Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Particle Separation Through Deterministic Lateral Displacement

organism contains, within its regulatory system, self-sustaining pacemaker circuitry that coordinates progression of the cell cycle. At a high level, all these cell-cycle control circuits exhibit parallels in mechanism and function. For example, phosphoryl-ation pathways are interconnected with tran-scriptional regulatory networks, critical positive and negative feedback pathways, and targeted proteolysis. These mechanisms lead to the cyclical presence and/or activation state of key regulatory proteins that in turn control the modular functions that implement the cell cycle. The specific cell-cycle regulatory proteins in bacteria and eukaryotic cells are not homologous; rather, the homologies are at the level of system design. 1 We report on a microfluidic particle-separation device that makes use of the asymmetric bifurcation of laminar flow around obstacles. A particle chooses its path deterministically on the basis of its size. All particles of a given size follow equivalent migration paths, leading to high resolution. The microspheres of 0.8, 0.9, and 1.0 micrometers that were used to characterize the device were sorted in 40 seconds with a resolution of ϳ10 nanometers, which was better than the time and resolution of conventional flow techniques. Bacterial artificial chromosomes could be separated in 10 minutes with a resolution of ϳ12%. Particles in solution are usually separated according to size by exclusion or hydrodynamic chromatography (1–5). In the former, a sample mixture is injected at one end of a tube packed with porous beads and then washed through the tube (1–3). Particles smaller than the pore size enter the beads, which lengthens their migration path, and so they are on average eluted later than larger particles. Zones of particles of a given size broaden, however, because particles in each zone take many different paths, leading to different retention times. This multipath effect reduces the resolution of size-exclusion chromatography (6–8). In hydrodynamic chromatography, a sample mixture is driven through a capillary by hy-drodynamic flow, which has a parabolic flow profile (4, 5). Large particles cannot intercept the low-velocity fluid near the capillary wall, and thus on average move faster and become separated from small particles. Multipath effects also limit the resolution of hydrodynamic chromatography, because each migration path samples different velocities in the parabolic flow (9, 10). Recently, devices that exploit both techniques have been miniaturized with the use of microfabrication technology (5, 10–12). Mi-crofabricated devices have also been designed that inherently rely on diffusion for separation. Particle mixtures are either repeatedly subject to spatially …
Report of the Science Community Workshop on the proposed First Sample Depot for the Mars Sample Return Campaign

The Mars 2020/Mars Sample Return (MSR) Sample Depot Science Community Workshop was held on September 28 and 30, 2022, to assess the Scientifically‐Return Worthy (SRW) value of the full collection of samples acquired by the rover Perseverance at Jezero Crater, and of a proposed subset of samples to be left as a First Depot at a location within Jezero Crater called Three Forks. The primary outcome of the workshop was that the community is in consensus on the following statement: The proposed set of ten sample tubes that includes seven rock samples, one regolith sample, one atmospheric sample, and one witness tube constitutes a SRW collection that: (1) represents the diversity of the explored region around the landing site, (2) covers partially or fully, in a balanced way, all of the International MSR Objectives and Samples Team scientific objectives that are applicable to Jezero Crater, and (3) the analyses of samples in this First Depot on Earth would be of fundamental importance, providing a substantial improvement in our understanding of Mars. At the conclusion of the meeting, there was overall community support for forming the First Depot as described at the workshop and placing it at the Three Forks site. The community also recognized that the diversity of the Rover Cache (the sample collection that remains on the rover after placing the First Depot) will significantly improve with the samples that are planned to be obtained in the future by the Perseverance rover and that the Rover Cache is the primary target for MSR to return to Earth.
Inflammatory breast cancer biomarker identification by simultaneous TGIRT-seq profiling of coding and non-coding RNAs in tumors and blood

Inflammatory breast cancer (IBC) is the most aggressive and lethal breast cancer subtype, but lags in biomarker identification. Here, we used an improved Thermostable Group II Intron Reverse Transcriptase RNA sequencing (TGIRT-seq) method to simultaneously profile coding and non-coding RNAs from tumors, PBMCs, and plasma of IBC and non-IBC patients and healthy donors. Besides RNAs from known IBC-relevant genes, we identified hundreds of other overexpressed coding and non-coding RNAs (p[≤]0.001) in IBC tumors and PBMCs, including higher proportions with elevated intron-exon depth ratios (IDRs), likely reflecting enhanced transcription resulting in accumulation of intronic RNAs. As a consequence, differentially represented protein-coding gene RNAs in IBC plasma were largely intron RNA fragments, whereas those in healthy donor and non-IBC plasma were largely fragmented mRNAs. Potential IBC biomarkers in plasma included T-cell receptor pre-mRNA fragments traced to IBC tumors and PBMCs; intron RNA fragments correlated with high IDR genes; and LINE-1 and other retroelement RNAs that we found globally up-regulated in IBC and preferentially enriched in plasma. Our findings provide new insights into IBC and demonstrate advantages of broadly analyzing transcriptomes for biomarker identification. The RNA-seq and data analysis methods developed for this study may be broadly applicable to other diseases.
Application of PSO Algorithm in 2D Otsu Image Segmentation

In image segmentation algorithms,the selection of optimal threshold is the key to segmentation.However,most threshold selection methods adopt the mode of exhaustive search so that the operation efficiency is low,the capability of noise resisting is weak,and error segmentation happens easily in these methods.A new algorithm of select optimal threshold is proposed based on chaos particle swarm optimization.The first step of image threshold method discussed in this paper is image denoising and making two-dimensional histogram of the image.The second one is to select appropriate values of gray level as initial population according to the two-dimensional histograms.Finally,the output of this algorithm is the optimal threshold.Using this threshold to partition off the pixels,image segmentation is implemented.Experimental results show that the proposed method can not only obtain ideal segmentation results but also decrease the computation cost reasonably,and it is suitable for real time application and conducive to improving the realtmime image processing.
The emergence of groups in the evolution of friendship networks

Friendship networks usually show a certain degree of segmentation: subgroups of friends. The explanation of the emergence of such groups from initially dyadic pair friendships is a difficult but important problem. In this paper we attempt to provide a first contribution to the explanation of subgroup formation in friendship networks by using the LS set as a definition for a friendship group. We construct a dynamic individual oriented model of friendship formation and provide preliminary simulation results that give an idea of how to continue the process of explaining group formation.
The application of simultaneous mainstem bronchus dilation with pulmonary artery stenting in the context of hypoplastic left heart syndrome

Abstract Hypoplastic left heart syndrome (HLHS) is a congenital diagnosis that necessitates immediate intervention at the beginning of life to ensure survival past infancy and to optimize left-side cardiac function. Often, these required procedures can lead to deleterious side effects and resultant complications. In this case report, we present a 15-month-old patient with HLHS who underwent multiple procedures, including two aortic arch surgeries. After the interventions, the patient experienced left main pulmonary bronchus compression along with pulmonary artery stenosis. In this case, we outline an approach to performing vascular dilation without compromise of airway patency.
Partial quadratic eigenvalue assignment in vibrating systems using acceleration and velocity feedback

The partial quadratic eigenvalue assignment problem (PQEVAP) is to shift a few undesired eigenvalues of a damped vibrating system to suitably chosen locations, while leaving the remaining eigenvalues and corresponding eigenvectors unchanged. In this paper, an algorithm for solving PQEVAPs and the minimum norm PQEVAP (MNPQEVAP) using acceleration and velocity feedback is proposed. It is shown that solving the PQEVAP here is transformed into solving an eigenvalue assignment of a linear system of a much lower order. Furthermore, the MNPQEVAP here can be efficiently solved by a gradient-based unconstrained optimization method with the derived gradient formula. This algorithm works directly on the second-order system model, and requires the knowledge of only the open-loop eigenvalues to be replaced and their corresponding eigenvectors. Lastly, through two numerical examples, the results of solving the MNPQEVAP under two different combined feedback signals, velocity and displacement signals, and acceleration and velocity signals, are compared from two points of view, i.e. the F-norms of their feedback matrices and the active control energy required from the actuators.
A Three-regime Model of Network Pruning

Recent work has highlighted the complex influence training hyperparameters, e.g., the number of training epochs, can have on the prunability of machine learning models. Perhaps surprisingly, a systematic approach to predict precisely how adjusting a specific hyperparameter will affect prunability remains elusive. To address this gap, we introduce a phenomenological model grounded in the statistical mechanics of learning. Our approach uses temperature-like and load-like parameters to model the impact of neural network (NN) training hyperparameters on pruning performance. A key empirical result we identify is a sharp transition phenomenon: depending on the value of a load-like parameter in the pruned model, increasing the value of a temperature-like parameter in the pre-pruned model may either enhance or impair subsequent pruning performance. Based on this transition, we build a three-regime model by taxonomizing the global structure of the pruned NN loss landscape. Our model reveals that the dichotomous effect of high temperature is associated with transitions between distinct types of global structures in the post-pruned model. Based on our results, we present three case-studies: 1) determining whether to increase or decrease a hyperparameter for improved pruning; 2) selecting the best model to prune from a family of models; and 3) tuning the hyperparameter of the Sharpness Aware Minimization method for better pruning performance.
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark

Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understanding. However, there is still a lack of high-quality motion datasets for small bio-robotics, which presents more challenging scenarios for long-term movement prediction and behavior control based on third-person observation. In this study, we introduce RatPose, a bio-robot motion prediction dataset constructed by considering the influence factors of individuals and environments based on predefined annotation rules. To enhance the robustness of motion prediction against these factors, we propose a Dual-stream Motion-Scenario Decoupling (\textit{DMSD}) framework that effectively separates scenario-oriented and motion-oriented features and designs a scenario contrast loss and motion clustering loss for overall training. With such distinctive architecture, the dual-branch feature flow information is interacted and compensated in a decomposition-then-fusion manner. Moreover, we demonstrate significant performance improvements of the proposed \textit{DMSD} framework on different difficulty-level tasks. We also implement long-term discretized trajectory prediction tasks to verify the generalization ability of the proposed dataset.
A Game of Competition for Risk

In this study, we present models where participants strategically select their risk levels and earn corresponding rewards, mirroring real-world competition across various sectors. Our analysis starts with a normal form game involving two players in a continuous action space, confirming the existence and uniqueness of a Nash equilibrium and providing an analytical solution. We then extend this analysis to multi-player scenarios, introducing a new numerical algorithm for its calculation. A key novelty of our work lies in using regret minimization algorithms to solve continuous games through discretization. This groundbreaking approach enables us to incorporate additional real-world factors like market frictions and risk correlations among firms. We also experimentally validate that the Nash equilibrium in our model also serves as a correlated equilibrium. Our findings illuminate how market frictions and risk correlations affect strategic risk-taking. We also explore how policy measures can impact risk-taking and its associated rewards, with our model providing broader applicability than the Diamond-Dybvig framework. We make our methodology and open-source code available at https://github.com/louisabraham/cfrgame Finally, we contribute methodologically by advocating the use of algorithms in economics, shifting focus from finite games to games with continuous action sets. Our study provides a solid framework for analyzing strategic interactions in continuous action games, emphasizing the importance of market frictions, risk correlations, and policy measures in strategic risk-taking dynamics.
SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual Learning

Deep neural networks (DNNs) struggle to learn in dynamic environments since they rely on fixed datasets or stationary environments. Continual learning (CL) aims to address this limitation and enable DNNs to accumulate knowledge incrementally, similar to human learning. Inspired by how our brain consolidates memories, a powerful strategy in CL is replay, which involves training the DNN on a mixture of new and all seen classes. However, existing replay methods overlook two crucial aspects of biological replay: 1) the brain replays processed neural patterns instead of raw input, and 2) it prioritizes the replay of recently learned information rather than revisiting all past experiences. To address these differences, we propose SHARP, an efficient neuro-inspired CL method that leverages sparse dynamic connectivity and activation replay. Unlike other activation replay methods, which assume layers not subjected to replay have been pretrained and fixed, SHARP can continually update all layers. Also, SHARP is unique in that it only needs to replay few recently seen classes instead of all past classes. Our experiments on five datasets demonstrate that SHARP outperforms state-of-the-art replay methods in class incremental learning. Furthermore, we showcase SHARP's flexibility in a novel CL scenario where the boundaries between learning episodes are blurry. The SHARP code is available at \url{https://github.com/BurakGurbuz97/SHARP-Continual-Learning}.
DAWN: An Improved BFS Algorithm for Solving Shortest Path Problems on Unweighted Graph

The shortest paths problem is a fundamental challenge in graph theory, with a broad range of potential applications. However, traditional serial algorithms often struggle to adapt to large-scale graphs. To address this issue, researchers have explored parallel computing as a solution. We propose an improved BFS (Breadth-First Search) algorithm achieving higher parallelism and scalability, which requires $O(E_{wcc}(i))$ and $O(S_{wcc} \cdot E_{wcc})$ times for SSSP and APSP problems, respectively, where $S_{wcc}$ and $E_{wcc}$ denote the number of nodes and edges included in the largest weakly connected component in graph. To evaluate the effectiveness of the novel algorithm, we tested it using real graphs from Stanford Network Analysis Platform and SuiteSparse Matrix Collection. Our algorithm outperformed the BFS and $\Delta$-stepping implementations from Gunrock, achieving a speedup of 313.763$\times$ and 338.862$\times$, respectively.
Using VGG16 Algorithms for classification of lung cancer in CT scans Image

Lung cancer is the leading reason behind cancer-related deaths within the world. Early detection of lung nodules is vital for increasing the survival rate of cancer patients. Traditionally, physicians should manually identify the world suspected of getting carcinoma. When developing these detection systems, the arbitrariness of lung nodules' shape, size, and texture could be a challenge. Many studies showed the applied of computer vision algorithms to accurate diagnosis and classification of lung nodules. A deep learning algorithm called the VGG16 was developed during this paper to help medical professionals diagnose and classify carcinoma nodules. VGG16 can classify medical images of carcinoma in malignant, benign, and healthy patients. This paper showed that nodule detection using this single neural network had 92.08% sensitivity, 91% accuracy, and an AUC of 93%.
Learning Linear Groups in Neural Networks

Employing equivariance in neural networks leads to greater parameter efficiency and improved generalization performance through the encoding of domain knowledge in the architecture; however, the majority of existing approaches require an a priori specification of the desired symmetries. We present a neural network architecture, Linear Group Networks (LGNs), for learning linear groups acting on the weight space of neural networks. Linear groups are desirable due to their inherent interpretability, as they can be represented as finite matrices. LGNs learn groups without any supervision or knowledge of the hidden symmetries in the data and the groups can be mapped to well known operations in machine learning. We use LGNs to learn groups on multiple datasets while considering different downstream tasks; we demonstrate that the linear group structure depends on both the data distribution and the considered task.
Black-Box Anomaly Attribution

When the prediction of a black-box machine learning model deviates from the true observation, what can be said about the reason behind that deviation? This is a fundamental and ubiquitous question that the end user in a business or industrial AI application often asks. The deviation may be due to a sub-optimal black-box model, or it may be simply because the sample in question is an outlier. In either case, one would ideally wish to obtain some form of attribution score -- a value indicative of the extent to which an input variable is responsible for the anomaly. In the present paper we address this task of ``anomaly attribution,'' particularly in the setting in which the model is black-box and the training data are not available. Specifically, we propose a novel likelihood-based attribution framework we call the ``likelihood compensation (LC),'' in which the responsibility score is equated with the correction on each input variable needed to attain the highest possible likelihood. We begin by showing formally why mainstream model-agnostic explanation methods, such as the local linear surrogate modeling and Shapley values, are not designed to explain anomalies. In particular, we show that they are ``deviation-agnostic,'' namely, that their explanations are blind to the fact that there is a deviation in the model prediction for the sample of interest. We do this by positioning these existing methods under the unified umbrella of a function family we call the ``integrated gradient family. '' We validate the effectiveness of the proposed LC approach using publicly available data sets. We also conduct a case study with a real-world building energy prediction task and confirm its usefulness in practice based on expert feedback.
A machine learning approach to the prediction of heat-transfer coefficients in micro-channels

The accurate prediction of the two-phase heat transfer coefficient (HTC) as a function of working fluids, channel geometries and process conditions is key to the optimal design and operation of compact heat exchangers. Advances in artificial intelligence research have recently boosted the application of machine learning (ML) algorithms to obtain data-driven surrogate models for the HTC. For most supervised learning algorithms, the task is that of a nonlinear regression problem. Despite the fact that these models have been proven capable of outperforming traditional empirical correlations, they have key limitations such as overfitting the data, the lack of uncertainty estimation, and interpretability of the results. To address these limitations, in this paper, we use a multi-output Gaussian process regression (GPR) to estimate the HTC in microchannels as a function of the mass flow rate, heat flux, system pressure and channel diameter and length. The model is trained using the Brunel Two-Phase Flow database of high-fidelity experimental data. The advantages of GPR are data efficiency, the small number of hyperparameters to be trained (typically of the same order of the number of input dimensions), and the automatic trade-off between data fit and model complexity guaranteed by the maximization of the marginal likelihood (Bayesian approach). Our paper proposes research directions to improve the performance of the GPR-based model in extrapolation.
Dust and inclination corrected star-formation and interstellar medium scaling relations in nearby galaxies

Following from our recent work, we present a detailed analysis of star-formation and interstellar medium (ISM) scaling relations, done on a representative sample of nearby galaxies. Hα images are analysed in order to derive the integrated galaxy luminosity, known as a more instantenous and accurate star-formation rate (SFR) tracer, and the required photometric and structural parameters. Dust and inclination corrected Hα luminosities, SFRs and related quantities are determined using a self-consistent method based on previous work prescriptions, which do not require the assumption of a dust attenuation curve and use of Balmer decrements (or other hydrogen recombination lines) to estimate the dust attenuation, with the advantage of determining dust opacities and dust masses along the way. We investigate the extent to which dust and inclination effects bias the specific parameters of these relations, the scatter and degree of correlation, and which relations are fundamental or are just a consequence of others. Most of our results are consistent within errors with other similar studies, while others come in opposition or are inconclusive. By comparing the B band optical and Hα (star-forming) discs scalelengths, we found on average, the star-formation distribution to be more extended than the stellar continuum emission one (the ratio being 1.10), this difference increasing with stellar mass. Similarly, more massive galaxies have a more compact stellar emission surface density than the star-formation one (average ratio of 0.77). The method proposed can be applied in larger scale studies of star-formation and ISM evolution, for normal low to intermediate redshift galaxies.