bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions
Authors: Li Oscar , Liu Hao , Chen Chaofan , Rudin Cynthia
Published: June 2022

Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability---they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.
Predicting Vehicular Travel Times by Modeling Heterogeneous Influences Between Arterial Roads
Authors: Achar Avinash , Sarangan Venkatesh , Regikumar Rohith , Sivasubramaniam Anand
Published: June 2022

Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest in the context of intelligent transportation systems. We address the problem of travel time prediction in arterial roads using data sampled from probe vehicles. There is only a limited literature on methods using data input from probe vehicles. The spatio-temporal dependencies captured by existing data driven approaches are either too detailed or very simplistic. We strike a balance of the existing data driven approaches to account for varying degrees of influence a given road may experience from its neighbors, while controlling the number of parameters to be learnt. Specifically, we use a NoisyOR conditional probability distribution (CPD) in conjunction with a dynamic Bayesian network (DBN) to model state transitions of various roads. We propose an efficient algorithm to learn model parameters. We also propose an algorithm for predicting travel times on trips of arbitrary durations. Using synthetic and real world data traces we demonstrate the superior performance of the proposed method under different traffic conditions.
Modeling Temporal Tonal Relations in Polyphonic Music Through Deep Networks With a Novel Image-Based Representation
Authors: Chuan Ching-Hua , Herremans Dorien
Published: June 2022

We propose an end-to-end approach for modeling polyphonic music with a novel graphical representation, based on music theory, in a deep neural network. Despite the success of deep learning in various applications, it remains a challenge to incorporate existing domain knowledge in a network without affecting its training routines. In this paper we present a novel approach for predictive music modeling and music generation that incorporates domain knowledge in its representation. In this work, music is transformed into a 2D representation, inspired by tonnetz from music theory, which graphically encodes musical relationships between pitches. This representation is incorporated in a deep network structure consisting of multilayered convolutional neural networks (CNN, for learning an efficient abstract encoding of the representation) and recurrent neural networks with long short-term memory cells (LSTM, for capturing temporal dependencies in music sequences). We empirically evaluate the nature and the effectiveness of the network by using a dataset of classical music from various composers. We investigate the effect of parameters including the number of convolution feature maps, pooling strategies, and three configurations of the network: LSTM without CNN, LSTM with CNN (pre-trained vs. not pre-trained). Visualizations of the feature maps and filters in the CNN are explored, and a comparison is made between the proposed tonnetz-inspired representation and pianoroll, a commonly used representation of music in computational systems. Experimental results show that the tonnetz representation produces musical sequences that are more tonally stable and contain more repeated patterns than sequences generated by pianoroll-based models, a finding that is directly useful for tackling current challenges in music and AI such as smart music generation.
Feature Enhancement Network: A Refined Scene Text Detector
Authors: Zhang Sheng , Liu Yuliang , Jin Lianwen , Luo Canjie
Published: June 2022

In this paper, we propose a refined scene text detector with a novel Feature Enhancement Network (FEN)for Region Proposal and Text Detection Refinement. Retrospectively, both region proposal with only 3 x 3 sliding-window feature and text detection refinement with single scale high level feature are insufficient, especially for smaller scene text. Therefore, we design a new FEN network with task-specific, low and high level semantic features fusion to improve the performance of text detection. Besides, since unitary position-sensitive RoI pooling in general object detection is unreasonable for variable text regions, an adaptively weighted position-sensitive RoI pooling layer is devised for further enhancing the detecting accuracy. To tackle the sample-imbalance problem during the refinement stage,we also propose an effective positives mining strategy for efficiently training our network. Experiments on ICDAR2011 and 2013 robust text detection benchmarks demonstrate that our method can achieve state-of-the-art results, outperforming all reported methods in terms of F-measure.
Video-Based Sign Language Recognition Without Temporal Segmentation
Authors: Huang Jie , Zhou Wengang , Zhang Qilin , Li Houqiang , Li Weiping
Published: June 2022

Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.
Learning Generative Neural Networks for 3D Colorization
Authors: Yang Zhenpei , Liu Lihang , Huang Qixing
Published: June 2022

Automatic generation of 3D visual content is a fundamental problem that sits at the intersection of visual computing and artificial intelligence. So far, most existing works have focused on geometry synthesis. In contrast, advances in automatic synthesis of color information, which conveys rich semantic information of 3D geometry, remain rather limited. In this paper, we propose to learn a generative model that maps a latent color parameter space to a space of colorizations across a shape collection. The colorizations are diverse on each shape and consistent across the shape collection. We introduce an unsupervised approach for training this generative model and demonstrate its effectiveness across a wide range of categories. The key feature of our approach is that it only requires one colorization per shape in the training data, and utilizes a neural network to propagate the color information of other shapes to train the generative model for each particular shape. This characteristics makes our approach applicable to standard internet shape repositories.
Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling
Authors: Ahuja Chaitanya , Morency Louis-Philippe
Published: June 2022

Recurrent neural networks have shown remarkable success in modeling sequences. However low resource situations still adversely affect the generalizability of these models. We introduce a new family of models, called Lattice Recurrent Units (LRU), to address the challenge of learning deep multi-layer recurrent models with limited resources.  LRU models achieve this goal by creating distinct (but coupled) flow of information inside the units: a first flow along time dimension and a second flow along depth dimension. It also offers a symmetry in how information can flow horizontally and vertically.  We analyze the effects of decoupling three different components of our LRU model: Reset Gate, Update Gate and Projected State. We evaluate this family of new LRU models on computational convergence rates and statistical efficiency.Our experiments are performed on four publicly-available datasets, comparing with Grid-LSTM and Recurrent Highway networks. Our results show that LRU has better empirical computational convergence rates and statistical efficiency values, along with learning more accurate language models.
Training and Evaluating Improved Dependency-Based Word Embeddings
Authors: Li Chen , Li Jianxin , Song Yangqiu , Lin Ziwei
Published: June 2022

Word embedding has been widely used in many natural language processing tasks. In this paper, we focus on learning word embeddings through selective higher-order relationships in sentences to improve the embeddings to be less sensitive to local context and more accurate in capturing semantic compositionality. We present a novel multi-order dependency-based strategy to composite and represent the context under several essential constraints. In order to realize selective learning from the word contexts, we automatically assign the strengths of different dependencies between co-occurred words in the stochastic gradient descent process. We evaluate and analyze our proposed approach using several direct and indirect tasks for word embeddings. Experimental results demonstrate that our embeddings are competitive to or better than state-of-the-art methods and significantly outperform other methods in terms of context stability. The output weights and representations of dependencies obtained in our embedding model conform to most of the linguistic characteristics and are valuable for many downstream tasks.
Synthesis of Orchestrations of Transducers for Manufacturing
Authors: De Giacomo Giuseppe , Vardi Moshe , Felli Paolo , Alechina Natasha , Logan Brian
Published: June 2022

In this paper, we model manufacturing processes and facilities as transducers (automata with output). The problem of whether a given manufacturing process can be realized by a given set of manufacturing resources can then be stated as an orchestration problem for transducers. We first consider the conceptually simpler case of uni-transducers (transducers with a single input and a single output port), and show that synthesizing orchestrations for uni-transducers is EXPTIME-complete. Surprisingly, the complexity remains the same for the more expressive multi-transducer case, where transducers have multiple input and output ports and the orchestration is in charge of dynamically connecting ports during execution.
Planning With Pixels in (Almost) Real Time
Authors: Bandres Wilmer , Bonet Blai , Geffner Hector
Published: June 2022

Recently, width-based planning methods have been shown to yield state-of-the-art results in the Atari 2600 video games. For this, the states were associated with the (RAM) memory states of the simulator. In this work, we consider the same planning problem but using the screen instead. By using the same visual inputs, the planning results can be compared with those of humans and learning methods. We show that the planning approach, out of the box and without training, results in scores that compare well with those obtained by humans and learning methods, and moreover, by developing an episodic, rollout version of the IW(k) algorithm, we show that such scores can be obtained in almost real time.
Generating Image Captions in Arabic Using Root-Word Based Recurrent Neural Networks and Deep Neural Networks
Authors: Jindal Vasu
Published: June 2022

Automatic caption generation of an image requires both computer vision and natural language processing techniques. Despite of advanced research in English caption generation, research on generating Arabic descriptions of an image is extremely limited. Semitic languages like Arabic are heavily influenced by root-words. We leverage this critical dependency of Arabic and in this paper are the first to generate captions of an image directly in Arabic using root-word based Recurrent Neural Networks and Deep Neural Networks. We report the first BLEU score for direct Arabic caption generation. Experimental results confirm that generating image captions using root-words directly in Arabic significantly outperforms the English-Arabic translated captions using state-of-the-art methods.
Asymmetric Joint Learning for Heterogeneous Face Recognition
Authors: Cao Bing , Wang Nannan , Gao Xinbo , Li Jie
Published: June 2022

Heterogeneous face recognition (HFR) refers to matching a probe face image taken from one modality to face images acquired from another modality. It plays an important role in security scenarios. However, HFR is still a challenging problem due to great discrepancies between cross-modality images. This paper proposes an asymmetric joint learning (AJL) approach to handle this issue. The proposed method transforms the cross-modality differences mutually by incorporating the synthesized images into the learning process which provides more discriminative information. Although the aggregated data would augment the scale of intra-classes, it also reduces the diversity (i.e. discriminative information) for inter-classes. Then, we develop the AJL model to balance this dilemma. Finally, we could obtain the similarity score between two heterogeneous face images through the log-likelihood ratio. Extensive experiments on viewed sketch database, forensic sketch database and near infrared image database illustrate that the proposed AJL-HFR method achieve superior performance in comparison to state-of-the-art methods.
Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition
Authors: Du Yang , Yuan Chunfeng , Li Bing , Hu Weiming , Yang Hao , Fu Zhikang , Zhao Lili
Published: June 2022

Feature extraction is a critical step in the task of action recognition. Hand-crafted features are often restricted because of their fixed forms and deep learning features are more effective but need large-scale labeled data for training. In this paper, we propose a new hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map(NOASSOM) to adaptively and learn effective features from data without supervision. NOASSOM is extended from Adaptive-Subspace Self-Organizing Map (ASSOM) which only deals with linear data and is trained with supervision by the labeled data. Firstly, by adding a nonlinear orthogonal map layer, NOASSOM is able to handle the nonlinear input data and it avoids defining the specific form of the nonlinear orthogonal map by a kernel trick. Secondly, we modify loss function of ASSOM such that every input sample is used to train model individually. In this way, NOASSOM effectively learns the statistic patterns from data without supervision. Thirdly, we propose a hierarchical NOASSOM to extract more representative features. Finally, we apply the proposed hierarchical NOASSOM to efficiently describe the appearance and motion information around trajectories for action recognition. Experimental results on widely used datasets show that our method has superior performance than many state-of-the-art hand-crafted features and deep learning features based methods.
Unsupervised Learning of Geometry From Videos With Edge-Aware Depth-Normal Consistency
Authors: Yang Zhenheng , Wang Peng , Xu Wei , Zhao Liang , Nevatia Ramakant
Published: June 2022

Learning to reconstruct depths from a single image by watching unlabeled videos via deep convolutional network (DCN) is attracting significant attention in recent years, e.g. (Zhou et al. 2017). In this paper, we propose to use surface normal representation for unsupervised depth estimation framework. Our estimated depths are constrained to be compatible with predicted normals, yielding more robust geometry results. Specifically, we formulate an edge-aware depth-normal consistency term, and solve it by constructing a depth-to-normal layer and a normal-to-depth layer inside of the DCN. The depth-to-normal layer takes estimated depths as input, and computes normal directions using cross production based on neighboring pixels. Then given the estimated normals, the normal-to-depth layer outputs a regularized depth map through local planar smoothness. Both layers are computed with awareness of edges inside the image to help address the issue of depth/normal discontinuity and preserve sharp edges. Finally, to train the network, we apply the photometric error and gradient smoothness to supervise both depth and normal predictions. We conducted experiments on both outdoor (KITTI) and indoor (NYUv2) datasets, and showed that our algorithm vastly outperforms state-of-the-art, which demonstrates the benefits of our approach.
Temporal-Difference Learning With Sampling Baseline for Image Captioning
Authors: Chen Hui , Ding Guiguang , Zhao Sicheng , Han Jungong
Published: June 2022

The existing methods for image captioning usually train the language model under the cross entropy loss, which results in the exposure bias and inconsistency of evaluation metric. Recent research has shown these two issues can be well addressed by policy gradient method in reinforcement learning domain attributable to its unique capability of directly optimizing the discrete and non-differentiable evaluation metric. In this paper, we utilize reinforcement learning method to train the image captioning model. Specifically, we train our image captioning model to maximize the overall reward of the sentences by adopting the temporal-difference (TD) learning method, which takes the correlation between temporally successive actions into account. In this way, we assign different values to different words in one sampled sentence by a discounted coefficient when back-propagating the gradient with the REINFORCE algorithm, enabling the correlation between actions to be learned. Besides, instead of estimating a "baseline" to normalize the rewards with another network, we utilize the reward of another Monte-Carlo sample as the "baseline" to avoid high variance. We show that our proposed method can improve the quality of generated captions and outperforms the state-of-the-art methods on the benchmark dataset MS COCO in terms of seven evaluation metrics.
Stack-Captioning: Coarse-to-Fine Learning for Image Captioning
Authors: Gu Jiuxiang , Cai Jianfei , Wang Gang , Chen Tsuhan
Published: June 2022

The existing image captioning approaches typically train a one-stage sentence decoder, which is difficult to generate rich fine-grained descriptions. On the other hand, multi-stage image caption model is hard to train due to the vanishing gradient problem. In this paper, we propose a coarse-to-fine multi-stage prediction framework for image captioning, composed of multiple decoders each of which operates on the output of the previous stage, producing increasingly refined image descriptions. Our proposed learning approach addresses the difficulty of vanishing gradients during training by providing a learning objective function that enforces intermediate supervisions. Particularly, we optimize our model with a reinforcement learning approach which utilizes the output of each intermediate decoder's test-time inference algorithm as well as the output of its preceding decoder to normalize the rewards, which simultaneously solves the well-known exposure bias problem and the loss-evaluation mismatch problem. We extensively evaluate the proposed approach on MSCOCO and show that our approach can achieve the state-of-the-art performance.
Learning Adaptive Hidden Layers for Mobile Gesture Recognition
Authors: Hu Ting-Kuei , Lin Yen-Yu , Hsiu Pi-Cheng
Published: June 2022

This paper addresses two obstacles hindering advances in accurate gesture recognition on mobile devices. First, gesture recognition performance is highly dependent on feature selection, but optimal features typically vary from gesture to gesture. Second, diverse user behaviors and mobile environments result in extremely large intra-class variations. We tackle these issues by introducing a new network layer, called an adaptive hidden layer (AHL), to generalize a hidden layer in deep neural networks and dynamically generate an activation map conditioned on the input. To this end, an AHL is composed of multiple neuron groups and an extra selector. The former compiles multi-modal features captured by mobile sensors, while the latter adaptively picks a plausible group for each input sample. The AHL is end-to-end trainable and can generalize an arbitrary subset of hidden layers. Through a series of AHLs, the great expressive power from exponentially many forward paths allows us to choose proper multi-modal features in a sample-specific fashion and resolve the problems caused by the unfavorable variations in mobile gesture recognition. The proposed approach is evaluated on a benchmark for gesture recognition and a newly collected dataset. Superior performance demonstrates its effectiveness.
FLIC: Fast Linear Iterative Clustering With Active Search
Authors: Zhao Jiaxing , Ren Bo , Hou Qibin , Cheng Ming-Ming , Rosin Paul
Published: June 2022

In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm named “active search” which explicitly considers neighboring continuity. Based on this search method, we design a back-and-forth traversal strategy and a "joint" assignment and update step to speed up the algorithm. Compared to earlier works, such as Simple Linear Iterative Clustering (SLIC) and its follow-ups, who use fixed search regions and perform the assignment and the update step separately, our novel scheme reduces the iteration number before convergence, as well as improves boundary sensitivity of the over-segmentation results. Extensive evaluations on the Berkeley segmentation benchmark verify that our method outperforms competing methods under various evaluation metrics. In particular, lowest time cost is reported among existing methods (approximately 30 fps for a 481321 image on a single CPU core). To facilitate the development of over-segmentation, the code will be publicly available.
Self-View Grounding Given a Narrated 360° Video
Authors: Chou Shih-Han , Chen Yi-Chun , Zeng Kuo-Hao , Hu Hou-Ning , Fu Jianlong , Sun Min
Published: June 2022

Narrated 360° videos are typically provided in many touring scenarios to mimic real-world experience. However, previous work has shown that smart assistance (i.e., providing visual guidance) can significantly help users to follow the Normal Field of View (NFoV) corresponding to the narrative.In this project, we aim at automatically grounding the NFoVs of a 360° video given subtitles of the narrative (referred to as ''NFoV-grounding"). We propose a novel Visual Grounding Model (VGM) to implicitly and efficiently predict the NFoVs given the video content and subtitles. Specifically, at each frame, we efficiently encode the panorama into feature map of candidate NFoVs using a Convolutional Neural Network (CNN) and the subtitles to the same hidden space using an RNN with Gated Recurrent Units (GRU). Then, we apply soft-attention on candidate NFoVs to trigger sentence decoder aiming to minimize the reconstruct loss between the generated and given sentence. Finally, we obtain the NFoV as the candidate NFoV with the maximum attention without any human supervision.To train VGM more robustly, we also generate a reverse sentence conditioning on one minus the soft-attention such that the attention focuses on candidate NFoVs less relevant to the given sentence. The negative log reconstruction loss of the reverse sentence (referred to as ''irrelevant loss") is jointly minimized to encourage the reverse sentence to be different from the given sentence. To evaluate our method, we collect the first narrated 360° videos dataset and achieve state-of-the-art NFoV-grounding performance.
MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training
Authors: Cheung Ernest , Wong Anson , Bera Aniket , Manocha Dinesh
Published: June 2022

We present a new method for training pedestrian detectors on an unannotated set of images. We produce a mixed reality dataset that is composed of real-world background images and synthetically generated static human-agents. Our approach is general, robust, and makes few assumptions about the unannotated dataset. We automatically extract from the dataset: i) the vanishing point to calibrate the virtual camera, and ii) the pedestrians' scales to generate a Spawn Probability Map, which is a novel concept that guides our algorithm to place the pedestrians at appropriate locations. After putting synthetic human-agents in the unannotated images, we use these augmented images to train a Pedestrian Detector, with the annotations generated along with the synthetic agents. We conducted our experiments using Faster R-CNN by comparing the detection results on the unannotated dataset performed by the detector trained using our approach and detectors trained with other manually labeled datasets. We showed that our approach improves the average precision by 5-13% over these detectors.
NUMERICAL INVESTIGATION OF HYDRAULIC FRACTURE PROPAGATION IN HETEROGENEOUS ROCKS USING THE DAMAGE MECHANICS MODEL
Authors: Yu Bo , Sun Dongliang
Published: June 2022

In this study, we use a three-dimensional damage mechanics-based finite element model to investigate hydraulic fracture propagation in heterogeneous rocks. In the proposed finite element model, the Weibull random distribution function is adopted to characterize the rock mechanical heterogeneity of the elastic modulus. This damage mechanics model can well reflect the damage evolution of hydraulic fracture propagation in heterogeneous rocks under tensile and compression states. The fully coupled fluid-solid system of the hydraulic fracturing problem is numerically solved by the Newton-Raphson iterative method. Our numerical results show that key factors including the degree of mechanical heterogeneity, tensile strength, elastic modulus, and natural fractures have a significant impact on hydraulic fracture propagation in heterogeneous rocks. Both the damage zone and the first principal stress contour present a random distribution due to the mechanical heterogeneity. In a naturally fractured heterogeneous reservoir, the damage zone presents a banded distribution, which has the same dip angle of natural fractures. Some natural fractures away from the injection point are also partially opened under the combined action shear and normal stress field. This investigation provides new insight into the formation mechanisms of complex fracture networks in heterogeneous rocks.
Interest Rates Expectations and Flow Dynamics in High Yield Corporate Debt Mutual funds
Authors: Banegas Ayelen , Finch Christopher
Published: June 2022

Fixed-income mutual funds saw massive outflows during the onset of the COVID-19 crisis, with funds investing primarily in high yield debt markets experiencing the largest redemptions, as a percentage of assets. In March 2020 alone, high yield bond (HYB) and bank loan (BL) mutual fund withdrawals reached an estimated 4.1 and 13.6 percent of assets under management (AUM), accounting for close to $10.4 and $11.4 billion, respectively. Following interventions from the Federal Reserve that helped restore credit market conditions and brought U.S. interest rates back to new lows, flow dynamics of HYB and BL funds began to diverge substantially.
The use of ex-situ nitrogen-doped olive oil-derived carbon nano-onions for application in chemi-resistive gas sensors to detect acetone at room temperature
Authors: Mongwe T , Ntuli T , Sikeyi L , Coville N , Mamo M , Serbena J , Nkadimeng M
Published: June 2022

This study reports on the synthesis of carbon nano-onions (CNOs; ca. d < 55 nm) and nitrogen-doped CNOs (N-CNOs) using a facile pyrolysis method and ex-situ doping of the CNOs. Elemental analysis of the N-CNOs revealed that their nitrogen content depended on the ammonia flow rate. Analysis of the N-CNOs revealed that they all exhibited structural defects. After the successful synthesis of CNOs and N-CNOs, polyvinylpyrrolidone (PVP):CNOs/N-CNOs:MnO2-nanorods (MONRs) composites were prepared and used as active sensing materials. In every case, the PVP polymer was used to stabilize the MONRs for acetone detection at 25 °C. The chemi-resistive gas sensors that showed the highest acetone sensitivity (pS = 2.0 x 10-4 ppm-1) was fabricated using a pristine CNOs (pCNOs) based composite. However, the N-CNOs based sensor (a15S) presented the lowest acetone limit of detection (LoD) at 1.2 ppm. The study implicated the effect of the nitrogen and oxygen content of the CNOs surfaces on the acetone detection. Thus, a higher sensitivity with lower LoD was observed at room temperature using the pCNOs based sensor, when compared to earlier literature reports.
Factors Influencing the Diagnosis of Chronic Inflammatory Demyelinating
Authors: Avdagic Amer , Tejas Mehta  , Krista Schoff  , Raghav Govindarajan
Published: June 2022

BACKGROUND: Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) is a neurological disorder that leads to demyelination of peripheral nerves presenting with an array of symptoms. Symptoms of CIDP include but are not limited to loss of sensation, loss of reflexes, tingling and pain, and weakness. European Federation Neurological Society (EFNS) has developed guidelines for the diagnosis of this disorder. The objective of this study is to look at the relationship between the EFNS diagnostic criteria and whether patients that have the diagnosis of CIDP met this criteria. Data collection was completed on the patients diagnosed with CIDP and then the patients that were diagnosed but did not meet the criteria were analyzed to see what common outliers exist that led to the diagnosis.

 

RESULTS: A total of 20 patients (13 males and 7 females) were included in the study. Eighty-three percent of patients that were correctly diagnosed using the EFNS/PNS guidelines displayed hyporeflexia at the time of their diagnosis. A large majority of the patients (83%) correctly diagnosed using the EFNS/PNS guidelines displayed distal weakness at the time of their diagnosis. At the time of their diagnosis, EMG showed that majority of those who did not meet the EFNS/PNS criteria had no nerves that displayed increased latency. Fifty-eight percent of those who did meet the criteria outlined by the EFNS/PNS guidelines had two or more nerves that presented with increased latency. Testing the velocity of patients displayed that all of the patients that did not meet the EFNS/PNS criteria did not present with nerves that had diminished velocity.

 

CONCLUSION: CIDP misdiagnosis continues to be an issue leading to mismanagement of these patients. This study showed a preference of the clinical component for diagnosis of CIDP even if electrophysiological criteria was not met.
RNA Extraction and RT-qPCR of Human Lung Organoids v2
Authors: Baumgardt Morris
Published: June 2022

This protocol describes the RNA extraction from human alveolar-like organoids followed by the perfomance of a RT-qPCR to quantify the amount of hostfactors (ACE2, TMPRSS2, and FURIN) of SARS-CoV-2. The protocol contains detailed steps for the lysis of human alveolar-like organoids, as wells as the RNA extraction followed by the reverse transcription into cDNA. In the next section the protocol describes in detail how to quantify the gene expression with the TaqMan gene expression kit and how to calculate the normalized Ct values.
The Contextual Dependence of the Interpretation of Constitutional Rights: An Analysis from the Point of View of Semantic Holism
Authors: Zezza Michele
Published: June 2022

The analysis aims to reflect on how the external context conditions the legal interpretation of the constitutional provisions that recognize fundamental rights. To this end, some methodological indications from the field of philosophy of language are adopted, implicitly defending the possibility of a transposition, not mechanical and uncritical but analogical, of some essential acquisitions of semantic holism.
Trans Justice Fights Trans Moral Panic
Authors: Mestre i Mestre Ruth M.
Published: June 2022

Between the summer of 2019 and the summer of 2021, a violent discussion about trans rights took place in Spain. This paper argues that the discussion can be understood as an instance of ‘social problems work’, more specifically as part of a moral crusade or a moral panic episode. Implicit in this is the idea that there has been an over-reaction to trans recognition and trans equality laws, publicly presented as a major social problem. This paper also provides legal arguments against the fears voiced in the discussion, by summarizing relevant ECHR case-law that agrees with an alternative feminist account of trans rights that de-pathologizes gender-identity self-determination. The paper thus suggests that a ‘cultural war’ over gender identity has been ignited and has yet to be fully fought and won.
Review on Theoretical Models of Void Evolution in Crystalline Particles
Authors:   ITMO University, Russia, Krasnitckii S.A. ,   Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, Russia,   Peter the Great St. Petersburg Polytechnic University, Russia, Gutkin M.Yu. ,   ITMO University, Russia,   Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, Russia,   Peter the Great St. Petersburg Polytechnic University, Russia
Published: June 2022

In the review, the up-to-date theoretical research of various aspects of void evolution problem in hollow crystalline micro- and nanostructures is summarized. A classification of hollow architectures of micro- and nanostructures distinguishing the main procedures of void (pore) production as well as the influence of the voids on functional properties of the devices based on hollow structures, is suggested. The factors responsible for the void evolution process are discussed. Finally, theoretical models of the void evolution describing shrinkage and growth processes in particles of various structures are considered in terms of kinetics and thermodynamics concepts.
Digital Transformation and Public Control: Perspectives of Influence
Authors: Lisina N. S.
Published: June 2022

The paper is devoted to the examination of the degree of influence of digital transformation on public control. The author assumes that, under the influence of digital transformation, the nature of public control will remain unchanged, this phenomenon will have a significant impact on the forms and methods of public control. It is determined that the use of digital technologies in public control is possible in three main directions: 1) receipt by the subject of public control of information about the activities of the controlled object; 2) analysis of the activities of the controlled object by the subject of public control in order to determine its compliance with public interests; 3) adoption by the subject of public control of measures to remedy the identified deviations. The paper analyzes the practice of using a number of digital technologies in public control, including the practice of using digital projects at federal level, namely: “Vash kontrol” [Your Control] and “Nasha Priroda” [Our Nature], as well as digital projects at the regional level — the project of the city of Moscow “Aktivnyi grzhdanin” [Active citizen]. In addition, the paper determines the prospects for their further development.
Countering Anti-Russian Sanctions in Payment and Currency Fields
Authors: Artemov N. M. , Sitnik A. A.
Published: June 2022

The paper is devoted to the study of anti-Russian sanctions imposed by the United States of America and foreign states and international organizations that have joined them, as well as the analysis of measures aimed at countering unfriendly actions undertaken against the monetary system of the Russian Federation. The paper examines the historical aspects of the anti-Russian policy of Western countries, analyzes provisions of regulatory legal acts and other official documents adopted by the United States and the countries of the European Union, aimed at destabilizing payment and currency relations in Russia. The authors investigate the counter-sanctions policy and measures aimed at ensuring the stability of the Russian monetary system in general and the national payment system in particular. It is concluded that the use of the dollar and the euro as a weapon of sanctions war, the practice of confiscating reserves and assets of countries pursuing policies that do not correspond to the interests of the United States and dependent states, restricting access of financial institutions of “rogue countries” to American and European financial resources, disconnecting credit institutions from SWIFT, and other sanctions inevitably lead to the formation of a parallel international financial system, of which a fundamentally new international monetary system will become a part.
The 27 February 2022 Lop Nor Earthquake: Detectability, Location, and Discrimination
Authors: Gibbons Steven J. , Chaves Esteban J. , Fisk Mark
Published: June 2022

A seismic event with mb 4.8 (Mw 4.2) was detected close to the Chinese Lop Nor nuclear test site on 27 February 2022. Waveforms recorded at regional and far regional distances in central Asia indicate greater likeness with previous earthquakes in the region than with historical nuclear tests. We investigate signal-to-noise ratio (SNR) at regional and global stations, and find the best signals in central Asia and Alaska. Lower SNR at stations in China, Europe, and Australia is likely related to the radiation pattern. A joint probabilistic location of the 2022 event and well-constrained historical nuclear tests indicates an epicenter near 41.88° N and 88.10° E, about 25 km northwest of the tunnel portion of the test site. A moment tensor inversion using high-quality regional signals indicates a nearly deviatoric source with a 72% double couple and a reverse fault mechanism. The centroid depth is 20–25 km, consistent with depth phases recorded in Alaska. The observed faulting geometry and source composition for the 2022 Lop Nor event is consistent with previous earthquakes in the region and the spatial alignment of local geomorphological features, indicating tectonic and not anthropogenic origin.
Economic impacts of agricultural policy responses to the outbreak of COVID-19
Authors: Jiménez Dora Elena , Saldarriaga-Isaza Adrián
Published: June 2022

The aim of this article is to evaluate the general economic impact of the policies focused on the agricultural sector decreed by the Colombian government within the State of Economic, Social, and Ecological Emergency to mitigate the negative effects of the COVID-19 pandemic on this sector. We use a computable general equilibrium model, calibrated with a social accounting matrix that was expanded to focus on specific crops and considered different sizes of agricultural production units. We find that, in general, this set of policies does not have adverse effects but increases the agricultural production of the units receiving the incentives, especially of small and medium-sized farms. In turn, they also increase the demand for unskilled labor, rural households’ disposable income, and consumption compared with pre-covid levels. The results are conditional on the options to finance this set of policies. Also, depending on the financing option, the income gap between rural and urban areas could either narrow or widen.
COMPARAÇÃO CLIMÁTICA DE SÃO JOSÉ DOS CAMPOS, PARAIBUNA E SÃO LUIZ DO PARAITINGA ENTRE 1940-1970
Authors: Santos Júlio César Pimenta dos , Bastos Eduardo Jorge de Brito
Published: June 2022

The use, control and monitoring of water resources each year are becoming more detailed and important for the maintenance and protection of the basins that supply the industry, population and agriculture. The macro- and micro-scale geographic and environmental characteristics are decisive for the microclimate and water availability. To classify the microclimate and water balance, the Thornthwaite method was used for the cities of the Vale do Paraíba Paulista: Paraibuna, São José dos Campos and São Luiz do Paraitinga, with the input of data from 1940 to 1970. The studied municipalities are geographically close, and analyzing land use and occupation, as well as IBGE data, we found their characteristics that differentiate the climate of the municipalities, the anthropogenic interference in areas of the municipality destined to urban and population growth, forest vegetation and the construction of the Paraibuna/Paraitinga dam. The aim of this work is to classify and indicate possible alterations in the microclimate of each municipality, showing that humid microclimates were found, but with different subclassifications, and that the use and occupation of the soil increase some natural characteristics, such as temperature and water storage.
Estudio de casos de Chile
Authors: Chibbaro Schwartz Arnaldo , García Muñoz Pedro , Ruiz Valdés Reinaldo , Morales Estupiñán César ,   BID Invest
Published: June 2022

Este documento aborda el desarrollo de la fruticultura en Chile y su inserción en los mercados internacionales, así como los bienes y políticas públicas y la organización gremial que la han posibilitado, ilustrándolo con el análisis de cinco casos de empresas frutícolas productoras y exportadoras de distintas especies (uva de mesa, paltas, cerezas y manzanas frescas y procesadas) y representativas de modelos empresariales diferentes (empresa asociativa, empresa familiar, empresa de integración vertical). Se dedica particular atención a los desafíos que se presentan a la fruticultura chilena y a las políticas públicas, para enfrentar un nuevo ciclo de desarrollo con un dinamismo similar al de las tres décadas pasadas. En relación al desarrollo del sector frutícola, se reseñan los factores estructurales y del entorno histórico que permitieron su desarrollo productivo exportador y su temprano y exitoso posicionamiento en los mercados globales. También se describen en detalle los bienes y políticas públicas que han sido soporte fundamental de ese desarrollo y las organizaciones gremiales que han sustentado la articulación entre empresas productoras y exportadoras, factores determinantes de una estrategia exportadora basada en la acción público-privada. De las cinco empresas analizadas, dos casos (Jorge Schmidt Exports y Gesex) corresponden a empresas de gran tamaño, integradas verticalmente, altamente capitalizadas y tecnificadas, relevantes en el ranking nacional de exportaciones de fruta de Chile y con inversiones en otros países. El tercer caso (Mi Fruta S.A), corresponde a un emprendimiento asociativo de pequeños productores que han logrado desarrollarse como exportadores exitosos a través de su posicionamiento en los mercados internacionales de comercio justo con el sello fair trade. El cuarto caso (Grupo Surfrut) describe la exitosa experiencia de una empresa familiar, productora y exportadora de fruta orgánica fresca y procesada de alta calidad, que se sitúa como la mayor en su rubro en el país y una de las más grandes a nivel mundial y en cuyo desarrollo se ha priorizado la innovación y la responsabilidad social empresarial. El quinto caso (Solymar) aborda la experiencia de una empresa más reciente, productora y exportadora de fruta fresca y especialmente cerezas, enfocada al mercado chino y que ha desarrollado interesantes estrategias de vinculación con sus compradores en China.
Research Insights: Did Demand for Telemedicine Grow During the COVID-19 Pandemic?
Authors: Busso Matías , González María P. , Scartascini Carlos
Published: June 2022

We exploit mobility restrictions imposed during the COVID-19 pandemic to show the existence of a larger potential demand for telemedicine services in a middle-income developing country. We find an increase in the number of calls by 230 percent during the pandemic. The effects were mostly driven by older individuals with pre-existing conditions who used the service for internal medicine consultations. After the mobility restrictions were lifted, the demand for telemedicine services remained high.
Proces nullitatis matrimonii drogą synodalną. Papież Franciszek do Roty Rzymskiej 27 stycznia 2022 roku
Authors: Góralski Wojciech
Published: June 2022

Z okazji inauguracji nowego roku sądowego w Rocie Rzymskiej, w dniu 27 stycznia 2022 r. papież Franciszek przyjął w Sali Klementyńskiej prałatów-audytorów wymienionego Trybunału Apostolskiego, a także jego adwokatów i pozostałych pracowników i współpracowników. W swoim przemówieniu podkreślił, że proces nullitatis matrimonii ma cechy synodalności, ponieważ jest wspólną drogą wszystkich jego uczestników prowadzącą do poznania prawdy o konkretnym małżeństwie. Uświadamia nie tylko sędziom rotalnym, ale wszystkim sędziom kościelnym, że wymierzanie sprawiedliwości w Kościele w tym zakresie wymaga ducha synodalnego: umiejętności słuchania, podejmowania dialogu i rozstrzygania. Ujawnia się w tym wrażliwość pasterska papieża Franciszka w stosunku do tych małżonków, którym nie powiodło się w życiu wspólnym, i którym, jak się wyraził, należy zaoferować „balsam miłosierdzia”.