Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted Ehrhart theory via mixed Hodge modules on toric varieties

We give a cohomological and geometrical interpretation for the weighted Ehrhart theory of a full-dimensional lattice polytope $P$, with Laurent polynomial weights of geometric origin. For this purpose, we calculate the motivic Chern and Hirzebruch characteristic classes of a mixed Hodge module complex $\mathcal{M}$ whose underlying cohomology sheaves are constant on the $\mathbb{T}$-orbits of the toric variety $X_P$ associated to $P$. Besides motivic coefficients, this also applies to the intersection cohomology Hodge module. We introduce a corresponding generalized Hodge $\chi_y$-polynomial of the ample divisor $D_P$ on $X_P$. Motivic properties of these characteristic classes are used to express this Hodge polynomial in terms of a very general weighed lattice point counting and the corresponding weighted Ehrhart theory. We introduce, for such a mixed Hodge modules complex $\mathcal{M}$ on $X$, an Ehrhart polynomial $E_{P,\mathcal{M}}$ generalizing the Hodge polynomial of $\mathcal{M}$ and satisfying a reciprocity formula and a purity formula fitting with the duality for mixed Hodge modules. This Ehrhart polynomial and its properties depend only on a Laurent polynomial weight function on the faces $Q$ of $P$. In the special case of the intersection cohomology mixed Hodge module, the weight function corresponds to Stanley's $g$-function of the polar polytope of $P$, hence it depends only on the combinatorics of $P$. In particular, we obtain a combinatorial formula for the intersection cohomology signature.
Compensating for charge sharing by a deep-learning method: a preliminary experimental study

Photon counting detectors (PCDs) bring valuable advantages to diagnostic computed tomography (CT), including lower noise and higher resolution than energy integrating detectors. However, there are still several nonideal factors preventing PCDs from meeting people's expectations, for example, charge sharing and pile up. In this paper, we did some preliminary work on charge sharing and conducted an experimental study using an XCounter PCD to compare the effects of no anti-coincidence, anti-coincidence by hardware and charge sharing compensation by a deep learning method. In our results, a smaller bias and standard deviation are obtained from deep learning method than directly from no-anti-coincidence mode of the detector. Our network also outperforms the anti-coincidence mode of the detector in the low energy bin and has smaller standard deviation in the high energy bin. The results validate that a deep learning method is suitable to compensate for charge sharing.
Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems

Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced. Unfortunately, both performance and solution feasibility can be significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. %as well as infeasibility. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon Control (RHC) approach leveraging Model Predictive Control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method.
Directional dual-tree rational-dilation complex wavelet transform

Dyadic discrete wavelet transform (DWT) has been used successfully in processing signals having non-oscillatory transient behaviour. However, due to the low Q-factor property of their wavelet atoms, the dyadic DWT is less effective in processing oscillatory signals such as embolic signals (ESs). ESs are extracted from quadrature Doppler signals, which are the output of Doppler ultrasound systems. In order to process ESs, firstly, a pre-processing operation known as phase filtering for obtaining directional signals from quadrature Doppler signals must be employed. Only then, wavelet based methods can be applied to these directional signals for further analysis. In this study, a directional dual-tree rational-dilation complex wavelet transform, which can be applied directly to quadrature signals and has the ability of extracting directional information during analysis, is introduced.
The AKT–mTOR axis regulates de novo differentiation of CD4+Foxp3+ cells

CD4+Foxp3+ regulatory T (T reg) cells play an essential role in maintaining immunological tolerance via their suppressive function on conventional CD4+ T (Tconv) cells. Repertoire studies suggest that distinct T cell receptor signaling pathways lead to T reg differentiation, but the signals that regulate T reg specification are largely unknown. We identify AKT as a strong repressor of entry into the T reg phenotype in vitro and in vivo. A constitutively active allele of AKT substantially diminished TGF-β–induced Foxp3 expression in a kinase-dependent manner and via a rapamycin-sensitive pathway, implicating the AKT–mammalian target of rapamycin axis. The observed impairment in Foxp3 induction was part of a broad dampening of the typical T reg transcriptional signature. Expression of active AKT at a stage before Foxp3 turn on during normal T reg differentiation in the thymus selectively impaired differentiation of CD4+Foxp3+ cells without any alteration in the positive selection of Tconv. Activated AKT, in contrast, did not affect established Foxp3 expression in T reg cells. These results place AKT at a nexus of signaling pathways whose proper activation has a strong and broad impact on the onset of T reg specification.
Grad-CAMO: Learning Interpretable Single-Cell Morphological Profiles from 3D Cell Painting Images

Despite their black-box nature, deep learning models are extensively used in image-based drug discovery to extract feature vectors from single cells in microscopy images. To better understand how these networks perform representation learning, we employ visual explainability techniques (e.g., Grad-CAM). Our analyses reveal several mechanisms by which supervised models cheat, exploiting biologically irrelevant pixels when extracting morphological features from images, such as noise in the background. This raises doubts regarding the fidelity of learned single-cell representations and their relevance when investigating downstream biological questions. To address this misalignment between researcher expectations and machine behavior, we introduce Grad-CAMO, a novel single-cell interpretability score for supervised feature extractors. Grad-CAMO measures the proportion of a model's attention that is concentrated on the cell of interest versus the background. This metric can be assessed per-cell or averaged across a validation set, offering a tool to audit individual features vectors or guide the improved design of deep learning architectures. Importantly, Grad-CAMO seamlessly integrates into existing workflows, requiring no dataset or model modifications, and is compatible with both 2D and 3D Cell Painting data. Additional results are available at https://github.com/eigenvivek/Grad-CAMO.
Submitted to Ieee Transactions on Cybernetics 1

—Motion behaviors of a rigid body can be characterized by a 6-dimensional motion trajectory, which contains position vectors of a reference point on the rigid body and rotations of this rigid body over time. This paper devises a Rotation and Relative Velocity (RRV) descriptor by exploring the local translational and rotational invariants of motion trajectories of rigid bodies, which is insensitive to noise, invariant to rigid transformation and scaling. A flexible metric is also introduced to measure the distance between two RRV descriptors. The RRV descriptor is then applied to characterize motions of a human body skeleton modeled as articulated interconnections of multiple rigid bodies. To illustrate the descriptive ability of the RRV de-scriptor, we explore it for different rigid body motion recognition tasks. The experimental results on benchmark datasets demonstrate that this simple RRV descriptor outperforms the previous ones regarding recognition accuracy without increasing computational cost.
Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy

We propose an unfolded accelerated projected-gradient descent procedure to estimate model and algorithmic parameters for image super-resolution and molecule localization problems in image microscopy. The variational lower-level constraint enforces sparsity of the solution and encodes different noise statistics (Gaussian, Poisson), while the upper-level cost assesses optimality w.r. t. ~the task considered. In more detail, a standard $\ell_2$ cost is considered for image reconstruction (e.g., deconvolution/super-resolution, semi-blind deconvolution) problems, while a smoothed $\ell_1$ is employed to assess localization precision in some exemplary fluorescence microscopy problems exploiting single-molecule activation. Several numerical experiments are reported to validate the proposed approach on synthetic and realistic ISBI data.
Malware detection in Android by network traffic analysis

A common behavior of mobile malware is transferring sensitive information of the cell phone user to malicious remote servers. In this paper, we describe and demonstrate in full detail, a method for detecting malware based on this behavior. For this, we first create an App-URL table that logs all attempts made by all applications to communicate with remote servers. Each entry in this log preserves the application id and the URI that the application contacted. From this log, with the help of a reliable and comprehensive domain blacklist, we can detect rogue applications that communicate with malicious domains. We further propose a behavioral analysis method using syscall tracing. Our work can be integrated with be behavioral analysis to build an intelligent malware detection model.
Berry Twist: a Twisting-Tube Soft Robotic Gripper for Blackberry Harvesting

As global demand for fruits and vegetables continues to rise, the agricultural industry faces challenges in securing adequate labor. Robotic harvesting devices offer a promising solution to solve this issue. However, harvesting delicate fruits, notably blackberries, poses unique challenges due to their fragility. This study introduces and evaluates a prototype robotic gripper specifically designed for blackberry harvesting. The gripper features an innovative fabric tube mechanism employing motorized twisting action to gently envelop the fruit, ensuring uniform pressure application and minimizing damage. Three types of tubes were developed, varying in elasticity and compressibility using foam padding, spandex, and food-safe cotton cheesecloth. Performance testing focused on assessing each gripper's ability to detach and release blackberries, with emphasis on quantifying damage rates. Results indicate the proposed gripper achieved an 82% success rate in detaching blackberries and a 95% success rate in releasing them, showcasing the promised potential for robotic harvesting applications.
Resolution Limit of Single-Photon LiDAR

Single-photon Light Detection and Ranging (LiDAR) systems are often equipped with an array of detectors for improved spatial resolution and sensing speed. However, given a fixed amount of flux produced by the laser transmitter across the scene, the per-pixel Signal-to-Noise Ratio (SNR) will decrease when more pixels are packed in a unit space. This presents a fundamental trade-off between the spatial resolution of the sensor array and the SNR received at each pixel. Theoretical characterization of this fundamental limit is explored. By deriving the photon arrival statistics and introducing a series of new approximation techniques, the Mean Squared Error (MSE) of the maximum-likelihood estimator of the time delay is derived. The theoretical predictions align well with simulations and real data.
ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition

In this work, we revisit the problem of semi-supervised named entity recognition (NER) focusing on extremely light supervision, consisting of a lexicon containing only 10 examples per class. We introduce ELLEN, a simple, fully modular, neuro-symbolic method that blends fine-tuned language models with linguistic rules. These rules include insights such as ''One Sense Per Discourse'', using a Masked Language Model as an unsupervised NER, leveraging part-of-speech tags to identify and eliminate unlabeled entities as false negatives, and other intuitions about classifier confidence scores in local and global context. ELLEN achieves very strong performance on the CoNLL-2003 dataset when using the minimal supervision from the lexicon above. It also outperforms most existing (and considerably more complex) semi-supervised NER methods under the same supervision settings commonly used in the literature (i.e., 5% of the training data). Further, we evaluate our CoNLL-2003 model in a zero-shot scenario on WNUT-17 where we find that it outperforms GPT-3.5 and achieves comparable performance to GPT-4. In a zero-shot setting, ELLEN also achieves over 75% of the performance of a strong, fully supervised model trained on gold data. Our code is available at: https://github.com/hriaz17/ELLEN.
S100P-Derived RAGE Antagonistic Peptide Reduces Tumor Growth and Metastasis

Purpose: The receptor for advanced glycation end products (RAGE) contributes to multiple pathologies, including diabetes, arthritis, neurodegenerative diseases, and cancer. Despite the obvious need, no RAGE inhibitors are in common clinical use. Therefore, we developed a novel small RAGE antagonist peptide (RAP) that blocks activation by multiple ligands. Experimental Design: RAGE and its ligands were visualized by immunohistochemical analysis of human pancreatic tissues, and siRNA was used to analyze their functions. Interactions between RAGE and S100P, S100A4, and HMGB-1 were measured by ELISA. Three S100P-derived small antagonistic peptides were designed, synthesized, and tested for inhibition of RAGE binding. The effects of the peptide blockers on NFκB-luciferase reporter activity was used to assess effects on RAGE-mediated signaling. The most effective peptide was tested on glioma and pancreatic ductal adenocarcinoma (PDAC) models. Results: Immunohistochemical analysis confirmed the expression of RAGE and its ligands S100P, S100A4, and HMGB-1 in human PDAC. siRNA silencing of RAGE or its ligands reduced the growth and migration of PDAC cells in vitro. The most effective RAP inhibited the interaction of S100P, S100A4, and HMGB-1 with RAGE at micromolar concentrations. RAP also reduced the ability of the ligands to stimulate RAGE activation of NFκB in cancer cells in vitro and in vivo. Importantly, systemic in vivo administration of RAP reduced the growth and metastasis of pancreatic tumors and also inhibited glioma tumor growth. Conclusion: RAP shows promise as a tool for the investigation of RAGE function and as an in vivo treatment for RAGE-related disorders. Clin Cancer Res; 18(16); 4356–64. ©2012 AACR.
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields

The segmentation and tracking of living cells play a vital role within the biomedical domain, particularly in cancer research, drug development, and developmental biology. These are usually tedious and time-consuming tasks that are traditionally done by biomedical experts. Recently, to automatize these processes, deep learning based segmentation and tracking methods have been proposed. These methods require large-scale datasets and their full potential is constrained by the scarcity of annotated data in the biomedical imaging domain. To address this limitation, we propose Biomedical Video Diffusion Model (BVDM), capable of generating realistic-looking synthetic microscopy videos. Trained only on a single real video, BVDM can generate videos of arbitrary length with pixel-level annotations that can be used for training data-hungry models. It is composed of a denoising diffusion probabilistic model (DDPM) generating high-fidelity synthetic cell microscopy images and a flow prediction model (FPM) predicting the non-rigid transformation between consecutive video frames. During inference, initially, the DDPM imposes realistic cell textures on synthetic cell masks which are generated based on real data statistics. The flow prediction model predicts the flow field between consecutive masks and applies that to the DDPM output from the previous time frame to create the next one while keeping temporal consistency. BVDM outperforms state-of-the-art synthetic live cell microscopy video generation models. Furthermore, we demonstrate that a sufficiently large synthetic dataset enhances the performance of cell segmentation and tracking models compared to using a limited amount of available real data.
Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transfer

In this paper, we propose a method to improve the accuracy of speech emotion recognition (SER) by using vision transformer (ViT) to attend to the correlation of frequency (y-axis) with time (x-axis) in spectrogram and transferring positional information between ViT through knowledge transfer. The proposed method has the following originality i) We use vertically segmented patches of log-Mel spectrogram to analyze the correlation of frequencies over time. This type of patch allows us to correlate the most relevant frequencies for a particular emotion with the time they were uttered. ii) We propose the use of image coordinate encoding, an absolute positional encoding suitable for ViT. By normalizing the x, y coordinates of the image to -1 to 1 and concatenating them to the image, we can effectively provide valid absolute positional information for ViT. iii) Through feature map matching, the locality and location information of the teacher network is effectively transmitted to the student network. Teacher network is a ViT that contains locality of convolutional stem and absolute position information through image coordinate encoding, and student network is a structure that lacks positional encoding in the basic ViT structure. In feature map matching stage, we train through the mean absolute error (L1 loss) to minimize the difference between the feature maps of the two networks. To validate the proposed method, three emotion datasets (SAVEE, EmoDB, and CREMA-D) consisting of speech were converted into log-Mel spectrograms for comparison experiments. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods in terms of weighted accuracy while requiring significantly fewer floating point operations (FLOPs). Overall, the proposed method offers an promising solution for SER by providing improved efficiency and performance.
$\Phi^p$ Amplitudes from the Positive Tropical Grassmannian: Triangulations of Extended Diagrams

The global Schwinger formula, introduced by Cachazo and Early as a single integral over the positive tropical Grassmannian, provides a way to uncover properties of scattering amplitudes which are hard to see in their standard Feynman diagram formulation. In a recent work, Cachazo and one of the authors extended the global Schwinger formula to general $\phi^p$ theories. When $p=4$, it was conjectured that the integral decomposes as a sum over cones which are in bijection with non-crossing chord diagrams, and further that these can be obtained by finding the zeroes of a piece-wise linear function, $H(x)$. In this note we give a proof of this conjecture. We also present a purely combinatorial way of computing $\phi^p$ amplitudes by triangulating a trivial extended version of non-crossing $(p-2)$-chord diagrams, called extended diagrams, and present a proof of the bijection between triangulated extended diagrams and Feynman diagrams when $p=4$. This is reminiscent of recent constructions using Stokes polytopes and accordiohedra. However, the $\phi^p$ amplitude is now partitioned by a new collection of objects, each of which characterizes a polyhedral cone in the positive tropical Grassmannian in the form of an associahedron or of an intersection of two associahedra. Moreover, we comment on the bijection between extended diagrams and double-ordered biadjoint scalar amplitudes. We also conjecture the form of the general piece-wise linear function, $H^{\phi^p}(x)$, whose zeroes generate the regions in which the $\phi^p$ global Schwinger formula decomposes into.
Wormhole solutions under the effect of dark matter in $f(R,L_m)$ gravity

In the background of $f(R, L_m)$ gravity, this work investigates three distinct dark matter halo profiles to test the possibility of generalised wormhole geometry within the galactic halo regions. The current study aims to accomplish these goals by examining various dark matter profiles including Universal Rotation Curves (URC), Navarro-Frenk-White (NFW) model-I, and NFW model-II inside two distinct $f(R, L_m)$ gravity models. According to the $f(R, L_m) = \frac{R}{2} + L_m^\alpha$ model, the DM halo density profiles produce suitable shape functions that meet all the necessary requirements for exhibiting the wormhole geometries with appropriate choice of free parameters. In addition, to examine DM profiles under the $f(R, L_m) = \frac{R}{2} + (1 + \lambda R)L_m$ model, we consider a specific shape function. Further, we observed that the derived solution from both two models violates the null energy constraints, confirming that the DM supports wormholes to maintain in the galactic halo.
SOURCE SEPARATION OF TEMPORALLY CORRELATED SOURCE USING BANK OF BAND PASS FILTERS

This paper introduces a new source separation algorithms exploiting the difference in the spectra shapes of the source signals. The proposed approach relies only on second-order statistics and estimates the mixing matrix by using eigenvalue decomposition of covariance matrix of prewithened sensor signals or alternatively an input output identification procedure using as inputs linear band pass filtered versions of the estimated colored sources. An adaptive implementation of the proposed technique is presented. The new algorithm shows to be computationally very simple and efficient. In addition and in contrast to other existing techniques, the covariance of the noise do not need to be modeled. The effectiveness of the proposed method is illustrated by some numerical simulations.