Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct and indirect reinforcement learning

Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision‐making and control tasks. In this paper, we classify RL into direct and indirect RL according to how they seek the optimal policy of the Markov decision process problem. The former solves the optimal policy by directly maximizing an objective function using gradient descent methods, in which the objective function is usually the expectation of accumulative future rewards. The latter indirectly finds the optimal policy by solving the Bellman equation, which is the sufficient and necessary condition from Bellman's principle of optimality. We study policy gradient (PG) forms of direct and indirect RL and show that both of them can derive the actor–critic architecture and can be unified into a PG with the approximate value function and the stationary state distribution, revealing the equivalence of direct and indirect RL. We employ a Gridworld task to verify the influence of different forms of PG, suggesting their differences and relationships experimentally. Finally, we classify current mainstream RL algorithms using the direct and indirect taxonomy, together with other ones, including value‐based and policy‐based, model‐based and model‐free.
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks

PURPOSE Segmentation of multiple organs-at-risk (OARs) is essential for MR-only radiation therapy treatment planning and MR-guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor-intensive, time-consuming, and prone to intra- and inter-observer variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency. METHODS We developed Automated deep Learning-based Abdominal Multi-Organ segmentation (ALAMO) technique based on 2D U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multi-view. The model takes in multi-slice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground-truth. The performance was measured using volume overlapping and surface distance. RESULTS The ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the 10 OARs, 9 achieved high Dice Similarity Coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference completed within one minute for a 3D volume of 320x288x180. Overall, the ALAMO model matched the state-of-the-art techniques in performance. CONCLUSION The proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands.
Rheoacoustic Gels: Tuning Mechanical and Flow Properties of Colloidal Gels with Ultrasonic Vibrations

Colloidal gels, where nanoscale particles aggregate into an elastic yet fragile network, are at the heart of materials that combine specific optical, electrical and mechanical properties. Tailoring the viscoelastic features of colloidal gels in real-time thanks to an external stimulus currently appears as a major challenge in the design of "smart" soft materials. Here we introduce "rheo-acoustic" gels, a class of materials that are sensitive to ultrasonic vibrations. By using a combination of rheological and structural characterization, we evidence and quantify a strong softening in three widely different colloidal gels submitted to ultrasonic vibrations (with submicron amplitude and frequency 20-500 kHz). This softening is attributed to micron-sized cracks within the gel network that may or may not fully heal once vibrations are turned off depending on the acoustic intensity. Ultrasonic vibrations are further shown to dramatically decrease the gel yield stress and accelerate shear-induced fluidization. Ultrasound-assisted fluidization dynamics appear to be governed by an effective temperature that depends on the acoustic intensity. Our work opens the way to a full control of elastic and flow properties by ultrasonic vibrations as well as to future theoretical and numerical modeling of such rheo-acoustic gels.
Filtrations in Module Categories, Derived Categories, and Prime Spectra

Let $R$ be a commutative noetherian ring. The notion of $n$-wide subcategories of ${\operatorname{\mathsf{Mod}}}\ R$ is introduced and studied in Matsui–Nam–Takahashi–Tri–Yen in relation to the cohomological dimension of a specialization-closed subset of ${\operatorname{Spec}}\ R$. In this paper, we introduce the notions of $n$-coherent subsets of ${\operatorname{Spec}}\ R$ and $n$-uniform subcategories of $\mathsf{D}({\operatorname{\mathsf{Mod}}}\ R)$ and explore their interactions with $n$-wide subcategories of ${\operatorname{\mathsf{Mod}}}\ R$. We obtain a commutative diagram that yields filtrations of subcategories of ${\operatorname{\mathsf{Mod}}}\ R$, $\mathsf{D}({\operatorname{\mathsf{Mod}}}\ R)$ and subsets of ${\operatorname{Spec}}\ R$ and complements classification theorems of subcategories due to Gabriel, Krause, Neeman, Takahashi, and Angeleri Hügel–Marks–Šťovíček–Takahashi–Vitória.
Algebraic Properties for Certain Form of the Members of Sequence on Generalized Modified Camassa-Holm Equation

We study the symmetry and integrability of a Generalized Modified Camassa-Holm Equation (GMCH) of the form $$u_{t}-u_{xxt}+2nu_{x}(u^2-u_{x}^2)^{n-1}(u-u_{xx})^2+(u^2-u_{x}^2)^{n}(u_{x}-u_{xxx})=0. $$ We observe that for increasing values of $n\in \mathbb{N}$, $\mathbb{N}$ denotes natural number, the above equation gives a family of equations in which nonlinearity is rapidly increasing as $n$ increases. However, this family has similar form of symmetries except the values of $n$. Interestingly the resultant second-order nonlinear ODE which is to be obtained from GMCH equation has eight dimensional symmetries. Hence the second-order nonlinear ODE is linearizable. Finally we conclude that the resultant second-order nonlinear ordinary differential equation which is obtained from the family of GMCH passes the Painleve Test also it posses the similar form of leading order, resonances and truncated series solution too.
A Compared Study Between Some Subspace Based Algorithms

The technology of face recognition has made some progress in recent years. After studying the PCA, 2DPCA, R1-PCA, L1-PCA, KPCA and KECA algorithms, in this paper ECA (2DECA) is proposed by extracting features in PCA (2DPCA) based on Renyi entropy contribution. And then we conduct a study on the 2DL1-PCA and 2DR1-PCA algorithms. On the basis of the experiments, this paper compares the difference of the recognition accuracy and operational efficiency between the above algorithms.
Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval

With the improvements in speech recognition and voice generation technologies over the last years, a lot of companies have sought to develop conversation understanding systems that run on mobile phones or smart home devices through natural language interfaces. Conversational assistants, such as Google Assistant and Microsoft Cortana, can help users to complete various types of tasks. This requires an accurate understanding of the user's information need as the conversation evolves into multiple turns. Finding relevant context in a conversation's history is challenging because of the complexity of natural language and the evolution of a user's information need. In this work, we present an extensive analysis of language, relevance, dependency of user utterances in a multi-turn information-seeking conversation. To this aim, we have annotated relevant utterances in the conversations released by the TREC CaST 2019 track. The annotation labels determine which of the previous utterances in a conversation can be used to improve the current one. Furthermore, we propose a neural utterance relevance model based on BERT fine-tuning, outperforming competitive baselines. We study and compare the performance of multiple retrieval models, utilizing different strategies to incorporate the user's context. The experimental results on both classification and retrieval tasks show that our proposed approach can effectively identify and incorporate the conversation context. We show that processing the current utterance using the predicted relevant utterance leads to a 38% relative improvement in terms of nDCG@20. Finally, to foster research in this area, we have released the dataset of the annotations.
Design of High-Frequency Trading Algorithm Based on Machine Learning

Based on iterative optimization and activation function in deep learning, we proposed a new analytical framework of high-frequency trading information, that reduced structural loss in the assembly of Volume-synchronized probability of Informed Trading ($VPIN$), Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Machine (SVM) to make full use of the order book information. Amongst the return acquisition procedure in market-making transactions, uncovering the relationship between discrete dimensional data from the projection of high-dimensional time-series would significantly improve the model effect. $VPIN$ would prejudge market liquidity, and this effectiveness backtested with CSI300 futures return.
Deforming cubulations of hyperbolic groups

We describe a procedure to deform cubulations of hyperbolic groups by ‘bending hyperplanes’. Our construction is inspired by related constructions like Thurston's Mickey Mouse example, walls in fibred hyperbolic 3‐manifolds and free‐by‐ Z groups, and Hsu–Wise turns. As an application, we show that every cocompactly cubulated Gromov‐hyperbolic group admits a proper, cocompact, essential action on a CAT(0) cube complex with a single orbit of hyperplanes. This answers (in the negative) a question of Wise, who proved the result in the case of free groups. We also study those cubulations of a general group G that are not susceptible to trivial deformations. We name these bald cubulations and observe that every cocompactly cubulated group admits at least one bald cubulation. We then apply the hyperplane‐bending construction to prove that every cocompactly cubulated hyperbolic group G admits infinitely many bald cubulations, provided G is not a virtually free group with Out(G) finite. By contrast, we show that the Burger–Mozes examples each admit a unique bald cubulation.
Non-classical current noise and light emission of an ac-driven tunnel junction

The nonsymmetrized current noise is crucial for the analysis of light emission in nanojunctions. The latter represent non-classical photon emitters whose description requires a full quantum approach. It was found experimentally that light emission can occur with a photon energy exceeding the applied dc voltage, which intuitively should be forbidden due to the Pauli principle. This overbias light emission cannot be described by the single-electron physics, but can be explained by two-electron or even three-electron processes, correlated by a local resonant mode in analogy to the well-known dynamical Coulomb blockade (DCB). Here, we obtain the nonsymmetrized noise for junctions driven by an arbitrarily shaped periodic voltage. We find that when the junction is driven, the overbias light emission exhibits intriguingly different features compared to the dc case. In addition to kinks at multiples of the bias voltage, side kinks appear at integer multiples of the ac driving frequency. Our work generalizes the DCB theory of light emission to driven tunnel junctions and opens the avenue for engineered quantum light sources, which can be tuned purely by applied voltages.
RNN . . . region i region j Location aware Attention i j T Temporal Convolution K Graph Message Passing Prediction for region j K

Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease intervention and control. Most existing work has either limited long-term prediction performance or lacks a comprehensive ability to capture spatio-temporal dependencies in data. Accurate and early disease forecasting models would markedly improve both epidemic prevention and managing the onset of an epidemic. In this paper, we design a cross-location attention based graph neural network (Cola-GNN) for learning time series embeddings and location aware attentions. We propose a graph message passing framework to combine learned feature embeddings and an attention matrix to model disease propagation over time. We compare the proposed method with state-of-the-art statistical approaches and deep learning models on real-world epidemic-related datasets from United States and Japan. The proposed method shows strong predictive performance and leads to interpretable results for long-term epidemic predictions.
Video-rate isotropic quantitative differential phase contrast microscopy based on color-multiplexed annular illumination

Differential phase contrast microscopy (DPC) provides high-resolution quantitative phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typically, illumination in DPC microscopic systems is designed with 2-axis half-circle amplitude patterns, which, however, reduce the temporal resolution of DPC, precluding observation of high-speed phenomenon. Efforts have been made to achieve video-rate DPC by using tri-mode illumination or adding multi-colored filter. However, the frequency responses of the PTFs has not been improved, leading to poor phase contrast and signal-to-noise ratio (SNR) for phase reconstruction. We present a video-rate isotropic quantitative phase imaging (QPI) method based on color-multiplexed differential phase contrast (DPC). In our method, the illumination source is modulated by an LCD into an annular color-multiplexed pattern matching the numerical aperture of the objective precisely to maximize the frequency response for both low and high frequencies (from 0 to 2NAobj). In addition, we propose an alternating illumination scheme to provide a perfectly circularly symmetrical phase transfer function (PTF), achieving isotropic imaging resolution and signal-to-noise ratio (SNR). A color camera records the light transmitted through the specimen, and three monochromatic intensity images at each color channel are then separated and utilized to recover the phase of the specimen. We present the derivation, implementation, simulation and experimental results demonstrating that our method accomplishes high resolution, noise-robustness and reconstruction accuracy at camera-limited frame rates.
Exome Sequencing Identifies Susceptibility Loci for Sarcoidosis Prognosis

Many sarcoidosis-associating immunological genes have been shown to be shared between other immune-mediated diseases. In Finnish sarcoidosis patients, good prognosis subjects more commonly have HLA-DRB1*03:01 and/or HLA-DRB1*04:01-DPB1*04:01 haplotype, but no marker for persistent disease have been found. The objective was to further pinpoint genetic differences between prognosis subgroups in relation to the HLA markers. Whole-exome sequencing was conducted for 72 patients selected based on disease activity (resolved disease, n = 36; persistent disease, n = 36). Both groups were further divided by the HLA markers (one/both markers, n = 18; neither of the markers, n = 18). The Finnish exome data from the Genome Aggregation Database was used as a control population in the WES sample. Statistical analyses included single-variant analysis for common variants and gene level analysis for rare variants. We attempted to replicate associated variants in 181 Finnish sarcoidosis patients and 150 controls. An association was found in chromosome 1p36.21 (AADACL3 and C1orf158), which has recently been associated with sarcoidosis in another WES study. In our study, variations in these genes were associated with resolved disease (AADACL3, p = 0.0001 and p = 0.0003; C1orf158, p = 7.03E-05). Another interesting chromosomal region also peaked, Leucocyte Receptor Complex in 19q13.42, but the association diminished in the replication sample. In conclusion, this WES study supports the previously found association in the region 1p36.21. Furthermore, a novel to sarcoidosis region was found, but additional studies are warranted to verify this association.
Frequency shifting confocal microscopy via azimuthally polarized Bessel-Gaussian beam

A new frequency-shifting confocal microscopy (FSCM) illuminated by an azimuthally polarized Bessel-Gaussian (BG) beam is investigated. The solid excitation spot is produced by the BG beam modulated by a spiral phase plate, and the donut excitation spot is directly obtained by the same BG beam. Through vector diffraction theory and two-view RL reconstruction algorithm, the optical transfer functions of two confocal imaging modes and the simulation imaging of FSCM are presented. The results show that, two illumination modes produced by the azimuthally polarized BG beam can enhance the spatial resolution of FSCM, the spatial resolution of reconstructed image is mainly depended on the illumination mode with higher frequency transfer efficiency, the small pinhole is helpful to improve the contrast and spatial resolution of image. When the iterations number is about 100, the reconstructed image has good quality. This FSCM is helpful to quickly realize super resolution and high contrast in cell imaging.
Poverty and its effect on neurobiology – What do we know?

Poverty is a common phenomenon in India with around 3% of its population living below the poverty line in 2019. [1] We as psychiatrists have always studied the interplay of developmental factors and their role in the genesis of psychopathology and their effects on psychosocial and brain development. This field of study has been labeled as developmental psychopathology and has been the backbone of clinical assessment in a child and adult psychiatry. [2] The recent trends in neurobiology research are now looking at the interplay of social factors and their effects on the developmental neurobiology and in turn on the genesis of psychiatric disorders. The contemporary neuroscience has studied the influence of poverty on cognitive, emotional, and stress regulation systems and looked at how these contextual factors associated with lack of food, shelter, education, and health-care influence neural development. [3] Various factors in brain development such as the analysis of neural plasticity and regulation of cognition, emotion, and stress have been studied with mixed results. [4]
Association between personality factors and early employment of mothers in medical undergraduates

Background: Personality of an individual can be influenced by a variety of factors, including parenting and mother–child interactions in childhood. Reduced time spent with mother due to early employment of mother can affect the personality development in a positive or negative way. Personality, in turn, can influence academic performance, learning, and coping in Bachelor of Medicine, Bachelor of Surgery (MBBS) students. This study aims at exploring the association of mother's employment in the initial period of life and personality traits in MBBS students. Methods: This was a cross-sectional study conducted in a tertiary care center. Consecutively selected 200 MBBS students of all the academic years were interviewed using a semi-structured pro forma and big five inventory scale. Data were statistically analyzed using mean, standard deviation, Chi-square test, and other tests. A P < 0.05 was considered statistically significant. Results: Mothers of 21% of the students worked during pregnancy, and mothers of 33% of the students started working within the first 6 months of delivery. Significantly, high score of extraversion, agreeableness, and openness was found in students whose mothers started working within the first 6 months of delivery. No significant correlation was found between early employment of mother and scores of neuroticism and conscientiousness. Neuroticism score was higher in final MBBS students, students living away from home, and those staying in nuclear families. Conclusion: Early employment in mother was associated with high extraversion, agreeableness, and openness in MBBS students.
A critical review of new competency-based curriculum for community medicine using various curricular review frameworks

The purpose of the present review is to examine the new competency-based undergraduate curriculum in community medicine against the established frameworks and criteria and suggest the way forward for achieving competencies expected of the Indian Medical Graduate (IMG). The new Graduate Medical Education Regulations, 2018, is based on Harden's concept of the curriculum. Hence, we reviewed the components of new curriculum against Harden's conceptualization of various components of the curriculum, and since it claims it is competency-based, we used Tyler's Goal/Objective-Based Evaluation. The new undergraduate curriculum has shown a move toward integration of course content and defined the competencies in more measurable terms. However, it appears that the earlier information-based curriculum corresponding to book chapters (”topics”) has been modified to specify higher cognitive domains with no explicit link between the IMG level curriculum outcomes to subject level intended learning outcomes (ILOs). The mechanism to link ILOs to assessment is also not clear and so needs more clarity. The assessment system hinted at in the current document is mostly based on the existing conventional system of 50% as pass cutoff, etc., against criterion-referenced assessment applicable to competencies that need to be performed. Furthermore, there is no guidance on the creation of educational opportunities and environment for students and faculty – perhaps it is left to “Curriculum Implementation Support Programme (CISP) Workshops. ” Hence, the need for preparing a roadmap/blueprint to learning experiences and assessment methods and levels and milestones to be reached at various phases of MBBS and during internship is required.
Raised Prosthetic Valve Gradients: What Should be the Approach?

The introduction of valve replacement surgery has dramatically improved the outcome of patients with valvular heart disease. Echocardiography plays an important role to determine the outcome of the surgery and follow up of these patients with prosthetic heart valve. Flow across the prosthesis is determined by using Doppler echocardiography. Whenever there is a high gradient across a prosthetic valve, echocardiography becomes challenging, as there are many causes, which may give rise to high prosthetic valve gradient. Some of these causes are prosthesis-related which need urgent intervention and some are non-prosthesis-related. A careful systematic echocardiographic approach, using 2D, 3D, Doppler and transesophageal echocardiography is crucial to find out the exact cause of high gradient.