Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of solvent and micellar encapsulation on the photostability of avobenzone

The photodegradation of avobenzone ( AV ), the only ultraviolet filter molecule approved by the Food and Drug Administration to absorb UVA radiation, is an important problem in sunscreen formulations. In this paper, the photophysics and photostability of AV in various solvent systems and in aqueous micelles are studied. AV in its keto-enol tautomer functions as an effective UVA protection agent. AV is highly susceptible to photoinduced diketonization in both nonpolar solvents and in aqueous aggregates but is considerably more stable in polar, protic solvents like methanol. By studying its stability in different surfactant solutions, we show that incorporation of AV into sodium dodecylsulfate (SDS) micelles can achieve stability levels comparable to neat methanol. Steady-state spectral shifts, fluorescence anisotropy, and time-resolved fluorescence decay measurements are all consistent with AV experiencing a polar environment after micellar encapsulation. It is proposed that AV is encapsulated in the palisade layer of the SDS micelles, which allows access to water molecules that facilitate the re-formation of the enol form after photon absorption and relaxation. Although the detailed mechanism of AV tautomerization remains unclear, this work suggests that tuning the chemical microenvironment of AV may be a useful strategy for improving sunscreen efficacy
Junior Research Group for ”Statistical Learning in Computational Biology”

Recent advances in high-throughput technologies have led to an exponential increase in biological data (such as genomic, epigenomic and proteomic data). To gain meaningful insights in such large data collections, efficient statistical learning methods are needed that take into account various sources of confounding such as batch effects or population structure, inherent to large biological data sets. We are interested in developing and applying new machine learning / statistical learning methods to solving computational biology problems and answering new biological questions. Application areas include the study of viruses like HIV, Hepatitis C or Influenza as well as the field of epigenetics. Method-wise we are interested in • integration of heterogeneous data sets • improving interpretability of non-linear estimators • efficient learning methods for large data sets.
Online Sufficient Dimension Reduction Through Sliced Inverse Regression

Sliced inverse regression is an eﬀective paradigm that achieves the goal of dimension reduction through replacing high dimensional covariates with a small number of linear combinations. It does not impose parametric assumptions on the dependence structure. More importantly, such a reduction of dimension is suﬃcient in that it does not cause loss of information. In this paper, we adapt the stationary sliced inverse regression to cope with the rapidly changing environments. We propose to implement sliced inverse regression in an online fashion. This online learner consists of two steps. In the ﬁrst step we construct an online estimate for the kernel matrix; in the second step we propose two online algorithms, one is motivated by the perturbation method and the other is originated from the gradient descent optimization, to perform online singular value decomposition. The theoretical properties of this online learner are established. We demonstrate the numerical performance of this online learner through simulations and real world applications. All numerical studies conﬁrm that this online learner performs as well as the batch learner.
International Journal of Management Sciences and Business Research, Dec-2019 ISSN (2226-8235) Vol-8, Issue 12

Author’s Details: (1) Paolo Biancone, Full Professor, Department of Management, University of Turin, C.so Unione Sovietica 218 bis, Turin, Italy (2) Maura Campra, Full Professor, Department of Economics and Business Studies, University of Piemonte Orientale ―Amedeo Avogadro‖, via E. Perrone, 18, Novara, Italy (3) Valerio Brescia, Research fellow, Department of Management, University of Turin, C.so Unione Sovietica 218 bis, Turin, Italy (4) Lucia Taruffo, guest lecturer, Department of Economics and Business Studies, University of Piemonte Orientale ―Amedeo Avogadro‖, via E. Perrone, 18, Novara, Italy Corresponding author: Valerio Brescia, Department of Management, University of Turin, C.so Unione Sovietica 218 bis, Turin, Italy, 10100, Tel. 0039 0116706139 Fax 0039 011 6706012. E-mail: valerio.brescia@unito.it Abstract: Great attention has been given in recent years to the means of evaluating university spin-offs and their impact on the social and economic context. Nevertheless, in literature, there is not an approach that takes into account the different elements linked both to the value of the organization and to the effects that technology and innovation have on the context. The study starting from the analysis of the different methods of evaluation of university spin-offs and the immaterial value of the spin-off proposes an approach that brings together the different elements. The case study of the University of Turin (Italy) highlights the components and characteristics that must be analyzed and which confirm what has already been highlighted by the reference theory. The study offers an integrated approach that can be adopted in other Italian and foreign universities, and that accentuates the attention on the gaps that should be explored in future studies.
Frame-wise representations of depth videos for action recognition

We investigate the of problem continuous action recognition from depth images. Further, we investigate the frame-wise classiﬁcation as a solution for the continuous action recognition problem. We present three types of depth data representation from depth frames, which are referred as single-reference representation , multiple-reference representation and no-reference representation . The ﬁrst two regard a global translation transformation while the third is a local (region-wise) similarity transformation of the depth data. These representations directly provide the descriptors of each depth image. We represent each frame of the video by bag-of-words (BOW) representation. We show that variation of the visual words over a temporal window accounts for the motion information which is integrated in each frame BOW representation, which we encode by temporal smoothing and temporal differentiation of frame BOW representation. Finally, once we have frame representations, standard classiﬁers such as random forests or support vector machines are used for frame-wise action classiﬁcation. We apply a simple voting scheme after frame-wise classiﬁcation for isolated action recognition to compare our method with state-of-the-art methods on isolated recognition and we able to achieve better performance than state-of-the-art methods.
A Novel Virtual 3D Brush Model Based on Variable Stiffness and Haptic Feedback

A novel variable stiffness 3D virtual brush model and haptic decoration technique suitable for the surface of the three-dimensional objects for the automobile industry are introduced based on real-time haptic feedback mechanism using a 6 DOFs input device, and the haptic behavior of an expressive virtual 3D brush with variable stiffness is studied in detail for the first time. First, the intrinsic mechanism between the deformation of real hair brush and the applied external forces (such as the bending moment) is analyzed and studied in detail by introducing a bending spring to express the basic mechanical behavior for the 3D hair brush. Based on this brush model, many important painting features can be simulated, such as the softer brush tip, brush flattening, and bristle spreading. And a useful algorithm (named the weighted-average distance) for dealing with collision checking among the two objects (3D clay and the 3D brush) is presented. As long as the brush head is close to the 3D object, within a tolerance range, the computational tactile sensation force will be emerged, and the interactive painting process is implemented actually on the outer surface of the virtual object. We then calculate the related bounding ball for deformed 3D brush using a fast ball-expanding search algorithm to determine the virtual projection plane. Based on the real-time deformation about the virtual brush head at a sampling point, the 2D painting footprints, which is produced between the brush head and virtual projection painting plane, is calculated and rendered. Next, the 3D painting footprint could be easily produced via mapping the 2D painting footprints onto the surface of the 3D model in real time. Finally, the 3D painting strokes are formed via controlling the exerted force and overlapping the virtual 3D painting footprints with different shape, size, and following the moving direction of the 3D brush. Experiment result shows that the adopted method can effectively enhance reality to users, with high performance.
Detection of Mergeable Wikipedia Articles Utilizing Multiple Similarity Measures

Wikipedia is the largest online encyclopedia, in which articles are edited by different volunteers with different thoughts and styles. Sometimes two or more articles’ titles are different but the themes of these articles are exactly the same or strongly similar. Administrators and editors are supposed to detect such article pairs and determine whether they should be merged together. We call an article pair is mergeable if it is discussed for possible merge, and a merged article pair is such that the pair is actually merged. In this paper, we propose a method to automatically determine whether an article pair is mergeable or merged. According to Wikipedia Guidelines for article merge, in the duplicate case, the article pairs are covering exactly the same contents. In the overlap case, the article pairs are covering related subjects that have a significant overlap. The content of an overlapped part is similar but the words in the pair can be extensively different, so methods that exploit semantic relatedness are necessary. We consider various textual similarities and semantic relatedness. For integrating word embeddings on the target dataset and the global large corpus, we propose linear and non-linear combinations of multiple embedding results and rebuilding word vectors for evaluating semantic relatedness. We clarify the differences between our method and previous researches for combining multiple word embeddings. We also deal with overlap cases by computing Jaccard similarity between article pairs. We combine Jaccard similarity, common-link article count and word embedding-based relatedness together, to predict whether the article pair should be merged. We explore the relationship between segment-level (paragraph-level) similarity and mergeable/merged article pairs, then propose Multimodal Similarity-Based Merge Prediction (MSBMP) which combines the proposed new features by Random Forest, to predict mergeable/merged article pairs. Our evaluations are performed on real mergeable and merged article pairs. Remarkable superiorities of MSBMP are shown, with apparent improvement from baselines of WikiSearch, TFIDF and word embeddings.
Modified Sliding-Mode Control Method for Synchronization a Class of Chaotic Fractional-Order Systems with Application in Encryption

In this study, we propose a secure communication scheme based on the synchronization of two identical fractional-order chaotic systems. The fractional-order derivative is in Caputo sense, and for synchronization, we use a robust sliding-mode control scheme. The designed sliding surface is taken simply due to using special technic for fractional-order systems. Also, unlike most manuscripts, the fractional-order derivatives of state variables can be chosen differently. The stability of the error system is proved using the Lyapunov stability of fractional-order systems. Numerical simulations illustrate the ability and effectiveness of the proposed method. Moreover, synchronization results are applied to secure communication using the masking method. The security analysis demonstrates that the introduced algorithm has a large keyspace, high sensitivity to encryption keys, higher security, and the acceptable performance speed.
Developing a Computing Identity Framework

This paper expands on knowledge of computing identity by building on what is known about prior identity models in science and mathematics education. The model theorizes three primary sub-constructs that contribute to the development of a computing identity: belief in one's performance/competence, interest, and recognition in computing. Drawing on data from a nationally representative survey of more than 1,700 college students at 22 colleges and universities, the study tested the alignment of the theorized model to the measures on the survey. Confirmatory Factor Analysis was used to validate whether the appropriate measures loaded on the three separate sub-constructs. Criterion-related validity was also established by testing whether the computing identity measures predicted the choice of a computer science career. The results reveal that a computing identity proxy based on the theorized measures was a highly significant predictor of students' computer science and information technology career choice (p < 0.0001). In addition, this work also established criterion-related validity by showing gender differences that had been found by prior work in computing. Finally, the theorized measures were found to be reliable and internally consistent. The educational understanding of computing identities may provide an important tool to help researchers and practitioners improve student persistence in computer science.
Spectrum Allocation with Power Controlin LBS Based D2D Cellular Mobile Networks

Over the past decade, the demand for mobile internet traffic has been rapidly increasing as the huge increase of the smart phone and mobile devices. D2D (Device to Device) is known that it reduce the traffic load of the base station and also improves the reliability of the network performance. However, D2D communication may share the same spectrum, which means that it shares the resource with cellular networks. Although D2D reduce the load on the base station through direct communication, it also results in the increase of interference due to the sharing of the cellular spectrum. It may occur the decrease of the network throughput as the mutual interference is increased. In this paper, we propose a spectrum allocation scheme to use the resources efficiently when the D2D link share the resources of the cellular network in the uplink. D2D communication utilizes the location information for allocating resources when the eNB know the location of all devices. The proposed scheme not only ensures the performance of the D2D communication but also decrease the computational complexity We applied the power control in order to attain the maximum throughput in the D2D underlay network. We also analyze several schemes to control the power of Cellular UE and D2D user in order to satisfy the minimum SINR value in uplink cellular network environment. Simulation results show that the proposed scheme provides the maximum throughput with the 30 % of computational complexity. In order to maximize total throughput, we compare the simulation results in case of controlling only the cellular UE or D2D user and both simultaneously. It is observed that power control is effective to achieve comparable throughput with a reduced complexity for spectrum allocation. 
Discover the Silver Lining Eﬀect: Machine Learning Techniques for Learning User’s Attitudes over Time on Social media

The way the users generate contents on social networks is descriptive of their aﬀective state (e.g., personality, emotions, empathy). The analysis of the digital footprints left by users on social networks, known as social media footprints (e.g,. comments, likes, videos), is a very hot topic in the last decade. The proposed research will analyze such an information with the aim of detecting the user’s typical attitudes. This will allow us to better proﬁle user’s social behavior, aiming to improve her satisfaction with personalized services. Speciﬁcally, we want to evaluate how user’s positive attitudes spread during social events or, more generically, during discussions about social issues. Furthermore, this work will pay attention on how user’s attitudes evolve over time w.r
Teaching Abstraction in Computer Science to 7th Grade Students

Abstraction is one of the most fundamental ideas in computer science (CS), and as such, according to Bruner [23], it should be taught spirally, starting as early as possible and revisited at every level of education. However, teaching CS abstraction to novices is a very challenging task, and CS educational research has often demonstrated students' difficulties in learning this idea, in different contexts and at different age levels. The challenge in teaching CS abstraction is even greater when dealing with young students, since according to theories on children's cognitive development, their abstraction abilities may still be not fully developed. In 2013, Armoni [5] introduced a framework for teaching abstraction in the context of algorithmic problem solving, intended for novice students. We studied the effect of this framework in an introductory CS course for 7th graders, in which Scratch was used as the programming language for implementing algorithmic solutions. Our findings indicate that the framework was highly effective for developing CS abstraction skills as well as other related skills and aspects, such as the tendency to provide explanations for solutions, the use of initialization processes, and the perception of the nature of CS. It also significantly improved students’ general CS performance in this course.
Nonplanar Crack Growth Simulation of Multiple Cracks Using Finite Element Method

This work deals with a 2D finite element simulation of nonplanar multiple cracks using fracture and crack propagation analysis. This analysis was performed by using the developed source code software written by Visual Fortran Language. This source code includes the adaptive mesh generation utilizing the advanced front method and also the mesh refinement process. In order to correctly represent the field singularity, the quarter-point singular elements are constructed around the tip of the crack. The crack growth criteria are used to predict the crack growth direction by utilizing the circumferential stress factor in calculating the yielding stress in elastic fracture assumptions. The stress intensity factor determination is one of the most critical procedures as it determines the crack initiation and propagation mechanism. Moreover, the stress intensity factor histories during the crack growth are measured with the use of equivalent domain integral methods. The crack path simulation and stress intensity factor calculations are compared with the literature and revealed that the results are in agreement with research carried in this domain.
This paper is included in the Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’20)

Starting in 2013, we set out to build a new database to act as the conﬁguration store for a high-performance cloud block storage system (Amazon EBS). This database needs to be not only highly available, durable, and scalable but also strongly consistent. We quickly realized that the constraints on availability imposed by the CAP theorem, and the realities of operating distributed systems, meant that we didn’t want one database. We wanted millions. Physalia is a transactional key-value store, optimized for use in large-scale cloud control planes, which takes advantage of knowledge of transaction patterns and infrastructure design to offer both high availability and strong consistency to millions of clients. Physalia uses its knowledge of datacenter topology to place data where it is most likely to be available. Instead of being highly available for all keys to all clients, Physalia focuses on being extremely available for only the keys it knows each client needs, from the perspective of that client. This paper describes Physalia in context of Amazon EBS, and some other uses within Amazon Web Services. We believe that the same patterns, and approach to design, are widely applicable to distributed systems problems like control planes, conﬁguration management, and service discovery.
Dynamic analysis of team strategy in professional football

One of the most important tactical decisions that a football manager must make is to determine the spatial configuration of the team, or formation, during different phases of a match. The selection of formations influences how aggressively a team plays, where they focus their attacks, and their overall playing style. We present an innovative new technique for dynamically measuring, classifying and studying team formations in professional football matches. Using a large sample of player tracking data, we measure the relative positioning of each team’s players in and out of possession of the ball over successive time intervals during each match. Applying hierarchical agglomerative clustering – using the Wasserstein metric to measure distances between formations – we have identified the unique set of offensive and defensive formations that teams deployed. We use these formation templates, in combination with Bayesian model selection criteria, to classify new formation observations, producing tactical summaries of each match. We identify each team’s preferred offensive and defensive formations, and study how managers reacted tactically to key events during their matches. Finally, we discuss how formation choices relate to playing style, and discuss other potential applications of our methodology.
Preconditioned conjugate gradient algorithms for graph regularized matrix completion

Low-rank matrix completion is the problem of recovering the missing entries of a data matrix by using the assumption that a good low-rank approximation to the true matrix is possible. Much attention has been paid recently to exploiting correlations between the column/row entities through side information to improve the matrix completion quality. In this paper, we propose an eﬃcient algorithm for solving the low-rank matrix completion with graph-based regularizers. Experiments on synthetic data show that our approach achieves signiﬁcant speedup compared to the alternating minimization scheme.
The Hint Factory: Automatic Generation of Contextualized Help for Existing Computer Aided Instruction

The Hint Factory is a novel application of Markov decision processes (MDPs), a reinforcement learning technique, to automatically generate contextualized hints from past student data. We describe the process of adding the Hint Factory to an existing, non-adaptive, software program used to teach deductive logic in an undergraduate Philosophy course, and we report the initial results of a pilot study to test the hint generation capabilities in a real class setting.
Comparison of Retinal Vessel Segmentation Techniques: A Review

- Retinal vessel segmentation is segmentation of an image to extract the vessels to diagnose various diseases such as diabetic retinopathy,pathology related diseases In this paper, various retinal vessel segmentation diseases has been discussed and compared for the early detection of the diseases.In this paper various techniques has been evaluated and it has been observed that retinal vessel segmentation using infinite perimeter in active contour model is best in case of accuracy.