Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of breast lesions using multi-parametric diffusion MRI and machine learning

Objective. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm. Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11 b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters, D m , α, and β and three IVIM parameters D diff, D perf, and f were estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers. Main Results. The 75% quantile, and median of D m ; 75% quantile of f; mean, median, and skewness of β; kurtosis of D perf; and 75% quantile of D diff were the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers. Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.
Synthesis of patchy particles using gaseous ligands

The collective self-assembly of colloidal particles can be influenced by the composition of the suspending medium, the bulk material of the particles themselves and, importantly, by their surface chemistry. This can be inhomogeneous or patchy to give an orientational dependence to the interaction potential between the particles. These additional constraints to the energy landscape then steer the self-assembly towards configurations of fundamental or applicational interest. We present a novel approach to modify the surface chemistry of colloidal particles to give them two polar patches, using gaseous ligands. In particular, we synthesize polar inverse patchy colloids, i.e., charged particles with two (fluorescent) patches of the opposite charge on their poles. We characterize the dependence of these charges on the pH of the suspending solution.
The generation of continental shelf waves

Assuming zero divergence, the equations of forced long waves in a uniformly rotating, homogeneous ocean are reduced to a single partial differential equation for the stream function. A shelf of exponential slope between a rigid continent and a sea of uniform depth is taken as a model, and certain other assumptions are made which appear physically reasonable. Calculations made on the basis of this simplified theory are in good qualitative agreement with observations of shelf waves, indicating that these waves are generated by the stress of the longshore component of the geostrophic wind.
PDMS-based porous membrane for medical applications: design, development, and fabrication

Organ-on-a-chip (OoC) is one of the most popular microfluidic chips and possesses various industrial, biomedical, and pharmaceutical applications. So far, many types of OoCs with different applications have been fabricated, most of which contain porous membranes useful as cell culture substrates. One of the challenging parts of OoC’s chips is porous membrane fabrication, making it a complex and sensitive process, which is an issue in microfluidic design. These membranes are made of various materials, the same as biocompatible polymer polydimethylsiloxane (PDMS). Besides OoC, these PDMS membranes can be applied in diagnosis, cell separating, trapping, and sorting. In the present study, a new approach has been presented to design and fabricate an efficient porous membrane in terms of time and cost. The fabrication method has fewer steps than previous techniques and employs more conventional approaches. The presented method for membrane fabrication is functional and a novel way to continue producing this product with a single mold and peeling off the membrane on each try. Merely one sacrificial layer (polyvinyl alcohol) and an O2 plasma surface treatment have been used for fabrication. Surface modification and sacrificial layer on the mold ease the peeling of the PDMS membrane. Transferring process of the membrane to the OoC device is explained, and a filtration test is presented to show the functionality of the PDMS membranes. Cell viability is investigated by MTT assay to ensure the PDMS porous membranes are suitable for microfluidic devices. Also, cell adhesion, cell count, and confluency are analyzed, showing almost the same results for the PDMS membranes and the control samples.
Brain-constrained neural modeling explains fast mapping of words to meaning

Abstract Although teaching animals a few meaningful signs is usually time-consuming, children acquire words easily after only a few exposures, a phenomenon termed “fast-mapping. ” Meanwhile, most neural network learning algorithms fail to achieve reliable information storage quickly, raising the question of whether a mechanistic explanation of fast-mapping is possible. Here, we applied brain-constrained neural models mimicking fronto-temporal-occipital regions to simulate key features of semantic associative learning. We compared networks (i) with prior encounters with phonological and conceptual knowledge, as claimed by fast-mapping theory, and (ii) without such prior knowledge. Fast-mapping simulations showed word-specific representations to emerge quickly after 1–10 learning events, whereas direct word learning showed word-meaning mappings only after 40–100 events. Furthermore, hub regions appeared to be essential for fast-mapping, and attention facilitated it, but was not strictly necessary. These findings provide a better understanding of the critical mechanisms underlying the human brain’s unique ability to acquire new words rapidly.
Pericyte stem cells induce Ly6G + cell accumulation and immunotherapy resistance in pancreatic cancer

We report the identification of a cell population that shares pericyte, stromal and stemness features, does not harbor the KrasG12D mutation and drives tumoral growth in vitro and in vivo. We term these cells pericyte stem cells (PeSCs) and define them as CD45−EPCAM−CD29+CD106+CD24+CD44+ cells. We perform studies with p48‐Cre;KrasG12D (KC), pdx1‐Cre;KrasG12D;Ink4a/Arffl/fl (KIC) and pdx1‐Cre;KrasG12D;p53R172H (KPC) and tumor tissues from PDAC and chronic pancreatitis patients. We also perform single‐cell RNAseq analysis and reveal a unique signature of PeSC. Under steady‐state conditions, PeSCs are barely detectable in the pancreas but present in the neoplastic microenvironment both in humans and mice. The coinjection of PeSCs and tumor epithelial cells leads to increased tumor growth, differentiation of Ly6G+ myeloid‐derived suppressor cells, and a decreased amount of F4/80+ macrophages and CD11c+ dendritic cells. This population induces resistance to anti‐PD‐1 immunotherapy when coinjected with epithelial tumor cells. Our data reveal the existence of a cell population that instructs immunosuppressive myeloid cell responses to bypass PD‐1 targeting and thus suggest potential new approaches for overcoming resistance to immunotherapy in clinical settings.
Kinetic Models for Systems of Interacting Agents Withmultiple Microscopic States

We propose and investigate general kinetic models %of Boltzmann type with transition probabilities that can describe the simultaneous change of multiple microscopic states of the interacting agents. These models can be applied to many problems in socio-economic sciences, where individuals may change both their compartment and their characteristic kinetic variable, as for instance kinetic models for epidemics or for international trade with possible transfers of agents. Mathematical properties of our kinetic model are proved, as existence and uniqueness of a solution for the Cauchy problem in suitable Wasserstein spaces. The quasi-invariant asymptotic regime, leading to simpler kinetic Fokker-Planck-type equations, is investigated and commented on in comparison with other existing models. Some numerical tests are performed in order to show time evolution of distribution functions and of meaningful macroscopic fields, even in case of non-constant interaction probabilities.
Dust-buried Compact Sources in the Dwarf Galaxy NGC 4449

Multiwavelength images from the Hubble Space Telescope covering the wavelength range 0.27–1.6 μm show that the central area of the nearby dwarf galaxy NGC 4449 contains several tens of compact sources that are emitting in the hydrogen recombination line Paβ (1.2818 μm) but are only marginally detected in Hα (0.6563 μm) and undetected at wavelengths λ ≤ 0.55 μm. An analysis of the spectral energy distributions (SEDs) of these sources indicates that they are likely relatively young stellar clusters heavily attenuated by dust. The selection function used to identify the sources prevents meaningful statistical analyses of their age, mass, and dust extinction distributions. However, these cluster candidates have ages ∼5–6 Myr and A V > 6 mag, according to their SED fits, and are extremely compact, with typical deconvolved radii of 1 pc. The dusty clusters are located at the periphery of the dark clouds within the galaxy and appear to be partially embedded. Density and pressure considerations indicate that the H ii regions surrounding these clusters may be stalled, and that pre-supernova (pre-SN) feedback has not been able to clear the clusters of their natal cocoons. These findings are in potential tension with existing models that regulate star formation with pre-SN feedback, since pre-SN feedback acts on short timescales, ≲4 Myr, for a standard stellar initial mass function. The existence of a population of dusty stellar clusters with ages >4 Myr, if confirmed by future observations, paints a more complex picture for the role of stellar feedback in controlling star formation.
Modular Deep Learning

Transfer learning has recently become the dominant paradigm of machine learning. Pre-trained models fine-tuned for downstream tasks achieve better performance with fewer labelled examples. Nonetheless, it remains unclear how to develop models that specialise towards multiple tasks without incurring negative interference and that generalise systematically to non-identically distributed tasks. Modular deep learning has emerged as a promising solution to these challenges. In this framework, units of computation are often implemented as autonomous parameter-efficient modules. Information is conditionally routed to a subset of modules and subsequently aggregated. These properties enable positive transfer and systematic generalisation by separating computation from routing and updating modules locally. We offer a survey of modular architectures, providing a unified view over several threads of research that evolved independently in the scientific literature. Moreover, we explore various additional purposes of modularity, including scaling language models, causal inference, programme induction, and planning in reinforcement learning. Finally, we report various concrete applications where modularity has been successfully deployed such as cross-lingual and cross-modal knowledge transfer. Related talks and projects to this survey, are available at https://www.modulardeeplearning. com/.
Defect-induced states, defect-induced phase transition and excitonic states in bent transition metal dichalcogenide (TMD) nanoribbons: density functional vs. many body theory

Two-dimensional (2D) transition metal dichalcogenide (TMD) materials have versatile electronic and optical properties. TMD nanoribbons show interesting properties due to reduced dimensionality, quantum confinement, and edge states. Tang et al. showed that the edge bands evolved with bending can tune the optical properties for various widths of TMD nanoribbons. Defects are commonly present in 2D TMD materials, and can dramatically change the material properties. In this following work, we investigate the interaction between the edge and the defect states in WS2 nanoribbons with line defects under different bending conditions, using density functional theory (DFT). We reveal interesting semiconducting-to-metallic phase transitions, suggesting potential applications in nano-electronics or molecular electronics. We also calculate the optical absorption of the nanoribbons with different defect positions with the many-body GW-BSE (Bethe-Salpeter equation) approach, revealing a tunable optical spectrum and diverse exciton states in the defected TMD nanoribbons.
New optical velocities of nearby dwarf LSB galaxies

We present radial velocities for nearby dwarf galaxy candidates found by Karachentseva & Karachentsev (1998) on the POSS-II films. Out of 118 observed objects, 88 have been detected in the H α line. Their median radial velocity is 1750 km s - 1 . A quarter of the galaxies belong to the Local Volume, having corrected radial velocities V L G < 500 km s - 1 . Some of them are members of the nearby groups around Maffei/IC 342, M 81, and NGC 6946.
ML-driven Hardware Cost Model for MLIR

During early optimization passes, compilers must make predictions for machine-dependent characteristics such as execution unit utilization, number of register spills, latency, throughput etc. to generate better code. Often a hand-written static/analytical hardware cost model is built into the compiler. However, the need for more sophisticated and varied predictions has become more pronounced with the development of deep learning compilers which need to optimize dataflow graphs. Such compilers usually employ a much higher level MLIR form as an IR representation before lowering to traditional LLVM-IR. A static/analytical cost model in such a scenario is cumbersome and error prone as the opcodes represent very high level algebraic/arithmetic operations. Hence, we develop a machine learning-based cost model for high-level MLIR which can predict different target variables of interest such as CPU/GPU/xPU utilization, instructions executed, register usage etc. By considering the incoming MLIR as a text input a la NLP models we can apply well-known techniques from modern NLP research to help predict hardware characteristics more accurately. We expect such precise ML-driven hardware cost models to guide our deep learning compiler in graph level optimizations around operator fusion, local memory allocation, kernel scheduling etc. as well as in many kernel-level optimizations such as loop interchange, LICM and unroll. We report early work-in -progress results of developing such models on high-level MLIR representing dataflow graphs emitted by Pytorch/Tensorflow-like frameworks as well as lower-level dialects like affine. We show that these models can provide reasonably good estimates with low error bounds for various hardware characteristics of interest and can be a go-to mechanism for hardware cost modelling in the future.
Safe Sequential Testing and Effect Estimation in Stratified Count Data

Sequential decision making significantly speeds up research and is more cost-effective compared to fixed-n methods. We present a method for sequential decision making for stratified count data that retains Type-I error guarantee or false discovery rate under optional stopping, using e-variables. We invert the method to construct stratified anytime-valid confidence sequences, where cross-talk between subpopulations in the data can be allowed during data collection to improve power. Finally, we combine information collected in separate subpopulations through pseudo-Bayesian averaging and switching to create effective estimates for the minimal, mean and maximal treatment effects in the subpopulations.
Error Estimation for Random Fourier Features

Random Fourier Features (RFF) is among the most popular and broadly applicable approaches for scaling up kernel methods. In essence, RFF allows the user to avoid costly computations on a large kernel matrix via a fast randomized approximation. However, a pervasive difficulty in applying RFF is that the user does not know the actual error of the approximation, or how this error will propagate into downstream learning tasks. Up to now, the RFF literature has primarily dealt with these uncertainties using theoretical error bounds, but from a user's standpoint, such results are typically impractical -- either because they are highly conservative or involve unknown quantities. To tackle these general issues in a data-driven way, this paper develops a bootstrap approach to numerically estimate the errors of RFF approximations. Three key advantages of this approach are: (1) The error estimates are specific to the problem at hand, avoiding the pessimism of worst-case bounds. (2) The approach is flexible with respect to different uses of RFF, and can even estimate errors in downstream learning tasks. (3) The approach enables adaptive computation, so that the user can quickly inspect the error of a rough initial kernel approximation and then predict how much extra work is needed. Lastly, in exchange for all of these benefits, the error estimates can be obtained at a modest computational cost.
A Supervisory Learning Control Framework for Autonomous & Real-time Task Planning for an Underactuated Cooperative Robotic task

We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to reposition an object within their shared work space. Control of multi-agent systems for manipulation tasks cannot rely on individual control strategies with little to no communication between the agents that serve the common objective through swarming. Instead a coordination strategy is required that queries subtasks to the individual agents. We formulate the problem in a Task And Motion Planning (TAMP) setting, while considering a decomposition strategy that allows us to treat the task and motion planning problems separately. We solve the supervisory planning problem offline using deep Reinforcement Learning techniques resulting into a supervisory policy capable of coordinating the two manipulators into a successful execution of the pick-and-place task. Additionally, a benefit of solving the task planning problem offline is the possibility of real-time (re)planning, demonstrating robustness in the event of subtask execution failure or on-the-fly task changes. The framework achieved zero-shot deployment on the real setup with a success rate that is higher than 90%.
Observational constraints on Tachyon inflation and reheating in f(Q) gravity

In this work we study one of the most appealing string theory-motivated models, we present a tachyonic inflationary model in the recently proposed symmetric teleparallel framework, and examine constraints on tachyon inflation with the exponential potential along with the reheating for a chosen $f(Q)$ gravity model. Considering a reheating phase parametrized by a number of e-folds $N_{re},$ a temperature $T_{re}$, and an equation of state $\omega _{re}$, we relate the reheating parameters as functions of the exponential tachyon potential, $f(Q)$ model, and\ the spectral index $n_{s}$ parameters. We argue that our model predicts inflationary e-folds bounded as $50\leq N\leq 64$. While for the reheating phase, wide ranges of reheating e-folds numbers and temperatures can be obtained as we increase $\omega_{re} $ towards the value $1/4$ according to recent Planck Data.
Self-supervised network distillation: an effective approach to exploration in sparse reward environments

Reinforcement learning can solve decision-making problems and train an agent to behave in an environment according to a predesigned reward function. However, such an approach becomes very problematic if the reward is too sparse and so the agent does not come across the reward during the environmental exploration. The solution to such a problem may be to equip the agent with an intrinsic motivation that will provide informed exploration during which the agent is likely to also encounter external reward. Novelty detection is one of the promising branches of intrinsic motivation research. We present Self-supervised Network Distillation (SND), a class of intrinsic motivation algorithms based on the distillation error as a novelty indicator, where the predictor model and the target model are both trained. We adapted three existing self-supervised methods for this purpose and experimentally tested them on a set of ten environments that are considered difficult to explore. The results show that our approach achieves faster growth and higher external reward for the same training time compared to the baseline models, which implies improved exploration in a very sparse reward environment. In addition, the analytical methods we applied provide valuable explanatory insights into our proposed models.
Harmonic generation in magnetized plasma for electromagnetic wave propagating parallel to external magnetic field

Harmonic generation has always been of fundamental interest in the study of the nonlinear nature of any physical system. In the present study, particle-in-cell simulations have been carried out to explore the harmonic generation of electromagnetic (EM) waves in a magnetized plasma. EM wave propagation is chosen to be parallel to the applied external magnetic field. The simulations show the excitation of odd higher harmonics of right circularly polarized waves and left circularly polarized waves when the incident wave is linearly polarized. The harmonic generation is maximum when the incident EM wave frequency matches the electron cyclotron frequency. When the incident EM wave has a circular polarization, no harmonics get excited. A theoretical understanding of these observations has also been provided. The studies thus show that by appropriately tailoring plasma parameters, EM waves of higher frequencies and the desired nature of circular polarization can be generated.