Miracle-dz/UniFAR-FTUs huggingface dataset:
High Serum S100A12 Levels Predict Poor Outcome After Acute Primary Intracerebral Hemorrhage

Objective Intracerebral hemorrhage (ICH) triggers an inflammatory cascade that damages brain tissues and worsens functional outcome. S100A12 functions to promote brain inflammation. We aimed to investigate the relationship between serum S100A12 levels and functional outcome in ICH patients. Methods Serum S100A12 levels were measured in 101 ICH patients hospitalized within 24 h after symptom onset. Poor functional outcome was defined as a modified Rankin scale of 3 or greater at 3 months after stroke. Early neurologic deterioration was defined as an increase of ≥4 points in the National Institutes of Health Stroke Scale (NIHSS) score or death at 24 hours from symptoms onset. Results High serum S100A12 levels were independently correlated with NIHSS score (t = 5.384, P < 0.001), hematoma volume (t = 4.221, P < 0.001) and serum C-reactive protein levels (t = 5.068, P < 0.001). Serum S100A12 levels were substantially higher in patients with a poor outcome (median, 66.5 versus 37.7 ng/mL; P < 0.001) or early neurological deterioration (median, 76.5 versus 40.1 ng/mL; P < 0.001) than in the other remainders, independently predicted a poor outcome (odds ratio, 1.035; 95% confidence interval, 1.007–1.064; P = 0.015) and early neurologic deterioration (odds ratio,1.032; 95% confidence interval, 1.003–1.060; P = 0.027), and significantly discriminated a poor outcome (area under curve, 0.794; 95% confidence interval, 0.702–0.868) and early neurologic deterioration (area under curve, 0.760; 95% confidence interval, 0.664–0.839) under receiver operating characteristic curve. Conclusion High serum S100A12 levels at admission are highly associated with the extent of inflammatory response, severity, a poor functional outcome and early neurologic deterioration in ICH patients, substantializing serum S100A12 as a promising prognostic biomarker for ICH.
Collision-free spatial motion of rigid bodies via topological complexity

The Topological complexity a la Farber $\text{TC}(-)$ is a homotopy invariant which have interesting applications in Robotics, specifically, in the robot motion planning problem. In this work we calculate the topological complexity of the configuration space of $k$ distinct rigid bodies without collisions in $\mathbb{R}^d$, for $d=2,3$. Furthermore, we present optimal algorithms which can be used in designing practical systems controlling motion of many rigid bodies moving in space without collisions. The motion planning algorithms we present in this work are easily implementable in practice.
A comprehensive survey of fingerprint presentation attack detection

Nowadays, the number of people that utilize either digital applications or machines is increasing exponentially. Therefore, trustworthy verification schemes are required to ensure security and to authenticate the identity of an individual. Since traditional passwords have become more vulnerable to attack, the need to adopt new verification schemes is now compulsory. Biometric traits have gained significant interest in this area in recent years due to their uniqueness, ease of use and development, user convenience and security. Biometric traits cannot be borrowed, stolen or forgotten like traditional passwords or RFID cards. Fingerprints represent one of the most utilized biometric factors. In contrast to popular opinion, fingerprint recognition is not an inviolable technique. Given that biometric authentication systems are now widely employed, fingerprint presentation attack detection has become crucial. In this review, we investigate fingerprint presentation attack detection by highlighting the recent advances in this field and addressing all the disadvantages of the utilization of fingerprints as a biometric authentication factor. Both hardwareand software-based state-of-the-art methods are thoroughly presented and analyzed for identifying real fingerprints from artificial ones to help researchers to design securer biometric systems.
Fair Meta-Learning: Learning How to Learn Fairly

Data sets for fairness relevant tasks can lack examples or be biased according to a specific label in a sensitive attribute. We demonstrate the usefulness of weight based meta-learning approaches in such situations. For models that can be trained through gradient descent, we demonstrate that there are some parameter configurations that allow models to be optimized from a few number of gradient steps and with minimal data which are both fair and accurate. To learn such weight sets, we adapt the popular MAML algorithm to Fair-MAML by the inclusion of a fairness regularization term. In practice, Fair-MAML allows practitioners to train fair machine learning models from only a few examples when data from related tasks is available. We empirically exhibit the value of this technique by comparing to relevant baselines.
Distribution of Pink-pigmented Facultative Methylotrophs Isolated from the Leaves of Potato Grown in Different Regions of Japan

Seed tubers of potato (Solanum tuberosum) plants were grown in five locations in Japan: Hokkaido, Yamagata, Ibaraki, Hiroshima, and Nagasaki. One hundred colonies of pink-pigmented facultative methylotrophs (PPFMs) were isolated from potato leaves at flowering time. The 16S rRNA gene sequences of isolates were determined, and clustering analysis (≥ 99% identity) identified 11 operational taxonomic units (OTUs). Phylogenetic analysis revealed that all OTUs were grouped in genus Methylobacterium. The OTU M9 was distributed among all five collection sites, and other OTUs were distributed among several collection sites. These data indicated that the distribution of PPFMs in the potato phyllosphere differed according to the plant’s geographical location. Discipline: Agricultural Environment Additional key words: 16S rRNA, Methylobacterium, operational taxonomic units, seed tuber
Partial Discharges in HVDC Cables - The Effect of the Temperature Gradient During Load Transients

The paper investigates the role of the space charge accumulation phenomena in the inception of Partial Discharges (PD) in a defect within the bulk of the dielectrics of a High Voltage Direct Current (HVDC) cable. It is widespread accepted that the number of PD occurring in an HVDC cable during its lifetime is largely less than that under AC voltage stress. This is essentially due to higher values of the voltage between the conductor and the screen needed to trigger PD under a steady DC voltage than the same value under AC stress. Nevertheless, due to the dependency of the electrical conductivity of the insulation on the electric field and temperature, PD in HVDC cables is strongly influenced by the temperature distribution and, consequently, on the load current. Thus, the main goal of this research is to evaluate the main parameters of the PD occurring in an HVDC cable during a load transient. Numerical simulations by using Comsol Multlphysics® are validated by the results obtained with an ad hoc experimental setup. A model DC cable, subjected to transient phenomena due to a current step and with an air void defect in the insulation material, is simulated and setup. On the other hand, an experimental setup is used to carry out PD measurements on the same case study. The results of this research show the strong dependency of the PD parameters on the temperature distribution varying over time during a load transient.
Elk (Cervus elaphus) grazing reduces volunteer soybean density

Abstract: Management of glyphosate resistant volunteer soybean can be challenging and costly in other glyphosate resistant crops. This study examined the effect of elk (Cervus elaphus) grazing on volunteer soybean (Glycine max) management. Volunteer soybean density was assessed in four fields grazed by elk and three without elk in western Manitoba, Canada. This study determined that elk grazing of soybean stubble significantly reduced soybean volunteer density in the following crop. These results demonstrate that winter elk grazing can be an effective management technique for volunteer soybeans.
Describing the phenomena of students’ representation in solving ill-posed and well-posed problems

Mathematical representation is an essential aspect of mathematical problem-solving. But students’ ability of an accurate representation in ill-posed problem-solving is still very minimal compared to that in well-posed problem-solving. However, ill-posed problem supported mathematical abstraction used in mathematical concept understanding. This study described the representations used by mathematics education students in solving ill-posed and well-posed problems. Thirty Indonesian matematics education students have solved ill-well posed problems by using think-aloud. Researchers also collected data using a video recorder and a field note. Data were analyzed by a constant comparative method so that it was obtained the different characteristics of representations between solving ill-posed and well-posed problems. The finding of the study showed that verbal and symbolic representations were used by subjects to compute, detect, and correct error. They also justified their answers in ill-posed problem-solving. However, the visual representation was only used by first subject to identify and correct error. The subjects lacked to expose necessary information to solve the ill-posed problem compared to the well-posed problem.
Disentangle, align and fuse for multimodal and zero-shot image segmentation

Magnetic resonance (MR) protocols rely on several sequences to properly assess pathology and organ status. Yet, despite advances in image analysis we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the information shared between modalities (largely an organ's anatomy) is beneficial for multi-modality multi-input processing and learning. However, we must overcome inherent anatomical misregistrations and disparities in signal intensity across the modalities to claim this benefit. We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, enabling semi-supervised and zero shot learning. Core to our method is learning a disentangled decomposition into anatomical and imaging factors. Shared anatomical factors from the different inputs are jointly processed and fused to extract more accurate segmentation masks. Image misregistrations are corrected with a Spatial Transformer Network, that non-linearly aligns the anatomical factors. The imaging factor captures signal intensity characteristics across different modality data, and is used for image reconstruction, enabling semi-supervised learning. Temporal and slice pairing between inputs are learned dynamically. We demonstrate applications in Late Gadolinium Enhanced (LGE) and Blood Oxygenation Level Dependent (BOLD) cardiac segmentation, as well as in T2 abdominal segmentation.
Using Deep Neural Networks for Estimating Loop Unrolling Factor

Optimizing programs requires deep expertise. On one hand, it is a tedious task, because it requires a lot of tests to find out the best combination of optimizations to apply with their best factors. On the other hand, this task is critical, because it may degrade the performance of programs instead of improving it. The automatization of this task can deal with this problem and permit to obtain good results. Optimizing loops that take the most significant part of the program execution time plays a crucial role to achieve best performance. In this paper, we address Loop unrolling optimization, by proposing a deep Neural Network model to predict the optimal unrolling factor for programs written for TIRAMISU. TIRAMISU is a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. TIRAMISU introduces a scheduling language with novel commands to explicitly manage the complexities that arise when targeting these systems.
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Microelectromechanical systems (MEMS) are of high interest for recent electronic applications. Their applications range from medicine to measurement technology, from microfluidics to the Internet of Things (IoT). In many cases, MEMS elements serve as sensors or actuators, e.g., in recent mobile phones, but also in future autonomously driving cars. Most MEMS elements are based on silicon, which is not deformed plastically under a load, as opposed to metals. While highly sophisticated solutions were already found for diverse MEMS sensors, actuators, and other elements, MEMS fabrication is less standardized than pure microelectronics, which sometimes blocks new ideas. One of the possibilities to overcome this problem may be the 3D printing approach. While most 3D printing technologies do not offer sufficient resolution for MEMS production, and many of the common 3D printing materials cannot be used for this application, there are still niches in which the 3D printing of MEMS enables producing new structures and thus creating elements for new applications, or the faster and less expensive production of common systems. Here, we give an overview of the most recent developments and applications in 3D printing of MEMS.
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension

In this paper, we propose the scheme for annotating large-scale multi-party chat dialogues for discourse parsing and machine comprehension. The main goal of this project is to help understand multi-party dialogues. Our dataset is based on the Ubuntu Chat Corpus. For each multi-party dialogue, we annotate the discourse structure and question-answer pairs for dialogues. As we know, this is the first large scale corpus for multi-party dialogues discourse parsing, and we firstly propose the task for multi-party dialogues machine reading comprehension.
False Data Injection Attacks Detection in Power System Using Machine Learning Method

False data injection attacks (FIDAs) against state estimation in power system are a problem that could not be effectively solved by traditional methods. In this paper, we use four outlier detection methods, namely one-Class SVM, Robust covariance, Isolation forest and Local outlier factor method from machine learning area in IEEE14 simulation platform for test and compare their performance. The accuracy and precision were estimated through simulation to observe the classification effect.
On the abundance of supersymmetric strings in AdS3 × S 3 × S 3 × S 1 describing BPS line operators

We study supersymmetric open strings in type IIB AdS3 × S 3 × S 3 × S 1 with mixed R–R and NS–NS fields. We focus on strings ending along a straight line at the boundary of AdS3, which can be interpreted as line operators in a dual CFT2. We study both classical configurations and quadratic fluctuations around them. We find that strings sitting at a fixed point in S 3 × S 3 × S 1, i.e. satisfying Dirichlet boundary conditions, are 1/2 BPS. We also show that strings sitting at different points of certain submanifolds of S 3 × S 3 × S 1 can still share some fraction of the supersymmetry. This allows to define supersymmetric smeared configurations by the superposition of them, which range from 1/2 BPS to 1/8 BPS. In addition to the smeared configurations, there are as well 1/4 BPS and 1/8 BPS strings satisfying Neumann boundary conditions. All these supersymmetric strings are shown to be connected by a network of interpolating BPS boundary conditions. Our study reveals the existence of a rich moduli of supersymmetric open string configurations, for which the appearance of massless fermionic fields in the spectrum of quadratic fluctuations is crucial.
Bermudagrass Control in Sugarcane in Brazil

Aims: Evaluation of the chemicals in controlling bermudagrass weed and effects on sugarcane selectivity. Study Design: Chamber growth studies: completely randomized design with nine treatments with five replicates. Field studies: Randomized block design with nine treatments with five replicates Place and Duration of Study: Instituto Agronômico, Centro de Cana, São Paulo State, Brazil, between February/2018 and December/2019. Methodology: Bermudagrass chemical control was studied in growth chamber in pots. In the first stage, imazapyr, clomazone, indaziflam, sulfentrazone and the control treatment were studied. In the second stage, imazapyr, clomazone, indaziflam were applied and a treatment with no herbicides was maintained. After 75 days of imazapyr application and 38 days of clomazone and indaziflam, clomazone + indaziflam and clomazone + sulfentrazone were applied, in addition to the control treatment. Sugar cane selectivity study was carried out in the field. Before sugarcane planting, imazapyr, clomazone, indaziflam were applied. After planting, clomazone + indaziflam and clomazone + sulfentrazone were applied, in addition to the control treatment. Results: Clomazone at 1050.0 g ha-1 applied as pre plant at 38 days before planting followed by clomazone at 1050.0 g ha-1 plus sulfentrazone at 650.0 g ha-1 applied 2 days after sugar cane planting was the best treatment for bermudagrass control and yield of the crop. Other viable options for control involved clomazone plus sulfentrazone used after imazapyr or indaziflam.
Robo-PlaNet: Learning to Poke in a Day

Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observations. This architecture is useful for learning tasks in which either the agent does not have access to meaningful states (like position/velocity of robotic joints) or where the observed states significantly deviate from the physical state of the agent (which is commonly the case in low-cost robots in the form of backlash or noisy joint readings). PlaNet, by design, interleaves phases of training the dynamics model with phases of collecting more data on the target environment, leading to long training times. In this work, we introduce Robo-PlaNet, an asynchronous version of PlaNet. This algorithm consistently reaches higher performance in the same amount of time, which we demonstrate in both a simulated and a real robotic experiment.
On a concept of quantum non-Markovianity weaker than CP-indivisibility

The problem of defining quantum non-Markovianity has proven elusive, with various inequivalent criteria put forth to address it. We consider the question: what is the weakest notion of quantum non-Markovianity of system dynamics that would account for any aspect of memory? That is, a process indicated to be non-Markovian according to any existing criterion is also non-Markovian according to this criterion, but the converse may not be true. Correspondingly, we identify true memorylessness with the idea of the quantum dynamics being "temporally self-similar", whereby the form of the intermediate map that propagates the state between two arbitrary time intervals is independent of the initial time $t_0$. This is identified with the quantum dynamical semigroup (QDS), renewing advocacy for a familiar candidate. Of practical importance is that our weaker criterion is useful for indicating the non-Markovianity of CP-divisible channels that manifest an effect of counteracting decoherence. This is illustrated with the examples of power-law noise (PLN) and Ornstein-Uhlenbeck Noise (OUN). For physically well motivated channels, we propose a geometric quantification of non-Markovianity according to our definition, which is shown to be applicable to stronger manifestations of quantum non-Markovianity, demonstrated here by application to various channels, including those that are CP-indivisible, P-indivisible and eternally non-Markovian. Finally, we indicate a few open questions that come up as natural consequences of this work.
Correcting Sociodemographic Selection Biases for Accurate Population Prediction from Social Media

Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population - a "selection bias". Across six tasks for predicting U.S. county population health statistics from Twitter, we explore standard restratification techniques - bias mitigation approaches that reweight people-specific variables according to how under-sampled their socio-demographic groups are. We found standard restratification provided no improvement and often degraded population prediction accuracy. The core reason for this seemed to be both shrunken and sparse estimates of each population's socio-demographics for which we thus develop and evaluate three methods to address: predictive redistribution to account for shrinking, as well as adaptive binning and informed smoothing to handle sparse socio-demographic estimates. We show each of our methods can significantly improve over the standard restratification approaches. Combining approaches, we find substantial improvements over non-restratified models, yielding a 44.9% increase in variance explained for predicting surveyed life satisfaction, and an 10.5% average increase across all tasks.
Novel Anthanthrone and Anthanthrene Co-polymers as p-Type Conjugated Semiconductors for Organic Photovoltaics

The design of advanced new generation conjugated materials with improved properties for photovoltaic applications has been the focus of research in recent years. This work reports on novel anthanthrone/anthracene and anthanthrene/anthracene co-polymers as p-type conjugated semiconductors in organic photovoltaics. The presence of anthracene component with the grafted alkyloxy chains enhanced the solubility and process-ability of the co-polymers. The photophysical and photovoltaic parameters greatly depend on ratio of components of the co-polymers and nature of the grafted chains. Devices using the blends of SV15 and PCBM in 1:2 ratio gave $J_{\mathrm{s}\mathrm{c}}$ 3.97 mA/cm2 and higb $V_{oc}$ 0.92 V with efficiency 1.7%.
Analyzing Bias in Sensitive Personal Information Used to Train Financial Models

Bias in data can have unintended consequences that propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can result in discrimination from certain financial instruments and services. At the same time, data privacy is of paramount importance, and recent data breaches have seen reputational damage for large institutions. Presented in this paper is a trusted model-lifecycle management platform that attempts to ensure consumer data protection, anonymization, and fairness. Specifically, we examine how datasets can be reproduced using deep learning techniques to effectively retain important statistical features in datasets whilst simultaneously protecting data privacy and enabling safe and secure sharing of sensitive personal information beyond the current state-of-practice.
Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

We consider the problem of decomposing higher-order moment tensors, i.e., the sum of symmetric outer products of data vectors. Such a decomposition can be used to estimate the means in a Gaussian mixture model and for other applications in machine learning. The $d$th-order empirical moment tensor of a set of $p$ observations of $n$ variables is a symmetric $d$-way tensor. Our goal is to find a low-rank tensor approximation comprising $r \ll p$ symmetric outer products. The challenge is that forming the empirical moment tensors costs $O(pn^d)$ operations and $O(n^d)$ storage, which may be prohibitively expensive; additionally, the algorithm to compute the low-rank approximation costs $O(n^d)$ per iteration. Our contribution is avoiding formation of the moment tensor, computing the low-rank tensor approximation of the moment tensor implicitly using $O(pnr)$ operations per iteration and no extra memory. This advance opens the door to more applications of higher-order moments since they can now be efficiently computed. We present numerical evidence of the computational savings and show an example of estimating the means for higher-order moments.