Miracle-dz/UniFAR-FTUs huggingface dataset:
Seismic Exploration of the Rocky Mountain Region

The Seismic Exploration of the Rocky Mountain Region, is a publication consisting of thirty-three articles. Ten articles are about seismic exploration of Rocky Mountain structure, seven are regarding seismic stratigraphy in this region and sixteen describe seismic exploration of basins in the Rocky Mountains. Also included are two introductory articles. This book integrates geology and geophysics for viable oil and gas exploration in the Rocky Mountains.
Robust Fault Detection and Set-Theoretic UIO for Discrete-Time LPV Systems With State and Output Equations Scheduled by Inexact Scheduling Variables

This paper proposes a novel robust fault detection (FD) approach and designs a set-theoretic unknown input observer (SUIO) for linear parameter-varying (LPV) systems with both state and output equations scheduled by inexact scheduling variables. First, for such LPV systems, we propose a novel robust FD method by combing the set theory with the unknown input observer, which considers the bounds of measurement errors of scheduling variables to generate FD-oriented sets. In general, as long as sensors with sufficiently high precision are equipped to measure the scheduling variables, the bounds of measurement errors of scheduling variables can be less conservative than those direct bounds of scheduling variables, which can reduce robust FD conservatism in this way. Second, we give the unknown input decoupling condition of SUIO for such LPV systems and propose an SUIO design method under this condition for robust state estimation (SE). Besides, stability conditions for the proposed methods are established via matrix inequalities. At the end of this paper, a case study is used to illustrate the effectiveness of the proposed methods.
Vasculature of the Suprachiasmatic Nucleus: Pathways for Diffusible Output Signals

Transplant studies demonstrate unequivocally that the suprachiasmatic nucleus (SCN) produces diffusible signals that can sustain circadian locomotor rhythms. There is a vascular portal pathway between the SCN and the organum vasculosum of the lamina terminalis in mouse brain. Portal pathways enable low concentrations of neurosecretions to reach specialized local targets without dilution in the systemic circulation. To explore the SCN vasculature and the capillary vessels whereby SCN neurosecretions might reach portal vessels, we investigated the blood vessels (BVs) of the core and shell SCN. The arterial supply of the SCN differs among animals, and in some animals, there are differences between the 2 sides. The rostral SCN is supplied by branches from either the superior hypophyseal artery (SHpA) or the anterior cerebral artery or the anterior communicating artery. The caudal SCN is consistently supplied by the SHpA. The rostral SCN is drained by the preoptic vein, while the caudal is drained by the basal vein, with variations in laterality of draining vessels. In addition, several key features of the core and shell SCN regions differ: Median BV diameter is significantly smaller in the shell than the core based on confocal image measurements, and a similar trend occurs in iDISCO-cleared tissue. In the cleared tissue, whole BV length density and surface area density are significantly greater in the shell than the core. Finally, capillary length density is also greater in the shell than the core. The results suggest three hypotheses: First, the distinct arterial and venous systems of the rostral and caudal SCN may contribute to the in vivo variations of metabolic and neural activities observed in SCN networks. Second, the dense capillaries of the SCN shell are well positioned to transport blood-borne signals. Finally, variations in SCN vascular supply and drainage may contribute to inter-animal differences.
Perspectives and experiences of Zambian pregnant and postpartum women receiving two intervention models to increase uptake of male partner HIV testing

ABSTRACT This study explored the experiences of pregnant women who received two intervention models for increasing uptake of male partner HIV testing in antenatal settings. As part of a randomised trial, we interviewed twenty participants who received partner notification services only while 22 received the partner notification plus HIV self-testing. Thematic analysis was used to analyse the data. Partner notification services helped to initiate discussions of HIV testing with partners, influence partners to undergo testing, and encouraged disclosure of HIV status. Some women experienced difficulties engaging partners due to fear of their partner's reaction. Some partners were unable to test due to time constraints. The partner notification plus HIV self-testing intervention, stimulated discussion about HIV testing; facilitated testing for men at their convenience; addressed privacy/confidentiality, and stigma concerns; and provided the opportunity to disclose HIV status. Some women feared disclosure and retribution in case of discordance results. There were also challenges with men making follow-ups for confirmatory HIV tests. The addition of HIV self-test kits to partner notification services can expand HIV testing services to male partners, including those of HIV-negative women. Additional efforts are needed to link men to appropriate HIV prevention, care, and treatment services.
Characterization of Variable Regions of the Gp120 Protein from HIV-1 Subtype C Virus Variants Obtained from Individuals at Different Disease Stages in Sub-Saharan Africa

Background: The development of a vaccine against HIV/AIDS capable of preventing virus infection has been hampered by the HIV envelope (Env) heterogeneity that makes it difficult to induce neutralizing antibodies against Env proteins from different HIV clades. Several studies have indicated that gp120 Env protein sequence tends to change considerably during the course of HIV disease which allows the virus to escape the immune responses. In order to define gp120 sequence changes, we have characterized the V1, V2, V4 and V5 variable regions of gp120 variants from 72 HIV-1-clade-C-infected subjects from South Africa and Swaziland, which were naïve to antiretroviral (ARV) therapy and at different disease stages. Sequence characteristics, such as aminoacid sequence length, presence of Putative N-Glycosylation Sites (PNGSs) and electric charge were investigated. Methods: According to the Avidity Index value and CD4+ T cell count, patients were classified for disease stage in three groups: recent, chronic and late stage, each one comprised of 24 patients. The V1 to V5 Env variable regions were directly PCR amplified from plasma virus RNA and sequenced. Results: A significant increase in the amino acid sequence length of V1 and V4 domains, and a corresponding increase of the “shifting” PNGSs were observed in the HIV variants obtained from individuals at chronic stage of disease, as compared to the recent infection group. Finally, a significant increase of the net electric positive charge of the V5 loop was found in the HIV variants from the group of subjects with late disease, as compared to the chronic disease group. Conclusion: We conclude that changes in sequence length, glycosylation pattern and net electrical charge in the variable V1, V4 and V5 regions of gp120 occur in the course of HIV infection, possibly in response to the pressure of the host immune response.
Tension-free vaginal tape: 162 cases in a district general hospital

From March 1999 to July 2002 a prospective study was carried out of 162 consecutive cases of urodynamically confirmed stress and mixed incontinence who underwent the tension-free vaginal tape (TVT) procedure. Patients were followed up at 6 weeks and 6 months and with a quality of life questionnaire at 1 year. The intraoperative complication rate was 7.6% and the postoperative complication rate was 18.8%. The subjective cure rate for patients suffering from urodynamic stress incontinence was 85%, with a further 11% experiencing significant improvement in their symptoms. The subjective cure rate for patients with mixed incontinence was 88%, with a further 9% experiencing significant improvement. The TVT procedure appears to be safe and effective for both stress and mixed incontinence for up to three years in a district general hospital. Complications in the short term are uncommon and can be managed easily.
Study of flavor dependence of the baryon-to-meson ratio in proton-proton collisions at 
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The production cross sections of ${\rm D^0}$ and $\Lambda^+_{\rm c}$ hadrons originating from beauty-hadron decays (i.e. non-prompt) were measured for the first time at midrapidity ($|y|<0.5$) by the ALICE Collaboration in proton-proton collisions at a center-of-mass energy $\sqrt{s}=13$ TeV. They are described within uncertainties by perturbative QCD calculations employing the fragmentation fractions of beauty quarks to baryons measured at forward rapidity by the LHCb Collaboration. The ${\rm b\overline{b}}$ production cross section per unit of rapidity at midrapidity, estimated from these measurements, is ${\rm d}\sigma_{\rm b\overline{b}}/{\rm d}y|_{|y|<0.5} = 83.1 \pm 3.5 (\mathrm{stat.}) \pm 5.4(\mathrm{syst.}) ^{+12.3}_{-3.2} (\mathrm{extrap.})\,\mu$b. The baryon-to-meson ratios are computed to investigate the hadronization mechanism of beauty quarks. The non-prompt $\Lambda^+_{\rm c}/{\rm D^0}$ production ratio has a similar trend to the one measured for the promptly produced charmed particles and to the p$/\pi^+$ and $\Lambda/{\rm K^0_S}$ ratios, suggesting a similar baryon-formation mechanism among light, strange, charm, and beauty hadrons. The $p_{\rm T}$-integrated non-prompt $\Lambda_{\rm c}/{\rm D^0}$ ratio is found to be significantly higher than the one measured in e$^+$e$^-$ collisions.
Inversion and Cubic Vectors for Permutrees

We introduce two generalizations of bracket vectors from binary trees to permutrees. These new vectors help describe algebraic and geometric properties of the rotation lattice of permutrees defined by Pilaud and Pons. The first generalization serves the role of an inversion vector for permutrees allowing us to define an explicit meet operation and provide a new constructive proof of the lattice property for permutree rotation lattices. The second generalization, which we call cubic vectors, allows for the construction of a cubic realization of these lattices which is proven to form a cubical embedding of the corresponding permutreehedra. These results specialize to those known about permutahedra and associahedra.
Transferable Models for Bioacoustics with Human Language Supervision

Passive acoustic monitoring offers a scalable, non-invasive method for tracking global biodiversity and anthropogenic impacts on species. Although deep learning has become a vital tool for processing this data, current models are inflexible, typically cover only a handful of species, and are limited by data scarcity. In this work, we propose BioLingual, a new model for bioacoustics based on contrastive language-audio pretraining. We first aggregate bioacoustic archives into a language-audio dataset, called AnimalSpeak, with over a million audio-caption pairs holding information on species, vocalization context, and animal behavior. After training on this dataset to connect language and audio representations, our model can identify over a thousand species' calls across taxa, complete bioacoustic tasks zero-shot, and retrieve animal vocalization recordings from natural text queries. When fine-tuned, BioLingual sets a new state-of-the-art on nine tasks in the Benchmark of Animal Sounds. Given its broad taxa coverage and ability to be flexibly queried in human language, we believe this model opens new paradigms in ecological monitoring and research, including free-text search on the world's acoustic monitoring archives. We open-source our models, dataset, and code.
Quantization Aware Factorization for Deep Neural Network Compression

Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks. Due to memory and power consumption limitations of mobile or embedded devices, the quantization step is usually necessary when pre-trained models are deployed. A conventional post-training quantization approach applied to networks with decomposed weights yields a drop in accuracy. This motivated us to develop an algorithm that finds tensor approximation directly with quantized factors and thus benefit from both compression techniques while keeping the prediction quality of the model. Namely, we propose to use Alternating Direction Method of Multipliers (ADMM) for Canonical Polyadic (CP) decomposition with factors whose elements lie on a specified quantization grid. We compress neural network weights with a devised algorithm and evaluate it's prediction quality and performance. We compare our approach to state-of-the-art post-training quantization methods and demonstrate competitive results and high flexibility in achiving a desirable quality-performance tradeoff.
SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference

Deep neural networks (DNNs) demonstrate outstanding performance across most computer vision tasks. Some critical applications, such as autonomous driving or medical imaging, also require investigation into their behavior and the reasons behind the decisions they make. In this vein, DNN attribution consists in studying the relationship between the predictions of a DNN and its inputs. Attribution methods have been adapted to highlight the most relevant weights or neurons in a DNN, allowing to more efficiently select which weights or neurons can be pruned. However, a limitation of these approaches is that weights are typically compared within each layer separately, while some layers might appear as more critical than others. In this work, we propose to investigate DNN layer importance, i.e. to estimate the sensitivity of the accuracy w.r. t. perturbations applied at the layer level. To do so, we propose a novel dataset to evaluate our method as well as future works. We benchmark a number of criteria and draw conclusions regarding how to assess DNN layer importance and, consequently, how to budgetize layers for increased DNN efficiency (with applications for DNN pruning and quantization), as well as robustness to hardware failure (e.g. bit swaps).
Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution

Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies. However, most of the current research in this area has prioritized the design of transformer blocks to capture global information, while overlooking the importance of incorporating high-frequency priors, which we believe could be beneficial. In our study, we conducted a series of experiments and found that transformer structures are more adept at capturing low-frequency information, but have limited capacity in constructing high-frequency representations when compared to their convolutional counterparts. Our proposed solution, the cross-refinement adaptive feature modulation transformer (CRAFT), integrates the strengths of both convolutional and transformer structures. It comprises three key components: the high-frequency enhancement residual block (HFERB) for extracting high-frequency information, the shift rectangle window attention block (SRWAB) for capturing global information, and the hybrid fusion block (HFB) for refining the global representation. Our experiments on multiple datasets demonstrate that CRAFT outperforms state-of-the-art methods by up to 0.29dB while using fewer parameters. The source code will be made available at: https://github.com/AVC2-UESTC/CRAFT-SR.git.
Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review

This comprehensive review systematically evaluates Machine Learning (ML) methodologies employed in the detection, prediction, and analysis of mental stress and its consequent mental disorders (MDs). Utilizing a rigorous scoping review process, the investigation delves into the latest ML algorithms, preprocessing techniques, and data types employed in the context of stress and stress-related MDs. The findings highlight that Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models consistently exhibit superior accuracy and robustness among all machine learning algorithms examined. Furthermore, the review underscores that physiological parameters, such as heart rate measurements and skin response, are prevalently used as stress predictors in ML algorithms. This is attributed to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. Additionally, the application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. The synthesis of this review identifies significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for detection and prediction of stress and stress-related MDs.
Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions

Chatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated the format of how a chatbot references previous conversations with a user and its effects on a user’s perceptions and privacy concerns. In a three-week longitudinal between-subjects study, 169 participants talked about their dental flossing habits to a chatbot that either, (1-None): did not explicitly reference previous user utterances, (2-Verbatim): referenced previous utterances verbatim, or (3-Paraphrase): used paraphrases to reference previous utterances. Participants perceived Verbatim and Paraphrase chatbots as more intelligent and engaging. However, the Verbatim chatbot also raised privacy concerns with participants. To gain insights as to why people prefer certain conditions or had privacy concerns, we conducted semi-structured interviews with 15 participants. We discuss implications from our findings that can help designers choose an appropriate format to reference previous user utterances and inform in the design of longitudinal dialogue scripting.
Confronting global 21-cm signal with ℤ3 symmetric dark matter models

While the ℤ3 symmetric dark matter models have shown tremendous prospects in addressing a number of (astro-)particle physics problems, they can leave interesting imprints on cosmological observations as well. We consider two such promising models: semi-annihilating dark matter (SADM) and Co-SIMP 2 → 3 interaction, and investigate their effects on the global 21-cm signal. SADM alone cannot address the EDGES dip but can perform better with the aid of an excess radio background, whereas Co-SIMP can naturally explain the EDGES absorption feature by virtue of an intrinsic cooling effect without invoking any such excess radiation. Hence, the latter model turns out to be a rare model within the domain of CDM, that uses leptophilic interaction to achieve the EDGES dip. Further, keeping in mind the ongoing debate between EDGES and SARAS 3 on the global 21-cm signal, we demonstrate that our chosen models can still remain viable in this context, even if the EDGES data requires reassessment in future. We then extend our investigation to possible reflections on the Dark Ages, followed by a consistency check with the CMB and BAO observations via Planck 2018(+BAO) datasets. This work thus presents a compelling case of exploring these interesting particle physics models in the light of different cosmological observations.
CERMET: Coding for Energy Reduction with Multiple Encryption Techniques - It's easy being green

This paper presents CERMET, an energy-efficient hardware architecture designed for hardware-constrained cryptosystems. CERMET employs a base cryptosystem in conjunction with network coding to provide both information-theoretic and computational security while reducing energy consumption per bit. This paper introduces the hardware architecture for the system and explores various optimizations to enhance its performance. The universality of the approach is demonstrated by designing the architecture to accommodate both asymmetric and symmetric cryptosystems. The analysis reveals that the benefits of this proposed approach are multifold, reducing energy per bit and area without compromising security or throughput. The optimized hardware architectures can achieve below 1 pJ/bit operations for AES-256. Furthermore, for a public key cryptosystem based on Elliptic Curve Cryptography (ECC), a remarkable 14.6X reduction in energy per bit and a 9.3X reduction in area are observed, bringing it to less than 1 nJ/bit.
RECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?

Over the past two decades, recommendation systems (RSs) have used machine learning (ML) solutions to recommend items, e.g., movies, books, and restaurants, to clients of a business or an online platform. Recipe recommendation, however, has not yet received much attention compared to those applications. We introduce RECipe as a multi-purpose recipe recommendation framework with a multi-modal knowledge graph (MMKG) backbone. The motivation behind RECipe is to go beyond (deep) neural collaborative filtering (NCF) by recommending recipes to users when they query in natural language or by providing an image. RECipe consists of 3 subsystems: (1) behavior-based recommender, (2) review-based recommender, and (3) image-based recommender. Each subsystem relies on the embedding representations of entities and relations in the graph. We first obtain (pre-trained) embedding representations of textual entities, such as reviews or ingredients, from a fine-tuned model of Microsoft's MPNet. We initialize the weights of the entities with these embeddings to train our knowledge graph embedding (KGE) model. For the visual component, i.e., recipe images, we develop a KGE-Guided variational autoencoder (KG-VAE) to learn the distribution of images and their latent representations. Once KGE and KG-VAE models are fully trained, we use them as a multi-purpose recommendation framework. For benchmarking, we created two knowledge graphs (KGs) from public datasets on Kaggle for recipe recommendation. Our experiments show that the KGE models have comparable performance to the neural solutions. We also present pre-trained NLP embeddings to address important applications such as zero-shot inference for new users (or the cold start problem) and conditional recommendation with respect to recipe categories. We eventually demonstrate the application of RECipe in a multi-purpose recommendation setting.