Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Derivatives of Rigid Body Dynamics Algorithms

Rigid body dynamics is a well-established framework in robotics. It can be used to expose the analytic form of kinematic and dynamic functions of the robot model. So far, two major algorithms, namely the recursive Newton-Euler algorithm (RNEA) and the articulated body algorithm (ABA), have been proposed to compute the inverse dynamics and the forward dynamics in a few microseconds. Evaluating their derivatives is an important challenge for various robotic applications (optimal control, estimation, co-design or reinforcement learning). However it remains time consuming, whether using finite differences or automatic differentiation. In this paper, we propose new algorithms to efficiently compute them thanks to closed-form formulations. Using the chain rule and adequate algebraic differentiation of spatial algebra, we firstly differentiate explicitly RNEA. Then, using properties about the derivative of function composition, we show that the same algorithm can also be used to compute the derivatives of ABA with a marginal additional cost. For this purpose, we introduce a new algorithm to compute the inverse of the joint-space inertia matrix, without explicitly computing the matrix itself. All the algorithms are implemented in our open-source C++ framework called Pinocchio. Benchmarks show computational costs varying between 3 microseconds (for a 7-dof arm) up to 17 microseconds (for a 36-dof humanoid), outperforming the alternative approaches of the state of the art.
Ultra-compact and highly efficient silicon polarization splitter and rotator

We propose and experimentally demonstrate an ultra-compact and highly efficient polarization splitter and rotator based on a silicon bent directional coupler structure. The TM-to-TE cross-polarization coupling occurs between the two parallel bent waveguides, if the phase matching condition is satisfied. Efficient polarization splitting and rotating are simultaneously achieved. The device is fabricated by a single step of exposure and etching. The measured peak TM-to-TE polarization conversion efficiency reaches 96.9%. The TM-to-TE conversion loss is lower than 1 dB in the wavelength range of 1544 nm–1585 nm, and the insertion loss for the TE polarization is lower than 0.3 dB in the wavelength regime of 1530 nm–1600 nm. The cross talk values are lower than −20 and −18 dB for the TE- and TM-polarizations over a wavelength range of 70 nm, respectively. The coupling length of the polarization splitter and rotator is 8. 77μm. To the best of our knowledge, our device achieves the shortest coupling length.
Biomaterial Scaffolds in Regenerative Therapy of the Central Nervous System

The central nervous system (CNS) is the most important section of the nervous system as it regulates the function of various organs. Injury to the CNS causes impairment of neurological functions in corresponding sites and further leads to long-term patient disability. CNS regeneration is difficult because of its poor response to treatment and, to date, no effective therapies have been found to rectify CNS injuries. Biomaterial scaffolds have been applied with promising results in regeneration medicine. They also show great potential in CNS regeneration for tissue repair and functional recovery. Biomaterial scaffolds are applied in CNS regeneration predominantly as hydrogels and biodegradable scaffolds. They can act as cellular supportive scaffolds to facilitate cell infiltration and proliferation. They can also be combined with cell therapy to repair CNS injury. This review discusses the categories and progression of the biomaterial scaffolds that are applied in CNS regeneration.
A chimeric mutant analysis in yeast cells suggests BiP independent regulation of the mammalian endoplasmic reticulum-stress sensor IRE1α

ABSTRACT An endoplasmic reticulum (ER)-located transmembrane protein, Ire1, triggers cytoprotective events upon ER stress. Chimeric yeast Ire1 carrying the luminal domain of the mammalian major Ire1 paralogue IRE1α is upregulated in ER-stressed yeast cells, but is poorly associated with the ER-located chaperone BiP even under non-stressed conditions. This observation contradicts the theory that BiP is the master regulator of IRE1α.
Extending CDFR for Overlapping Community Detection

In many real-world networks, a node often belongs to multiple communities. Therefore, overlapping community detection is an important task in social network analysis. CDFR is an efficient algorithm recently proposed for non-overlapping community detection. In this paper, CDFR is extended for overlapping community detection, and the extended algorithm is called as OCDFR. In OCDFR, CDFR is first called to obtain a non-overlapping community partition. Then, find the kth NGC (k=1, 2, 3,.) node for each node, and record the fuzzy relations between them. Based on the fuzzy relation between a node and its kth NGC, five decision methods are proposed to determine whether it can join the communities to which its kth NGC belongs. Experimental results on real-world networks and synthetic networks demonstrate that OCDFR is effective and highly competitive.
Modeling of the plasma chemistry and plasma–surface interactions in reactive plasmas

In this paper, an overview is given of modeling activities going on in our research group, for describing the plasma chemistry and plasma–surface interactions in reactive plasmas. The plasma chemistry is calculated by a fluid approach or by hybrid Monte Carlo (MC)–fluid modeling. An example of both is illustrated in the first part of the paper. The example of fluid modeling is given for a dielectric barrier discharge (DBD) in CH4/O2, to describe the partial oxidation of CH4 into value-added chemicals. The example of hybrid MC–fluid modeling concerns an inductively coupled plasma (ICP) etch reactor in Ar/Cl2/O2, including also the description of the etch process. The second part of the paper deals with the treatment of plasma–surface interactions on the atomic level, with molecular dynamics (MD) simulations or a combination of MD and MC simulations.
Scalable Methods for 8-bit Training of Neural Networks

Quantized Neural Networks (QNNs) are often used to improve network efficiency during the inference phase, i.e. after the network has been trained. Extensive research in the field suggests many different quantization schemes. Still, the number of bits required, as well as the best quantization scheme, are yet unknown. Our theoretical analysis suggests that most of the training process is robust to substantial precision reduction, and points to only a few specific operations that require higher precision. Armed with this knowledge, we quantize the model parameters, activations and layer gradients to 8-bit, leaving at a higher precision only the final step in the computation of the weight gradients. Additionally, as QNNs require batch-normalization to be trained at high precision, we introduce Range Batch-Normalization (BN) which has significantly higher tolerance to quantization noise and improved computational complexity. Our simulations show that Range BN is equivalent to the traditional batch norm if a precise scale adjustment, which can be approximated analytically, is applied. To the best of the authors' knowledge, this work is the first to quantize the weights, activations, as well as a substantial volume of the gradients stream, in all layers (including batch normalization) to 8-bit while showing state-of-the-art results over the ImageNet-1K dataset.
Atorvastatin Downregulates In Vitro Methyl Methanesulfonate and Cyclophosphamide Alkylation-Mediated Cellular and DNA Injuries

Statins are 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors, and this class of drugs has been studied as protective agents against DNA damages. Alkylating agents (AAs) are able to induce alkylation in macromolecules, causing DNA damage, as DNA methylation. Our objective was to evaluate atorvastatin (AVA) antimutagenic, cytoprotective, and antigenotoxic potentials against DNA lesions caused by AA. AVA chemopreventive ability was evaluated using antimutagenicity assays (Salmonella/microsome assay), cytotoxicity, cell cycle, and genotoxicity assays in HepG2 cells. The cells were cotreated with AVA and the AA methyl methanesulfonate (MMS) or cyclophosphamide (CPA). Our datum showed that AVA reduces the alkylation-mediated DNA damage in different in vitro experimental models. Cytoprotection of AVA at low doses (0.1–1.0 μM) was observed after 24 h of cotreatment with MMS or CPA at their LC50, causing an increase in HepG2 survival rates. After all, AVA at 10 μM and 25 μM had decreased effect in micronucleus formation in HepG2 cells and restored cell cycle alterations induced by MMS and CPA. This study supports the hypothesis that statins can be chemopreventive agents, acting as antimutagenic, antigenotoxic, and cytoprotective components, specifically against alkylating agents of DNA.
Multivariate time series modeling using VARMAX

Two examples of Vector Autoregressive Moving Average modeling with exogenous variables are given in this presentation. Data is from the real world. One example is about a two-dimensional time series for wages and prices in Denmark that spans more than a hundred years. The other is about the market for agricultural products, especially eggs! These examples give a general overview of the many possibilities offered by PROC VARMAX, such as handling of seasonality, causality testing and Bayesian modeling, and so on. INTRODUCTION The aim of this paper is to illustrate some of the many features in PROC VARMAX which is dedicated multivariate time series analysis. The name VARMAX is an abbreviation for Vector Autoregressive Moving Average models with eXogenous variables. This procedure is updated in the most recent version of Analytic Products 13.1 as of December 2013. These models are multivariate generalizations of univariate ARMA models which have been popular since the book by Box and Jenkins (1976). For multivariate problems more interesting hypotheses could be tested in a dynamic model setup. But on the other hand the model building is more complicated. Some of the many facilities offered by PROC VARMAX are demonstrated by two examples in this paper. For a more examples the reader is referred to an upcoming book, Milhøj(2014). TIME SERIES ANALYSIS OF THE DANISH WAGE-PRICE RELATION FOR 150 YEARS In the first example two time series are considered: A yearly price index, denoted P, and a wage index 1818 to 1981, denoted W, for the years 1818 to 1981. The series of course exhibit a form of exponential growth, so it is natural to consider the logarithmically transformed series, LP = log(P) and LW = log(W). The basic application of the procedure VARMAX for modeling the two series simultaneously is simple as given by the next program. The option DIF=. indicates that both series are differenced in order to achieve stationarity. proc varmax data=sasmts.wageprice print=all plots=all; model lp lw/dif=(lp(1) lw(1)); run; This output has three main parts – basic statistical properties of the series, estimation of a particular model and checks of the fitted model. If the option PRINTALL is included, many more features of the model are presented leading to a huge output. The series of first differences passes the Dickey Fuller testing rejecting that a second order difference is needed. By an algorithm to choose an appropriate order of a VARMA model an ARMA(4,1) model is selected. But it turns out that all parameters of the moving average term are insignificant, so an AR(4) will be used in the following. This model is applied by adding the option P=4 to the model statement. Far from all autoregressive parameters are significant, as seen by the schematic plot, Output 1, of the autoregressive coefficients. Schematic Representation of Parameter Estimates Variable/ Lag C AR1 AR2 AR3 AR4 lp . ++ . + lw + ++ +. + is > 2*std error, is < -2*std error, . is between, * is N/A Output 1. A quick view of significant parameters
SemEval 2018 Task 4: Character Identification on Multiparty Dialogues

Character identification is a task of entity linking that finds the global entity of each personal mention in multiparty dialogue. For this task, the first two seasons of the popular TV show Friends are annotated, comprising a total of 448 dialogues, 15,709 mentions, and 401 entities. The personal mentions are detected from nominals referring to certain characters in the show, and the entities are collected from the list of all characters in those two seasons of the show. This task is challenging because it requires the identification of characters that are mentioned but may not be active during the conversation. Among 90+ participants, four of them submitted their system outputs and showed strengths in different aspects about the task. Thorough analyses of the distributed datasets, system outputs, and comparative studies are also provided. To facilitate the momentum, we create an open-source project for this task and publicly release a larger and cleaner dataset, hoping to support researchers for more enhanced modeling.
Pocket Labs Supported IoT Teaching

IoT is both a concept and a specific platform with large variety of applications that rapidly become inseparable part of everyday life not only improving it, but making it more interesting and fun. ICT based, it is devoted to interactions with environment that are usually not available with traditional ICT equipment and platforms. IoT is at the same time both complementary and compatible with exist-ing non IoT world, which offers computing power and resources to IoT, making it a unique and powerful combination. Pocket Lab is a relatively new teaching concept that supports students’ creativity and initiative allowing for carrying and experimenting with real equipment at a time and place of choice, much like using of regular text books for studying. Although the IoT & Pocket Labs are not nec-essarily interconnected or mutually conditioned, this paper discusses such a real case of teaching practice, where the Pocket Labs are a natural solution for teach-ing of IoT. The paper deals with one semester teaching experience of IoT as a university course. Obtained results and experience may be quite general except for university students profile defined with their previous education and knowledge. Besides the main goal of the course which is an introduction to IoT, some other aims were exploring the students’ motivation for studying of IoT as a new technology and emphasizing the importance of new original ideas and views being as important as mastering the IoT technologies.
Two Transcripts of FBXO5 Promote Migration and Osteogenic Differentiation of Human Periodontal Ligament Mesenchymal Stem Cells

Objectives Enhanced migration and osteogenic differentiation of mesenchymal stem cells (MSCs) are beneficial for MSC-mediated periodontal tissue regeneration, a promising method for periodontitis treatment. FBXO5, a member of the F-box protein family, is involved in the osteogenic differentiation of MSCs. Here, we investigated the effect of FBXO5 on human periodontal ligament stem cells (hPDLSCs). Materials and Methods hPDLSCs were isolated from periodontal ligament tissue. Lentivirus FBXO5 shRNA was used to silence FBXO5 expression. Two transcripts of FBXO5 were overexpressed and transduced into hPDLSCs via retroviral infection. Migration and osteogenic differentiation of hPDLSCs were evaluated using the scratch migration assay, alkaline phosphatase (ALP) activity, ALP staining, alizarin red staining, western blotting, and real-time polymerase chain reaction. Results The expression of FBXO5 was upregulated after osteogenic induction in hPDLSCs. FBXO5 knockdown attenuated migration, inhibited ALP activity and mineralization, and decreased RUNX2, OSX, and OCN expression, while the overexpression of two transcript isoforms significantly accelerated migration, enhanced ALP activity and mineralization, and increased RUNX2, OSX, and OCN expression in hPDLSCs. Conclusions Both isoforms of FBXO5 promoted the migration and osteogenic differentiation potential of hPDLSCs, which identified a potential target for improving periodontal tissue regeneration.
Identification of pyridinoline, a collagen crosslink, as a novel intrinsic ligand for the receptor for advanced glycation end-products (RAGE)

ABSTRACT Advanced glycation end-products (AGEs) elicit inflammatory responses via the receptor for AGEs (RAGE) and participate in the pathogenesis of diabetic complications. An earlier study showed that 3-hydroxypyridinium (3-HP), a common moiety of toxic AGEs such as glyceraldehyde-derived pyridinium (GLAP) and GA-pyridine, is essential for the interaction with RAGE. However, the physiological significance of 3-HP recognition by RAGE remains unclear. We hypothesized that pyridinoline (Pyr), a collagen crosslink containing the 3-HP moiety, could have agonist activity with RAGE. To test this hypothesis, we purified Pyr from bovine achilles tendons and examined its cytotoxicity to rat neuronal PC12 cells. Pyr elicited toxicity to PC12 cells in a concentration-dependent manner, and this effect was attenuated in the presence of either the anti-RAGE antibody or the soluble form of RAGE. Moreover, surface plasmon resonance-based analysis showed specific binding of Pyr to RAGE. These data indicate that Pyr is an intrinsic ligand for RAGE. Abbreviations: AGEs: advanced glycation end-products; RAGE: receptor for advanced glycation end-products; DAMPs: damage-associated molecular patterns; PRR: pattern recognition receptor; TLR: toll-like receptor; GLAP: glyceraldehyde-derived pyridinium; 3-HP: 3-hydroxypyridinium; Pyr: pyridinoline; HFBA: heptafluorobutyric acid; GST: glutathione S-transferase; SPR: surface plasmon resonance; ECM: extracellular matrix; EMT: epithelial to mesenchymal transition GRAPHICAL ABSTRACT Pyridinoline, a collagen crosslink released during collagen degradation, acts as an intrinsic ligand for RAGE.
Defining Forensic Authorship Attribution for Limited Samples from Social Media

Forensic author attribution (FAA) has its roots in stylometry research and has most recently grown to include intelligent systems approaches. But to be useful in the legal context, there are additional considerations for the design and development of such systems. In this paper, we provide a survey of the existing research in author attribution, and we propose research characteristics required to move the science towards acceptance in forensic applications, as required in court. The paper goes on to present our initial research on optimizing a common n-gram approach in the context of the PAN12 author attribution dataset and a large Twitter dataset. We conclude with a discussion of our findings and future work toward open
Distortionless Beamforming Optimized With $\ell _1$ -Norm Minimization

We propose a beamforming method that minimizes the <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math> </inline-formula> norm of a beamformer output vector under the same distortionless constraint as that of the conventional minimum power distortionless response (MPDR) beamformer. Using the <inline-formula> <tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> norm makes the beamformer output sparse. This leads to reducing the residual elements of the interference signal. In addition, the sensitivity of the proposed beamformer can be controlled by adding a norm constraint as in the MPDR beamformer. The proposed method improved the signal-to-interference-noise ratio by 7 dB from that of the MPDR beamformer for reverberation time <inline-formula><tex-math notation="LaTeX">$T_{60}$</tex-math></inline-formula> = 300 ms in a simulation.
Data-Driven Secret Santa

In 2016, the Bank of Georgia and the Georgian Post conducted a nation-wide secret santa for the residents of the country Georgia. For 2017, they wanted to repeat the campaign while using the collected data from the previous year to improve the experience of the participants as well as the overall quality of the campaign. There were three main objectives in the project: 1) Extract information from the userprovided text fields. This was done through a combination of ad hoc linguistic and probabilistic methods. 2) Building a recommendation system that recommends items from local businesses in order to boost local economy. This was accomplished through combining item-item collaborative filtering with the demographic information of the users. And 3) creating a matching algorithm that maximizes the number of participants who both sent and received a gift. We trained a hybrid model with a Random Forest and Logistic Regression. The results were: 1417 extracted keywords, over 450 different local businesses connected with the users and increasing the percentage of people who sent out as well as received a gift by 14% compared to the previous year. ∗Affiliation at the time of the project Pulsar AI
Cellular and Biophysical Pipeline for the Screening of Peroxisome Proliferator-Activated Receptor Beta/Delta Agonists: Avoiding False Positives

Peroxisome proliferator-activated receptor beta/delta (PPARß/δ) is considered a therapeutic target for metabolic disorders, cancer, and cardiovascular diseases. Here, we developed one pipeline for the screening of PPARß/δ agonists, which reduces the cost, time, and false-positive hits. The first step is an optimized 3-day long cellular transactivation assay based on reporter-gene technology, which is supported by automated liquid-handlers. This primary screening is followed by a confirmatory transactivation assay and by two biophysical validation methods (thermal shift assay (TSA) and (ANS) fluorescence quenching), which allow the calculation of the affinity constant, giving more information about the selected hits. All of the assays were validated using well-known commercial agonists providing trustworthy data. Furthermore, to validate and test this pipeline, we screened a natural extract library (560 extracts), and we found one plant extract that might be interesting for PPARß/δ modulation. In conclusion, our results suggested that we developed a cheaper and more robust pipeline that goes beyond the single activation screening, as it also evaluates PPARß/δ tertiary structure stabilization and the ligand affinity constant, selecting only molecules that directly bind to the receptor. Moreover, this approach might improve the effectiveness of the screening for agonists that target PPARß/δ for drug development.