Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of chitosan coating containing Nepeta pogonosperma extract on shelf life of chicken fillets during chilled storage

Abstract Chicken meat is highly susceptible to microbial and chemical spoilage due to its high moisture and protein content. The use of edible coatings contains herbal extracts with antioxidant and antibacterial properties that help to extend the shelf life of meat products. In this study, the effect of chitosan coating (2%) and Nepeta pogonosperma extract (NPe) (0.2% and 0.6%) and their combination on chemical properties (pH, peroxide value (PV), thiobarbituric acid index (TBARS), total volatile basic nitrogen (TVB‐N)) and microbial (aerobic mesophilic and psychrotrophic microorganisms, lactic acid bacteria, Enterobacteriaceae and Pseudomonas sp.) of chicken fillets were studied over a 12‐day refrigerated storage period compared to the control sample. The results of NPe DPPH radical scavenging activity (DRSA) showed that IC50 and total phenolic contents values were 94.65 μg/ml and 113.53 mg GAE/g extract, respectively. Statistical results showed that the rate of increase in pH, PV, TBARS, and TVB‐N of all coated treatments were lower than control. Microbial analysis results showed a decrease in the growth of different bacteria in chitosan‐treated combined with NPe compared to the control sample during chilled storage. Chicken fillets coated with chitosan and 0.6% NPe displayed a longer shelf life compared to other samples.
Nitrogen‐Doped Hierarchically Porous Carbon Materials with Enhanced Performance for Supercapacitor

The hierarchical porous carbon (HPC) with high specific surface area is a promising material for supercapacitors due to its effective ion diffusion paths and low transport resistance. Herein, a nitrogen-doped hierarchical porous carbon (NHPC) material was successfully synthesized from a gelatin and graphene oxide composite via a combined solvation volatilization/nano-CaCO3 template/KOH activation method. The obtained NHPC exhibits a large surface area (SBET=1091m2 g-1) as well as a combination of electrical double layer capacitance and pseudocapacitance behavior. Benefiting from the advanced synergistic effect of nitrogen-doped property and hierarchically porous structure, NHPC shows a specific capacitance of 234 F g-1 at 0.5 A g-1 in a three-electrode system. Moreover, supercapacitor based on NHPC exhibits an excellent cycle life (retaining > 98% after 5000 cycles) and a high energy density up to 9.43 Wh kg-1.
Early oocyte triggering followed by in vitro maturation is a good approach in women with resistance ovary syndrome: A case-series

Abstract Background Some women represent the inability to respond to endogenous and exogenous gonadotropins during in vitro fertilization/intracytoplasmic sperm injection cycles leading to the follicular developmental arrest. The women with resistant ovaries could benefit from in vitro maturation. Case This case-series presents pregnancies resulting from initially scheduled conventional in vitro fertilization which led to arrested cycles because of resistant ovary syndrome. The protocol was changed to early oocyte triggering for 15 women due to the small follicles ≤ 12 mm in diameter on day 15 after stimulation with high doses of exogenous gonadotrophins instead of cycle cancellation. Germinal vesicle and metaphase I oocytes that were retrieved from follicles were matured in vitro and inseminated by intracytoplasmic sperm injection. Twenty formed embryos were transferred on day 3 after oocyte retrieval. This resulted in a 30.76% chemical pregnancy out of which no abortion occurred. Therefore, we reported a 30.76% singleton ongoing pregnancy. Conclusion It seems that early oocyte triggering followed by in vitro maturation may be considered as a good modality in women experiencing follicular resistance to gonadotropins. These cycles can be rescued from cancellation with satisfactory clinical outcomes.
Association of metformin use on metabolic acidosis in diabetic patients with chronic hepatitis B‐related cirrhosis and renal impairment

Metformin is an oral anti‐hyperglycemic recommended by the American Diabetes Association (ADA) as a preferred initial pharmacologic agent for type 2 diabetes. Metabolic acidosis is a rare yet severe side effect of it. We examined the association of metformin use and dosage on the risk of metabolic acidosis in diabetic patients with different degrees of chronic hepatitis B (CHB)‐related cirrhosis and chronic kidney disease (CKD).
Functional Perspective of Products from Pears cv. Pituralka

Pears cv. Pituralka are autochthon old cultivar from west Slovenian region and northern part of Italy, that were traditionally consumed as cooked or baked meals. In our study we developed new products and defined their nutritional value. Different thermical treatments (cooking and baking) were used for preservation of these products in sugar solution with or without an ascorbic or citric acid and packed in vacuum in a thermostable polyamide/polypropylene (PA/PP) foil. The products made from pears cv. Pituralka were sensory evaluated. The results show that Pituralka pears can be consumed only if they are thermally processed. Containing high amount of total fiber (6–7 g/100 g) products have functional perspective as prebiotics. Having low acidity level (total amount of acids 12.9–15.3 mg/100 g) products from pears cv. Pituralka have very acceptable sensory properties, especially for people with dyspepsia. However low amount of vitamin C (0.5 mg/100 g) has affect on higher sensitivity to oxidation, that needs to be taken into consideration during processing and storage.
Estimation When Both Covariance and Precision Matrices are Sparse

We offer a method to estimate a covariance matrix in the special case that both the covariance matrix and the precision matrix are sparse - a constraint we call double sparsity. The estimation method is maximum likelihood, subject to the double sparsity constraint. In our method, only a particular class of sparsity pattern is allowed: both the matrix and its inverse must be subordinate to the same chordal graph. Compared to a naive enforcement of double sparsity, our chordal graph approach exploits a special algebraic local inverse formula. This local inverse property makes computations that would usually involve an inverse (of either precision matrix or covariance matrix) much faster. In the context of estimation of covariance matrices, our proposal appears to be the first to find such special pairs of covariance and precision matrices.
SCIDA: Self-Correction Integrated Domain Adaptation From Single- to Multi-Label Aerial Images

Most publicly available datasets for image classification are with single labels, while images are inherently multilabeled in our daily life. Such an annotation gap makes many pretrained single-label classification models fail in practical scenarios. For aerial images, this annotation issue is more concerned: Aerial data naturally cover a relatively large land area with multiple labels, while annotated aerial datasets currently publicly available (e.g., UCM and AID) are single-labeled. As manually annotating multilabel aerial images (MAIs) would be time-/ labor-consuming, we propose a novel self-correction integrated domain adaptation (SCIDA) method for automatic multilabel learning. SCIDA is weakly supervised, i.e., automatically learning the multilabel image classification model from using massive, publicly available single-label images. To achieve this goal, we propose a novel labelwise self-correction (LWC) module to better explore underlying label correlations. This module also makes the unsupervised domain adaptation (UDA) from single-label to multilabel data possible. For model training, the proposed method uses single-label information yet requires no prior knowledge of multilabeled data and predicts labels for MAIs. Through extensive evaluations, the proposed model, which is trained with single-labeled MAI-AID-s and MAI-UCM-s datasets, achieves much better performances than comparative methods on our collected multiscene aerial image dataset. The code and data are available on GitHub (https://github.com/Ryan315/Single2multi-DA).
Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model

Data analysis has become a necessity in the modern era of cricket. Everything from effective team management to match win predictions use some form of analytics. Meaningful data representations are necessary for efficient analysis of data. In this study we investigate the use of adaptive (learnable) embeddings to represent inter-related features (such as players, teams, etc). The data used for this study is collected from a classical T20 tournament IPL (Indian Premier League). To naturally facilitate the learning of meaningful representations of features for accurate data analysis, we formulate a deep representation learning framework which jointly learns a custom set of embeddings (which represents our features of interest) through the minimization of a contrastive loss. We base our objective on a set of classes obtained as a result of hierarchical clustering on the overall run rate of an innings. It's been assessed that the framework ensures greater generality in the obtained embeddings, on top of which a task based analysis of overall run rate prediction was done to show the reliability of the framework.
Fast predictions of lattice energies by continuous isometry invariants of crystal structures

Crystal Structure Prediction (CSP) aims to discover solid crystalline materials by optimizing periodic arrangements of atoms, ions or molecules. CSP takes weeks of supercomputer time because of slow energy minimizations for millions of simulated crystals. The lattice energy is a key physical property, which determines thermodynamic stability of a crystal but has no simple analytic expression. Past machine learning approaches to predict the lattice energy used slow crystal descriptors depending on manually chosen parameters. The new area of Periodic Geometry offers much faster isometry invariants that are also continuous under perturbations of atoms. Our experiments on simulated crystals confirm that a small distance between the new invariants guarantees a small difference of energies. We compare several kernel methods for invariant-based predictions of energy and achieve the mean absolute error of less than 5kJ/mole or 0.05eV/atom on a dataset of 5679 crystals.
$\alpha$-Hypergeometric Uncertain Volatility Models and their Connection to 2BSDEs

In this article we propose a α-hypergeometric model with uncertain volatility (UV) where we derive a worst-case scenario for option pricing. The approach is based on the connexion between a certain class of nonlinear partial differential equations of HJB-type (G-HJB equations), that govern the nonlinear expectation of the UV model and that provide an alternative to the difficult model calibration problem of UV models, and second-order backward stochastic differential equations (2BSDEs). Using asymptotic analysis for the G-HJB equation and the equivalent 2BSDE representation, we derive a limit model that provides an accurate description of the worst-case price scenario in cases when the bounds of the UV model are slowly varying. The analytical results are tested by numerical simulations using a deep learning based approximation of the underlying 2BSDE.
Who’s Waldo? Linking People Across Text and Images

We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image. In contrast to prior work in visual grounding, which is predominantly object-based, our new task masks out the names of people in captions in order to encourage methods trained on such image–caption pairs to focus on contextual cues, such as the rich interactions between multiple people, rather than learning associations between names and appearances. To facilitate this task, we introduce a new dataset, Who’s Waldo, mined automatically from image–caption data on Wikimedia Commons. We propose a Transformer-based method that outperforms several strong baselines on this task, and release our data to the research community to spur work on contextual models that consider both vision and language. Code and data are available at: https://whoswaldo.github. io
Logistics and trade flows in selected ECOWAS Countries: An empirical verification

This study investigates the role of logistics and its six components on trade flows in selected Economic Community of West Africa States (ECOWAS) countries. The impact of other macro-economic variables on trade flows was also investigated. Ten countries were selected in twelve years period. We decomposed trade flows into import and export trade. The World Bank Logistics performance index was used as a measure of logistics performance. The LPI has six components, and the impact of these components on trade flows were also examined. The random-effect model was used to explain the cross-country result that was obtained. The results showed that logistics has significant impact on import trade and also, it has positive but weak impact on export trade. The study revealed that competence in local logistics service industry and timeliness of shipment has positive and significant impact on import trade. It was also found that Competence of local logistics service industry, ease and affordability of handling shipment and timeliness of shipment in reaching final destination has positive and significant relation with export trade. Efficiency and effectiveness of custom has a positive and significant impact on import trade. The study recommended the introduction of the single window system and improvement in border management to reduce the cost associated with Logistics and thereby enhance trade.
SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation

We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on generative commonsense reasoning, a.k. a. the CommonGen task, through experiments using both BART and T5 models. Through extensive automatic and human evaluation, we show that SAPPHIRE noticeably improves model performance. An in-depth qualitative analysis illustrates that SAPPHIRE effectively addresses many issues of the baseline model generations, including lack of commonsense, insufficient specificity, and poor fluency.
Modular forms, projective structures, and the four squares theorem

It is well-known that Lagrange’s four-square theorem, stating that every natural number may be written as the sum of four squares, may be proved using methods from the classical theory of modular forms and theta functions. We revisit this proof. In doing so, we concentrate on geometry and thereby avoid some of the tricky analysis that is often encountered. Guided by projective differential geometry we find a new route to Lagrange’s theorem. An artist’s impression of the action of Γ0(4) on the unit disc. In the Polish wycinanka lowicka style by Katarzyna Nurowska. 2010 Mathematics Subject Classification. 11F03, 11F27, 53A20. 1 ar X iv :2 10 8. 06 43 3v 1 [ m at h. N T ] 1 4 A ug 2 02 1 2 M. EASTWOOD AND B. MOORE
NeuraCrypt is not private

NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sensitive dataset to an encoded dataset so that (1) it is still possible to train machine learning models on the encoded data, but (2) an adversary who has access only to the encoded dataset can not learn much about the original sensitive dataset. We break NeuraCrypt privacy claims, by perfectly solving the authors' public challenge, and by showing that NeuraCrypt does not satisfy the formal privacy definitions posed in the original paper. Our attack consists of a series of boosting steps that, coupled with various design flaws, turns a 1% attack advantage into a 100% complete break of the scheme.
CVaR-based Safety Analysis for the Infinite Time Setting

We develop a risk-averse safety analysis method for stochastic systems on discrete infinite time horizons. Our method quantifies the notion of risk for a control system in terms of the severity of a harmful random outcome in a fraction of worst cases, whereas classical methods quantify risk in terms of probabilities. The theoretical arguments are based on the analysis of a value iteration algorithm on an augmented state space. We provide conditions to guarantee the existence of an optimal policy on this space. We illustrate the method numerically using an example from the domain of stormwater management.
DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants

Named entity recognition (NER) is usually developed and tested on text from well-written sources. However, in intelligent voice assistants, where NER is an important component, input to NER may be noisy because of user or speech recognition error. In applications, entity labels may change frequently, and non-textual properties like topicality or popularity may be needed to choose among alternatives. We describe a NER system intended to address these problems. We test and train this system on a proprietary user-derived dataset. We compare with a baseline text-only NER system; the baseline enhanced with external gazetteers; and the baseline enhanced with the search and indirect labelling techniques we describe below. The final configuration gives around 6% reduction in NER error rate. We also show that this technique improves related tasks, such as semantic parsing, with an improvement of up to 5% in error rate.
Isotropy groups of the action of orthogonal similarity on symmetric matrices

We find an algorithmic procedure that enables to compute and to describe the structure of the isotropy subgroups of the group of complex orthogonal matrices with respect to the action of similarity on complex symmetric matrices. A key step in our proof is to solve a certain rectangular block upper-triangular Toeplitz matrix equation.
Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera-IMU System

The Rolling Shutter (RS) mechanism is widely used in consumer-grade cameras, which are essential parts in smartphones and autonomous vehicles. RS leads to image distortion when the camera moves relative to the scene while capturing images. This effect needs to be considered in structure-from-motion, and vision-aided odometry, for which recent studies have extended Global Shutter (GS) methods to RS cameras. So far, it has been unclear how the RS affects spatiotemporal calibration of the camera in a multi-sensor system, which is crucial for good performance of aforementioned applications. This work takes the camera-IMU system as an example and looks into how the RS affects its spatiotemporal calibration. We develop a continuous-time calibration method for a RS camera-IMU system based on basis splines by using a calibration target. Considering the RS, every observation of a landmark on the target corresponds to a unique camera pose fitted by B-splines. Tests conducted on simulated data generated from public calibration datasets showed that the RS can noticeably affect accuracy of the camera extrinsic parameters, causing errors about 0.5° in orientation and 2 cm in translation with a setting common to smartphone cameras. With real data collected by two industrial camera-IMU systems, we found that considering the RS effect gives more accurate and consistent spatiotemporal calibration. These tests also revealed the impact of alternative IMU models and of the continuous-time versus discrete-time representation on camera extrinsic calibration, and the degradation of visual inertial odometry by extrinsic calibration errors.
Efficient Anytime CLF Reactive Planning System for a Bipedal Robot on Undulating Terrain

We propose and experimentally demonstrate a reactive planning system for bipedal robots on unexplored, challenging terrain. The system includes: a multilayer local map for assessing traversability; an anytime omnidirectional control Lyapunov function for use with a rapidly exploring random tree star (RRT*) that generates a vector field for specifying motion between nodes; a subgoal finder when the final goal is outside of the current map; and a finite-state machine to handle high-level mission decisions. The system also includes a reactive thread that copes with robot deviations via a vector field, defined by a closed-loop feedback policy. The vector field provides real-time control commands to the robot's gait controller as a function of instantaneous robot pose. The system is evaluated on various challenging outdoor terrains and cluttered indoor scenes in both simulation and experiment on Cassie Blue, a bipedal robot with 20 degrees of freedom. All implementations are coded in C++ with the robot operating system and are available at https://github.com/UMich-BipedLab/CLF_reactive_planning_system.
AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting

As a special field in deep learning, Graph Neural Networks (GNNs) focus on extracting intrinsic network features and have drawn unprecedented popularity in both academia and industry. Most of the state-of-the-art GNN models offer expressive, robust, scalable and inductive solutions empowering social network recommender systems with rich network features that are computationally difficult to leverage with graph traversal based methods. Most recent GNNs follow an encoder-decoder paradigm to encode high dimensional heterogeneous information from a subgraph onto one low dimensional embedding space. However, one single embedding space usually fails to capture all aspects of graph signals. In this work, we propose boosting-based meta learner for GNNs, which automatically learns multiple projections and the corresponding embedding spaces that captures different aspects of the graph signals. As a result, similarities between sub-graphs are quantified by embedding proximity on multiple embedding spaces. AdaGNN performs exceptionally well for applications with rich and diverse node neighborhood information. Moreover, AdaGNN is compatible with any inductive GNNs for both node-level and edge-level tasks.
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning

Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks. Recent progress in model-based RL allows agents to be much more data-efficient, as it enables them to learn behaviors of visual environments in imagination by leveraging an internal World Model of the environment. Improved sample efficiency can also be achieved by reusing knowledge from previously learned tasks, but transfer learning is still a challenging topic in RL. Parameter-based transfer learning is generally done using an all-or-nothing approach, where the network's parameters are either fully transferred or randomly initialized. In this work we present a simple alternative approach: fractional transfer learning. The idea is to transfer fractions of knowledge, opposed to discarding potentially useful knowledge as is commonly done with random initialization. Using the World Model-based Dreamer algorithm, we identify which type of components this approach is applicable to, and perform experiments in a new multi-source transfer learning setting. The results show that fractional transfer learning often leads to substantially improved performance and faster learning compared to learning from scratch and random initialization.