Miracle-dz/UniFAR-FTUs huggingface dataset:
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting

Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that generated the observed data. A key question is whether one can still construct hypothesis tests and confidence sets with proper coverage and high power in a so-called likelihood-free inference (LFI) setting; that is, a setting where the likelihood is not explicitly known but one can forward-simulate observable data according to a stochastic model. In this paper, we present $\texttt{ACORE}$ (Approximate Computation via Odds Ratio Estimation), a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest. We also present a goodness-of-fit procedure for checking whether the constructed tests and confidence regions are valid. $\texttt{ACORE}$ is based on the key observation that the LRT statistic, the rejection probability of the test, and the coverage of the confidence set are conditional distribution functions which often vary smoothly as a function of the parameters of interest. Hence, instead of relying solely on samples simulated at fixed parameter settings (as is the convention in standard Monte Carlo solutions), one can leverage machine learning tools and data simulated in the neighborhood of a parameter to improve estimates of quantities of interest. We demonstrate the efficacy of $\texttt{ACORE}$ with both theoretical and empirical results. Our implementation is available on Github.
Investigating the interaction between gradient-only line searches and different activation functions

Gradient-only line searches (GOLS) adaptively determine step sizes along search directions for discontinuous loss functions resulting from dynamic mini-batch sub-sampling in neural network training. Step sizes in GOLS are determined by localizing Stochastic Non-Negative Associated Gradient Projection Points (SNN-GPPs) along descent directions. These are identified by a sign change in the directional derivative from negative to positive along a descent direction. Activation functions are a significant component of neural network architectures as they introduce non-linearities essential for complex function approximations. The smoothness and continuity characteristics of the activation functions directly affect the gradient characteristics of the loss function to be optimized. Therefore, it is of interest to investigate the relationship between activation functions and different neural network architectures in the context of GOLS. We find that GOLS are robust for a range of activation functions, but sensitive to the Rectified Linear Unit (ReLU) activation function in standard feedforward architectures. The zero-derivative in ReLU's negative input domain can lead to the gradient-vector becoming sparse, which severely affects training. We show that implementing architectural features such as batch normalization and skip connections can alleviate these difficulties and benefit training with GOLS for all activation functions considered.
De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and Non-smooth Predictors

In spite of several notable efforts, explaining the generalization of deterministic non-smooth deep nets, e.g., ReLU-nets, has remained challenging. Existing approaches for non-smooth deep nets typically need to bound the Lipschitz constant of such deep nets but such bounds are quite large, may even increase with the training set size yielding vacuous generalization bounds. In this paper, we present a new de-randomized PAC-Bayes margin bound for deterministic non-convex and non-smooth predictors, e.g., ReLU-nets. The bound depends on a trade-off between the $L_2$-norm of the weights and the effective curvature (`flatness') of the predictor, avoids any dependency on the Lipschitz constant, and yields meaningful (decreasing) bounds with the increase in training set size. We first develop a de-randomization argument for non-convex but smooth predictors, e.g., linear deep networks (LDNs). We then consider non-smooth predictors which for any given input realizes as a smooth predictor, e.g., ReLU-nets become some LDN for any given input, but the realized smooth predictor can be different for different inputs. For such non-smooth predictors, we introduce a new PAC-Bayes analysis which takes advantage of the smoothness of the realized predictors, e.g., LDN, for a given input. After careful de-randomization, we get a bound for the deterministic non-smooth predictor. We present empirical results to illustrate the efficacy of our bounds over changing training set size and randomness in labels.
Local polarization in oxygen-deficient 
LaMnO3
 induced by charge localization in the Jahn-Teller distorted structure

The functional properties of transition metal perovskite oxides are known to result from a complex interplay of magnetism, polarization, strain, and stoichiometry. Here, we show that for materials with a cooperative Jahn-Teller distortion, such as LaMnO$_3$ (LMO), the orbital order can also couple to the defect chemistry and induce novel material properties. At low temperatures, LMO exhibits a strong Jahn-Teller distortion that splits the $e_g$ orbitals of the high-spin Mn$^{3+}$ ions and leads to alternating long, short, and intermediate Mn--O bonds. Our DFT+$U$ calculations show that, as a result of this orbital order, the charge localization in LMO upon oxygen vacancy formation differs from other manganites, like SrMnO$_3$, where the two extra electrons reduce the two Mn sites adjacent to the vacancy. In LMO, relaxations around the defect depend on which type of Mn--O bond is broken, affecting the $d$-orbital energies and leading to asymmetric and hence polar excess-electron localization with respect to the vacancy. Moreover, we show that the Mn--O bond lengths, orbital order and consequently the charge localization and polarity are tunable via strain.
On the generalization of bayesian deep nets for multi-class classification

Generalization bounds which assess the difference between the true risk and the empirical risk have been studied extensively. However, to obtain bounds, current techniques use strict assumptions such as a uniformly bounded or a Lipschitz loss function. To avoid these assumptions, in this paper, we propose a new generalization bound for Bayesian deep nets by exploiting the contractivity of the Log-Sobolev inequalities. Using these inequalities adds an additional loss-gradient norm term to the generalization bound, which is intuitively a surrogate of the model complexity. Empirically, we analyze the affect of this loss-gradient norm term using different deep nets.
Improving STDP-based Visual Feature Learning with Whitening

In recent years, spiking neural networks (SNNs) emerge as an alternative to deep neural networks (DNNs). SNNs present a higher computational efficiency – using low-power neuromorphic hardware – and require less labeled data for training – using local and unsupervised learning rules such as spike timing-dependent plasticity (STDP). SNNs have proven their effectiveness in image classification on simple datasets such as MNIST. However, to process natural images, a pre-processing step is required. Difference-of-Gaussians (DoG) filtering is typically used together with on-center / off-center coding, but it results in a loss of information that decreases the classification performance. In this paper, we propose to use whitening as a pre-processing step before learning features with STDP. Experiments on CIFAR-10 show that whitening allows STDP to learn visual features that are visually closer to the ones learned with standard neural networks, with a significantly increased classification performance as compared to DoG filtering. We also propose an approximation of whitening as convolution kernels that is computationally cheaper to learn and more suited to be implemented on neuromorphic hardware. Experiments on CIFAR-10 show that it performs similarly to regular whitening. Cross-dataset experiments on CIFAR-10 and STL-10 also show that it is stable across datasets, making it possible to learn a single whitening transformation to process different datasets.
Electron irradiation of multilayer PdSe2 field effect transistors

Palladium diselenide ( PdSe2) is a recently isolated layered material that has attracted a lot of interest for its pentagonal structure, air stability and electrical properties that are largely tunable by the number of layers. In this work, multilayer PdSe2 is used as the channel of back-gate field-effect transistors, which are studied under repeated electron irradiations. Source-drain Pd electrodes enable contacts with resistance below 350kΩμm. The transistors exhibit a prevailing n-type conduction in high vacuum, which reversibly turns into ambipolar electric transport at atmospheric pressure. Irradiation by 10keV electrons suppresses the channel conductance and promptly transforms the device from n-type to p-type. An electron fluence as low as 160e−/nm2 dramatically changes the transistor behavior, demonstrating a high sensitivity of PdSe2 to electron irradiation. The sensitivity is lost after a few exposures, with a saturation condition being reached for fluence higher than ∼4000e−/nm2. The damage induced by high electron fluence is irreversible as the device persists in the radiation-modified state for several hours, if kept in vacuum and at room temperature. With the support of numerical simulation, we explain such a behavior by electron-induced Se atom vacancy formation and charge trapping in slow trap states at the Si/SiO2 interface.
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations

Deep reinforcement learning has succeeded in sophisticated games such as Atari, Go, etc. Real-world decision making, however, often requires reasoning with partial information extracted from complex visual observations. This paper presents Discriminative Particle Filter Reinforcement Learning (DPFRL), a new reinforcement learning framework for partial and complex observations. DPFRL encodes a differentiable particle filter with learned transition and observation models in a neural network, which allows for reasoning with partial observations over multiple time steps. While a standard particle filter relies on a generative observation model, DPFRL learns a discriminatively parameterized model that is training directly for decision making. We show that the discriminative parameterization results in significantly improved performance, especially for tasks with complex visual observations, because it circumvents the difficulty of modelling observations explicitly. In most cases, DPFRL outperforms state-of-the-art POMDP RL models in Flickering Atari Games, an existing POMDP RL benchmark, and in Natural Flickering Atari Games, a new, more challenging POMDP RL benchmark that we introduce. We further show that DPFRL performs well for visual navigation with real-world data.
On the Microscopic Modeling of Vehicular Traffic on General Networks

We introduce a formalism to deal with the microscopic modeling of vehicular traffic on a road network. Traffic on each road is uni-directional, and the dynamics of each vehicle is described by a Follow-the-Leader model. From a mathematical point of view, this amounts to define a system of ordinary differential equations on an arbitrary network. A general existence and uniqueness result is provided, while priorities at junctions are shown to hinder the stability of solutions. We investigate the occurrence of the Braess paradox in a time-dependent setting within this model. The emergence of Nash equilibria in a non-stationary situation results in the appearance of Braess type paradoxes, and this is supported by numerical simulations.
Fine properties of solutions to the Cauchy problem for a fast diffusion equation with Caffarelli–Kohn–Nirenberg weights

We investigate fine global properties of nonnegative, integrable solutions to the Cauchy problem for the Fast Diffusion Equation with weights (WFDE) $u_t=|x|^\gamma\mathrm{div}\left(|x|^{-\beta}\nabla u^m\right)$ posed on $(0,+\infty)\times\mathbb{R}^d$, with $d\ge 3$, in the so-called good fast diffusion range $m_c<m<1$, within the range of parameters $\gamma, \beta$, optimal for the validity of the so-called Caffarelli-Kohn-Nirenberg inequalities. It is a natural question to ask in which sense such solutions behave like the Barenblatt $\mathfrak{B}$ (fundamental solution): for instance, asymptotic convergence, i.e. $\|u(t)-\mathfrak{B}(t)\|_{{\rm L}^p(\mathbb{R}^d)}\xrightarrow[]{t\to\infty}0$, is well known for all $1\le p\le \infty$, while only few partial results tackle a finer analysis of the tail behaviour. We characterize the maximal set of data $\mathcal{X}\subset{\rm L}^1_+(\mathbb{R}^d)$ that produces solutions which are pointwise trapped between two Barenblatt (Global Harnack Principle), and uniformly converge in relative error (UREC), i.e. ${\rm d}_\infty(u(t))=\|u(t)/\mathcal{B}(t)-1\|_{{\rm L}^\infty(\mathbb{R}^d)}\xrightarrow[]{t\to\infty}0$. Such characterization is in terms of an integral condition on $u(t=0)$. To the best of our knowledge, analogous issues for the linear heat equation $m=1$, do not possess such clear answers. Our characterization is also new for the classical, non-weighted, FDE. We are able to provide minimal rates of convergence to $\mathcal{B}$ in different norms. Such rates are almost optimal in the non weighted case, and become optimal for radial solutions. To complete the panorama, we show that solutions with data in ${\rm L}^1_+(\mathbb{R}^d)\setminus\mathcal{X}$, preserve the same "fat" spatial tail for all times, hence UREC fails.
Feasibility and preliminary efficacy of a self-hypnosis intervention available on the web for cancer survivors with insomnia

Insomnia is highly prevalent among cancer survivors. Previous research suggests cancer survivors may prefer non-pharmacological insomnia treatments and are especially amenable to alternative treatments such as hypnosis. Yet, little is known about the applicability of hypnosis to treat insomnia among cancer survivors. This study investigated feasibility and preliminary efficacy of self-hypnosis recordings available on the web to improve sleep, fatigue, mood, and quality of life in cancer survivors. Twenty-eight cancer survivors with insomnia were randomly assigned to a self-hypnosis group that immediately received a 4-week self-hypnosis intervention for insomnia (n = 14) or to a waitlist control group (n = 14). The main outcomes of sleep, fatigue, mood, and quality of life were assessed via online preand post-treatment questionnaires and sleep diaries. Intervention usage, perceived impact, and feedback were also obtained. Overall adjusted effect sizes show small self-hypnosis treatment effects in sleep, fatigue, mood, and quality of life; however, with this small sample size, improvements were not found to be statistically significant. The intervention appears feasible as most participants utilized the intervention and most perceived it as at least somewhat helpful. Additional research is needed to determine whether this self-treatment option is efficacious for cancer survivors with insomnia.
Effect of Health Coaching Approach Based on Motivational Interview on Improving Undesirable Nutritional Habits of Housewives

Abstract Background and aims: Nutrition disorders are a common risk factor for many clinical disorders, and effective methods for resolving it are motivational interviewing methods that can be useful in encouraging people to promote lifestyle. In recent years, motivational interviews have come up with community health and often people in need of a change in nutritional habits are so vast that they can maintain their health. Material and method: This interventional study was carried out on all housewives referring to Mazdak Health home. They were selected by simple sampling method. The sample size was 35 in each subgroup. Food intake was assessed by a feed frequency questionnaire (FFQ) and the energy consumption in kilocalories per day was calculated using Nutritionist 4 software. Using SPSS18 and Chi-square tests, the exact test Fisher, T and Independent Tests, Mann-Whitney, and Wilcoxon were analyzed. Results: The mean score of healthy nutrition index after intervention was significantly different in intervention and control groups (p-value = 0.004). Also, in whole grain (p-value = 0.001), dairy (p-value = 0.01) Fruits (p-value = 0.01), vegetables (p-value = 0.003) had a significant difference between the mean of intervention and control groups after intervention. There were no significant differences between the other indices after the intervention in the intervention and control groups. Conclusions: The intervention has been effective and generally increases the index of healthy nutrition index after intervention. But on components of dairy, sodium, refined grains, absent calories, proteins have not had any effect.
Cancer Stem Cells in Pheochromocytoma and Paraganglioma

Pheochromocytoma (PCC) and paraganglioma (PGL) are rare neuroendocrine tumors associated with high cardiovascular morbidity and variable risk of malignancy. The current therapy of choice is surgical resection. Nevertheless, PCCs/PGLs are associated with a lifelong risk of tumor persistence or recurrence. A high rate of germline or somatic mutations in numerous genes has been found in these tumors. For some, the tumorigenic processes are initiated during embryogenesis. Such tumors carry gene mutations leading to pseudohypoxic phenotypes and show more immature characteristics than other chromaffin cell tumors; they are also often multifocal or metastatic and occur at an early age, often during childhood. Cancer stem cells (CSCs) are cells with an inherent ability of self-renewal, de-differentiation, and capacity to initiate and maintain malignant tumor growth. Targeting CSCs to inhibit cancer progression has become an attractive anti-cancer therapeutic strategy. Despite progress for this strategy for solid tumors such as neuroblastoma, brain, breast, and colon cancers, no substantial advance has been made employing similar strategies in PCCs/PGLs. In the current review, we discuss findings related to the identification of normal chromaffin stem cells and CSCs, pathways involved in regulating the development of CSCs, and the importance of the stem cell niche in development and maintenance of CSCs in PCCs/PGLs. Additionally, we examine the development and feasibility of novel CSC-targeted therapeutic strategies aimed at eradicating especially recurrent and metastatic tumors.
Efficient Single-Gene and Gene Family Editing in the Apicomplexan Parasite Eimeria tenella Using CRISPR-Cas9

Eimeria species are pathogenic protozoa with a wide range of hosts and the cause of poultry coccidiosis, which results in huge economic losses to the poultry industry. These parasites encode a genome of ∼8000 genes that control a highly coordinated life cycle of asexual replication and sexual differentiation, transmission, and virulence. However, the function and physiological importance of the large majority of these genes remain unknown mostly due to the lack of tools for systematic analysis of gene functions. Here, we report the first application of CRISPR-Cas9 gene editing technology in Eimeria tenella for analysis of gene function at a single gene level as well as for systematic functional analysis of an entire gene family. Using a transgenic line constitutively expressing Cas9, we demonstrated successful and efficient loss of function through non-homologous end joining as well as guided homologous recombination. Application of this approach to the study of the localization of EtGRA9 revealed that the gene encodes a secreted protein whose cellular distribution varied during the life cycle. Systematic disruption of the ApiAp2 transcription factor gene family using this approach revealed that 23 of the 33 factors expressed by this parasite are essential for development and survival in the host. Our data thus establish CRISPR-Cas9 as a powerful technology for gene editing in Eimeria and will set the stage for systematic functional analysis of its genome to understand its biology and pathogenesis, and will make it possible to identify and validate new targets for coccidiosis therapy.
Tumor-Associated Macrophages: Recent Insights and Therapies

Macrophages, which have functions of engulfing and digesting foreign substances, can clear away harmful matter, including cellular debris and tumor cells. Based on the condition of the internal environment, circulating monocytes give rise to mature macrophages, and when they are recruited into the tumor microenvironment and in suitable conditions, they are converted into tumor-associated macrophages (TAMs). Generally, macrophages grow into two main groups called classically activated macrophages (M1) and alternatively activated macrophages (M2). M2 and a small fraction of M1 cells, also known as TAMs, not only lack the function of phagocytizing tumor cells but also help these tumor cells escape from being killed and help them spread to other tissues and organs. In this review, we introduce several mechanisms by which macrophages play a role in the immune regulation of tumor cells, including both killing factors and promoting effects. Furthermore, the targeted therapy for treating tumors based on macrophages is also referred to in our review. We confirm that further studies of macrophage-focused therapeutic strategies and their use in clinical practice are needed to verify their superior efficacy and potential in cancer treatment.
The Myelin Water Fraction Serves as a Marker for Age-Related Myelin Alterations in the Cerebral White Matter – A Multiparametric MRI Aging Study

Quantitative MRI modalities, such as diffusion tensor imaging (DTI) or magnetization transfer imaging (MTI) are sensitive to the neuronal effects of aging of the cerebral white matter (WM), but lack the specificity for myelin content. Myelin water imaging (MWI) is highly specific for myelin and may be more sensitive for the detection of changes in myelin content inside the cerebral WM microstructure. In this multiparametric imaging study, we evaluated the performance of myelin water fraction (MWF) estimates as a marker for myelin alterations during normal-aging. Multiparametric MRI data derived from DTI, MTI and a novel, recently-proposed MWF-map processing and reconstruction algorithm were acquired from 54 healthy subjects (aged 18–79 years) and region-based multivariate regression analysis was performed. MWFs significantly decreased with age in most WM regions (except corticospinal tract) and changes of MWFs were associated with changes of radial diffusivity, indicating either substantial alterations or preservation of myelin content in these regions. Decreases of fractional anisotropy and magnetization transfer ratio were associated with lower MWFs in commissural fiber tracts only. Mean diffusivity had no regional effects on MWF. We conclude that MWF estimates are sensitive for the assessment of age-related myelin alterations in the cerebral WM of normal-aging brains.