Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Prior Driven Regularized Deep Learning for Sonar Image Classification

Deep learning has been recently shown to improve performance in the domain of synthetic aperture sonar (SAS) image classification. Given the constant resolution with a range of SAS, it is no surprise that deep learning techniques perform so well. Despite deep learning’s recent success, there are still compelling open challenges in reducing the high false alarm rate and enabling success when training imagery is limited, which is a practical challenge that distinguishes the SAS classification problem from standard image classification set-ups where training imagery may be abundant. We address these challenges by exploiting prior knowledge that humans use to grasp the scene. These include unconscious elimination of the image speckle and localization of objects in the scene. We introduce a new deep learning architecture that incorporates these priors with the goal of improving automatic target recognition (ATR) from SAS imagery. Our proposal—called SPDRDL, structural prior driven regularized deep learning—incorporates the previously mentioned priors in a multitask convolutional neural network (CNN) and requires no additional training data when compared to traditional SAS ATR methods. Two structural priors are enforced via regularization terms in the learning of the network: 1) structural similarity prior—enhanced imagery (often through despeckling) aids human interpretation and is semantically similar to the original imagery and 2) structural scene context priors—learned features ideally encapsulate target centering information; hence learning may be enhanced via a regularization that encourages fidelity against known ground truth target shifts (relative target position from scene center). Experiments on a challenging real-world data set reveal that SPDRDL outperforms state-of-the-art deep learning and other competing methods for SAS image classification.
Raw-x-vector: Multi-scale Time Domain Speaker Embedding Network

State-of-the-art text-independent speaker verification systems typically use cepstral features or filter bank energies of speech utterances as input features. With the ability of deep neural networks to learn representations from raw data, recent studies attempted to extract speaker embeddings directly from raw waveforms and showed competitive results. In this paper, we propose a new speaker embedding called raw-x-vector for speaker verification in the time domain, combining a multi-scale waveform encoder and an x-vector network architecture. We show that the proposed approach outperforms existing raw-waveform-based speaker verification systems by a large margin. We also show that the proposed multi-scale encoder improves over single-scale encoders for both the proposed system and another state-of-the-art raw-waveform-based speaker verification systems. A further analysis of the learned filters shows that the multi-scale encoder focuses on different frequency bands at its different scales while resulting in a more flat overall frequency response than any of the single-scale counterparts.
Anchor-based Bilingual Word Embeddings for Low-Resource Languages

Good quality monolingual word embeddings (MWEs) can be built for languages which have large amounts of unlabeled text. MWEs can be aligned to bilingual spaces using only a few thousand word translation pairs. For low resource languages training MWEs monolingually results in MWEs of poor quality, and thus poor bilingual word embeddings (BWEs) as well. This paper proposes a new approach for building BWEs in which the vector space of the high resource source language is used as a starting point for training an embedding space for the low resource target language. By using the source vectors as anchors the vector spaces are automatically aligned during training. We experiment on English-German, English-Hiligaynon and English-Macedonian. We show that our approach results not only in improved BWEs and bilingual lexicon induction performance, but also in improved target language MWE quality as measured using monolingual word similarity.
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation

Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typically fine-tuned on hundreds of thousands of data points, an infeasible requirement when applying summarization to new, niche domains. In this work, we introduce a novel and generalizable method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner. WikiTransfer fine-tunes pretrained models on pseudo-summaries, produced from generic Wikipedia data, which contain characteristics of the target dataset, such as the length and level of abstraction of the desired summaries. WikiTransfer models achieve state-of-the-art, zero-shot abstractive summarization performance on the CNN-DailyMail dataset and demonstrate the effectiveness of our approach on three additional diverse datasets. These models are more robust to noisy data and also achieve better or comparable few-shot performance using 10 and 100 training examples when compared to few-shot transfer from other summarization datasets. To further boost performance, we employ data augmentation via round-trip translation as well as introduce a regularization term for improved few-shot transfer. To understand the role of dataset aspects in transfer performance and the quality of the resulting output summaries, we further study the effect of the components of our unsupervised fine-tuning data and analyze few-shot performance using both automatic and human evaluation.
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling

Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors. This approach introduces a trade-off between disentangled representation learning and reconstruction quality since the model does not have enough capacity to learn correlated latent variables that capture detail information present in most image data. To overcome this trade-off, we present a novel multi-stage modelling approach where the disentangled factors are first learned using a preexisting disentangled representation learning method (such as $\beta$-TCVAE); then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables, adding detail information while maintaining conditioning on the previously learned disentangled factors. Taken together, our multi-stage modelling approach results in a single, coherent probabilistic model that is theoretically justified by the principal of D-separation and can be realized with a variety of model classes including likelihood-based models such as variational autoencoders, implicit models such as generative adversarial networks, and tractable models like normalizing flows or mixtures of Gaussians. We demonstrate that our multi-stage model has much higher reconstruction quality than current state-of-the-art methods with equivalent disentanglement performance across multiple standard benchmarks.
Simvastatin Posttreatment Controls Inflammation and Improves Bacterial Clearance in Experimental Sepsis

Sepsis is characterized by a life-threatening organ dysfunction caused by an unbalanced host response to microbe infection that can lead to death. Besides being currently the leading cause of death in intensive care units worldwide, sepsis can also induce long-term consequences among survivors, such as cognitive impairment. Statins (lipid-lowering drugs widely used to treat dyslipidemia) have been shown to possess pleiotropic anti-inflammatory and antimicrobial effects. These drugs act inhibiting 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase, an enzyme that catalyzes the conversion of HMG-CoA to mevalonate, the limiting step in cholesterol biosynthesis. In this work, we evaluated the therapeutic effects of simvastatin in an animal model of sepsis. In previous study from our group, statin pretreatment avoided cognitive damage and neuroinflammation in sepsis survivors. Herein, we focused on acute inflammation where sepsis was induced by cecal ligation and puncture (CLP), and the animals were treated with simvastatin (2 mg/kg) 6 h after surgery. We measured plasma biochemical markers of organ dysfunction, cell migration, cell activation, bacterial elimination, production of nitric oxide 24 h after CLP, survival rate for 7 days, and cognitive impairment 15 days after CLP. One single administration of simvastatin 6 h after CLP was able to prevent both liver and kidney dysfunction. In addition, this drug decreased cell accumulation in the peritoneum as well as the levels of TNF-α, MIF, IL-6, and IL-1β. Simvastatin diminished the number of bacterial colony forming units (CFU) and increased the production of nitric oxide production in the peritoneum. Simvastatin treatment increased survival for the first 24 h, but it did not alter survival rate at the end of 7 days. Our results showed that posttreatment with simvastatin hampered organ dysfunction, increased local production of nitric oxide, improved bacterial clearance, and modulated inflammation in a relevant model of sepsis.
MARS: Multimacro Architecture SRAM CIM-Based Accelerator With Co-Designed Compressed Neural Networks

Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the large storage overheads and the substantial computational cost of CNNs are problematic in hardware accelerators. Computing-in-memory (CIM) architecture has demonstrated great potential to effectively compute large-scale matrix–vector multiplication. However, the intensive multiply and accumulation (MAC) operations executed on CIM macros remain bottlenecks for further improvement of energy efficiency and throughput. To reduce computational costs, model compression is a widely studied method to shrink the model size. For implementation in a static random access memory (SRAM) CIM–based accelerator, the model compression algorithm must consider the hardware limitations of CIM macros. In this study, a software and hardware co-design approach is proposed to design MARS, a SRAM-based CIM (SRAM CIM)-based CNN accelerator that can utilize multiple SRAM CIM macros as processing units and support a sparse CNN, and an SRAM CIM-aware model compression algorithm that considers a CIM architecture to reduce the number of network parameters. With the proposed hardware software co-designed method, MARS can reach over 700 and 400 FPS for CIFAR-10 and CIFAR-100, respectively. In addition, MARS achieves 52.3 and 88.2 TOPs/W in VGG16 and ResNet18, respectively.
Probing the CGM of low-redshift dwarf galaxies using FIRE simulations

Observations of ultraviolet (UV) metal absorption lines have provided insight into the structure and composition of the circumgalactic medium (CGM) around galaxies. We compare these observations with the low-redshift (z ≤ 0.3) CGM around dwarf galaxies in high-resolution cosmological zoom-in runs in the FIRE-2 (Feedback In Realistic Environments) simulation suite. We select simulated galaxies that match the halo mass, stellar mass, and redshift of the observed samples. We produce absorption measurements using trident for UV transitions of C iv, O vi, Mg ii, and Si iii. The FIRE equivalent width (EW) distributions and covering fractions for the C iv ion are broadly consistent with observations inside 0.5Rvir, but are underpredicted for O vi, Mg ii, and Si iii. The absorption strengths of the ions in the CGM are moderately correlated with the masses and star formation activity of the galaxies. The correlation strengths increase with the ionization potential of the ions. The structure and composition of the gas from the simulations exhibit three zones around dwarf galaxies characterized by distinct ion column densities: the discy interstellar medium, the inner CGM (the wind-dominated regime), and the outer CGM (the IGM accretion-dominated regime). We find that the outer CGM in the simulations is nearly but not quite supported by thermal pressure, so it is not in hydrostatic equilibrium, resulting in halo-scale bulk inflow and outflow motions. The net gas inflow rates are comparable to the star formation rate of the galaxy, but the bulk inflow and outflow rates are greater by an order of magnitude, with velocities comparable to the virial velocity of the halo. These roughly virial velocities (${\sim } 100 \, \rm km\, s^{-1}$) produce large EWs in the simulations. This supports a picture for dwarf galaxies in which the dynamics of the CGM at large scales are coupled to the small-scale star formation activity near the centre of their haloes.
Lifetime Prevalence and Correlates of Patient-Perceived Medical Errors Experienced in the U.S. Ambulatory Setting: A Population-Based Study

Background: The rate of safety harm self-perceived medical errors and harms reported in the U.S. ambulatory system is not well characterized. Objectives: To determine the prevalence of U.S. adult ambulatory care patient self-perceived safety harms and to gauge the degree of association between harms with various patient characteristics and outcomes. Methods: A large U.S. cross-sectional online survey of 9206 ambulatory care adults was assessed for their perception of medical errors and harms during care (misdiagnosis, mistakes in care, and wrong or delayed treatment) and also included patient demographics, health status, comorbidities, insurance status, income, barriers to care (affordability, transportation, and family and social support), number of visits to primary health care services in the past 12 months, and use of urgent or emergency care in the last 12 months. Results: The overall rate of self-perceived medical errors and harms among adult patients in the ambulatory care setting was 36%. Female patients, independent of age, and those with multiple comorbidities or barriers to care, reported the highest number of medical errors. Utilization of multiple providers was associated with a greater number of reported medical errors, often resulting in changing health care providers. Patients who reported having trouble affording health care or navigating the system to receive care also reported higher levels of harm. They were cared for by multiple providers, often switch providers, and their care is associated with greater utilization of health care resources. Patients reporting the highest rates of harm had greater use of hospital and emergency room care. Conclusions: This large U.S. adult ambulatory care study provides evidence that patient self-perceived medical errors and harms reported by patients are common. Patient self-perceived medical errors and harms occur most commonly in women, with poor health, limitation of activities, and who have three or more comorbidities.
Beirut explosion: Energy yield from the fireball time evolution in the first 230 milliseconds.

The evolution of the fireball resulting from the August 2020 Beirut explosion is traced using amateur footage taken during the first $\sim$ 230 $ms$ after the detonation. 38 frames separated by $\sim$ 16.66 - 33.33 $ms$ are extracted from 6 different videos located precisely on the map. Measurements of the time evolution of the radius $R_{t}$ of the shock wave are traced by the fireball at consecutive time sequence $t$. Pixel scales for the videos are calibrated by de-projecting the existing grains silos building for which accurate drawings are available and by defining the line of sight incident angles. The energy available to drive the shock wave at early times can be calculated through $E = 10^{2b} K \rho_{o}$ where $b$ is a fitted parameter dependant on the relation between $R_t$ and $t$. $K$ is a constant depending on the ratio of specific heats of the atmosphere and $\rho_0$ is the undisturbed gas density. A total energy yield of $E \approx 1.3\times 10^{12}$ Joules with a lower bound of $9.8\times 10^{11}$ and an upper bound of $\sim 1.7\times 10^{12}$ or the equivalent of $\sim 310_{235}^{405}$ tons of T.N. T. is found. Our energy yields are different from other published studies using the same method. This can present an argument that if the compound that exploded is fuel rich ammonium nitrate (ANFO), then the actual mass that detonated is less than officially claimed.
Hodge-Newton filtration for $p$-divisible groups with ramified endomorphism structure

Let $\mathcal{O}_K$ be a complete discrete valuation ring of mixed characteristic $(0,p)$ with perfect residue field. We prove the existence of the Hodge-Newton filtration for $p$-divisible groups over $\mathcal{O}_K$ with additional endomorphism structure for the ring of integers of a finite, possibly ramified field extension of $\mathbb{Q}_p$. The argument is based on the Harder-Narasimhan theory for finite flat group schemes over $\mathcal{O}_K$. In particular, we describe a sufficient condition for the existence of a filtration of $p$-divisible groups over $\mathcal{O}_K$ associated to a break point of the Harder-Narasimhan polygon.
Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale

Automatic evaluation of language generation systems is a well-studied problem in Natural Language Processing. While novel metrics are proposed every year, a few popular metrics remain as the de facto metrics to evaluate tasks such as image captioning and machine translation, despite their known limitations. This is partly due to ease of use, and partly because researchers expect to see them and know how to interpret them. In this paper, we urge the community for more careful consideration of how they automatically evaluate their models by demonstrating important failure cases on multiple datasets, language pairs and tasks. Our experiments show that metrics (i) usually prefer system outputs to human-authored texts, (ii) can be insensitive to correct translations of rare words, (iii) can yield surprisingly high scores when given a single sentence as system output for the entire test set.
Seeding the Solar System with Life: Mars, Venus, Earth, Moon, Protoplanets

Abstract In the space of the entire universe, the only conclusive evidence of life, is found on Earth. Although the ultimate source of all life is unknown, many investigators believe Earth, Mars, and Venus may have been seeded with life when these planets, and the sun, were forming in a galactic cluster of thousands of stars and protoplanets. Yet others hypothesize that while and after becoming established members of this solar system, these worlds became contaminated with life during the heavy bombardment phase when struck by millions of life-bearing meteors, asteroids, comets and oceans of ice. Because bolide impacts may eject tons of life-bearing debris into space, and as powerful solar winds may blow upper atmospheric organisms into space, these three planets may have repeatedly exchanged living organisms for billions of years. In support of these hypotheses is evidence suggestive of stromatolites, algae, and lichens on Mars, fungi on Mars and Venus, and formations resembling fossilized acritarchs and metazoans on Mars, and fossilized impressions resembling microbial organisms on the lunar surface, and dormant microbes recovered from the interior of a lunar camera. The evidence reviewed in this report supports the interplanetary transfer hypothesis and that Earth may be seeding this solar system with life.
A 21st Century Evil: Immunopathology and New Therapies of COVID-19

Coronavirus Disease 2019 (COVID-19) has been classified as a global threat, affecting millions of people and killing thousands. It is caused by the SARS-CoV-2 virus, which emerged at the end of 2019 in Wuhan, China, quickly spreading worldwide. COVID-19 is a disease with symptoms that range from fever and breathing difficulty to acute respiratory distress and death, critically affecting older patients and people with previous comorbidities. SARS-CoV-2 uses the angiotensin-converting enzyme 2 (ACE2) receptor and mainly spreads through the respiratory tract, which it then uses to reach several organs. The immune system of infected patients has been demonstrated to suffer important alterations, such as lymphopenia, exhausted lymphocytes, excessive amounts of inflammatory monocytes and macrophages, especially in the lungs, and cytokine storms, which may contribute to its severity and difficulty of establishing an effective treatment. Even though no specific treatment is currently available, several studies have been investigating potential therapeutic strategies, including the use of previously approved drugs and immunotherapy. In this context, this review addresses the interaction between SARS-CoV-2 and the patient’s host immune system during infection, in addition to discussing the main immunopathological mechanisms involved in the development of the disease and potential new therapeutic approaches.
Anticipated User Stereotypes Systematically Affect the Social Acceptability of Mobile Devices

Understanding social perception is crucial when designing socially accepted mobile devices. Using the stereotype content model (SCM), recent work showed that mobile devices systematically attract stereotypical users’ warmth and competence. It was concluded that the SCM can predict a device’s social acceptability. There is, however, no empirical evidence for the assumption that the SCM can predict social acceptability and it also unclear what causes a device’s stereotypical perception. In this paper, we first verify that the SCM’s dimensions strongly correlate with social acceptance and show that social acceptance can be explained through stereotypical perception. In a second study, we independently asked participants to assess the warmth and competence of mobile devices, human stereotypes, and the probability that human stereotypes use the devices. We found that warmth and competence of anticipated stereotypical users predict a device’s position in the SCM. The combined results of both studies show that the stereotypical perception of anticipated users can explain the social acceptability of mobile devices.
Mitochondrial DNA Mutation Analysis in Breast Cancer: Shifting From Germline Heteroplasmy Toward Homoplasmy in Tumors

Studies have suggested a potential role of somatic mitochondrial mutations in cancer development. To analyze the landscape of somatic mitochondrial mutation in breast cancer and to determine whether mitochondrial DNA (mtDNA) mutational burden is correlated with overall survival (OS), we sequenced whole mtDNA from 92 matched-paired primary breast tumors and peripheral blood. A total of 324 germline variants and 173 somatic mutations were found in the tumors. The most common germline allele was 663G (12S), showing lower heteroplasmy levels in peripheral blood lymphocytes than in their matched tumors, even reaching homoplasmic status in several cases. The heteroplasmy load was higher in tumors than in their paired normal tissues. Somatic mtDNA mutations were found in 73.9% of breast tumors; 59% of these mutations were located in the coding region (66.7% non-synonymous and 33.3% synonymous). Although the CO1 gene presented the highest number of mutations, tRNA genes (T,C, and W), rRNA 12S, and CO1 and ATP6 exhibited the highest mutation rates. No specific mtDNA mutational profile was associated with molecular subtypes of breast cancer, and we found no correlation between mtDNA mutational burden and OS. Future investigations will provide insight into the molecular mechanisms through which mtDNA mutations and heteroplasmy shifting contribute to breast cancer development.