Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Algorithms for Exact IR Drop De-Embedding in Analog Multiply–Accumulate Computing

IR drop that comes from the line resistance is a well-known issue of the crossbar structure. When the latter is used in the in-memory computing, this issue sets the limit for the analog computing parallelism. In this work, the previously unappreciated linear network modeling is proven to be sufficient for its calibration. Fast algorithms exploiting this simplification are proposed for an exact and efficient de-embedding of the IR drop. Physical pictures are offered for a more intuitive understanding. Scaling in the input and output peripherals is proposed to overcome the dynamic range overflow issue while maintaining the memristor cell precision. An all-in-one-chip MNIST demonstration is presented. Neural network scheduling and gear-like convolution are proposed for real-time processing. Full MNIST test set with the calibration shows a 98.70% accuracy, with 0.45% loss from the software baseline and a 3.58% improvement upon the without-calibration case. Besides inference deployment, these methods may also be generalized to edge learning where the computation resources are limited.
Recurrence properties of quantum observables in wave packet dynamics

We investigate the recurrence properties of the time series of quantum mechanical expectation values, in terms of two representative models for a single-mode radiation field interacting with a nonlinear medium. From recurrence-time distributions, return maps and recurrence plots, we conclude that the dynamics of appropriate observables pertaining to the field can vary from quasiperiodicity to hyperbolicity, depending on the extent of the nonlinearity and of the departure from coherence of the initial state of the field. We establish that, in a simple bipartite model in which the field is effectively an open quantum system, a decaying exponential recurrence-time distribution, characteristic of a hyperbolic dynamical system, is associated with chaotic temporal evolution as characterized by a positive Liapunov exponent.
Simulations of magnetized multiphase galactic disc regulated by supernovae explosions

Context. What exactly controls star formation in the Galaxy remains controversial. In particular, the role of feedback and magnetic field are still partially understood.Aims. We investigate the role played by supernovae feedback and magnetic field on the star formation and the structure of the Galactic disc. Methods. We perform numerical simulations of the turbulent, magnetized, self-gravitating, multi-phase, supernovae regulated ISM within a 1 kpc stratified box. We implemented various schemes for the supernovae. This goes from a random distribution at a fixed rate to distributions for which the supernovae are spatially and temporally correlated to the formation of stars. To study the influence of magnetic field on star formation, we perform both hydrodynamical and magneto-hydrodynamical simulations. Results. We find that supernovae feedback has a drastic influence on the galactic evolution. The supernovae distribution plays a very significant role. When the supernovae are not correlated with star formation events, they do not significantly modify the very high star formation rate obtained without feedback. When the supernovae follow the accretion, the star formation rate can be reduced by a factor up to 30. The magnetic field is also playing a significant role. It reduces the star formation rate by a factor up to 2−3 and reduces the number of collapse sites by a factor of about 2. Conclusions. The exact correlation between the supernovae and the dense gas appears to have significant consequences on the galactic disc evolution and the star formation. This implies that small scale studies are necessary to understand and quantify the feedback efficiency. The magnetic field does influence the star formation at galactic scales by reducing the star formation rate and the number of star formation sites.
Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer

BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations are only the most well-known mechanisms of homologous recombination deficiency (HR-d) pathway, and patients displaying HR-d behave similarly to BRCA mutated patients. HRd assessment can be challenging and is progressively overcoming BRCA testing not only for prognostic information but more importantly for drugs prescriptions. However, HR testing is not already integrated in clinical practice, it is quite expensive and it is not refundable in many countries. Selecting patients who are more likely to benefit from this assessment (BRCA 1/2 WT patients) at an early stage of the diagnostic process, would allow an optimization of genomic profiling resources. In this study, we sought to explore whether somatic BRCA1/2 genes status can be predicted using computational pathology from standard hematoxylin and eosin histology. In detail, we adopted a publicly available, deep-learning-based weakly supervised method that uses attention-based learning to automatically identify sub regions of high diagnostic value to accurately classify the whole slide (CLAM). The same model was also tested for progression free survival (PFS) prediction. The model was tested on a cohort of 664 (training set: n = 464, testing set: n = 132) ovarian cancer patients, of whom 233 (35.1%) had a somatic BRCA 1/2 mutation. An area under the curve of 0.7 and 0.55 was achieved in the training and testing set respectively. The model was then further refined by manually identifying areas of interest in half of the cases. 198 images were used for training (126/72) and 87 images for validation (55/32). The model reached a zero classification error on the training set, but the performance was 0.59 in terms of validation ROC AUC, with a 0.57 validation accuracy. Finally, when applied to predict PFS, the model achieved an AUC of 0.71, with a negative predictive value of 0.69, and a positive predictive value of 0.75. Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. These actions may allow the model to improve performances for all the considered outcomes.
Searching for a 0.1-1 keV Cosmic Axion Background

Primordial decays of string theory moduli at z ∼ 10 naturally generate a dark radiation Cosmic Axion Background (CAB) with 0.1 1 keV energies. This CAB can be detected through axionphoton conversion in astrophysical magnetic fields to give quasi-thermal excesses in the extreme ultraviolet and soft X-ray bands. Substantial and observable luminosities may be generated even for axion-photon couplings ≪ 10GeV. We propose that axion-photon conversion may explain the observed excess emission of soft X-rays from galaxy clusters, and may also contribute to the diffuse unresolved cosmic X-ray background. We list a number of correlated predictions of the scenario.
Subtle leakage of a Majorana mode into a quantum dot

We investigate quantum transport through a quantum dot connected to source and drain leads and side coupled to a topological superconducting nanowire (Kitaev chain) sustaining Majorana end modes. Using a recursive Green’s-function approach, we determine the local density of states of the system and find that the end Majorana mode of the wire leaks into the dot, thus, emerging as a unique dot level pinned to the Fermi energy eF of the leads. Surprisingly, this resonance pinning, resembling, in this sense, a “Kondo resonance,” occurs even when the gate-controlled dot level edot(Vg) is far above or far below eF . The calculated conductance G of the dot exhibits an unambiguous signature for the Majorana end mode of the wire: In essence, an off-resonance dot [edot(Vg) � eF ], which should have G = 0, shows, instead, a conductance e 2 /2h over a wide range of Vg due to this pinned dot mode. Interestingly, this pinning effect only occurs when the dot level is coupled to a Majorana mode; ordinary fermionic modes (e.g., disorder) in the wire simply split and broaden (if a continuum) the dot level. We discuss experimental scenarios to probe Majorana modes in wires via these leaked/pinned dot modes.
Denoising Application of Magnetotelluric Low-Frequency Signal Processing

As magnetotelluric (MT) is an important method for exploring the geoelectrical structure of the underground, it has motivated in-depth research and application by many geophysicists. Nevertheless, due to the influence of the environment, the collected data are interfered with strong humanistic noise, which might result in a loss of their authenticity. To solve the above problems, many time–frequency domain methods have emerged. Based on the advantages of singular value decomposition (SVD) denoising, we propose a method of MT noisy data processing based on multiresolution singular value decomposition (MSVD) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), which overcomes the lack of flexibility in the construction of the matrix in SVD data processing. First, we introduce a new signal processing method by generalizing SVD to MSVD to obtain more accurate signal characteristics. Due to the difficulty of matrix selection, we suggest the singular value contribution rate as the standard to determine the suitable Hankel matrix and use MSVD to perform effective decomposition. Second, we propose the ICEEMDAN algorithm for removing impulse noise, which efficiently processes each modal component through adaptively decomposition of different thresholds. Experiments on synthetic and realistic data demonstrate that our proposed method can separate the large-scale contours of the MT noisy data and improve the time-domain waveform quality of low-frequency signal. The apparent resistivity–phase curves, coherence, and signal-to-noise ratio (SNR) are all obviously promoted.
RUNX1/CEBPA Mutation in Acute Myeloid Leukemia Promotes Hypermethylation and Indicates for Demethylation Therapy

Acute myeloid leukemia (AML) is a rapidly progressing heterogeneous disease with a high mortality rate, which is characterized by hyperproliferation of atypical immature myeloid cells. The number of AML patients is expected to increase in the near future, due to the old-age-associated nature of AML and increased longevity in the human population. RUNX1 and CEBPA, key transcription factors (TFs) of hematopoiesis, are frequently and independently mutated in AML. RUNX1 and CEBPA can bind TET2 demethylase and attract it to their binding sites (TFBS) in cell lines, leading to DNA demethylation of the regions nearby. Since TET2 does not have a DNA-binding domain, TFs are crucial for its guidance to target genomic locations. In this paper, we show that RUNX1 and CEBPA mutations in AML patients affect the methylation of important regulatory sites that resulted in the silencing of several RUNX1 and CEBPA target genes, most likely in a TET2-dependent manner. We demonstrated that hypermethylation of TFBS in AML cells with RUNX1 mutations was associated with resistance to anticancer chemotherapy. Demethylation therapy restored expression of the RUNX1 target gene, BIK, and increased sensitivity of AML cells to chemotherapy. If our results are confirmed, mutations in RUNX1 could be an indication for prescribing the combination of cytotoxic and demethylation therapies.
Facilitation of Hand Proprioceptive Processing in Paraplegic Individuals with Long-Term Wheelchair Sports Training

Previous studies have revealed drastic changes in motor processing in individuals with congenital or acquired limb deficiencies and dysfunction. However, little is known about whether their brains also exhibit characteristic proprioceptive processing. Using functional magnetic resonance imaging, we examined the brain activity characteristics of four individuals with congenital or acquired paraplegia (paraplegic group) who underwent long-term wheelchair sports training, when they passively experienced a right-hand movement (passive task) and when they actively performed a right-hand motor task (active task), compared to 37 able-bodied individuals (control group). Compared with the control group, the paraplegic group showed significantly greater activity in the foot section of the left primary motor cortex and in the inferior frontoparietal proprioceptive network during the passive task. In the paraplegic group, the left intraparietal sulcus region was activated during the passive task, but suppressed during the active task, which was not observed in the control group. This shows the facilitation of hand proprioceptive processing and unique usage of the intraparietal sulcus region in proprioceptive motor processing in the brains of paraplegic individuals with long-term wheelchair sports training.
X-RAY AND ULTRAVIOLET EMISSION FROM THE RECURRENT NOVA RS OPHIUCHI IN QUIESCENCE: SIGNATURES OF ACCRETION AND SHOCKED GAS

RS Ophiuchi is a recurrent nova system that experiences outbursts every ∼20 years, implying accretion at a high rate onto a massive white dwarf. However, previous X-ray observations of the system in quiescence have detected only faint emission that is difficult to reconcile with the high accretion rate (>2 × 10−8 M☉ yr−1) predicted by nova theory for such frequent outbursts. Here, we use new Chandra and XMM-Newton observations obtained 537 and 744 days after the 2006 outburst to constrain both the accretion rate onto the white dwarf and the properties of the nova ejecta at these times. We detect low level UV variability with the XMM-Newton Optical Monitor on day 744 that is consistent with accretion disk flickering, and use this to place a lower limit on the accretion rate at this epoch. The X-ray spectra in both observations are well described by a two component thermal plasma model. We identify the first component as the nova shell, which can emit X-rays for up to a decade after the outburst. The other component likely arises in the accretion disk boundary layer, and can be equally well fit by a single temperature plasma or a cooling flow model. Although the flux of the single temperature model implies an accretion rate that is 40 times too low to power the observed nova outburst rate (assuming that half of the accretion luminosity is emitted as X-rays in the boundary layer), the best-fit cooling flow model implies < 1.2 × 10−8 M☉ yr−1 537 days after the outburst, which is within a factor of two of the theoretical accretion rate required to power an outburst every 20 years. Furthermore, we place an upper limit on the accretion rate through an optically thick region of the boundary layer of 2.0 × 10−8 M☉ yr−1. Thus, these new quiescence data are consistent with the accretion rate expectations of nova theory. Finally, we discuss the possible origins of the low temperature associated with the accretion component, which is a factor of 10 lower than in T CrB, an otherwise similar recurrent nova.
Sensitization to common aeroallergens in the atopic population of Malatya Province, Turkey: A retrospective analysis using skin prick test

Aim: Asthma and allergic rhinitis (AR) are chronic disorders that occur frequently and affect the quality of life. It is important to identify aeroallergens in a certain region to diagnose and treat asthma and AR in susceptible persons. We aimed to determine the distribution of aeroallergen sensitization rates using the skin prick test (SPT) in adult patients with AR and/or asthma in Malatya Province, Turkey. Materials and Methods: Between May 2018 and December 2021, we retrospectively reviewed the data of patients with AR and/or asthma and sensitivity to at least one aeroallergen on the SPT. Results: Of the 419 patients enrolled in our study, 334 (79.7%) had only AR, 68 (16.2%) had both AR and asthma, and 17 (4.1%) had only asthma. A total of 259 (61.8%) patients were women, and the mean age was 30.52 ± 10.06 years. Sensitization to grass mix was most common (71.8%), and its rate was higher among patients with only AR and those with AR and asthma (75.1% vs. 64.7%, p = 0.001) than in patients with only asthma. However, the sensitization rate to Dermatophagoides pteronyssinus was higher in patients with only asthma (47.1%, p < 0.001) than in the other groups. Conclusions: We found that grass mix was the most common sensitizing aeroallergen in accordance with the geographic and climatic characteristics of Malatya Province. This information may help with diagnosis, management, and prevention of AR and asthma.
Regularization of Automorphic Functions of Manifolds with Special Kähler Geometry

In this paper we find automorphic functions of coset manifolds with special K\"ahler geometry. We use \zeta-functions to regularize an infinite product over integers which belong to a duality-invariant lattice, this product is known to produce duality-invariant functions. In turn these functions correspond to Eisenstein series which can be understood as string theory amplitudes that receive contributions from BPS states. The Ansatz is constructed using the coset manifold SU(1,n)\over SU(n) \times U(1) as an example but it can be generalized. Automorphic functions play an important role in the calculation of threshold corrections to gauge coupling and other stringy phenomena. We also find some connections between the theory of Abelian varieties and moduli spaces of Calabi-Yau manifolds
Application of second-order-accurate Total Variation Diminishing (TVD) schemes to the Euler equations in general geometries

A one-parameter family of explicit and implicit second-order-accurate, entropy satisfying, total variation diminishing (TVD) schemes was developed by Harten. These TVD schemes were the property of not generating spurious oscillations for one-dimensional nonlinear scalar hyperbolic conservation laws and constant coefficient hyperbolic systems. Application of these methods to one- and two-dimensional fluid flows containing shocks (in Cartesian coordinates) yields highly accurate nonoscillatory numerical solutions. The goal of this work is to expand these methods to the multidimensional Euler equations in generalized coordinate systems. Some numerical results of shock waves impinging on cylindrical bodies are compared with MacCormack's method.
MOMENTS ABOUT SMALLEST SAMPLE VALUE

Abstract : A new type of moments has been achieved by substituting in the central moments the smallest value of the sample for its mean. The new moments have the same advantage as the central moments of being independent of the location parameter but for certain value of the shape parameter they have less variance and thus are preferable for estimating purposes. The asymptotic properties of four estimators, three of them composed of the new moments and one of them of central moments have been examined. It could be concluded that for the shape parameter alpha = or is > 0.5 the estimator, which was composed of the first and second order moments of the new type, was by far the most efficient one. Small-sample properties of the new-moments estimators have been appraised by use of extensive Monte-Carlo studies and it could be stated that the same conclusion applies also to small and moderate sample sizes.
iSpec: An integrated software framework for the analysis of stellar spectra

iSpec is an integrated spectroscopic software framework suitable for the creation of spectral libraries such as the Benchmark Stars library and the determination of atmospheric parameters (i.e. effective temperature, surface gravity, metallicity) and individual chemical abundances. The framework can be used in automatic massive analysis, but it also includes a user-friendly visual interface that can easily interoperate with other astronomical applications such as TOPCAT, VOSpec and splat which provide access to the Virtual Observatory.
Analysis of the efficacy of autologous peripheral blood stem cell transplantation in high-risk neuroblastoma

Objective: This study aimed to analyze the efficacy of autologous peripheral blood stem cell transplantation for high-risk neuroblastoma in China. Methods: The data of 90 high-risk neuroblastoma patients treated with the CCCG-NB 2015 regimen were reviewed. The baseline clinicopathological characteristics and prognosis were analyzed and compared. In addition, the prognoses of tandem autologous stem cell transplantation and single autologous stem cell transplantation groups were compared. Results: The results of survival analysis showed that autologous peripheral blood stem cell transplantation based on this pretreatment regimen significantly improved the prognosis of children in the high-risk group. The 3-year event-free survival (EFS) and overall survival (OS) rates for the transplantation group and the nontransplantation group were 65.5% vs. 41.3% (p=0.023) and 77.1% vs. 57.9% (p=0.03), respectively. There was no difference in the distribution of baseline clinical case characteristics between the single transplantation group and the tandem transplantation group (p>0.05), and there was no significant difference in EFS and OS between the two groups (p>0.05). Conclusion: Based on this pretreatment programme, autologous peripheral blood stem cell transplantation is safe and tolerable and significantly improves the prognosis of children in the high-risk group. The value of tandem autologous stem cell transplantation is worthy of further discussion, which should consider various aspects such as the transplantation medication regimen and the patient's state.
Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per the requirements. Due to their lightweight properties, Internet of Things applications do not consume many computing resources. Therefore, it is common that a small-scale data center can accommodate thousands of Internet of Things applications. However, in small-scale fog computing environments, task scheduling of applications is limited to offering low latency and response times. In this paper, we propose a latency-aware task scheduling method for Internet of Things applications based on artificial intelligence in small-scale fog computing environments. The core concept of the proposed task scheduling is to use artificial neural networks with partitioning capabilities. With the partitioning technique for artificial neural networks, multiple edge servers are able to learn and calculate hyperparameters in parallel, which reduces scheduling times and service level objectives. Performance evaluation with state-of-the-art studies shows the effectiveness and efficiency of the proposed task scheduling in small-scale fog computing environments while introducing negligible energy consumption.
Gender, Business, and Social Capital in the Abyei Area between the Two Sudans

ABSTRACT This article statistically examines how competitive communities perceive conflict, cooperation, and social cohesion drivers across the borders of two Sudans. We found that perceptions differ by gender, relationship types, and the assets competing groups depend on for their livelihoods. Social cohesion in Abyei appears gendered and asset-based, with Ngok women reporting greater mistrust of their Missiriya neighbors. These women also see business opportunities to rebuild the trust across ethnic groups in the agro-pastoral economy. Supporting narratives and programs that support business for peace in Abyei may serve social construction for peace and open more holistic dimensions of social contract making.