Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional-LSTM for Multi-Image to Single Output Medical Prediction

Medical head CT-scan imaging has been successfully combined with deep learning for medical diagnostics of head diseases and lesions[1]. State of the art classification models and algorithms for this task usually are based on 3d convolution layers for volumetric data on a supervised learning setting (1 input volume, 1 prediction per patient) or 2d convolution layers in a supervised setting (1 input image, 1 prediction per image). However a very common scenario in developing countries is to have the volume metadata lost due multiple reasons for example formatting conversion in images (for example .dicom to jpg), in this scenario the doctor analyses the collection of images and then emits a single diagnostic for the patient (with possibly an unfixed and variable number of images per patient) , this prevents it from being possible to use state of the art 3d models, but also is not possible to convert it to a supervised problem in a (1 image,1 diagnostic) setting because different angles or positions of the images for a single patient may not contain the disease or lesion. In this study we propose a solution for this scenario by combining 2d convolutional[2] models with sequence models which generate a prediction only after all images have been processed by the model for a given patient \(i\), this creates a multi-image to single-diagnostic setting \(y^i=f(x_1,x_2,.,x_n)\) where \(n\) may be different between patients. The experimental results demonstrate that it is possible to get a multi-image to single diagnostic model which mimics human doctor diagnostic process: evaluate the collection of patient images and then use important information in memory to decide a single diagnostic for the patient.
Variational quantum algorithm for estimating the quantum Fisher information

The Quantum Fisher information (QFI) quantifies the ultimate precision of estimating a parameter from a quantum state, and can be regarded as a reliability measure of a quantum system as a quantum sensor. However, estimation of the QFI for a mixed state is in general a computationally demanding task. In this work we present a variational quantum algorithm called Variational Quantum Fisher Information Estimation (VQFIE) to address this task. By estimating lower and upper bounds on the QFI, based on bounding the fidelity, VQFIE outputs a range in which the actual QFI lies. This result can then be used to variationally prepare the state that maximizes the QFI, for the application of quantum sensing. In contrast to previous approaches, VQFIE does not require knowledge of the explicit form of the sensor dynamics. We simulate the algorithm for a magnetometry setup and demonstrate the tightening of our bounds as the state purity increases. For this example, we compare our bounds to literature bounds and show that our bounds are tighter.
Nutritive value of marketable heads and outer leaves of white head cabbage cultivated at different nitrogen rates

ABSTRACT The effect of different nitrogen treatments (100–400 kg N ha−1) on yield and quality of marketable cabbage heads and outer green leaves (waste fraction) of two cultivars of Brassica oleracea convar. capitata var. alba L. grown in two years field experiment was studied. During harvest, the marketable cabbage heads and the outer leaves (not adjacent leaves of cabbage) were removed; total and marketable yield of cabbage were evaluated as well as nutritive value was determined based on content of some components, that is, dry matter, ascorbic acid, soluble polyphenols, total flavonoids, nitrates, total nitrogen and antioxidant activity using 1,1-diphenyl-2-picrylhydrazyl (DPPH). The study showed a significant effect of nitrogen fertilization on the total and marketable yield of the tested cultivars of cabbage. The weight of total cabbage heads was increasing according to higher nitrogen fertilization from 100 up 400 kg N ha−1 for both cultivars. The outer leaves of cabbage characterized by a significantly higher nutritive value compared with marketable head, which is supported by several-fold higher content of total flavonoids, soluble polyphenols and over the 100% increase in ascorbic acid content. Although the outer green leaves of cabbage are discarded after harvesting, they possess high nutritive potential which can be practically used or transformed into an added value product.
Subgroups of the direct product of graphs of groups with free abelian vertex groups

A result of Baumslag and Roseblade states that a finitely presented subgroup of the direct product of two free groups is virtually a direct product of free groups. In this paper we generalise this result to the class of cyclic subgroup separable graphs of groups with free abelian vertex groups and cyclic edge groups. More precisely, we show that a finitely presented subgroup of the direct product of two groups in this class is virtually $H$-by-(free abelian), where $H$ is the direct product of two groups in the class. In particular, our result applies to 2-dimensional coherent right-angled Artin groups and residually finite tubular groups. Furthermore, we show that the multiple conjugacy problem and the membership problem are decidable for finitely presented subgroups of the direct product of two $2$-dimensional coherent RAAGs.
Energy Conservation in the Thin Layer Approximation: III. The Spherical Relativistic Case for Supernovae

The theory of the conservation of energy in the thin layer approximation has been extended to special relativity. Four models for the density of the circumstellar medium are analyzed, which are represented by constant, power law, exponential and Emden (n=5) profile for density. The astrophysical results are presented in a numerical way, except for a Taylor expansion of the four trajectories in the surrounding of the origin. The free parameters of the models are particularized for SN1993j, for which the radius versus time is known. Some evaluations on the time dilation are presented.
Supernova 2018cuf: A Type IIP Supernova with a Slow Fall from Plateau

We present multiband photometry and spectroscopy of SN 2018cuf, a Type IIP (“P” for plateau) supernova (SN) discovered by the Distance Less Than 40 Mpc Survey within 24 hr of explosion. SN 2018cuf appears to be a typical SN IIP, with an absolute V-band magnitude of −16.73 ± 0.32 at maximum and a decline rate of 0.21 ± 0.05 mag/50 days during the plateau phase. The distance of the object was constrained to be 41.8 ± 5.7 Mpc by using the expanding photosphere method. We used spectroscopic and photometric observations from the first year after the explosion to constrain the progenitor of SN 2018cuf using both hydrodynamic light-curve modeling and late-time spectroscopic modeling. The progenitor of SN 2018cuf was most likely a red supergiant of about 14.5 M⊙ that produced 0.04 ± 0.01 M⊙ 56Ni during the explosion. We also found ∼0.07 M⊙ of circumstellar material (CSM) around the progenitor is needed to fit the early light curves, where the CSM may originate from presupernova outbursts. During the plateau phase, high-velocity features at ∼11,000 km s−1 were detected in both the optical and near-infrared spectra, supporting the possibility that the ejecta were interacting with some CSM. A very shallow slope during the postplateau phase was also observed, and it is likely due to a low degree of nickel mixing or the relatively high nickel mass in the SN.
Temporal blocking of finite-difference stencil operators with sparse “off-the-grid” sources

Stencil kernels dominate a range of scientific applications, including seismic and medical imaging, image processing, and neural networks. Temporal blocking is a performance optimization that aims to reduce the required memory bandwidth of stencil computations by re-using data from the cache for multiple time steps. It has already been shown to be beneficial for this class of algorithms. However, applying temporal blocking to practical applications’ stencils remains challenging. These computations often consist of sparsely located operators not aligned with the computational grid (“off-the-grid”). Our work is motivated by modelling problems in which source injections result in wavefields that must then be measured at receivers by interpolation from the grided wavefield. The resulting data dependencies make the adoption of temporal blocking much more challenging. We propose a methodology to inspect these data dependencies and reorder the computation, leading to performance gains in stencil codes where temporal blocking has not been applicable. We implement this novel scheme in the Devito domain-specific compiler toolchain. Devito implements a domain-specific language embedded in Python to generate optimized partial differential equation solvers using the finite-difference method from high-level symbolic problem definitions. We evaluate our scheme using isotropic acoustic, anisotropic acoustic, and isotropic elastic wave propagators of industrial significance. After auto-tuning, performance evaluation shows that this enables substantial performance improvement through temporal blocking over highly-optimized vectorized spatially-blocked code of up to 1.6x.
Robust Privatization with Nonspecific Tasks and the Optimal Privacy-Utility Tradeoff

Privacy-preserving data release mechanisms aiming to minimize the privacy leakage under utility constraints of nonspecific tasks are studied through the lens of information theory. An information theoretic framework extending Sankar et al. [1] is proposed. While the private feature to be protected is typically determined and known by the users who release their data, the specific task where the release data is utilized is usually unknown. To address the lack of information of the specific task, utility constraints laid on a set of multiple possible tasks are considered. The mechanism protects the privacy of a given feature of the to-be-released data while satisfying utility constraints of all possible tasks in the set. First, the single-letter characterization of the privacy-utility tradeoff region is derived. Characterization of the minimum privacy under log-loss utility constraints turns out to be a non-convex optimization problem involving mutual information in the objective function and the constraints. Second, focusing on the case where the raw data consists of multiple independent components, we show that the above optimization problem can be decomposed into multiple parallel privacy funnel (PF) problems [2] with different weightings, each of which includes only a single utility constraint. We explicitly derive the optimal solution to each PF problem when the private feature is a deterministic function of a data component. The solution is characterized by the leakage-free threshold, and the minimum leakage is zero while the utility constraint is below the threshold. Once the utility requirement is above the threshold, the privacy leakage increases linearly. Finally, we show that the optimal weighting of each privacy funnel problem can be found by solving a linear program (LP). Numerical results are shown to illustrate the robustness of our approach.
Performance of Dual-Augmented Lagrangian Method and Common Spatial Patterns applied in classification of Motor-Imagery BCI

Motor-imagery based brain-computer interfaces (MI-BCI) have the potential to become ground-breaking technologies for neurorehabilitation, the reestablishment of non-muscular communication and commands for patients suffering from neuronal disorders and disabilities, but also outside of clinical practice, for video game control and other entertainment purposes. However, due to the noisy nature of the used EEG signal, reliable BCI systems require specialized procedures for features optimization and extraction. This paper compares the two approaches, the Common Spatial Patterns with Linear Discriminant Analysis classifier (CSP-LDA), widely used in BCI for extracting features in Motor Imagery (MI) tasks, and the Dual-Augmented Lagrangian (DAL) framework with three different regularization methods: group sparsity with row groups (DAL-GLR), dual-spectrum (DAL-DS) and l1-norm regularization (DAL-L1). The test has been performed on 7 healthy subjects performing 5 BCI-MI sessions each. The preliminary results show that DAL-GLR method outperforms standard CSP-LDA, presenting 6.9% lower misclassification error (p-value = 0.008) and demonstrate the advantage of DAL framework for MI-BCI.
Estimation of age-specific susceptibility to influenza in the Netherlands and its relation to loss of CD8+ T-cell memory

The magnitude of influenza epidemics is largely determined by the number of susceptible individuals at the start of the influenza season. Susceptibility, in turn, is influenced by antigenic drift. The evolution of influenza’s B-cell epitopes has been charted thoroughly, and only recently evidence for T-cell driven evolution is accumulating. We investigate the relation between susceptibility to influenza, and antigenic drift at CD8+ T-cell epitopes over a 45-year timespan. We estimate age-specific susceptibility with data reported by general practitioners, using a disease-transmission model in a Bayesian framework. We find large variation in susceptibility, both between seasons and age classes. Although it is often assumed that antigenic drift drives the variation in susceptibility, we do not find evidence for a relation between drift and susceptibility in our data. This suggests that other factors determining the variation in susceptibility play a dominating role, or that complex influenza-infection histories obscure any direct effects. Preface to this bioRχiv pre-print We are currently in the process of making this manuscript ready for re-submission, and are resolving some issues brought forward by our referees. Most importantly, we aim to better incorporate the co-circulation of the various influenza A and B subtypes during the different seasons, both in the estimation of susceptibility and antigenic drift.
On the stabilizing effect of rotation in the 3d Euler equations

While it is well known that constant rotation induces linear dispersive effects in various fluid models, we study here its effect on long time nonlinear dynamics in the inviscid setting. More precisely, we investigate stability in the 3d rotating Euler equations in R3$\mathbb {R}^3$ with a fixed speed of rotation. We show that for any M>0$\mathcal {M}> 0$ , axisymmetric initial data of sufficiently small size ε lead to solutions that exist for a long time at least ε−M$\varepsilon ^{-\mathcal {M}}$ and disperse. This is a manifestation of the stabilizing effect of rotation, regardless of its speed. To achieve this we develop an anisotropic framework that naturally builds on the available symmetries. This allows for a precise quantification and control of the geometry of nonlinear interactions, while at the same time giving enough information to obtain dispersive decay via adapted linear dispersive estimates.
Tip Avalanche Photodiode—A New Generation Silicon Photomultiplier Based on Non-Planar Technology

The Silicon Photomultiplier (SiPM) is a mature photodetector concept that is applied in a variety of applications ranging from medical imaging to automotive LiDAR systems. Over the last few years, improvements of the sensor performance are gradually approaching to a saturation. In this work we present our new concept to overcome the intrinsic limitations of planar configurations of electrodes. Our non-planar technology is based on focusing and enhancing the electric fields by tip-like electrodes. The shape of the electric field and the lack of typical micro-cell edges, allows us to exclude cell separation boundaries and eliminate dead space around active cell areas. Our design provides a high-density micro-cell layout with a high geometric efficiency. It resolves the well-known trade-off between the detection efficiency and the dynamic range. The first “Tip Avalanche Photodiode” (TAPD) prototypes show a remarkable geometric efficiency above 80% for a micro-cell pitch of 15 $\mu \text{m}$ . This directly translates into a photon detection efficiency (PDE) record peak value of 73% at 600nm with respect to the state-of-the-art SiPMs. Moreover, the PDE remains above a value of 45% up to a wavelength of 800nm with another record value of 22% at 905nm. The reduced micro-cell capacity allows for a fast recovery time below 4ns, which improves the operation at high photon rates. Overall, the TAPD is anticipated to be a very promising SiPM generation for various wide-spectral and high-dynamic-range applications in health science, biophysics, particle physics and LiDARs.
A RISC-V SystemC-TLM simulator

This work presents a SystemC-TLM based simulator for a RISC-V microcontroller. This simulator is focused on simplicity and easy expandable of a RISC-V. It is built around a full RISC-V instruction set simulator that supports full RISC-V ISA and extensions M, A, C, Zicsr and Zifencei. The ISS is encapsulated in a TLM-2 wrapper that enables it to communicate with any other TLM-2 compatible module. The simulator also includes a very basic set of peripherals to enable a complete SoC simulator. The running code can be compiled with standard tools and using standard C libraries without modifications. The simulator is able to correctly execute the riscv-compliance suite. The entire simulator is published as a docker image to ease its installation and use by developers. A porting of FreeRTOSv10.2. 1 for the simulated SoC is also published.
Object Permanence Through Audio-Visual Representations

As robots perform manipulation tasks and interact with objects, it is probable that they accidentally drop objects (e.g., due to an inadequate grasp of an unfamiliar object) that subsequently bounce out of their visual fields. To enable robots to recover from such errors, we draw upon the concept of object permanence—objects remain in existence even when they are not being sensed (e.g., seen) directly. In particular, we developed a multimodal neural network model—using a partial, observed bounce trajectory and the audio resulting from drop impact as its inputs—to predict the full bounce trajectory and the end location of a dropped object. We empirically show that: 1) our multimodal method predicted end locations close in proximity (i.e., within the visual field of the robot’s wrist camera) to the actual locations and 2) the robot was able to retrieve dropped objects by applying minimal vision-based pick-up adjustments. Additionally, we show that our method outperformed five comparison baselines in retrieving dropped objects. Our results contribute to enabling object permanence for robots and error recovery from object drops.
Eliminating the Barriers: Demystify Wi-Fi Baseband Design And Introduce PicoScenes Wi-Fi Sensing Platform

Research on Wi-Fi sensing over the past decade has been thriving, but not smooth. Three major barriers severely hamper the research, namely, the unknown baseband design and its influence on CSI, inability to access the low-level hardware controls and the lack of a flexible and versatile software toolkit for hardware control. This paper tries to break the above three barriers from the following aspects. First, an in-depth study on the baseband design of QCA9300, the popular CSI-enabled Wi-Fi NIC, is presented. The lessons learned is of great guiding significance for understanding what other commercial off-the-shelf NICs. Second, several valuable features of QCA9300 are unlocked for research, such as the arbitrary tuning for both the carrier frequency and baseband sampling rate. By leveraging the unlocked features, we identify three important types of CSI distortion, and pinpoint their origin through extensive evaluations. Last, we develop and release PicoScenes, a powerful, hardware-unified and extensible Wi-Fi sensing system. PicoScenes allows direct access to the unlocked features of QCA9300 and IWL5300, and therefore greatly facilitate the research on Wi-Fi sensing. It also supports the SDR-based Wi-Fi sensing by embedding a 802.11a/g/n/ac/ax software baseband implementation. We release PicoScenes at this https URL.
Cascading Failure From Targeted Road Network Disruptions

Recent natural disasters have shown that urban road networks are susceptible to cascading failures as evidenced by city scale traffic jams. Further, the rise of internet connected vehicles and smart city infrastructure leads to the potential for hackers and nation states to target disruptions that maximize the potential for cascading failure. While cascading failure in networks have been studied extensively, the spatiotemporal nature of how cascades spread is lacking. Here, we quantify the potential for targeted disruptions on urban traffic networks. Guided by microscopic traffic simulations, we develop a theoretical framework for predicting the growth in cascading traffic jams around road disruptions. We apply our framework to the city of Boston using previously validated origin-destination pairs from cell phone location records. Application of our framework to Boston revels that a targeted disruption of roads leads to disproportionately large proportion of shortest time routes being blocked, due to the relative importance of a few key roads. We find that an initial targeted disruption quickly impacts a significant portion of incoming traffic. Depending on the percentage of initial disruption, vehicle density, and characteristic free flow trip time the cascade occurs on the minutes to hours timescale. However, connected component analysis reveals that route redundancy provides an order of magnitude improvement in fragility, demonstrating the potential for strengthening transportation against network failure.
Identification and characterization of the grapevine flagelline eceptor (VvFLS2) and search for the receptor to chito oligosaccharides in grapevine

Pattern-recognition receptors (PRRs) play a key role in plant immunity by assuring recognition of microbe-associated molecular patterns (MAMPs), signature of microbial presence. MAMP perception constitutes the first layer of pathogen detection and activates defense mechanisms that aim to block the intruder. This study brings an insight into how grapevine (Vitis vinifera) perceives two MAMPs: the flagellin-derived flg22 peptide and chitin, which are conserved motifs occurring over the whole bacterial and fungal classes, respectively. This study analyzed MAMP-triggered early signaling events, defense gene expression and also the efficiency of elicited defense against gray mold and downy mildew diseases. These two MAMPs are active in grapevine suggesting that perception systems exist. So far, no PRR is known for this crop.Given the availability of grapevine genome, we could identify in silico putative grapevine receptors (VvFLS2, VvCERK1-3 and VvCEBiP1-2) that might function as PRRs for flg22 and chitin, respectively. Their functional characterization was firstly achieved by complementation assays in the corresponding A. thaliana mutants and, secondly, by a gene silencing strategy in grapevine.Our results permitted the identification of VvFLS2, the V. vinifera receptor for the bacterial flagellin. The function of VvFLS2 was demonstrated by restoring the flg22 responsiveness in the Arabidopsis fls2 null mutant. Thus, our work provides the first description of an active grapevine PRR-MAMP pair. We further compared VvFLS2 and the Arabidopsis receptor, AtFLS2, in their capability to perceive flagellin-derived flg22 epitopes from endophytic or pathogenic bacteria. Our data clearly show that VvFLS2 differentially recognizes flg22 from different bacteria and suggest that flagellin from the beneficial plant growth-promoting rhizobacteria (PGPR) Burkholderia phytofirmans has evolved to evade grapevine immune recognition system. We also obtained preliminary data on chitin sensing system in grapevine and show that VvCERK3 might be a functional ortholog of AtCERK1 by partly restoring the oxidative burst triggered by chitin in the Arabidopsis cerk1-2 mutant.