Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the immune response in kidney transplant patients.

Chapter 1: Introduction Literature Review 1.1 Historical overview 1.2 Renal transplantation 1.3 Transplantation immunology 1.3. 1 The major histocompatibility complex 1.3. 2 T lymphocyte alloantigen recognition 1.3. 3 T lymphocyte activation 1.4 Immunosuppressive drugs 1.4. 1 Calcineurin inhibitors 1.4. 2 mTOR inhibitors (mammalian "Targets of rapamycin") 1.4. 3 Mode of action 1.5 Complications of immunosuppressive therapy 1.5. 1 Rejection 1.5. 1.1 Hyperacute rejection 1.5. 1.2 Acute rejection 1.5. 1.3 Chronic rejection 1.5. 2 Infection 1.5. 3 Malignancy 1.6 A TP bioluminescence assay 1.6. 1 Adenosine Triphosphate (ATP) 1.6. 2 Bioluminescence
Using Deep Features of Only Objects to Describe Images

Inspired by recent advances in leveraging multiple modalities in machine translation, we introduce an encoderdecoder pipeline that uses (1) specific objects within an image and their object labels (2) a language model for decoding joint embedding of object features and the object labels. Our pipeline merges prior detected objects from the image and their object labels and then learns the sequences of captions describing the particular image. The decoder model learns to extract descriptions for the image from scratch by decoding the joint representation of the object visual features and their object classes conditioned by the encoder component. The idea of the model is to concentrate only on the specific objects of the image and their labels for generating descriptions of the image rather than visual feature of the entire image. The model needs to be calibrated more by adjusting the parameters and settings to result in better accuracy and performance.
An HAM-Based Analytic Modeling Methodology for Memristor Enabling Fast Convergence

Memristor has great application prospects in various high-performance electronic systems, such as memory, artificial intelligence, and neural networks, due to its fast speed, nano-scale dimensions, and low-power consumption. However, traditional nonanalytic models and lately reported analytic models for memristor have the problems of nonconvergence and slow convergence, respectively. These problems lay great obstacles in the analysis, simulation and design of memristor. To address these problems, a modeling methodology for analytic approximate solution of the state variable in memristor is proposed in this work. This methodology solves the governing equation of memristor by adopting the Homotopy Analysis Method (HAM) for the first time, and the convergence performance of the methodology is enhanced by an optimized convergence-control parameter in the HAM. The simulation results, compared with the reported analytic models, demonstrate that the HAM-based modeling methodology achieves faster convergence while guaranteeing sufficient accuracy. Based on the methodology, it can be simultaneously revealed that highly nonlinearity is the potential sources of slow convergence in analytic models, which is beneficial for the analysis and design guidance. In addition, a Spice subcircuit is constructed based on the obtained HAM model, and then it is integrated into an oscillator to verify its applicability. Due to the generality of HAM, this methodology may be easily extended to other memory devices.
Optimal Resource Allocation in Wireless Control Systems via Deep Policy Gradient

In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to packet dropouts, proper allocation of limited resources, e.g. transmission power, across plants is critical for maintaining reliable operation. In this paper, we formulate the design of an optimal resource allocation policy that uses current plant states and wireless channel states to assign resources used to send control actuation information back to plants. While this problem is challenging due to its infinite dimensionality and need for explicit system model and state knowledge, we propose the use of deep reinforcement learning techniques to find neural network-based resource allocation policies. In particular, we use model-free policy gradient methods to directly learn continuous power allocation policies without knowledge of plant dynamics or communication models. Numerical simulations demonstrate the strong performance of learned policies relative to baseline resource allocation methods in settings where state information is available both with and without noise.
Multitask Learning For Different Subword Segmentations In Neural Machine Translation

In Neural Machine Translation (NMT) the usage of sub􏰃words and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words. However, selecting the optimal subword segmentation involves a trade-off between expressiveness and flexibility, and is language and dataset-dependent. We present Block Multitask Learning (BMTL), a novel NMT architecture that predicts multiple targets of different granularities simultaneously, removing the need to search for the optimal segmentation strategy. Our multi-task model exhibits improvements of up to 1.7 BLEU points on each decoder over single-task baseline models with the same number of parameters on datasets from two language pairs of IWSLT15 and one from IWSLT19. The multiple hypotheses generated at different granularities can be combined as a post-processing step to give better translations, which improves over hypothesis combination from baseline models while using substantially fewer parameters.
Distributed Estimation via Network Regularization

We propose a new method for distributed estimation of a linear model by a network of local learners with heterogeneously distributed datasets. Unlike other ensemble learning methods, in the proposed method, model averaging is done continuously over time in a distributed and asynchronous manner. To ensure robust estimation, a network regularization term which penalizes models with high local variability is used. We provide a finite-time characterization of convergence of the weighted ensemble average and compare this result to centralized estimation. We illustrate the general applicability of the method in two examples: estimation of a Markov random field using wireless sensor networks and modeling prey escape behavior of birds based on a real-world dataset.
Fine-Grained Object Detection over Scientific Document Images with Region Embeddings

We study the problem of object detection over scanned images of scientific documents. We consider images that contain objects of varying aspect ratios and sizes and range from coarse elements such as tables and figures to fine elements such as equations and section headers. We find that current object detectors fail to produce properly localized region proposals over such page objects. We revisit the original R-CNN model and present a method for generating fine-grained proposals over document elements. We also present a region embedding model that uses the convolutional maps of a proposal's neighbors as context to produce an embedding for each proposal. This region embedding is able to capture the semantic relationships between a target region and its surrounding context. Our end-to-end model produces an embedding for each proposal, then classifies each proposal by using a multi-head attention model that attends to the most important neighbors of a proposal. To evaluate our model, we collect and annotate a dataset of publications from heterogeneous journals. We show that our model, referred to as Attentive-RCNN, yields a 17% mAP improvement compared to standard object detection models.
Perceptual Speech Enhancement via Generative Adversarial Networks

Automatic speech recognition (ASR) systems are of vital importance nowadays in commonplace tasks such as speech-to-text processing and language translation. This created the need of an ASR system that can operate in realistic crowded environments. Thus, speech enhancement is now considered as a fundamental building block in newly developed ASR systems. In this paper, a generative adversarial network (GAN) based framework is investigated for the task of speech enhancement of audio tracks. A new architecture based on CasNet generator and additional perceptual loss is incorporated to get realistically denoised speech phonetics. Finally, the proposed framework is shown to quantitatively outperform other GAN-based speech enhancement approaches.
Collaborative Filtering with A Synthetic Feedback Loop

We propose a novel learning framework for recommendation systems, assisting collaborative filtering with a synthetic feedback loop. The proposed framework consists of a "recommender" and a "virtual user. " The recommender is formulizd as a collaborative-filtering method, recommending items according to observed user behavior. The virtual user estimates rewards from the recommended items and generates the influence of the rewards on observed user behavior. The recommender connected with the virtual user constructs a closed loop, that recommends users with items and imitates the unobserved feedback of the users to the recommended items. The synthetic feedback is used to augment observed user behavior and improve recommendation results. Such a model can be interpreted as the inverse reinforcement learning, which can be learned effectively via rollout (simulation). Experimental results show that the proposed framework is able to boost the performance of existing collaborative filtering methods on multiple datasets.
My title

We propose a method for guiding a photographer to rotate her/his smartphone camera to obtain an image that overlaps with another image of the same scene. The other image is taken by another photographer from a different viewpoint. Our method is applicable even when the images do not have overlapping fields of view. Straightforward applications of our method include sharing attention to regions of interest for social purposes, or adding missing images to improve structure for motion results. Our solution uses additional images of the scene, which are often available since many people use their smartphone cameras regularly. These images may be available online from other photographers who are present at the scene. Our method avoids 3D scene reconstruction; it relies instead on a new representation that consists of the spatial orders of the scene points on two axes, x and y. This representation allows a sequence of points to be chosen efficiently and projected onto the photographer’s images, using epipolar point transfer. Overlaying these epipolar lines on the live preview of the camera produces a convenient interface to guide the user. The method was tested on challenging datasets of images and succeeded in guiding a photographer from one view to a non-overlapping destination view.
CSL-TR-15-21 November 6 , 2015

Data assimilation is the process to fuse information from priors, observations of nature, and numerical models, in order to obtain best estimates of the parameters or state of a physical system of interest. Presence of large errors in some observational data, e.g., data collected from a faulty instrument, negatively affect the quality of the overall assimilation results. This work develops a systematic framework for robust data assimilation. The new algorithms continue to produce good analyses in the presence of observation outliers. The approach is based on replacing the traditional L2 norm formulation of data assimilation problems with formulations based on L1 and Huber norms. Numerical experiments using the Lorenz-96 and the shallow water on the sphere models illustrate how the new algorithms outperform traditional data assimilation approaches in the presence of data outliers.
An atomic beam fluorescence locked magneto-optical trap for krypton atoms

We report here an atomic beam loaded magneto-optical trap (MOT) for metastable krypton atoms in which the fluorescence signal from the atomic beam is used to lock the cooling laser frequency. The fluorescence signal is generated by exciting the metastable krypton atomic beam using a probe laser beam (i.e. part of the cooling laser beam) intersecting the atomic beam at an angle. A spectral shift in the fluorescence signal can be achieved by varying the angle between the probe laser beam and the atomic beam to obtain the desired frequency detuning to lock the cooling laser frequency. This has been used to optimize the number of cold atoms in the MOT. The dependence of the peak height and slope of the atomic beam fluorescence (ABF) locking signal on the RF power in the discharge tube and pressure in the observation chamber of the setup has been studied to correlate its effect on the number of atoms in the MOT.
Low Rank Tensor Completion for Multiway Visual Data

Tensor completion recovers missing entries of multiway data. Teh missing of entries could often be caused during teh data acquisition and transformation. In dis paper, we provide an overview of recent development in low rank tensor completion for estimating teh missing components of visual data, e. g. , color images and videos. First, we categorize these methods into two groups based on teh different optimization models. One optimizes factors of tensor decompositions wif predefined tensor rank. Teh other iteratively updates teh estimated tensor via minimizing teh tensor rank. Besides, we summarize teh corresponding algorithms to solve those optimization problems in details. Numerical experiments are given to demonstrate teh performance comparison when different methods are applied to color image and video processing.
Recovery of LeTID by low intensity illumination: Reaction kinetics, completeness and threshold temperature

District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
Do dogs live in joint families? Understanding allo-parental care in free-ranging dogs

Cooperative breeding is an excellent example of altruistic cooperation in social groups. Domestic dogs have evolved from cooperatively hunting and breeding ancestors, but have adapted to a facultatively social scavenging lifestyle on streets, and solitary living in human homes. Pets typically breed and reproduce under human supervision, but free-ranging dogs can provide insights into the natural breeding biology of dogs. We conducted a five year-long study on parental care of free-ranging dogs in India. We observed widespread allo-parenting by both adult males and females. Allomothers provided significantly less care that the mothers, but the putative fathers showed comparable levels of care with the mothers. However, the nature of care varied; mothers invested more effort in feeding and allogrooming, while the putative fathers played and protected more. We were unsure of the relatedness of the pups with the putative fathers, but all the allomothers were maternal relatives of the pups, which provides support for both the “benefit-of-philopatry” and “assured fitness returns” hypotheses. Free-ranging dogs are not cooperative breeders like wolves, but are more similar to communal breeders. Their breeding biology bears interesting similarities with the human joint family system.
Multiquark-Adequate QCD Sum Rules: the Case of Flavour-Exotic Tetraquarks

Frequently, theoretical discussions of multiquark hadron states prove to be contaminated or even dominated by contributions of conventional hadrons. For the approach to QCD bound states in terms of QCD sum rules, we show how to get rid of all unwanted ordinary-hadron ballast by boiling down the traditional QCD sum-rule formalism to the nonconventional aspects in the focus of interest.
GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction

Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). Both methods provide complementary approaches to accelerating MRI acquisition. In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors. The proposed method, called GrappaNet, performs progressive reconstruction by first mapping the reconstruction problem to a simpler one that can be solved by a traditional parallel imaging methods using a neural network, followed by an application of a parallel imaging method, and finally fine-tuning the output with another neural network. The entire network can be trained end-to-end. We present experimental results on the recently released fastMRI dataset and show that GrappaNet can generate higher quality reconstructions than competing methods for both 4x and 8x acceleration.
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning

We develop a general reinforcement learning framework for mean field control (MFC) problems. Such problems arise for instance as the limit of collaborative multi-agent control problems when the number of agents is very large. The asymptotic problem can be phrased as the optimal control of a non-linear dynamics. This can also be viewed as a Markov decision process (MDP) but the key difference with the usual RL setup is that the dynamics and the reward now depend on the state's probability distribution itself. Alternatively, it can be recast as a MDP on the Wasserstein space of measures. In this work, we introduce generic model-free algorithms based on the state-action value function at the mean field level and we prove convergence for a prototypical Q-learning method. We then implement an actor-critic method and report numerical results on two archetypal problems: a finite space model motivated by a cyber security application and a continuous space model motivated by an application to swarm motion.
Detecting double neutron stars with LISA

We estimate the properties of the double neutron star (DNS) population that will be observable by the planned space-based interferometer Laser Interferometer Space Antenna (LISA). By following the gravitational radiation-driven evolution of DNSs generated from rapid population synthesis of massive binary stars, we estimate that around 35 DNSs will accumulate a signal-to-noise ratio above 8 over a 4-yr LISA mission. The observed population mainly comprises Galactic DNSs (94 per cent), but detections in the LMC (5 per cent) and SMC (1 per cent) may also be expected. The median orbital frequency of detected DNSs is expected to be 0.8 mHz, and many of them will be eccentric (median eccentricity of 0.11). LISA is expected to localize these DNSs to a typical angular resolution of 2°. We expect the best-constrained DNSs to have eccentricities known to a few parts in a thousand, chirp masses measured to better than 1 per cent fractional uncertainty, and sky localization at the level of a few arcminutes. The orbital properties will provide insights into DNS progenitors and formation channels. The localizations may allow neutron star natal kick magnitudes to be constrained through the Galactic distribution of DNSs, and make it possible to follow up the sources with radio pulsar searches. LISA is also expected to resolve ∼104 Galactic double white dwarfs, many of which may have binary parameters that resemble DNSs; we discuss how the combined measurement of binary eccentricity, chirp mass, and sky location may aid the identification of a DNS.
Adaptive Loss Scaling for Mixed Precision Training

Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy compared to the existing state-of-the-art MPT and single-precision floating point