Miracle-dz/UniFAR-FTUs huggingface dataset:
Redispersible Spray-Dried Powder Containing Nanoencapsulated Curcumin: the Drying Process Does Not Affect Neuroprotection In vitro

A redispersible spray-dried formulation containing curcumin-loaded, lipid-core nanocapsules (LNC-C) was developed for oral administration. The neuroprotective activity of curcumin after the spray-drying process was evaluated in vitro. The spray-dried powder (SD-LNC-C) was produced using a drying adjuvant composed of a blend of maltodextrin and l-leucine (90:10 w/w). Acceptable process yield (~ 70%) and drug content (6.5 ± 0.2 mg g−1) were obtained. SD-LNC-C was formed by smooth, spherical-shaped particles, and confocal Raman analysis indicated the distribution of the LNC-C on the surface of the leucine/maltodextrin agglomerates. The surface of the agglomerates was formed by a combination of LNC-C and adjuvants, and laser diffraction showed that SD-LNC-C had adequate aqueous redispersion, with no loss of controlled drug release behaviour of LNC-C. The in vitro curcumin activity against the lipopolysaccharide (LPS)-induced proinflammatory response in organotypic hippocampal slice cultures was evaluated. Both formulations (LNC-C and SD-LNC-C) reduced TNF-α to similar levels. Therefore, neuroprotection of curcumin in vitro may be improved by nanoencapsulation followed by spray-drying, with no loss of this superior performance. Hence, the redispersible spray-dried powder proposed here represents a suitable approach for the development of innovative nanomedicines containing curcumin for the prevention/treatment of neurodegenerative diseases.
Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ∼2800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ∼2.5 per cent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
Coded trace reconstruction in a constant number of traces

The coded trace reconstruction problem asks to construct a code <tex>$C \subset \{0,1\}^{n}$</tex> such that any <tex>$x\in C$</tex> is recoverable from independent outputs (“traces”) of <tex>$x$</tex> from a binary deletion channel (BDC). We present binary codes of rate <tex>$1-\varepsilon$</tex> that are efficiently recoverable from <tex>$\exp(O_{q}(\log^{1/3}(\frac{1}{\varepsilon})))$</tex> (a constant independent of <tex>$n$</tex>) traces of a <tex>$\text{BDC}_{q}$</tex> for any constant deletion probability <tex>$q\in(0,1)$</tex>. We also show that, for rate <tex>$1 -\varepsilon$</tex> binary codes, <tex>$\tilde{\Omega}(\log^{5/2}(1/\varepsilon))$</tex> traces are required. The results follow from a pair of black-box reductions that show that average-case trace reconstruction is essentially equivalent to coded trace reconstruction. We also show that there exist codes of rate <tex>$1 -\varepsilon$</tex> over an <tex>$O_{\varepsilon}(1)$</tex>-sized alphabet that are recoverable from <tex>$O(\log(1/\varepsilon))$</tex> traces, and that this is tight.
All group‐based latin squares possess near transversals

In a latin square of order n, a near transversal is a collection of n −1 cells which intersects each row, column, and symbol class at most once. A longstanding conjecture of Brualdi, Ryser, and Stein asserts that every latin square possesses a near transversal. We show that this conjecture is true for every latin square that is main class equivalent to the Cayley table of a finite group.
Topological Photonic Integrated Circuits Based on Valley Kink States

Valley pseudospin, a new degree of freedom in photonic lattices, provides an intriguing way to manipulate photons and enhance the robustness of optical networks. Here, topological waveguiding, refracting, resonating, and routing of valley‐polarized photons in integrated circuits are experimentally demonstrated. Specifically, it is shown that at the domain wall between photonic crystals of different topological valley phases, there exists a topologically protected valley kink state that is backscattering‐free at sharp bends and terminals. These valley kink states are further harnessed for constructing high‐Q topological photonic crystal cavities with tortuously shaped cavity geometries. A novel optical routing scheme at an intersection of multiple valley kink states is also demonstrated, where light splits counterintuitively due to the valley pseudospin of photons. These results can not only lead to robust optical communication and signal processing, but also open the door for fundamental research of topological photonics in areas such as lasing, quantum photon‐pair generation, and optomechanics.
MLP Aware Scheduling Techniques in Multithreaded Processors

Major chip manufacturers have all introduced Multithreaded processors. These processors are used for running a variety of workloads. Efficient resource utilization is an important design aspect in such processors. Particularly, it is important to take advantage of available memory-level parallelism(MLP). In this paper I propose a MLP aware operating system (OS) scheduling algorithm for Multithreaded Multi-core processors. By observing the MLP available in each thread and by balancing it with available MLP resources in the system the OS will come up with a new schedule of threads for the next quantum that could potentially improve overall performance. We do a qualitative comparison of our solution with other hardware and software techniques. This work can be extended by doing a quantitative evaluation and by further refining the scheduling optimization.
RWR-GAE: Random Walk Regularization for Graph Auto Encoders

Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space. Graph autoencoders, as one of the widely adapted deep models, have been proposed to learn graph embeddings in an unsupervised way by minimizing the reconstruction error for the graph data. However, its reconstruction loss ignores the distribution of the latent representation, and thus leading to inferior embeddings. To mitigate this problem, we propose a random walk based method to regularize the representations learnt by the encoder. We show that the proposed novel enhancement beats the existing state-of-the-art models by a large margin (upto 7.5\%) for node clustering task, and achieves state-of-the-art accuracy on the link prediction task for three standard datasets, cora, citeseer and pubmed. Code available at this https URL.
On the cosmological performance of photometrically classified supernovae with machine learning

The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin Observatory Legacy Survey of Space and Time (LSST), will be unfeasible. The development of automated classification processes based on photometry has thus become crucial. In this paper we investigate the performance of machine learning (ML) classification on the final cosmological constraints using simulated lightcurves from The Supernova Photometric Classification Challenge, released in 2010. We study the use of different feature sets for the lightcurves and many different ML pipelines based on either decision tree ensembles or automated search processes. To construct the final catalogs we propose a threshold selection method, by employing a \emph{Bias-Variance tradeoff}. This is a very robust and efficient way to minimize the Mean Squared Error. With this method we were able to get very strong cosmological constraints, which allowed us to keep $\sim 75\%$ of the total information in the type Ia SNe when using the SALT2 feature set and $\sim 33\%$ for the other cases (based on either the Newling model or on standard wavelet decomposition).
The first law of black hole thermodynamics for Taub-NUT spacetime

This article focuses on the new approach to the nature of a nut parameter $n$, usually treated as a dual charge to the mass. Instead of thinking about it as a gravitational analog of the magnetic monopole, we will show that the Taub-NUT spacetime conceals a peculiar rotation coming from the Misner strings depending on the nut value. Besides reconciling a number of confusing results present in the literature, this allows us to establish a consistent description of the black hole thermodynamics for the Lorentzian Taub--NUT spacetime with essential contribution from the Misner strings to the angular momentum and total entropy.
An adaptive s-step conjugate gradient algorithm with dynamic basis updating

The adaptive s-step CG algorithm is a solver for sparse symmetric positive definite linear systems designed to reduce the synchronization cost per iteration while still achieving a user-specified accuracy requirement. In this work, we improve the adaptive s-step conjugate gradient algorithm by the use of iteratively updated estimates of the largest and smallest Ritz values, which give approximations of the largest and smallest eigenvalues of A, using a technique due to G.Meurant and P.Tichý (2018). The Ritz value estimates are used to dynamically update parameters for constructing Newton or Chebyshev polynomials so that the conditioning of the s-step bases can be continuously improved throughout the iterations. These estimates are also used to automatically set a variable related to the ratio of the sizes of the error and residual, which was previously treated as an input parameter. We show through numerical experiments that in many cases the new algorithm improves upon the previous adaptive s-step approach both in terms of numerical behavior and reduction in number of synchronizations.
Keypoint detection and description on image curves

KEYPOINT DETECTION AND DESCRIPTION ON IMAGE CURVES Image curves are one of the choices for representing interest points which also provide discriminative information about images. Boundary of regions and contour of shapes are real-time instances of image curves. In this thesis, we propose two approaches for keypoint detection and description on image curves. To extract keypoints on image curves, we compute the extrema curvature of region boundaries. This mechanism improves repeatability of keypoints on 3D data. For the description of image curves, shape contours are used. This is similar to approaches that describe the features based on shapes and image gradients. Unlike these approaches, we combine spatial and directional information of tangent directions to extract a feature vector that leads to improved matching and recognition on several standard computer vision tasks such as character and object recognition.
Mutation Testing for Ethereum Smart Contract

Smart contract is a special program that manages digital assets on blockchain. It is difficult to recover the loss if users make transactions through buggy smart contracts, which cannot be directly fixed. Hence, it is important to ensure the correctness of smart contracts before deploying them. This paper proposes a systematic framework to mutation testing for smart contracts on Ethereum, which is currently the most popular open blockchain for deploying and running smart contracts. Fifteen novel mutation operators have been designed for Ethereum Smart Contracts (ESC), in terms of keyword, global variable/function, variable unit, and error handling. An empirical study on 26 smart contracts in four Ethereum DApps has been conducted to evaluate the effectiveness of mutation testing. The experimental results show that our approach can outperform the coverage-based approach on defect detection rate (96.01% vs. 55.68%). The ESC mutation operators are effective to reveal real defects and we found 117 out of 729 real bug reports are related to our operators. These show the great potential of using mutation testing for quality assurance of ESC.
Differentially Private Aggregated Mobility Data Publication Using Moving Characteristics

With the rapid development of GPS enabled devices (smartphones) and location-based applications, location privacy is increasingly concerned. Intuitively, it is widely believed that location privacy can be preserved by publishing aggregated mobility data, such as the number of users in an area at some time. However, a recent attack shows that these aggregated mobility data can be exploited to recover individual trajectories. In this paper, we first propose two differentially private basic schemes for aggregated mobility data publication, namely direct perturbation and threshold perturbation, which preserve location privacy of users and especially resist the trajectory recovery attack. Then, we explore the moving characteristics of mobile users, and design an improved scheme named static hybrid perturbation by combining the two basic schemes according to the moving characteristics. Since static hybrid perturbation works only for static data, which are entirely available before publishing, we further adapt the static hybrid perturbation by combining it with linear regression, and yield another improved scheme named dynamic hybrid perturbation. The dynamic hybrid perturbation works also for dynamic data, which are generated on the fly during publication. Privacy analysis shows that the proposed schemes achieve differential privacy. Extensive experiments on both simulated and real datasets demonstrate that all proposed schemes resist the trajectory recovery attack well, and the improved schemes significantly outperform the basic schemes.
Exploring the Effect of an Item's Neighborhood on its Sellability in eCommerce

Predicting the sale of an item is a critical problem in eCommerce search. Typically, items are independently predicted with a probability of sale for a given search query. But in a dynamic marketplace like eBay, even for a single product, there are various different factors distinguishing one item from another which can influence the purchase decision for the user. Users have to make a purchase decision by considering all of these options. Majority of the existing learning to rank algorithms model the relative relevance between labeled items only at the loss functions like pairwise or list-wise losses. But they are limited to point-wise scoring functions where items are ranked independently based on the features of the item itself. In this paper, we study the influence of an item's neighborhood to its purchase decision. Here, we consider the neighborhood as the items ranked above and below the current item in search results. By adding delta features comparing items within a neighborhood and learning a ranking model, we are able to experimentally show that the new ranker with delta features outperforms our baseline ranker in terms of Mean Reciprocal Rank (MRR). The ranking model with proposed delta features result in $3-5\%$ improvement in MRR over the baseline model. We also study impact of different sizes for neighborhood. Experimental results show that neighborhood size $3$ perform the best based on MRR with an improvement of $4-5\%$ over the baseline model.
Assessing the Quality of Scientific Papers

A multitude of factors are responsible for the overall quality of scientific papers, including readability, linguistic quality, fluency,semantic complexity, and of course domain-specific technical factors. These factors vary from one field of study to another. In this paper, we propose a measure and method for assessing the overall quality of the scientific papers in a particular field of study. We evaluate our method in the computer science domain, but it can be applied to other technical and scientific fields.Our method is based on the corpus linguistics technique. This technique enables the extraction of required information and knowledge associated with a specific domain. For this purpose, we have created a large corpus, consisting of papers from very high impact conferences. First, we analyze this corpus in order to extract rich domain-specific terminology and knowledge. Then we use the acquired knowledge to estimate the quality of scientific papers by applying our proposed measure. We examine our measure on high and low scientific impact test corpora. Our results show a significant difference in the measure scores of the high and low impact test corpora. Second, we develop a classifier based on our proposed measure and compare it to the baseline classifier. Our results show that the classifier based on our measure over-performed the baseline classifier. Based on the presented results the proposed measure and the technique can be used for automated assessment of scientific papers.
Interactive topic hierarchy revision for exploring a collection of online conversations

In the last decade, there has been an exponential growth of asynchronous online conversations (e.g. blogs), thanks to the rise of social media. Analyzing and gaining insights from such discussions can be quite challenging for a user, especially when the user deals with hundreds of comments that are scattered around multiple different conversations. A promising solution to this problem is to automatically mine the major topics from conversations and organize them into a hierarchical structure. However, the resultant topic hierarchy can be noisy and/or it may not match the user’s current information needs. To address this problem, we introduce a novel human-in-the-loop approach that allows the user to revise the topic hierarchy based on her feedback. We incorporate this approach within a visual text analytics system that helps users in analyzing and getting insights from conversations by exploring and revising the topic hierarchy. We evaluated the resulting system with real users in a lab-based study. The results from the user study, when compared to its counterpart that does not support interactive revisions of a hierarchical topic model, provide empirical evidence of the potential utility of our system in terms of both performance and subjective measures. Finally, we summarize generalizable lessons for introducing human-in-the-loop computation within a visual text analytics system.
From primordial black holes abundance to primordial curvature power spectrum (and back)

In the model where Primordial Black Holes (PBHs) form from large primordial curvature (C) perturbations, i.e., CPBHs, constraints on PBH abundance provide in principle constraints on the primordial curvature power spectrum. This connection however depends necessarily on the details of PBH formation mechanism. In this paper we provide, for the first time, constraints on the primordial curvature power spectrum from the latest limits on PBH abundance, taking into account all the steps from gravitational collapse in real space to PBH formation. In particular, we use results from numerical relativity simulations and peak theory to study the conditions for PBH formation for a range of perturbation shapes, including non-linearities, perturbation profile and a careful treatment of smoothing and filtering scales. We then obtain updated PBH formation conditions and translate that into primordial spectrum constraints for a wide range of shapes and abundances. These updated constraints cover a range of scales not probed by other cosmological observables. Our results show that the correct and accurate modelling of non-linearities, filtering and typical perturbation profile, is crucial for deriving meaningful cosmological implications.
BGD-based Adam algorithm for time-domain equalizer in PAM-based optical interconnects

To the best of our knowledge, for the first time, we propose adaptive moment estimation (Adam) algorithm based on batch gradient descent (BGD) to design a time-domain equalizer (TDE) for PAM-based optical interconnects. Adam algorithm has been widely applied in the fields of artificial intelligence. For TDE, BGD-based Adam algorithm can obtain globally optimal tap coefficients without being trapped in locally optimal tap coefficients. Therefore, fast and stable convergence can be achieved by BGD-based Adam algorithm with low mean square error. Meanwhile, BGD-based Adam algorithm is implemented by parallel processing, which is more efficient than conventional serial algorithms, such as least mean square and recursive least square algorithms. The experimental results demonstrate that BGD-based Adam feed-forward equalizer works well in 120-Gbit/s PAM8 optical interconnects. In conclusion, BGD-based Adam algorithm shows great potential for converging the tap coefficients of TDE in future optical interconnects.
New families of non-parity-time-symmetric complex potentials with all-real spectra

New families of non-parity-time-symmetric complex potentials with all-real spectra are derived by the supersymmetry method and the pseudo-Hermiticity method. With the supersymmetry method, we find families of non-parity-time-symmetric complex partner potentials which share the same spectrum as base potentials with known real spectra, such as the (complex) Wadati potentials. Different from previous supersymmetry derivations of potentials with real spectra, our derivation does not utilize discrete eigenmodes of base potentials. As a result, our partner potentials feature explicit analytical expressions which contain free functions. With the pseudo-Hermiticity method, we derive a new class of non-parity-time-symmetric complex potentials with free functions and constants, whose eigenvalues appear as conjugate pairs. This eigenvalue symmetry forces the spectrum to be all-real for a wide range of choices of these functions and constants in the potential. Tuning these free functions and constants, phase transition can also be induced, where conjugate pairs of complex eigenvalues emerge in the spectrum.
Effect of superfluid matter of a neutron star on the tidal deformability

We study the effect of superfluidity on the tidal response of a neutron star in a general relativistic framework. In this work, we take a dual-layer approach where the superfluid matter is confined in the core of the star. Then, the superfluid core is encapsulated with an envelope of ordinary matter fluid which acts effectively as the low-density crustal region of the star. In the core, the matter content is described by a two-fluid model where only the neutrons are taken as superfluid and the other fluid consists of protons and electrons making it charge neutral. We calculate the values of various tidal love numbers of a neutron star and discuss how they are affected due to the presence of entrainment between the two fluids in the core. We also emphasize that more than one tidal parameter is necessary to probe superfluidity with the gravitational wave from the binary inspiral.