Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinetics of Lumefantrine Thermal Decomposition Employing Isoconversional Models and Artificial Neural Network

Thermal analysis can be used to determine shelf-life and kinetic parameters in pharmaceutical systems. This work investigates the kinetic of lumefantrine thermal decomposition, an antimalarial, using non-isothermal and isothermal experimental data. The non-isothermal conditions are analyzed applying Vyazovkin method, while isothermal conditions employ models fitting procedure and artificial neural network. Lumefantrine was characterized by powder X-ray diffraction and Fourier transform infrared spectroscopy. The initial stage of lumefantrine thermal decomposition, about 5% of conversion, corresponds to the loss of chlorine and hydroxyl, being correctly predicted by the neural network as a complex event. At room temperature, the D3 model is appropriate to describe the process, once the half-life time is 19 months, in agreement with manufacturer. Isoconversional model determined the activation energy along the whole process while isothermal methodology determined the global value considering the entire process. The results provide important information for the pharmaceutical industry to assay levels of acceptable lumefantrine contents.
Endovascular devascularization of hypervascular tumors in neurooncology

Ендоваскулярна нейрорентгенохірургія • 2014 • No1(7) The main method of hypervascular tumors (HVTs) treatment remains surgical. However, the complexity and invasiveness of surgical access, and a high risk of massive intraoperative bleeding, often make it impossible for radical removal of such lesions [25]. Traditional methods of intraand postoperative bleedings prevention do not always provide the desirable effect. Endovascular techniques have been constantly improved, indications developed, possible difficulties and complications defined [5]. The first reports about the endovascular approach using in the treatment of tumors are dated 1904, when R.H. Dawbarn performed transcarotid embolization of face sarcoma by means of paraffin and gasoline mix [6]. In 1972 R.E. Heckster reported that preoperative embolization of the tumor eases its surgical removal and becomes effective, safe alternative to bandaging of external carotid artery (ECA) branches [12]. There is still no consensus on the indications for endovascular interventions before surgical treatment stage [8, 18]. Technical aspects of its performance are insufficiently developed, the problem of choosing the optimal quantity and quality of materials for embolization is not solved. Besides, have not been evaluated the effectiveness of endovascular treatment depending on morphology, location and stage of development of HVTs specifics and the use of permanent occlusion as an independent method of treatment of vascular anomalies [14, 16]. The objective — to investigate the results of endovascular devascularization of tumors to develop new approaches to the treatment of hypervascular neoplasms in neurooncology. D.V. SCHEGLOV 1, J.G. MAMMADOV-OGLY 2
Influence of the pH of li-rich Li1.2Mn0.54Ni0.13Co0.13O2 on the electrochemical performance by sol–gel method

Abstract The positive electrode material of the lithium-rich layered oxide has poor capability, and its cycle stability and rate characterizations have not been satisfactory. The lithium-rich cathode material Li1.2Mn0.54Ni0.13Co0.13O2 with different particle sizes was obtained by adjusting the pH by sol–gel method. The results show that the electrochemical performance of the material is optimal at pH = 9.0. It initial discharge capacity of 262.5 mAh/g at 0.1 C between 2.0 and 4.8 V. The high reversible discharge capacities is still of 161.0 and 133.7 mAh/g after 100 cycles at 0.5 C and 2 C, with a high capacity retention of 80.46% and 84.14%, respectively. This excellent electrochemical performance is attributed to smaller and uniform particles
Improvement of Dissolution Rate of Gliclazide Using Solid Dispersions with Aerosil 380 and Its Effect on Alloxan Induced Diabetic Rats

The main objective of this research is to conduct a comprehensive study for enhancing the aqueous solubility of poorly water soluble gliclazide using hydrophilic fumed silica particles (Aerosil® 380) and evaluating the influence of silica on drug release profile and pharmacological activity on alloxan induced diabetic rats. Solid dispersions (SD’s) of gliclazide were prepared using solvent evaporation method. The dissolution profiles and solid state characterization of the SD’s prepared were all evaluated. The dissolution rate of gliclazide in the SD’s with fumed silica (weight ratio, 1:1) was approximately 38%, which is about 10 fold higher than that of the pure drug after 30 min. After forming the SD’s, gliclazide changed into an amorphous state, which can infer from differential scanning calorimetry (DSC) and powder X-ray diffraction (PXRD). Fourier transform infrared spectroscopy (FTIR) also revealed the formation of weak hydrogen bonding through the interactions between the secondary amine groups of gliclazide and silanol groups of silica particles in the SD’s. The rapid dissolution rate from the SD’s might be attributed to the amorphization of drug, improved specific surface area and wettability than the original drug crystals. Further, we investigated the antidiabetic effects of SD’s of gliclazide in alloxan induced diabetic rats. The SD’s of gliclazide decrease the blood glucose level 64% whereas the conventional gliclazide decreases only 37% in diabetic rats. Lipid profiles, kidney and liver functions are remarkably improved in diabetic rat treated with SD’s of gliclazide than that of conventional gliclazide. These results suggest that SD’s of gliclazide have much more bioavailability and hence are more pharmacologically active than that of conventional gliclazide form.
Synthesis of novel N-(naphthalen-1-yl)propanamide derivatives and evaluation their antimicrobial activity

Abstract New N-(naphthalen-1-yl)propanamide was synthesized and studied for their antimicrobial activity. The final compounds were procured by reacting N-(naphthalen-1-yl)propanamide with some 2-mercapto aryl or dithiocarbamate salt derivatives. Their structures were determined by 1H-NMR and 13C-NMR spectroscopy and LC-MS/MS spectral data. The synthesized compounds were investigated for their antimicrobial activities against 10 bacteria and 10 fungi species. All compounds showed notable activity. Especially, compounds 2-[(5-methyl-1,3,4-thiadiazol-2-yl)thio]-N-(naphthalen-1-yl)propanamide (2a), 2-(benzothiazol-2-ylthio)-N-(naphthalen-1-yl)propenamide (2b), 2-[(1-methyl-1H-imidazol-2-yl)thio]-N-(naphthalen-1-yl)propanamide (2c), and N-(naphthalen-1-yl)-2-[(5-nitro-1H-benzimidazol-2-yl)thio]propanamide (2e) exhibited antifungal activity against at least one fungus at the half potency of ketoconazole. Also, 2-[(1-methyl-1H-imidazol-2-yl)thio]-N-(naphthalen-1-yl)propanamide (2c), N-(naphthalen-1-yl)-2-[(5-nitro-1H-benzimidazol-2-yl)thio]propanamide (2e) and 2-[(4-methyl-4H-1,2,4-triazol-3-yl)thio]-N-(naphthalen-1-yl)propanamide (2f) showed anti-gram-positive bacterial activity at the half potency of chloramphenicol. Only one compound, 2-(benzothiazol-2-ylthio)-N-(naphthalen-1-yl)propanamide (2b) displayed anti-gram-negative bacterial activity against Yersinia enterocolitica (Y53). Graphical Abstract
Active galactic nuclei winds as the origin of the H2 emission excess in nearby galaxies

In most galaxies, the fluxes of rotational H2 lines strongly correlate with star formation diagnostics [such as polycyclic aromatic hydrocarbons (PAHs)], suggesting that H2 emission from warm molecular gas is a minor by-product of star formation. We analyse the optical properties of a sample of 309 nearby galaxies derived from a parent sample of 2015 objects observed with the Spitzer Space Telescope. We find a correlation between the [O i]λ6300 emission-line flux and kinematics and the H2 S(3) 9.665 $\mu\mathrm{ m}$/PAH 11.3 $\mu\mathrm{ m}$. The [O i]λ6300 kinematics in active galactic nuclei (AGNs) cannot be explained only by gas motions due to the gravitational potential of their host galaxies, suggesting that AGN-driven outflows are important to the observed kinematics. While H2 excess also correlates with the fluxes and kinematics of ionized gas (probed by [O iii]), the correlation with [O i] is much stronger, suggesting that H2 and [O i] emissions probe the same phase or tightly coupled phases of the wind. We conclude that the excess of H2 emission seen in AGNs is produced by shocks due to AGN-driven outflows and in the same clouds that produce the [O i] emission. Our results provide an indirect detection of neutral and molecular winds and suggest a new way to select galaxies that likely host molecular outflows. Further ground- and space-based spatially resolved observations of different phases of the molecular gas (cold, warm, and hot) are necessary to test our new selection method.
Wavelet-based Methods for Numerical Solutions of Differential Equations

Wavelet theory has been well studied in recent decades. Due to their appealing features such as sparse multiscale representation and fast algorithms, wavelets have enjoyed many tremendous successes in the areas of signal/image processing and computational mathematics. This paper primarily intends to shed some light on the advantages of using wavelets in the context of numerical differential equations. We shall identify a few prominent problems in this field and recapitulate some important results along these directions. Wavelet-based methods for numerical differential equations offer the advantages of sparse matrices with uniformly bounded small condition numbers. We shall demonstrate wavelets' ability in solving some one-dimensional differential equations: the biharmonic equation and the Helmholtz equation with high wave numbers (of magnitude $O(10^4)$ or larger).
Heuristics for Symmetric Rectilinear Matrix Partitioning

Partitioning sparse matrices and graphs is a common and important problem in many scientific and graph analytics applications. In this work, we are concerned with a spatial partitioning called rectilinear partitioning (also known as generalized block distribution) of sparse matrices, which is needed for tiled (or {\em blocked}) execution of sparse matrix and graph analytics kernels. More specifically, in this work, we address the problem of symmetric rectilinear partitioning of square matrices. By symmetric, we mean having the same partition on rows and columns of the matrix, yielding a special tiling where the diagonal tiles (blocks) will be squares. We propose five heuristics to solve two different variants of this problem, and present a thorough experimental evaluation showing the effectiveness of the proposed algorithms.
Human-Centric Program Synthesis

Program synthesis techniques offer significant new capabilities in searching for programs that satisfy high-level specifications. While synthesis has been thoroughly explored for input/output pair specifications (programming-by-example), this paper asks: what does program synthesis look like beyond examples? What actual issues in day-to-day development would stand to benefit the most from synthesis? How can a human-centric perspective inform the exploration of alternative specification languages for synthesis? I sketch a human-centric vision for program synthesis where programmers explore and learn languages and APIs aided by a synthesis tool.
Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-BasedPatient Similarity Learning

Models have been proposed to extract temporal patterns from longitudinal electronic health records (EHR) for clinical predictive models. However, the common relations among patients (e.g., receiving the same medical treatments) were rarely considered. In this paper, we propose to learn patient similarity features as phenotypes from the aggregated patient-medical service matrix using non-negative matrix factorization. On real-world medical claim data, we show that the learned phenotypes are coherent within each group, and also explanatory and indicative of targeted diseases. We conducted experiments to predict the diagnoses for Chronic Lymphocytic Leukemia (CLL) patients. Results show that the phenotype-based similarity features can improve prediction over multiple baselines, including logistic regression, random forest, convolutional neural network, and more.
Robust and Adaptive Sequential Submodular Optimization

Emerging applications of control, estimation, and machine learning, from target tracking to decentralized model fitting, pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously used across time. Therefore, many researchers have proposed solutions within discrete optimization frameworks where the optimization is performed over finite sets. By exploiting notions of discrete convexity, such as submodularity, the researchers have been able to provide scalable algorithms with provable suboptimality bounds. In this article, we consider such problems but in adversarial environments, where in every step a number of the chosen elements in the optimization is removed due to failures/attacks. Specifically, we consider for the first time a sequential version of the problem that allows us to observe the failures and adapt, while the attacker also adapts to our response. We call the novel problem robust sequential submodular maximization (RSM). Generally, the problem is computationally hard and no scalable algorithm is known for its solution. However, in this article, we propose robust and adaptive maximization (RAM), the first scalable algorithm. RAM runs in an online fashion, adapting in every step to the history of failures. Also, it guarantees a near-optimal performance, even against any number of failures among the used elements. Particularly, RAM has both provable per-instance a priori bounds and tight and/or optimal a posteriori bounds. Finally, we demonstrate RAM's near-optimality in simulations across various application scenarios, along with its robustness against several failure types, from worst-case to random.
Using E-Graphs for CAD Parameter Inference

Computational fabrication is increasingly popular among end-users and makers in the form of 3D printing and laser cutting. Using Computer-Aided Design (CAD) tools to construct models from scratch is a significant barrier to entry for new users, so many online repositories provide ready-to-print though difficult-to-edit triangle meshes of popular designs. Recent work has proposed program synthesis techniques to automatically decompile triangle meshes to easier-to-edit Constructive Solid Geometry (CSG) designs. While these synthesized CSG designs simplify editing some models, they are flat, i.e., they do not contain loops or other forms of parameterization, which makes editing designs with repeated structure challenging. This paper presents an algorithm for lifting CSG designs to CAD programs, preserving equivalence to the original CSGs but parameterizing over repetitive structures. Our technique takes as input a flat CSG and infers parameters that capture the latent structure of the CSG using an equality-graph based rewrite mechanism combined with constraint solvers for inferring arithmetic operations. Our algorithm synthesizes CAD programs with (possibly nested) loops and arithmetic expressions including trigonometric functions. We implemented our algorithm in a tool called tool and evaluated it by running it on 16 designs collected from popular model-sharing websites and found that it reduces code size by 64% on average, and exposes the underlying structure for 81% of the models.
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics. Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated actions, it can drift away from demonstrated states due to error accumulation. Recent methods based on reinforcement learning (RL), such as inverse RL and generative adversarial imitation learning (GAIL), overcome this issue by training an RL agent to match the demonstrations over a long horizon. Since the true reward function for the task is unknown, these methods learn a reward function from the demonstrations, often using complex and brittle approximation techniques that involve adversarial training. We propose a simple alternative that still uses RL, but does not require learning a reward function. The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by encouraging it to return to demonstrated states upon encountering new, out-of-distribution states. We accomplish this by giving the agent a constant reward of r=+1 for matching the demonstrated action in a demonstrated state, and a constant reward of r=0 for all other behavior. Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q-learning or off-policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation. Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo.
Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

We tackle the human motion imitation, appearance transfer, and novel view synthesis within a unified framework, which means that the model once being trained can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints (pose) to estimate the human body structure. However, they only expresses the position information with no abilities to characterize the personalized shape of the individual person and model the limbs rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape. To preserve the source information, such as texture, style, color, and face identity, we propose a Liquid Warping GAN with Liquid Warping Block (LWB) that propagates the source information in both image and feature spaces, and synthesizes an image with respect to the reference. Specifically, the source features are extracted by a denoising convolutional auto-encoder for characterizing the source identity well. Furthermore, our proposed method is able to support a more flexible warping from multiple sources. In addition, we build a new dataset, namely Impersonator (iPER) dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis. Extensive experiments demonstrate the effectiveness of our method in several aspects, such as robustness in occlusion case and preserving face identity, shape consistency and clothes details. All codes and datasets are available on https://svip-lab.github. io/project/impersonator.html.
Affleck-Dine inflation

The Affleck-Dine mechanism in its simplest form provides baryogenesis from the out-of-equilibrium evolution of a complex scalar field with a simple renormalizable potential. We show that such a model, supplemented by nonminimal coupling to gravity, can also provide inflation, consistent with Planck constraints, simultaneously with the generation of the baryon asymmetry. The predictions of the model include significant tensor-to-scalar ratio and possibly baryon isocurvature fluctuations. The reheating temperature is calculable, making the model fully predictive. We require color triplet scalars for reheating and transfering the primordial baryon asymmetry to quarks; these could be observable at colliders. They can also be probed at higher scales by searches for quark compositeness in dijet angular distributions, and flavor-changing neutral current effects.
Gamma-Ray Burst Afterglows in the Multimessenger Era: Numerical Models and Closure Relations

Gamma-ray bursts (GRBs) associated with gravitational wave events are, and will likely continue to be, viewed at a larger inclination than GRBs without gravitational wave detections. As demonstrated by the afterglow of GW 170817A, this requires an extension of the common GRB afterglow models, which typically assume emission from an on-axis top-hat jet. We present a characterization of the afterglows arising from structured jets, providing a framework covering both successful and choked jets. We compute new closure relations for decelerating structured jets and compare them with the established relations for energy injection and refreshed shock models. The temporal slope before the jet break is found to be a simple function of the ratio between the viewing angle and effective opening angle of the jet. A numerical model to calculate synthetic light curves and spectra is publicly available as the open-source Python package afterglowpy.
DARTS: Dialectal Arabic Transcription System

We present the speech to text transcription system, called DARTS, for low resource Egyptian Arabic dialect. We analyze the following; transfer learning from high resource broadcast domain to low-resource dialectal domain and semi-supervised learning where we use in-domain unlabeled audio data collected from YouTube. Key features of our system are: A deep neural network acoustic model that consists of a front end Convolutional Neural Network (CNN) followed by several layers of Time Delayed Neural Network (TDNN) and Long-Short Term Memory Recurrent Neural Network (LSTM); sequence discriminative training of the acoustic model; n-gram and recurrent neural network language model for decoding and N-best list rescoring. We show that a simple transfer learning method can achieve good results. The results are further improved by using unlabeled data from YouTube in a semi-supervised setup. Various systems are combined to give the final system that achieves the lowest word error on on the community standard Egyptian-Arabic speech dataset (MGB-3).
D3M: A Deep Domain Decomposition Method for Partial Differential Equations

A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs). The solution of PDEs can be formulated as the solution of a constrained optimization problem, and we design a hierarchical neural network framework to solve this optimization problem. Through decomposing a PDE system into components parts, our D3M builds local neural networks on physical subdomains independently (which can be implemented in parallel), so as to obtain efficient neural network approximations for complex problems. Our analysis shows that the D3M approximation solution converges to the exact solution of the underlying PDEs. The accuracy and the efficiency of D3M are validated and demonstrated with numerical experiments.
A Framework for Data-Driven Robotics

We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show how to apply this framework to accomplish three different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly. Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth.