement by the EDGES collaboration, we derive constraints on the fraction of the dark matter that is in the form of primordial black holes (PBHs) with masses in the range 1015–1017 g. Improving upon previous analyses, we consider the effect of the X-ray heating of the intergalactic medium on these constraints, and also use the full shape of the 21-cm absorption feature in our inference. In order to account for the anomalously deep absorption amplitude, we also consider an excess radio background motivated by LWA1 and ARCADE2 observations. Because the heating rate induced by PBH evaporation evolves slowly, the data favour a scenario in which PBH-induced heating is accompanied by X-ray heating. Also, for the same reason, using the full measurement across the EDGES observation band yields much stronger constraints on PBHs than just the redshift of absorption. We find that 21-cm observations exclude f PBH ≳ 10-9.7 at 95% CL for M PBH = 1015 g. This limit weakens approximately as M 4 PBH towards higher masses, thus providing the strongest constraints on ultralight evaporating PBHs as dark matter over the entire mass range 1015–1017 g. Under the assumption of a simple spherical gravitational collapse based on the Press-Schechter formalism, we also derive bounds on the curvature power spectrum at extremely small scales (k ∼ 1015 Mpc-1). This highlights the usefulness of global 21-cm measurements, including non-detections, across wide frequency bands for probing exotic physical processes.
Structural Evidence for Ultrafast Polarization Rotation in Ferroelectric/Dielectric Superlattice Nanodomains

Structural evidence for ultrafast polarization rotation in ferroelectric/dielectric superlattice nanodomains Hyeon Jun Lee1, Youngjun Ahn1, Samuel D. Marks1, Eric C. Landahl2, Shihao Zhuang1, M. Humed Yusuf3, Matthew Dawber3, Jun Young Lee4, Tae Yeon Kim4, Sanjith Unithrattil4, Sae Hwan Chun5, Sunam Kim5, Intae Eom5, Sang-Yeon Park5, Kyung Sook Kim5, Sooheyong Lee6,7, Ji Young Jo4, Jiamian Hu1, and Paul G. Evans1,*
Unsupervised knowledge-transfer for learned image reconstruction

Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches usually require a large amount of high-quality paired training data, which is often not available in medical imaging. To circumvent this issue we develop a novel unsupervised knowledge-transfer paradigm for learned reconstruction within a Bayesian framework. The proposed approach learns a reconstruction network in two phases. The first phase trains a reconstruction network with a set of ordered pairs comprising of ground truth images of ellipses and the corresponding simulated measurement data. The second phase fine-tunes the pretrained network to more realistic measurement data without supervision. By construction, the framework is capable of delivering predictive uncertainty information over the reconstructed image. We present extensive experimental results on low-dose and sparse-view computed tomography showing that the approach is competitive with several state-of-the-art supervised and unsupervised reconstruction techniques. Moreover, for test data distributed differently from the training data, the proposed framework can significantly improve reconstruction quality not only visually, but also quantitatively in terms of PSNR and SSIM, when compared with learned methods trained on the synthetic dataset only.
Generalization by design: Shortcuts to Generalization in Deep Learning

We take a geometrical viewpoint and present a unifying view on supervised deep learning with the Bregman divergence loss function - this entails frequent classification and prediction tasks. Motivated by simulations we suggest that there is principally no implicit bias of vanilla stochastic gradient descent training of deep models towards"simpler"functions. Instead, we show that good generalization may be instigated by bounded spectral products over layers leading to a novel geometric regularizer. It is revealed that in deep enough models such a regularizer enables both, extreme accuracy and generalization, to be reached. We associate popular regularization techniques like weight decay, drop out, batch normalization, and early stopping with this perspective. Backed up by theory we further demonstrate that"generalization by design"is practically possible and that good generalization may be encoded into the structure of the network. We design two such easy-to-use structural regularizers that insert an additional \textit{generalization layer} into a model architecture, one with a skip connection and another one with drop-out. We verify our theoretical results in experiments on various feedforward and convolutional architectures, including ResNets, and datasets (MNIST, CIFAR10, synthetic data). We believe this work opens up new avenues of research towards better generalizing architectures.
Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions

This paper investigates the existing resource management approaches in Cloud Data Centres for energy and thermal efficiency. It identifies the need for integrated computing and cooling systems management and learning-based solutions in resource management systems. A taxonomy on energy and thermal efficient resource management in data centres is proposed based on an in-depth analysis of the literature. Furthermore, a detailed survey on existing approaches is conducted according to the taxonomy and recent advancements including machine learning-based resource management approaches and cooling management technologies are discussed.
Internal excitations of global vortices

We investigate the spectrum of linearized excitations of global vortices in 2+1 dimensions. After identifying the existence of localized excitation modes, we compute the decay time scale of the first two and compare the results to the numerical evolution of the full non-linear equations. We show numerically how the interaction of vortices with an external source of radiation or other vortices can excite these modes dynamically. We then simulate the formation of vortices in a phase transition and their interaction with a thermal bath estimating the amplitudes of these modes in each case. These numerical experiments indicate that even though, in principle, vortices are capable of storing a large amount of energy in these internal excitations, this does not seem to happen dynamically. We then explore the evolution of a network of vortices in an expanding (2+1) dimensional background, in particular in a radiation dominated universe. We find that vortices are still excited after the course of the cosmological evolution but again the level of excitation is very small. The extra energy in the vortices in these cosmological simulations never exceeds the 1% level of the total mass of the core of the vortex.
Machine-Learning-Derived Entanglement Witnesses

In this work, we show a correspondence between linear support vector machines (SVMs) and entanglement witnesses, and use this correspondence to generate entanglement witnesses for bipartite and tripartite qubit (and qudit) target entangled states. An SVM allows for the construction of a hyperplane that clearly delineates between separable states and the target entangled state; this hyperplane is a weighted sum of observables ('features') whose coefficients are optimized during the training of the SVM. We demonstrate with this method the ability to obtain witnesses that require only local measurements even when the target state is a non-stabilizer state. Furthermore, we show that SVMs are flexible enough to allow us to rank features, and to reduce the number of features systematically while bounding the inference error. This allows us to derive W state witnesses capable of detecting entanglement with fewer measurement terms than the fidelity method dominant in today's literature. The utility of this approach is demonstrated on quantum hardware furnished through the IBM Quantum Experience.
Phase-Aberration Correction in Shear-Wave Elastography Imaging Using Local Speed-of-Sound Adaptive Beamforming

Shear wave elasticity imaging (SWEI) is a non-invasive imaging modality that provides tissue elasticity information by measuring the travelling speed of an induced shear-wave. It is commercially available on clinical ultrasound scanners and popularly used in the diagnosis and staging of liver disease and breast cancer. In conventional SWEI methods, a sequence of acoustic radiation force (ARF) pushes are used for inducing a shear-wave, which is tracked using high frame-rate multi-angle plane wave imaging (MA-PWI) to estimate the shear-wave speed (SWS). Conventionally, these plane waves are beamformed using a constant speed-of-sound (SoS), assuming an a-priori known and homogeneous tissue medium. However, soft tissues are inhomogeneous, with intrinsic SoS variations. In this work, we study the SoS effects and inhomogeneities on SWS estimation, using simulation and phantoms experiments with porcine muscle as an abbarator, and show how these aberrations can be corrected using local speed-of-sound adaptive beamforming. For shear-wave tracking, we compare standard beamform with spatially constant SoS values to software beamforming with locally varying SoS maps. We show that, given SoS aberrations, traditional beamforming using a constant SoS, regardless of the utilized SoS value, introduces a substantial bias in the resulting SWS estimations. Average SWS estimation disparity for the same material was observed over 4.3 times worse when a constant SoS value is used compared to that when a known SoS map is used for beamforming. Such biases are shown to be corrected by using a local SoS map in beamforming, indicating the importance of and the need for local SoS reconstruction techniques.
Differentially private federated deep learning for multi-site medical image segmentation

Collaborative machine learning techniques such as federated learning (FL) enable the training of models on effectively larger datasets without data transfer. Recent initiatives have demonstrated that segmentation models trained with FL can achieve performance similar to locally trained models. However, FL is not a fully privacy-preserving technique and privacy-centred attacks can disclose confidential patient data. Thus, supplementing FL with privacy-enhancing technologies (PTs) such as differential privacy (DP) is a requirement for clinical applications in a multi-institutional setting. The application of PTs to FL in medical imaging and the trade-offs between privacy guarantees and model utility, the ramifications on training performance and the susceptibility of the final models to attacks have not yet been conclusively investigated. Here we demonstrate the first application of differentially private gradient descent-based FL on the task of semantic segmentation in computed tomography. We find that high segmentation performance is possible under strong privacy guarantees with an acceptable training time penalty. We furthermore demonstrate the first successful gradient-based model inversion attack on a semantic segmentation model and show that the application of DP prevents it from divulging sensitive image features.
Separation Guided Speaker Diarization in Realistic Mismatched Conditions

We propose a separation guided speaker diarization (SGSD) approach by fully utilizing a complementarity of speech separation and speaker clustering. Since the conventional clustering-based speaker diarization (CSD) approach cannot well handle overlapping speech segments, we investigate, in this study, separation-based speaker diarization (SSD) which inherently has the potential to handle the speaker overlap regions. Our preliminary analysis shows that the state-of-the-art Conv-TasNet based speech separation, which works quite well on the simulation data, is unstable in realistic conversational speech due to the high mismatch speaking styles in simulated training speech and read speech. In doing so, separation-based processing can assist CSD in handling the overlapping speech segments under the realistic mismatched conditions. Specifically, several strategies are designed to select between the results of SSD and CSD systems based on an analysis of the instability of the SSD system performances. Experiments on the conversational telephone speech (CTS) data from DIHARD-III Challenge show that the proposed SGSD system can significantly improve the performance of state-of-the-art CSD systems, yielding relative diarization error rate reductions of 20.2% and 20.8% on the development set and evaluation set, respectively.
Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications

—In this paper, we propose a novel approach to support domain-speciﬁc Model-Driven Software Engineering (MDSE) for the real-world use-case scenarios of smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). We argue that the majority of available data in the nature for Artiﬁcial Intelligence (AI), speciﬁcally Machine Learning (ML) are unlabeled. Hence, unsupervised and/or semi-supervised ML approaches are the practical choices. However, prior work in the literature of MDSE has considered supervised ML approaches, which only work with labeled training data. Our proposed approach is fully implemented and integrated with an existing state-of-the-art MDSE tool to serve the CPS/IoT domain. Moreover, we validate the proposed approach using a portion of the open data of the REFIT reference dataset for the smart energy systems domain. Our model-to-code transformations (code generators) provide the full source code of the desired IoT services out of the model instances in an automated manner. Currently, we generate the source code in Java and Python. The Python code is responsible for the ML functionalities and uses the APIs of several ML libraries and frameworks, namely Scikit-Learn, Keras and TensorFlow. For unsupervised and semi-supervised learning, the APIs of Scikit-Learn are deployed. In addition to the pure MDSE approach, where certain ML methods, e.g., K-Means, Mini-Batch K-Means, DB-SCAN, Spectral Clustering, Gaussian Mixture Model, Self-Training, Label Propagation and Label Spreading are supported, a more ﬂexible, hybrid approach is also enabled to support the practitioner in deploying a pre-trained ML model with any arbitrary architecture and learning algorithm
Toward a More Complex Understanding of Natal Super Star Clusters with Multiwavelength Observations

Henize 2–10 (He 2–10) is a nearby (D = 9 Mpc) starbursting blue compact dwarf galaxy that boasts a high star formation rate and a low-luminosity active galactic nucleus. He 2–10 is also one of the first galaxies in which embedded super star clusters (SSCs) were discovered. SSCs are massive, compact star clusters that will impact their host galaxies dramatically when their massive stars evolve. Here, we discuss radio, submillimeter, and infrared observations of He 2–10 from 1.87 μm to 6 cm in high angular resolution (∼0.3″), which allows us to disentangle individual clusters from aggregate complexes as identified at lower resolution. These results indicate the importance of spatial resolution to characterize SSCs, as low resolution studies of SSCs average over aggregate complexes that may host SSCs at different stages of evolution. We explore the thermal, nonthermal, and dust emission associated with the clusters along with dense molecular tracers to construct a holistic review of the natal SSCs that have yet to dramatically disrupt their parent molecular clouds. We assess the production rate of ionizing photons, extinction, total mass, and the star formation efficiency (SFE) associated with the clusters. Notably, we find that the SFE for the some of the natal clusters is high (>70%), which suggests that these clusters could remain bound even after the gas is dispersed from the system from stellar feedback mechanisms. If they remain bound, these SSCs could survive to become objects indistinguishable from globular clusters.
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity

Neural network pruning is a fruitful area of research with surging interest in high sparsity regimes. Benchmarking in this domain heavily relies on faithful representation of the sparsity of subnetworks, which has been traditionally computed as the fraction of removed connections (direct sparsity). This definition, however, fails to recognize unpruned parameters that detached from input or output layers of underlying subnetworks, potentially underestimating actual effective sparsity: the fraction of inactivated connections. While this effect might be negligible for moderately pruned networks (up to 10-100 compression rates), we find that it plays an increasing role for thinner subnetworks, greatly distorting comparison between different pruning algorithms. For example, we show that effective compression of a randomly pruned LeNet-300-100 can be orders of magnitude larger than its direct counterpart, while no discrepancy is ever observed when using SynFlow for pruning [Tanaka et al., 2020]. In this work, we adopt the lens of effective sparsity to reevaluate several recent pruning algorithms on common benchmark architectures (e.g., LeNet-300-100, VGG-19, ResNet-18) and discover that their absolute and relative performance changes dramatically in this new and more appropriate framework. To aim for effective, rather than direct, sparsity, we develop a low-cost extension to most pruning algorithms. Further, equipped with effective sparsity as a reference frame, we partially reconfirm that random pruning with appropriate sparsity allocation across layers performs as well or better than more sophisticated algorithms for pruning at initialization [Su et al., 2020]. In response to this observation, using a simple analogy of pressure distribution in coupled cylinders from physics, we design novel layerwise sparsity quotas that outperform all existing baselines in the context of random pruning.
Semantic Segmentation Alternative Technique: Segmentation Domain Generation

Detecting objects of interest in images was always a compelling task to automate. In recent years this task was more and more explored using deep learning techniques, mostly using region-based convolutional networks. In this project we propose an alternative semantic segmentation technique making use of Generative Adversarial Networks. We consider semantic segmentation to be a domain transfer problem. Thus, we train a feed forward network (FFNN) to receive as input a seed real image and generate as output its segmentation mask.
Edge-Powered Assisted Driving For Connected Cars

Assisted driving for connected cars is one of the main applications that 5G-and-beyond networks shall support. In this work, we propose an assisted driving system leveraging the synergy between connected vehicles and the edge of the network infrastructure, in order to envision global traffic policies that can effectively drive local decisions. Local decisions concern individual vehicles, e.g., which vehicle should perform a lane-change manoeuvre and when; global decisions, instead, involve whole traffic flows. Such decisions are made at different time scales by different entities, which are integrated within an edge-based architecture and can share information. In particular, we leverage a queuing-based model and formulate an optimization problem to make global decisions on traffic flows. To cope with the problem complexity, we then develop an iterative, linear-time complexity algorithm called Bottleneck Hunting (BH). We show the performance of our solution using a realistic simulation framework, integrating a Python engine with ns-3 and SUMO, and considering two relevant services, namely, lane change assistance and navigation, in a real-world scenario. Results demonstrate that our solution leads to a reduction of the vehicles’ travel times by 66 percent in the case of lane change assistance and by 20 percent for navigation, compared to traditional, local-coordination approaches.
Sarcasm Detection: A Comparative Study

Sarcasm detection is the task of identifying irony containing utterances in sentiment-bearing text. However, the figurative and creative nature of sarcasm poses a great challenge for affective computing systems performing sentiment analysis. This article compiles and reviews the salient work in the literature of automatic sarcasm detection. Thus far, three main paradigm shifts have occurred in the way researchers have approached this task: 1) semi-supervised pattern extraction to identify implicit sentiment, 2) use of hashtag-based supervision, and 3) incorporation of context beyond target text. In this article, we provide a comprehensive review of the datasets, approaches, trends, and issues in sarcasm and irony detection.
A systematic reclassification of Type IIn supernovae

Type IIn supernovae (SNe IIn) are a relatively infrequently observed subclass of SNe whose photometric and spectroscopic properties are varied. A common thread among SNe IIn is the complex multiple-component hydrogen Balmer lines. Owing to the heterogeneity of SNe IIn, online data bases contain some outdated, erroneous, or even contradictory classifications. SN IIn classification is further complicated by SN ‘impostors’ and contamination from underlying H ii regions. We have compiled a catalogue of systematically classified nearby (redshift z < 0.02) SNe IIn using the Open Supernova Catalogue (OSC). We present spectral classifications for 115 objects previously classified as SNe IIn. Our classification is based on results obtained by fitting multiple Gaussians to the H α profiles. We compare classifications reported by the OSC and Transient Name Server (TNS) along with the best matched templates from snid . We find that 28 objects have been misclassified as SNe IIn. TNS and OSC can be unreliable; they disagree on the classifications of 51 of the objects and contain a number of erroneous classifications. Furthermore, OSC and TNS hold misclassifications for 34 and 12 (respectively) of the transients we classify as SNe IIn. In total, we classify 87 SNe IIn. We highlight the importance of ensuring that online data bases remain up to date when new or even contemporaneous data become available. Our work shows the great range of spectral properties and features that SNe IIn exhibit, which may be linked to multiple progenitor channels and environment diversity. We set out a classification scheme for SNe IIn based on the H α profile that is not greatly affected by the inhomogeneity of SNe IIn.
Weakly Supervised Named Entity Tagging with Learnable Logical Rules

We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguating entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. Specifically, we introduce compound rules that are composed from simple rules to increase the precision of boundary detection and generate more diverse pseudo labels. We further design a dynamic label selection strategy to ensure pseudo label quality and therefore avoid overfitting the neural tagger. Experiments on three datasets demonstrate that our method outperforms other weakly supervised methods and even rivals a state-of-the-art distantly supervised tagger with a lexicon of over 2,000 terms when starting from only 20 simple rules. Our method can serve as a tool for rapidly building taggers in emerging domains and tasks. Case studies show that learned rules can potentially explain the predicted entities.