pwc-archive/papers-with-abstracts huggingface dataset:
Shock Symmetry and Business Cycle Synchronization: Is Monetary Unification Feasible among CAPADR Countries?
Authors: Jafet Baca
Date: 2021-12-03 00:00:00

In light of the ongoing integration efforts, the question of whether CAPADR economies may benefit from a single currency arises naturally. This paper examines the feasibility of an Optimum Currency Area (OCA) within seven CAPADR countries. We estimate SVAR models to retrieve demand and supply shocks between 2009:01 - 2020:01 and determine their extent of symmetry. We then go on to compute two regional indicators of dispersion and the cost of inclusion into a hypothetical OCA for each country. Our results indicate that asymmetric shocks tend to prevail. In addition, the dispersion indexes show that business cycles have become more synchronous over time. However, CAPADR countries are still sources of cyclical divergence, so that they would incur significant costs in terms of cycle correlation whenever they pursue currency unification. We conclude that the region does not meet the required symmetry and synchronicity for an OCA to be appropiate.
Financial Markets, Financial Institutions and International Trade: Examining the causal links for Indian Economy
Authors: Ummuhabeeba Chaliyan, Mini P. Thomas
Date: 2021-12-03 00:00:00

This study investigates whether a uni-directional or bi-directional causal relationship exists between financial development and international trade for Indian economy, during the time period from 1980 to 2019. The empirical analysis utilizes three measures of financial development created by IMF, namely, financial institutional development index, financial market development index and a composite index of financial development, encompassing dimensions of financial access, depth and efficiency. Johansen cointegration, vector error correction model and vector auto regressive model are estimated to examine the long run relationship and short run dynamics among the variables of interest. The econometric results indicate that there is indeed a long run causal relationship between the composite index of financial development and trade openness. Cointegration is also found to exist between trade openness and index of financial market development. However, there is no evidence of cointegration between financial institutional development and trade openness. Granger causality test results indicate the presence of uni-directional causality running from composite index of financial development to trade openness. Financial market development is also found to Granger cause trade openness. In contrast, trade openness is found to promote financial institutional development in the short run. Empirical evidence thus underlines the importance of formulating policies which recognize the role of well-developed financial markets in accelerating international trade of Indian economy.
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning
Authors: Wei Jiang, Alexander M. Haimovich, Mark Govoni, Timothy Garner, Osvaldo Simeone
Date: 2021-12-03 00:00:00
Tasks: Meta-Learning, Transfer Learning

This paper addresses the problem of fast learning of radar detectors with a limited amount of training data. In current data-driven approaches for radar detection, re-training is generally required when the operating environment changes, incurring large overhead in terms of data collection and training time. In contrast, this paper proposes two novel deep learning-based approaches that enable fast adaptation of detectors based on few data samples from a new environment. The proposed methods integrate prior knowledge regarding previously encountered radar operating environments in two different ways. One approach is based on transfer learning: it first pre-trains a detector such that it works well on data collected in previously observed environments, and then it adapts the pre-trained detector to the specific current environment. The other approach targets explicitly few-shot training via meta-learning: based on data from previous environments, it finds a common initialization that enables fast adaptation to a new environment. Numerical results validate the benefits of the proposed two approaches compared with the conventional method based on training with no prior knowledge. Furthermore, the meta-learning-based detector outperforms the transfer learning-based detector when the clutter is Gaussian.
THz Band Channel Measurements and Statistical Modeling for Urban Microcellular Environments
Authors: Naveed A. Abbasi, Jorge Gomez-Ponce, Revanth Kondaveti, Ashish Kumar, Eshan Bhagat, Rakesh N S Rao, Shadi Abu-Surra, Gary Xu, Charlie Zhang, Andreas F. Molisch
Date: 2021-12-03 00:00:00

The THz band (0.1-10 THz) has attracted considerable attention for next-generation wireless communications, due to the large amount of available bandwidth that may be key to meet the rapidly increasing data rate requirements. Before deploying a system in this band, a detailed wireless channel analysis is required as the basis for proper design and testing of system implementations. One of the most important deployment scenarios of this band is the outdoor microcellular environment, where the Transmitter (Tx) and the Receiver (Rx) have a significant height difference (typically $ \ge 10$ m). In this paper, we present double-directional (i.e., directionally resolved at both link ends) channel measurements in such a microcellular scenario encompassing street canyons and an open square. Measurements are done for a 1 GHz bandwidth between 145-146 GHz and an antenna beamwidth of 13 degree; distances between Tx and Rx are up to 85 m and the Tx is at a height of 11.5 m from the ground. The measurements are analyzed to estimate path loss, shadowing, delay spread, angular spread, and multipath component (MPC) power distribution. These results allow the development of more realistic and detailed THz channel models and system performance assessment.
The impact of varying electrical stimulation parameters on neuromuscular response
Authors: Dhruv Pai, Mentor Kip Ludwig
Date: 2021-12-03 00:00:00

High density neurostimulation systems are coming to market to help spinal cord injury patients by stimulating and recording neuromuscular function. However, the parameter space that these systems have to explore is exceedingly large, and would need an artificial intelligence (AI) system to optimize. We need a platform that will allow us to determine the optimal parameter space for these systems. Our project aims to build a platform for mapping and controlling neuromuscular activity, as a high-throughput testbed for implementing and testing closed-loop neuromuscular activity. This abstract presents the first phase (the mapping phase) of building that testbed by combining multi-electrode stimulation/recording with visual motion-tracking. A 3D-printed rectangular raceway was used with 4 pairs of differential recording electrodes, and two stimulation electrodes embedded in the raceway bed. Non-anesthetized earthworms were placed on the raceway with their head section on the stimulating electrodes. Bipolar sinusoidal stimulation pulses of a range of voltages (2 to 6Vp-p), pulse durations (2 ms to 6.7 ms), and a burst rate of 1 pulse per second were applied, and action potentials and physical motion were recorded and analyzed. Action potentials were found to correlate with expansion/contraction displacements of worm segments, and voltage increases were shown to increase action potential propagation amplitude. Using the multiple electrode recording allowed us to capture the wave propagation of action potential pulse over the length of the worm. Feasibility of a platform to simultaneously monitor action potentials and motion of earthworms with real-time mapping was demonstrated.
The Gardner problem and cycle slipping bifurcation for type 2 phase-locked loops
Authors: Nikolay V. Kuznetsov, Dmitry G. Arseniev, Mikhail V. Blagov, Mikhail Y. Lobachev, Zhouchao Wei, Marat V. Yuldashev, Renat V. Yuldashev
Date: 2021-12-02 00:00:00

In the present work, a second-order type 2 PLL with a piecewise-linear phase detector characteristic is analysed. An exact solution to the Gardner problem on the lock-in range is obtained for the considered model. The solution is based on a study of cycle slipping bifurcation and improves well-known engineering estimates.
Real Time Monitoring and Control of Neonatal Incubator using IOT
Authors: Rasha M. Abd El-Aziz, Ahmed I. Taloba
Date: 2021-11-27 00:00:00

The care of new born babies are the most important and sensitive part of bio-medical domain. Some new born babies have a higher risk of mortality due to their gestational age or their birth weight. Most of the premature babies born on 32-37 weeks of gestation and are deceased due to their unmet need for warmth. The neonatal incubator is a device used to nourish the premature babies by providing a controlled and closed environment. This incubator provides the babies with optimum temperature, relative humidity, optimum light and appropriate level of oxygen which are same as that in the womb. But babies in the incubators have a risk of losing those babies lives due to the improper monitoring of the it which causes accidents like gas leakage and short circuits due to overheating which leads to bursting of incubators. Thus, the objective of this paper is to overcome the drawbacks of an unmonitored incubator and develops an affordable and safe device for real-time monitoring of the neonatal incubator. a low cost yet effective apparatus for monitoring the important parameters like pulse rate, temperature, humidity, gas and light of the premature baby inside an incubator. The sensed data are passed to the doctors or nurses wirelessly by the Arduino UNO via Internet of Things (IoT) so as to take necessary actions at times to maintain an appropriate environment for the safety of the lives of premature babies.
A formalism for the long-distance magnetic field generated by populations of neurons
Authors: Alexis Garcia-Rodriguez, Alain Destexhe
Date: 2021-12-03 00:00:00

Brain activity can be measured using magnetic fields located at some distance from the brain, a technique called magneto-encephalography (MEG). The origin of such magnetic fields are the ionic currents involved in neuronal activity. While these processes are well known at the microscopic scale, it is less clear how large populations of neurons generate magnetic fields. Here, we attempt to make such a link by deriving a mean-field theory of magnetic field generation by populations of neurons. We use the concept of current dipoles to build a theory that can be applied to populations of neurons. We also evaluate the contributions of neuronal current sources such as the synaptic currents or action potential currents. This theory should be useful to calculate the magnetic field located at long distances from neuronal populations, such as in MEG.
Leaving Flatland: Advances in 3D behavioral measurement
Authors: Jesse D. Marshall, Tianqing Li, Joshua H. Wu, Timothy W. Dunn
Date: 2021-12-03 00:00:00

Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the true kinematics of animal movement. Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision. Continued advances at the intersection of deep learning and computer vision will facilitate 3D tracking across more anatomical features, with less training data, in additional species, and within more natural, occlusive environments. 3D behavioral measurement enables unique applications in phenotyping, investigating the neural basis of behavior, and designing artificial agents capable of imitating animal behavior.
Artificial Intelligence-driven Image Analysis of Bacterial Cells and Biofilms
Authors: Shankarachary Ragi, Md Hafizur Rahman, Jamison Duckworth, Kalimuthu Jawaharraj, Parvathi Chundi, Venkataramana Gadhamshetty
Date: 2021-12-02 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate reducing bacteria that are implicated in microbiologically influenced corrosion problems. Our goal is to automate the process of extracting the geometrical properties of the DA-G20 cells from the scanning electron microscopy (SEM) images, which is otherwise a laborious and costly process. These geometric properties are a biofilm phenotype that allow us to understand how the biofilm structurally adapts to the surface properties of the underlying metals, which can lead to better corrosion prevention solutions. We adapt two deep learning models: (a) a deep convolutional neural network (DCNN) model to achieve semantic segmentation of the cells, (d) a mask region-convolutional neural network (Mask R-CNN) model to achieve instance segmentation of the cells. These models are then integrated with moment invariants approach to measure the geometric characteristics of the segmented cells. Our numerical studies confirm that the Mask-RCNN and DCNN methods are 227x and 70x faster respectively, compared to the traditional method of manual identification and measurement of the cell geometric properties by the domain experts.
Interval Reachability Analysis using Second-Order Sensitivity
Authors: Pierre-Jean Meyer, Murat Arcak
Date: 2019-11-21 00:00:00
Tasks: Sensitivity

We propose a new approach to compute an interval over-approximation of the finite time reachable set for a large class of nonlinear systems. This approach relies on the notions of sensitivity matrices, which are the partial derivatives representing the variations of the system trajectories in response to variations of the initial states. Using interval arithmetics, we first over-approximate the possible values of the second-order sensitivity at the final time of the reachability problem. Then we exploit these bounds and the evaluation of the first-order sensitivity matrices at a few sampled initial states to obtain an over-approximation of the first-order sensitivity, which is in turn used to over-approximate the reachable set of the initial system. Unlike existing methods relying only on the first-order sensitivity matrix, this new approach provides guaranteed over-approximations of the first-order sensitivity and can also provide such over-approximations with an arbitrary precision by increasing the number of samples.
Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer
Authors: Tao Wang, Hong Liu, Pinhao Song, Tianyu Guo, Wei Shi
Date: 2021-12-05 00:00:00
Tasks: Decoder, Occluded Person Re-Identification, Person Re-Identification

Occluded person re-identification is a challenging task as human body parts could be occluded by some obstacles (e.g. trees, cars, and pedestrians) in certain scenes. Some existing pose-guided methods solve this problem by aligning body parts according to graph matching, but these graph-based methods are not intuitive and complicated. Therefore, we propose a transformer-based Pose-guided Feature Disentangling (PFD) method by utilizing pose information to clearly disentangle semantic components (e.g. human body or joint parts) and selectively match non-occluded parts correspondingly. First, Vision Transformer (ViT) is used to extract the patch features with its strong capability. Second, to preliminarily disentangle the pose information from patch information, the matching and distributing mechanism is leveraged in Pose-guided Feature Aggregation (PFA) module. Third, a set of learnable semantic views are introduced in transformer decoder to implicitly enhance the disentangled body part features. However, those semantic views are not guaranteed to be related to the body without additional supervision. Therefore, Pose-View Matching (PVM) module is proposed to explicitly match visible body parts and automatically separate occlusion features. Fourth, to better prevent the interference of occlusions, we design a Pose-guided Push Loss to emphasize the features of visible body parts. Extensive experiments over five challenging datasets for two tasks (occluded and holistic Re-ID) demonstrate that our proposed PFD is superior promising, which performs favorably against state-of-the-art methods. Code is available at https://github.com/WangTaoAs/PFD_Net
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks
Authors: Zixuan Ke, Bing Liu, Hu Xu, Lei Shu
Date: 2021-12-05 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Classification, Continual Learning, Contrastive Learning, Incremental Learning, Knowledge Distillation, Sentiment Analysis, Sentiment Classification, Transfer Learning

This paper studies continual learning (CL) of a sequence of aspect sentiment classification(ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product. The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs. To our knowledge, this setting has not been studied before for ASC. This paper proposes a novel model called CLASSIC. The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing. Experimental results show the high effectiveness of CLASSIC.
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Authors: Zixuan Ke, Bing Liu, Nianzu Ma, Hu Xu, Lei Shu
Date: 2021-12-05 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Continual Learning, Language Modeling, Language Modelling, Sentiment Analysis, Transfer Learning

Continual learning (CL) learns a sequence of tasks incrementally with the goal of achieving two main objectives: overcoming catastrophic forgetting (CF) and encouraging knowledge transfer (KT) across tasks. However, most existing techniques focus only on overcoming CF and have no mechanism to encourage KT, and thus do not do well in KT. Although several papers have tried to deal with both CF and KT, our experiments show that they suffer from serious CF when the tasks do not have much shared knowledge. Another observation is that most current CL methods do not use pre-trained models, but it has been shown that such models can significantly improve the end task performance. For example, in natural language processing, fine-tuning a BERT-like pre-trained language model is one of the most effective approaches. However, for CL, this approach suffers from serious CF. An interesting question is how to make the best use of pre-trained models for CL. This paper proposes a novel model called CTR to solve these problems. Our experimental results demonstrate the effectiveness of CTR
Causal Distillation for Language Models
Authors: Zhengxuan Wu, Atticus Geiger, Josh Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah D. Goodman
Date: 2021-12-05 00:00:00
Proceeding: NAACL 2022 7
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Understanding, Question Answering

Distillation efforts have led to language models that are more compact and efficient without serious drops in performance. The standard approach to distillation trains a student model against two objectives: a task-specific objective (e.g., language modeling) and an imitation objective that encourages the hidden states of the student model to be similar to those of the larger teacher model. In this paper, we show that it is beneficial to augment distillation with a third objective that encourages the student to imitate the causal computation process of the teacher through interchange intervention training(IIT). IIT pushes the student model to become a causal abstraction of the teacher model - a simpler model with the same causal structure. IIT is fully differentiable, easily implemented, and combines flexibly with other objectives. Compared with standard distillation of BERT, distillation via IIT results in lower perplexity on Wikipedia (masked language modeling) and marked improvements on the GLUE benchmark (natural language understanding), SQuAD (question answering), and CoNLL-2003 (named entity recognition).
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
Authors: Feng Xu, Chuang Zhu, Wenqi Tang, Ying Wang, Yu Zhang, Jie Li, Hongchuan Jiang, Zhongyue Shi, Jun Liu, Mulan Jin
Date: 2021-12-04 00:00:00
Tasks: Multiple Instance Learning, Specificity, whole slide images

Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model. Results: The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density ($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and orientation ($p$ = 0.012). Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC. The codes and dataset are available at https://github.com/bupt-ai-cz/BALNMP
Label-Efficient Semantic Segmentation with Diffusion Models
Authors: Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov, Valentin Khrulkov, Artem Babenko
Date: 2021-12-06 00:00:00
Proceeding: ICLR 2022 4
Tasks: Denoising, Segmentation, Semantic Segmentation, Super-Resolution

Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-the-art generative performance. The superior performance of diffusion models has made them an appealing tool in several applications, including inpainting, super-resolution, and semantic editing. In this paper, we demonstrate that diffusion models can also serve as an instrument for semantic segmentation, especially in the setup when labeled data is scarce. In particular, for several pretrained diffusion models, we investigate the intermediate activations from the networks that perform the Markov step of the reverse diffusion process. We show that these activations effectively capture the semantic information from an input image and appear to be excellent pixel-level representations for the segmentation problem. Based on these observations, we describe a simple segmentation method, which can work even if only a few training images are provided. Our approach significantly outperforms the existing alternatives on several datasets for the same amount of human supervision.