Miracle-dz/UniFAR-FTUs huggingface dataset:
Bidirectional Retrieval Made Simple Jônatas Wehrmann

This paper provides a very simple yet effective characterlevel architecture for learning bidirectional retrieval models. Aligning multimodal content is particularly challenging considering the difficulty in finding semantic correspondence between images and descriptions. We introduce an efficient character-level inception module, designed to learn textual semantic embeddings by convolving raw characters in distinct granularity levels. Our approach is capable of explicitly encoding hierarchical information from distinct base-level representations (e.g., characters, words, and sentences) into a shared multimodal space, where it maps the semantic correspondence between images and descriptions via a contrastive pairwise loss function that minimizes order-violations. Models generated by our approach are far more robust to input noise than state-of-the-art strategies based on word-embeddings. Despite being conceptually much simpler and requiring fewer parameters, our models outperform the state-of-the-art approaches by 4.8% in the task of description retrieval and 2.7% (absolute R@1 values) in the task of image retrieval in the popular MS COCO retrieval dataset. We also show that our models present solid performance for text classification, specially in multilingual and noisy domains.
Correlation functions for lipid membrane dynamics obtained from NMR spectroscopy

Department of Radiology, University of Arizona, Tucson, Arizona 85724~Received 15 October 1996! Nuclear magnetic resonance ~NMR! studies of the spin relaxation of lipid membranes provide a powerfultool for investigating the dynamics of these important biological structural elements. Here spectral densities ofmotion for various dynamical models have been ﬁtted to
The synthesis and evaluation of anti-acetylcholinesterase activity of some 4 ( 3 H )-quinazolinone derivatives bearing substituted 1 , 3 , 4-thiadiazole

According to the 2015 World Alzheimer Report, 46.8 million people worldwide are living with dementia in 2015. This number will almost double every 20 years. Much of the increase will take place in low and middle income countries (lMıcs): in 2015, 58% of all people with dementia live in lMıcs, rising to 63% in 2030 and 68% in 2050. The Report updates data on the prevalence, incidence, cost and trends of dementia worldwide. ıt also estimates how these numbers will increase in the future, leaving us with no doubt that dementia, including Alzheimer’s disease and other causes, is one of the biggest global public health and social care challenges facing people today and in the future (1).
Investigating the physicochemical properties of phenazopyridine hydrochloride using high-performance liquid chromatography and UV-Visible spectrophotometry

The purpose of this study is to develop high-performance liquid chromatography (HPLC) and ultravioletvisible (UV-Vis) spectrophotometry methods for the determination of pKa, log P, and log D values of phenazopyridine hydrochloride (PHCl). For the HPLC analysis, the pH of the mobile phase was plotted against the capacity factor, and the pKa value was obtained from a sigmoidal relationship. Meanwhile, for the UV-Vis spectrophotometric study, the absorbance of PHCl was plotted against pH at 332 nm to obtain the pKa value. The log P and log D (at pH 2.0) values were then calculated based on the concentration of PHCl in the octanol/water phase. Using the HPLC method, the pKa value was established as 5.05 (± 0.15). Meanwhile, the spectroscopic technique afforded pKa values of 5.20 (± 0.22) and 5.17 (± 0.17) via a classical sigmoidal curve and the Albert and Serjeant method. The log D (at pH 2.0) and log P values of PHCl were 1.61 (± 0.16) and 2.62 (± 0.11), respectively. The obtained results are in good agreement with each other. The pKa, log P, and log D values of PHCl can be determined by HPLC and UV-Vis spectrophotometry with reproducible results.
Antioxidant and anti-inflammatory activity of fruit , leaf and branch extracts of Paliurus spina-christi P . Mill

In this study, it is aimed to investigate antioxidant and anti-inflammatory activities of ethanol extracts and their n-hexane, chloroform, ethyl acetate, and aqueous ethanol fractions of the fruits, leaves, and branches of Paliurus spinachristi P. Mill. (Rhamnaceae). The antioxidant activity of the extracts was tested by DPPH and ABTS methods and the results were expressed as IC50 values. The total phenolic content was determined by Folin-Ciocalteu method and the results were expressed as mg of gallic acid equivalent per g dry extract. Anti-inflammatory activity of extracts were evaluated against 5lipoxygenase enzyme. In particular, all extracts obtained from branches of P. spina-christi, except hexane extract, showed strong antioxidant activity against DPPH and ABTS radicals. Ethyl acetate and ethanol extracts of branches of P. spina-christi showed the highest antioxidant activity in DPPH and ABTS assays with IC50 values of 15.54 and 22.06 μg/ml, respectively. Also, the highest total phenol content was found in the ethyl acetate extract of branches of P. spina-christi (286.6 mg/g). Ethyl acetate and ethanol extracts of leaves of P. spina-christi with IC50 values of 66.28 and 76.75 μg/ml were found to have the best anti-inflammatory activity in the lipoxygenase (LOX) inhibition assay used to evaluate anti-inflammatory activities of extracts. These results demonstrate that ethanol and ethyl acetate extracts from branches and leaves of Paliurus spina-christi have significant antioxidant and anti-inflammatory activity. Also, these results confirm the folk use of the plant as an antiinflammatory medicine.
MAKING CHANGE HAPPEN IN THE MIDDLE By Robert Farrell ENGAGEMENT : A LEADERSHIP IMPERATIVE

This paper seeks to provide library managers with a theoretical framework for thinking about how change is effected by those in middle management positions. Starting from the principles that change takes place within socio-culturally bounded contexts and is most successful when approached indirectly, two scenarios characteristic of many situations requiring change middle managers commonly face are then put forward. Following each scenario, a possible solution or path towards change is advanced in order to provide the reader with models for putting into practice the theoretical ideas presented. A methodology that combines theoretical frameworks and practical scenarios is adopted in order to ground theory in practice and thereby lead readers toward what might be called a “praxis” of change making.
Quantum-chemical free energy calculation of vinpocetine molecule release from sodium alginate

Microencapsulation is a method of obtaining traditional materials in a unique form that is able to protect substances from the effects of environmental factors that cause their decomposition. This method is used in medicine and pharmacy, where drugs and enzymes are encapsulated in biodegradable microcapsules. The aim of the study was a possibility of the use of sodium alginate for microencapsulation of vinpocetine on the basis of comparative analysis of thermodynamic characteristics concerned with the efficiency of vinpocetine release from sodium alginate into water and ethanol. The following methods of investigations were applied: The molecular modeling of the process of vinpocetine release from the microcapsules into various media was carried out for: water with pH value 2.0, 7.0, and ethyl alcohol 95%. The molecular dynamics method in the Bioevrik program was implemented to the modeling “polymer-vinpocetine” systems in the aqueous and ethanol environments. Results of the study: It was found that vinpocetine release is mostly energy-efficient into aqueous medium with рН=2, as compared with aqueous medium at рН= 7 and 95% ethyl alcohol.
Natural Language Understanding Discriminator System Action ( Policy ) Semantic Frame State Representation Real Experience Dialogue State Tracking Dialogue Policy Learning Natural Language Generation Simulated Experience

This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning. To obviate DDQ’s high dependency on the quality of simulated experiences, we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data. Experiments show that D3Q significantly outperforms DDQ by controlling the quality of simulated experience used for planning. The effectiveness and robustness of D3Q is further demonstrated in a domain extension setting, where the agent’s capability of adapting to a changing environment is tested.1
Learning Face Deblurring Fast and Wide Meiguang Jin University of Bern Switzerland

Portrait images and photos containing faces are ubiquitous on the web and the predominant subject of images shared via social media. Especially selfie images taken with lightweight smartphone cameras are susceptible to camera shake. Despite significant progress in the field of image deblurring over the last decade, the performance of stateof-the-art deblurring methods on blurry face images is still limited. In this work, we present a novel deep learning architecture that is designed to be computationally fast and exploits a very wide receptive field to return sharp face images even in challenging scenarios. Our network features an effective resampling convolution operation that ensures a wide receptive field from the very first layers, while at the same time being highly computationally efficient. We also show that batch normalization prevents networks from yielding high-quality image results and introduce instance normalization instead. We demonstrate our architecture on face deblurring as well as other more general scenes. Extensive experiments with state-of-the-art methods demonstrate the effectiveness of our proposed network, in terms of run-time, accuracy, and robustness to ISO levels as well as gamma correction.
Recurrent Pixel Embedding for Instance Grouping — Supplementary Material

In this supplementary material, we provide proofs of the propositions introduced in the main paper for understanding our objective function and grouping mechanism. Then, we provide the details of the mean-shift algorithm, computation of gradients and how it is adapted for recurrent grouping. We illustrate how the gradients are back-propagated to the input embedding using a toy example. Finally, we include more qualitative results on boundary detection and instance segmentation.
Captioning via Hierarchical Reinforcement Learning : Supplementary Material

Fig. 1 demonstrated a visualization example where the associated attentions of the learned text segments over video frames were plotted. Clearly, when generating different text segments, the HRL model attended to different temporal frames. For example, when the model was producing the segment is cooking on the stove, the first halve of the video, which contained the action cooking, played a more important role with larger attention values.
Dark matter concentrations in galactic nuclei according to polytropic models

We calculate the radial profiles of galaxies where the nuclear region is self-gravitating, consisting of self-interacting dark matter (SIDM) with F degrees of freedom. For sufficiently high density this dark matter becomes collisional, regardless of its behaviour on galaxy scales. Our calculations show a spike in the central density profile, with properties determined by the dark matter microphysics, and the densities can reach the ‘mean density’ of a black hole (from dividing the black hole mass by the volume enclosed by the Schwarzschild radius). For a galaxy halo of given compactness (χ ≡ 2GM/Rc2), certain values for the dark matter entropy yield a dense central object lacking an event horizon. For some soft equations of state of the SIDM (e.g. F 6), there are multiple horizonless solutions at given compactness. Although light propagates around and through a sphere composed of dark matter, it is gravitationally lensed and redshifted. While some calculations give non-singular solutions, others yield solutions with a central singularity. In all cases, the density transitions smoothly from the central body to the dark matter envelope around it, and to the galaxy’s dark matter halo. We propose that pulsar timing observations will be able to distinguish between systems with a centrally dense dark matter sphere (for different equations of state) and conventional galactic nuclei that harbour a supermassive black hole.
In vitro gene silencing effect of chitosan/shRNA PDGF-D nanoparticles in breast cancer

Breast cancer is the most common cancer worldwide in women and it is highly malignant and fatal. PDGF-D plays role in regulation of many cellular processes such as angiogenesis. PDGF-D is overexpressed in many types of cancers and promote tumor growth and metastasis. Silencing of PDGF-D gene by using shRNA with an appropriate carrier system may decrease tumor growth and metastasis. In our study, we prepared chitosan nanoparticles loaded with five different shRNA plasmids targeting different exons of PDGF-D gene. Then, nanoparticles were characterized in vitro and transfection efficiency of these nanoparticles were investigated in breast cancer cell lines (MCF- 7, MDA-MB-231 and MDA-MB-435). The effects of single and multiple shRNA sequences, molecular weight of chitosan (150 kDa and 400 kDa) and the amount of shRNA (100 and 500 μg) on the characterization and transfection efficiencies of nanoparticles have been studied. Size of nanoparticles changed between 200-400 nm and approximately 95-100% encapsulation efficiency were obtained. Release of shRNA changed with the molecular weight of chitosan. It was obtained that formulation containing shRNA plasmid targeting PDGF-D exon 6 (NP1) has the highest silencing efficiency in MDA-MB-231 cell line. It was also evaluated that chitosan can be a suitable gene delivery system for shRNA targeting PDGF-D.
Learning to Understand Image Blur

While many approaches have been proposed to estimate and remove blur in a photo, few efforts were made to have an algorithm automatically understand the blur desirability: whether the blur is desired or not, and how it affects the quality of the photo. Such a task not only relies on low-level visual features to identify blurry regions, but also requires high-level understanding of the image content as well as user intent during photo capture. In this paper, we propose a unified framework to estimate a spatially-varying blur map and understand its desirability in terms of image quality at the same time. In particular, we use a dilated fully convolutional neural network with pyramid pooling and boundary refinement layers to generate high-quality blur response maps. If blur exists, we classify its desirability to three levels ranging from good to bad, by distilling high-level semantics and learning an attention map to adaptively localize the important content in the image. The whole framework is end-to-end jointly trained with both supervisions of pixel-wise blur responses and image-wise blur desirability levels. Considering the limitations of existing image blur datasets, we collected a new large-scale dataset with both annotations to facilitate training. The proposed methods are extensively evaluated on two datasets and demonstrate state-of-the-art performance on both tasks.
Antidiabetic effects of Salvia triloba and Thymus praecox subsp. skorpilii var. skorpilii in a rat model of streptozotocin/nicotinamide-induced diabetes

Some Salvia and Thymus species of Lamiaceae family come into prominence with strong antidiabetic effects. Compared to the other species, there are limited studies on antidiabetic activity of Salvia triloba (ST) and Thymus praecox subsp. skorpilii var. skorpilii (TPS). The aim of this study was to adjust the dosage and to determine the antidiabetic effects of methanol extracts of ST and TPS in streptozotocin/nicotinamide-induced diabetic rats. Type II diabetes mellitus (T2DM) was induced by a single intraperitoneal (i.p.) injection of streptozotocin (STZ) dissolved in 0.1 M cold citrate buffer (pH 4.5) at a dose of 55 mg/kg/body weight (b.w.) and nicotinamide (100 mg/kg/b.w.) was given prior to STZ injection. For adjusting dosage, oral glucose tolerance test (OGTT) was used while insulin tolerance test (ITT), OGTT, blood glucose levels and animal weights were used to evaluate the antidiabetic effects of ST and TPS. According to the OGTT, the most effective doses for ST and TPS were 200 mg/kg and 100 mg/kg, respectively. At the end of three weeks, blood glucose levels of control goup reached to 462.50 mg/dl, whereas ST and TPS-treated groups blood glucose levels decreased less than 200.00 mg/dl. In conclusion, the present study suggests that both of ST and TPS methanolic extracts may be of therapeutic benefit in diabetes and thus need to further studies.
Copper availability as related to soil copper fractions in oxisols under liming

The knowledge of the chemical forms of copper in soils and the relationships of these forms with soil copper availability are important for predicting the copper behavior in the soil-plant system. The present work studies the influence of liming on the available contents of copper as well as on the forms of copper fractions in six types of Oxisols. Soil samples, with and without liming, received copper at rates of 0.0, 20.0 and 40.0 mg dm-3 and remained incubated for 30 days. Then, available copper was extracted with Mehlich-1, Mehlich-3, DTPA and EDTA solutions, and analyzed by atomic absorption spectrophotometry. Additionally, soil samples were extracted in a sequential procedure to determine Cu in fractions of soil, as follows: exchangeable-Cu fraction, organic matter-Cu fraction, Mn oxide-Cu fraction, amorphous Fe oxide-Cu fraction, crystalline Fe oxide-Cu fraction, residual-Cu fraction, and the total Cu content in the soil. Soil samples to which Cu was added presented higher Cu retention in the organic matter fraction with a small percentage retained in the exchangeable-Cu fraction. Liming resulted in a decrease of Cu in the exchangeable and organic matter fractions and an increase in the Fe and Mn oxide fractions and in the residual fraction. Without liming, the organic matter fraction presented the highest contribution to Cu content found in the soil extracts obtained with all extractors, except EDTA. For treatments with liming, Cu contents in the organic matter fraction were better correlated to Cu contents in extracts obtained with DTPA and Mehlich-3.
Multistage Adversarial Losses for Pose-Based Human Image Synthesis

Human image synthesis has extensive practical applications e.g. person re-identification and data augmentation for human pose estimation. However, it is much more challenging than rigid object synthesis, e.g. cars and chairs, due to the variability of human posture. In this paper, we propose a pose-based human image synthesis method which can keep the human posture unchanged in novel viewpoints. Furthermore, we adopt multistage adversarial losses separately for the foreground and background generation, which fully exploits the multi-modal characteristics of generative loss to generate more realistic looking images. We perform extensive experiments on the Human3.6M dataset and verify the effectiveness of each stage of our method. The generated human images not only keep the same pose as the input image, but also have clear detailed foreground and background. The quantitative comparison results illustrate that our approach achieves much better results than several state-of-the-art methods.
Quality by Design (QbD) Approach for Optimization of Microemulsion based Topical Gel

This study aimed to develop a microemulsion based topical gel formulation of Aceclofenac, an oral NSAID with known major complications like dyspepsia, ulcer and bleeding. Topical route overcomes the above complications and provides the additional advantage of site specific delivery. Aceclofenac being BCS class II drug pose the formulation problem because of lower aqueous solubility. Thus development of microemulsion based gel will be of greater benefit as oil phase will aid in drug solubilisation and its smaller size will facilitate higher permeation. The components of the formulation include LabrafacLipophile WL1349, Cremophor EL and Transcutol P as oil phase, surfactant and cosurfactant, respectively. The effect of oil to surfactant/co-surfactant ratio and carbopol 940 on drug release and viscosity profile of the formulation was studied by 3 2 factorial design. Dynamic light scattering showed that this formulation had a well defined spherical shape and uniform size ( 90 nm) with narrow polydispersity index (0.234) and a zeta potential -7.02 mV. The drug release from the optimized batch containing 1:9 oil to surfactant/co-surfactant ratio and 1.015 %w/w Carbopol 940 was found to be 73.85±2.07% at 180 min. The results revealed that the developed microemulsion gel has great potential for topical delivery of Aceclofenac.
Leveraging the Bayesian Filtering Paradigm for Vision-Based Facial Affective State Estimation

Estimating a person's affective state from facial information is an essential capability for social interaction. Automatizing such a capability has therefore increasingly driven multidisciplinary research for the past decades. At the heart of this issue are very challenging signal processing and artificial intelligence problems driven by the inherent complexity of human affect. We therefore propose a principled framework for designing automated systems capable of continuously estimating the human affective state from an incoming stream of images. First, we model human affect as a dynamical system and define the affective state in terms of valence, arousal and their higher-order derivatives. We then pose the affective state estimation problem as a Bayesian filtering problem and provide a solution based on Kalman filtering (KF) for probabilistic reasoning over time, combined with multiple instance sparse Gaussian processes (MI-SGP) for inferring affect-related measurements from image sequences. We quantitatively and qualitatively evaluate our proposed framework on the AVEC 2012 and AVEC 2014 benchmark datasets and obtain state-of-the-art results using the baseline features as input to our MI-SGP-KF model. We therefore believe that leveraging the Bayesian filtering paradigm can pave the way for further enhancing the design of automated systems for affective state estimation.
Exploiting vector instructions with generalized stream fusion

Ideally, a program written as a composition of concise, self-contained components should perform as well as the equivalent hand-written version where the functionality of what was many components has been manually combined into a monolithic implementation. That is, programmers should not have to sacrifice code clarity or good software engineering practices to obtain performance---we want compositionality without a performance penalty. This work shows how to attain this goal for high-level Haskell in the domain of sequence-processing functions, which includes applications such as array processing. Prior work on stream fusion shows how to automatically transform some high-level sequence-processing functions into efficient implementations. It has been used to great effect in Haskell libraries for manipulating byte arrays, Unicode text, and unboxed vectors. However some operations, like vector append, do not perform well within the stream fusion framework. Others, like SIMD computation using the SSE and AVX instructions available on modern x86 chips, do not seem to fit in the stream fusion framework at all. We describe generalized stream fusion, which solves these issues through a careful choice of stream representation. Benchmarks show that high-level Haskell code written using our compiler and libraries can produce code that is faster than both compiler- and hand-vectorized C.