Miracle-dz/UniFAR-FTUs huggingface dataset:
The development and validation of pathological sections based U-Net deep learning segmentation model for the detection of esophageal mucosa and squamous cell neoplasm

Background Deep learning methods have demonstrated great potential for processing high-resolution images. The U-Net model, in particular, has shown proficiency in the segmentation of biomedical images. However, limited research has examined the application of deep learning to esophageal squamous cell carcinoma (ESCC) segmentation. Therefore, this study aimed to develop deep learning segmentation systems specifically for ESCC. Methods A Visual Geometry Group (VGG)-based U-Net neural network architecture was utilized to develop the segmentation models. A pathological image cohort of surgical specimens was used for model training and internal validation, with two additional endoscopic biopsy section cohort for external validation. Model efficacy was evaluated across several metrics including Intersection over Union (IOU), accuracy, positive predict value (PPV), true positive rate (TPR), specificity, dice similarity coefficient (DSC), area under the receiver operating characteristic curve (AUC), and F1-Score. Results Surgical samples from ten patients were analyzed retrospectively, with each biopsy section cohort encompassing five patients. Transfer learning models based on U-Net weights yielded optimal results. For mucosa segmentation, the in internal validation achieved 93.81% IOU, with other parameters exceeding 96% (96.96% accuracy, 96.45% PPV, 96.65% TPR, 98.41% specificity, 96.81% DSC, 96.11% AUC, and 96.55% F1-Score). The tumor segmentation model attained an IOU of 91.95%, along with other parameters surpassing 95% (95.90% accuracy, 95.62% PPV, 95.71% TPR, 97.88% specificity, 95.81% DSC, 94.92% AUC, and 95.67% F1-Score). In the external validation for tumor segmentation model, IOU was 59.86% for validation database 1 (72.74% for accuracy, 76.03% for PPV, 77.17% for TPR, 83.80% for specificity, 74.89% for DSC, 71.83% for AUC, and 76.60% for F1-Score), and 50.88% for validation cohort 2 (68.03% for accuracy, 59.02% for PPV, 66.87% for TPR, 78.48% for specificity, 67.44% for DSC, 64.68% for AUC, and 62.70% for F1-Score). Conclusions The models exhibited satisfactory results, paving the way for their potential deployment on standard computers and integration with other artificial intelligence models in clinical practice in the future. However, limited to the size of study, the generalizability of models is impaired in the external validation, larger pathological section cohort would be needed in future development to ensure robustness and generalization.
ViR: Towards Efficient Vision Retention Backbones

Vision Transformers (ViTs) have attracted a lot of popularity in recent years, due to their exceptional capabilities in modeling long-range spatial dependencies and scalability for large scale training. Although the training parallelism of self-attention mechanism plays an important role in retaining great performance, its quadratic complexity baffles the application of ViTs in many scenarios which demand fast inference. This effect is even more pronounced in applications in which autoregressive modeling of input features is required. In Natural Language Processing (NLP), a new stream of efforts has proposed parallelizable models with recurrent formulation that allows for efficient inference in generative applications. Inspired by this trend, we propose a new class of computer vision models, dubbed Vision Retention Networks (ViR), with dual parallel and recurrent formulations, which strike an optimal balance between fast inference and parallel training with competitive performance. In particular, ViR scales favorably for image throughput and memory consumption in tasks that require higher-resolution images due to its flexible formulation in processing large sequence lengths. The ViR is the first attempt to realize dual parallel and recurrent equivalency in a general vision backbone for recognition tasks. We have validated the effectiveness of ViR through extensive experiments with different dataset sizes and various image resolutions and achieved competitive performance. Code: https://github.com/NVlabs/ViR
Ozone as Redox Bioregulator in Preventive Medicine: The Molecular and Pharmacological Basis of the Low-Dose Ozone Concept—A Review

The best form of prevention against human infection through bacteria, viruses, and other parasites is ozone disinfection of wastewater and drinking water as a highly effective, well-known method. Various preclinical studies showed promising results, which are being revisited and reconsidered in times of pandemics and led to interesting results in recent clinical trials and reports, as presented by the example of protective measures against COVID-19 in particularly vulnerable clinical personnel. The application of ozone in the form of the low-dose concept induces its regulation by interference of ozone or its peroxides into the redox equilibrium of the biological system, which finally results in the restoration of the glutathione equilibrium. The antioxidant system is activated, the immune system is modulated, and thus the defense mechanisms are improved. In patients with rheumatoid arthritis, repeated ozone treatments have led to new findings in “immunomodulation” through ozone. The more effective immune response is discussed as the response of innate immune memory and opens interesting aspects for complementary treatment of autoimmune diseases.
Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building

In this paper, we describe our submission to the BabyLM Challenge 2023 shared task on data-efficient language model (LM) pretraining (Warstadt et al., 2023). We train transformer-based masked language models that incorporate unsupervised predictions about hierarchical sentence structure into the model architecture. Concretely, we use the Structformer architecture (Shen et al., 2021) and variants thereof. StructFormer models have been shown to perform well on unsupervised syntactic induction based on limited pretraining data, and to yield performance improvements over a vanilla transformer architecture (Shen et al., 2021). Evaluation of our models on 39 tasks provided by the BabyLM challenge shows promising improvements of models that integrate a hierarchical bias into the architecture at some particular tasks, even though they fail to consistently outperform the RoBERTa baseline model provided by the shared task organizers on all tasks.
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes

Tensor decomposition is an important tool for multiway data analysis. In practice, the data is often sparse yet associated with rich temporal information. Existing methods, however, often under-use the time information and ignore the structural knowledge within the sparsely observed tensor entries. To overcome these limitations and to better capture the underlying temporal structure, we propose Dynamic EMbedIngs fOr dynamic Tensor dEcomposition (DEMOTE). We develop a neural diffusion-reaction process to estimate dynamic embeddings for the entities in each tensor mode. Specifically, based on the observed tensor entries, we build a multi-partite graph to encode the correlation between the entities. We construct a graph diffusion process to co-evolve the embedding trajectories of the correlated entities and use a neural network to construct a reaction process for each individual entity. In this way, our model can capture both the commonalities and personalities during the evolution of the embeddings for different entities. We then use a neural network to model the entry value as a nonlinear function of the embedding trajectories. For model estimation, we combine ODE solvers to develop a stochastic mini-batch learning algorithm. We propose a stratified sampling method to balance the cost of processing each mini-batch so as to improve the overall efficiency. We show the advantage of our approach in both simulation study and real-world applications. The code is available at https://github.com/wzhut/Dynamic-Tensor-Decomposition-via-Neural-Diffusion-Reaction-Processes.
Cavity magnomechanics: from classical to quantum

Hybrid quantum systems based on magnons in magnetic materials have made significant progress in the past decade. They are built based on the couplings of magnons with microwave photons, optical photons, vibration phonons, and superconducting qubits. In particular, the interactions among magnons, microwave cavity photons, and vibration phonons form the system of cavity magnomechanics (CMM), which lies in the interdisciplinary field of cavity QED, magnonics, quantum optics, and quantum information. Here, we review the experimental and theoretical progress of this emerging field. We first introduce the underlying theories of the magnomechanical coupling, and then some representative classical phenomena that have been experimentally observed, including magnomechanically induced transparency, magnomechanical dynamical backaction, magnon-phonon cross-Kerr nonlinearity, etc. We also discuss a number of theoretical proposals, which show the potential of the CMM system for preparing different kinds of quantum states of magnons, phonons, and photons, and hybrid systems combining magnomechanics and optomechanics and relevant quantum protocols based on them. Finally, we summarize this review and provide an outlook for the future research directions in this field.
Collective phenomena study in small systems using the bulk of particle production in high-multiplicity pp collisions at $\sqrt{s}$ =13 TeV with ALICE

The heavy-ion collisions (A--A) at the Large Hadron Collider (LHC) energies have confirmed the production of the quark-gluon plasma (QGP), a new state of nuclear matter where quarks and gluons are deconfined. The light-flavour hadrons ($\pi$, K, p), constitute the bulk of the produced particles, carry useful information of the collision geometry, collective behaviour and thermal property of the QGP. The measurements of light-flavour hadron production in small collision systems (pp and p--A) at the LHC energies have shown the onset of collective phenomena (e.g. radial flow and long-range correlations) that resemble what is typically observed in nucleus-nucleus collisions and attributed to the formation of a deconfined system of quarks and gluons. The new results of the identified light-flavour particle production measured in high-multiplicity triggered pp collisions at $\sqrt{s}=13$~TeV of ALICE Run 2 will be presented in search of collective behaviour in small collision systems. The transverse momenta $p_{\rm T}$-spectra of the identified particles show hardening at the mid-$p_{\rm T}$. The mean transverse momenta ($\langle p_{\rm T} \rangle$) are shown as a function of charged-particle multiplicity. The ratios of $p_{\rm T}$-spectra and the ratios of the integrated yields of kaon- and proton-to-pion are also presented and compared with published results.
Beyond Best-Fits and Model Selection -- Introducing"Reliability"of cusp-core inference of dark matter halos

We introduce the notion of a Bayesian analysis motivated `reliability' that gives a truer distinction of cusp-core and other halo-parameters (like mass-concentration) in an ensemble of observed galaxies. Our approach goes beyond the standard statistical techniques of parameter estimation and model fitting. We create hundreds of thousands of realistic mock SPARC RCs, with both cuspy and cored DM density profiles as model inputs. These RCs carefully incorporate the details of SPARC data such as the nature of observed uncertainties and different sources of scatters arising from observation, presence of baryons, DM mass-concentration, etc. Bayesian analysis of these mock RCs enables us to reconstruct and identify the parameter space in galaxy observable and theory where one can venture beyond best-fits to a preferred DM halo model or model selections between different density models. We find that it is imperative to choose low stellar surface density ($\Sigma_{\star}$) galaxies for reliable cusp-vs-core distinction; for example, RC data for galaxies with $\Sigma_{\star} \leq 2.5$ is needed for a 75\% confidence in distinguishing cusps from cores. Similarly, we also find that for correct estimations of the halo masses and concentrations, the RCs need to be measured to at least a radial distance $\geq 0. 8r_s$ where $r_s$ is the scale radii of the corresponding DM halo density profiles. Out of the total $\sim$ 135 SPARC galaxies, using our reliability criteria, we find that only 21 RCs clear the bar to be used for any unbiased cusp-core distinction as well as DM halo mass-concentration estimates at $\geq$75\% reliability confidence level. With $\geq$66\% ( $\geq$50\%) reliability settings, the sample size increases to 44 (59). Interestingly, in the $\geq 75$\% reliable subsample, there are 5 times more galaxies that are reliably cored than cuspy. [Abridged]
Proposta para o aproveitamento de biogás na obtenção de créditos de carbono em aterro sanitário e compostagem com aeração horizontal em camadas

PhD Thesis Postgraduate Program in Water Resources and Environmental Sanitation Institute of Hydraulic Research Federal University of Rio Grande do Sul, RS, Brazil PROPOSAL FOR THE ENJOYMENT OF BIOGAS IN OBTAINING CARBON CREDITS IN LANDFILL AND COMPOSTING WITH AERATION HORIZONTAL LAYERED Author: Tiago Luis Gomes Advisor: Gino Roberto Gehling Date and Place: Porto Alegre, May 17, 2013 The anaerobic digestion process opportunities, about controlled landfills and composting systems, are related to a biogas formation process through the biodegradation of organic substances in a predominantly anaerobic environment. The Caturrita’s landfill, located in Santa Maria – RS Brazil, has around 467,929 tons of deposited residues in one of its cells. The recovery of methane between 2013 and 2033, considering an availability of 70%, was estimated at 17,529,657 m3. Admitting the carbon credits and electric energy commercialization, during the same period, was estimated and would result in USD 2,131,438, that is, a monthly profits of USD 8,881. In a composting system, where anaerobic environments may occur, the horizontal pipelines system used showed that the CH4 production decreased with advantage in relation to vertical pipelines. For this experiment, Organic matter (OM) was aerated at a rate of 0.557 m air·d·kg, as determined by dry weight (DW), and the internal temperature and humidity of the windrows were monitored. The calculated variation of the pressure loss for the pipelines in the experimental widrow with horizontal pipelines proposal was between 1.56% and 2.56% at a maximum internal temperature of 70.8°C, unlike the windrow with vertical pipelines, which reached a maximum temperature of 47.1°C. In both systems, the humidity was greater than 50%. The cost per metre for commercial scale implementation is equivalent to USD 21.22, considering the use of alternative materials with the necessary mechanical and thermal resistance values. It was observed in the experimental pile with horizontal pipelines proposal the reduction of NH4 simultaneously to the elevation of NO3 in less time than in the pile with vertical pipelines. Furthermore, with the alternative aeration system, the CH4 emission would be insignificant, because the process is essentially aerobic. For composting system with vertical pipelines used in the company, considering only the emanation of CH4, the environmental cost can be estimated between USD 2,059 and USD 75,841 per year for an amount of 50,000 t of processed agribusiness residues. For Caturrita’s landfill, the environmental cost can be estimated between USD 7,733 and USD 52,287 per year. With a CH4 recovery of 70%, by burning or conversion into electrical energy, the maximum estimated environmental cost would fall to USD 15,686 for the estimated quantity and disposed of in landfill Caturrita between 1999 and 2008 of 467,929 tons. Key-words: Carbon credits, Landfill, composting, horizontal pipelines, environmental cost.
Circuit Width Estimation via Effect Typing and Linear Dependency (Long Version)

Circuit description languages are a class of quantum programming languages in which programs are classical and produce a description of a quantum computation, in the form of a quantum circuit. Since these programs can leverage all the expressive power of high-level classical languages, circuit description languages have been successfully used to describe complex and practical quantum algorithms, whose circuits, however, may involve many more qubits and gate applications than current quantum architectures can actually muster. In this paper, we present Proto-Quipper-R, a circuit description language endowed with a linear dependent type-and-effect system capable of deriving parametric upper bounds on the width of the circuits produced by a program. We prove both the standard type safety results and that the resulting resource analysis is correct with respect to a big-step operational semantics. We also show that our approach is expressive enough to verify realistic quantum algorithms.
Multi-Domain Polarization for Enhancing the Physical Layer Security of MIMO Systems

A novel Physical Layer Security (PLS) framework is conceived for enhancing the security of wireless communication systems by exploiting multi-domain polarization in Multiple-Input Multiple-Output (MIMO) systems. We design a sophisticated key generation scheme based on multi-domain polarization and the corresponding receivers. An in-depth analysis of the system’s secrecy rate is provided, demonstrating the confidentiality of our approach in the presence of eavesdroppers having strong computational capabilities. More explicitly, our simulation results and theoretical analysis corroborate the advantages of the proposed scheme in terms of its bit error rate (BER), block error rate (BLER), and maximum achievable secrecy rate. Our findings indicate that the innovative PLS framework effectively enhances the security and reliability of wireless communication systems. For example, in a <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> MIMO setup, the proposed PLS strategy exhibits an improvement of 2dB compared to conventional MIMO, systems at a BLER of <inline-formula> <tex-math notation="LaTeX">$2\cdot 10^{-5}$ </tex-math></inline-formula> while the eavesdropper’s BLER reaches 1.
Antioxidant Edible Films Based on Pear Juice and Pregelatinized Cassava Starch: Effect of the Carbohydrate Profile at Different Degrees of Pear Ripeness

Edible films based on fruit and vegetable purees combined with different food-grade biopolymeric binding agents (e.g., pectin, gelatin, starch, sodium alginate) are recognized as interesting packaging materials that benefit from the physical, mechanical, and barrier properties of biopolymers as well as the sensory and nutritional properties of purees. In the current contribution, edible antioxidant films based on pear juice and pregelatinized cassava starch were developed. In particular, the suitability of using pregelatinized cassava starch for the non-thermal production of these novel edible films was evaluated. In addition, the effects on the films’ properties derived from the use of pear juice instead of the complete puree, from the content of juice used, and from the carbohydrate composition associated with the ripening of pears were all studied. The produced films were characterized in terms of their total polyphenol content, water sensitivity, and water barrier, optical, mechanical and antioxidant properties. Results showed that the use of pear juice leads to films with enhanced transparency compared with puree-based films, and that juice concentration and carbohydrate composition associated with the degree of fruit ripeness strongly govern the films’ properties. Furthermore, the addition of pregelatinized cassava starch at room temperature discloses a significant and favorable impact on the cohesiveness, lightness, water resistance, and adhesiveness of the pear-juice-based films, which is mainly attributed to the effective interactions established between the starch macromolecules and the juice components.
Optimal control theory for maximum power of Brownian heat engines

The pursuit of achieving the maximum power in microscopic thermal engines has gained increasing attention in recent studies of stochastic thermodynamics. We employ the optimal control theory to study the performance of Brownian heat engines and determine the optimal heat-engine cycles in generic damped situation, which were previously known only in the overdamped and the underdamped limits. These optimal cycles include two isothermal processes, two adiabatic processes, and an extra isochoric relaxation process at the upper stiffness constraint. Our results not only interpolate the optimal cycles between the overdamped and the underdamped limits, but also determine the appropriate friction coefficient of the Brownian heat engine to achieve the maximum power. These findings offer valuable insights for the development of high-performance Brownian heat engines in experimental setups.
Relationship Between Explosive and Effusive Volcanism in the Montes Apenninus Region of the Moon

Lunar Pyroclastic Deposits (LPDs) are sites of explosive volcanism and often occur in areas of effusive volcanism on the Moon. On Earth, it has been observed that most volcanism has both effusive and explosive phases, whereas on the Moon, these two types of volcanism have typically been considered separately. We hypothesize that the relationship between explosive and effusive volcanism on the Moon is similar to what is observed on the Earth, where individual eruptions can experience multiple phases rather than one type of volcanism always preceding another or occurring separately. We present observations from the Moon Mineralogy Mapper detailing compositional relationships between volcanic features in the lunar Montes Apenninus region. We evaluated whether co‐located LPDs and effusive features (e.g., rilles, mare) could have erupted from the same volcanic vent or even at the same time based on their compositional similarities and stratigraphic relationships. We found that the LPDs have varied stratigraphic relationships with co‐located effusive features. We identified LPDs near sinuous rilles that may be related to the formation of the rille, where explosive and effusive volcanism occurred at the same vent (e.g., Mozart Rille), and LPDs that may be unrelated to the rille (e.g., Rimae Bode and Rima Bode LPD). Our results suggest that lunar volcanism can mirror terrestrial volcanism, with explosive and effusive eruptions demonstrating more complex dynamics and relationships than previously thought. This variability suggests that the relationship between LPDs and nearby volcanic features cannot be generalized for studies on their resource potential, eruption styles, or deposit volume.