pwc-archive/papers-with-abstracts huggingface dataset:
AI-Based Secure NOMA and Cognitive Radio enabled Green Communications: Channel State Information and Battery Value Uncertainties
Authors: Saeed Sheikhzadeh, Mohsen Pourghasemian, Mohammad R. Javan, Nader Mokari, Eduard A. Jorswieck
Date: 2021-06-30 00:00:00

In this paper, the security-aware robust resource allocation in energy harvesting cognitive radio networks is considered with cooperation between two transmitters while there are uncertainties in channel gains and battery energy value. To be specific, the primary access point harvests energy from the green resource and uses time switching protocol to send the energy and data towards the secondary access point (SAP). Using power-domain non-orthogonal multiple access technique, the SAP helps the primary network to improve the security of data transmission by using the frequency band of the primary network. In this regard, we introduce the problem of maximizing the proportional-fair energy efficiency (PFEE) considering uncertainty in the channel gains and battery energy value subject to the practical constraints. Moreover, the channel gain of the eavesdropper is assumed to be unknown. Employing the decentralized partially observable Markov decision process, we investigate the solution of the corresponding resource allocation problem. We exploit multi-agent with single reward deep deterministic policy gradient (MASRDDPG) and recurrent deterministic policy gradient (RDPG) methods. These methods are compared with the state-of-the-art ones like multi-agent and single-agent DDPG. Simulation results show that both MASRDDPG and RDPG methods, outperform the state-of-the-art methods by providing more PFEE to the network.
A Weather-Dependent Hybrid RF/FSO Satellite Communication for Improved Power Efficiency
Authors: Olfa Ben Yahia, Eylem Erdogan, Gunes Karabulut Kurt, Ibrahim Altunbas, Halim Yanikomeroglu
Date: 2021-06-30 00:00:00
Tasks: Diversity

Recent studies have shown that satellite communication (SatCom) will have a fundamental role in the next generation non-terrestrial networks (NTN). In SatCom, radio-frequency (RF) or free-space optical (FSO) communications can be used depending on the communication environment. Motivated by the complementary nature of RF and FSO communication, we propose a hybrid RF/FSO transmission strategy for SatCom, where the satellite selects RF or FSO links depending on the weather conditions obtained from the context-aware sensor. To quantify the performance of the proposed network, we derive the outage probability expressions by considering different weather conditions. Moreover, asymptotic analysis is conducted to obtain the diversity order. Furthermore, we investigate the impact of non-zero boresight pointing errors and illustrate the benefits of the aperture averaging to mitigate the effect of misalignment and atmospheric turbulence. Finally, we suggest effective design guidelines that can be useful for system designers. The results have shown that the proposed strategy performs better than the dual-mode conventional hybrid RF/FSO communication in terms of outage probability offering some power gain.
Does Collective Genetic Regulation exist?
Authors: J. M. Deutsch
Date: 2021-06-29 00:00:00

Does regulation in the genome use collective behavior, similar to the way the brain or deep neural networks operate? Here I make the case for why having a genomic network capable of a high level of computation would be strongly selected for, and suggest how it might arise from biochemical processes that succeed in regulating in a collective manner, very different than the usual way we think about genetic regulation.
Do grid codes afford generalization and flexible decision-making?
Authors: Linda Q. Yu, Seongmin A. Park, Sarah C. Sweigart, Erie D. Boorman, Matthew R. Nassar
Date: 2021-06-30 00:00:00
Tasks: Decision Making, Hippocampus

Behavioral flexibility is learning from previous experiences and planning appropriate actions in a changing or novel environment. Successful behavioral adaptation depends on internal models the brain builds to represent the relational structure of an abstract task. Emerging evidence suggests that the well-known roles of the hippocampus and entorhinal cortex (HC-EC) in integrating spatial relationships into cognitive maps can be extended to map the transition structure between states in non-spatial abstract tasks. However, what the EC grid-codes actually compute to afford generalization remains elusive. We introduce two non-exclusive ideas regarding what grid-codes may represent to afford higher-level cognition. One idea is that grid-codes are eigenvectors of the successor representation (SR) learned online during a task. This view assumes that the grid codes serve as an efficient basis function for learning and representing experienced relationships between entities. Subsequently, the grid codes facilitate generalization in novel contexts such as when the goal changes. The second idea is that the grid-codes reflect the inferred global task structure. This view assumes that the grid-code represents a structural code that is factorized from specific sensory content, enabling structural information to be transferred across tasks. Subsequently, the brain could afford one-shot inferences without requiring experience. The ability to generalize experiences and make appropriate decisions in novel situations is critical for both animals and machines. Here we review proposed computations of the grid-code in the brain, which is potentially critical to behavioral flexibility.
A Computational Model of Infant Learning and Reasoning with Probabilities
Authors: Thomas R Shultz, Ardavan S Nobandegani
Date: 2021-06-30 00:00:00
Tasks: Mathematical Proofs

Recent experiments reveal that 6- to 12-month-old infants can learn probabilities and reason with them. In this work, we present a novel computational system called Neural Probability Learner and Sampler (NPLS) that learns and reasons with probabilities, providing a computationally sufficient mechanism to explain infant probabilistic learning and inference. In 24 computer simulations, NPLS simulations show how probability distributions can emerge naturally from neural-network learning of event sequences, providing a novel explanation of infant probabilistic learning and reasoning. Three mathematical proofs show how and why NPLS simulates the infant results so accurately. The results are situated in relation to seven other active research lines. This work provides an effective way to integrate Bayesian and neural-network approaches to cognition.
Win-Stay-Lose-Shift as a self-confirming equilibrium in the iterated Prisoner's Dilemma
Authors: Minjae Kim, Jung-Kyoo Choi, Seung Ki Baek
Date: 2021-01-10 00:00:00

Evolutionary game theory assumes that players replicate a highly scored player's strategy through genetic inheritance. However, when learning occurs culturally, it is often difficult to recognize someone's strategy just by observing the behaviour. In this work, we consider players with memory-one stochastic strategies in the iterated prisoner's dilemma, with an assumption that they cannot directly access each other's strategy but only observe the actual moves for a certain number of rounds. Based on the observation, the observer has to infer the resident strategy in a Bayesian way and chooses his or her own strategy accordingly. By examining the best-response relations, we argue that players can escape from full defection into a cooperative equilibrium supported by Win-Stay-Lose-Shift in a self-confirming manner, provided that the cost of cooperation is low and the observational learning supplies sufficiently large uncertainty.
Compiling Elementary Mathematical Functions into Finite Chemical Reaction Networks via a Polynomialization Algorithm for ODEs
Authors: Mathieu Hemery, François Fages, Sylvain Soliman
Date: 2021-06-30 00:00:00

The Turing completeness result for continuous chemical reaction networks (CRN) shows that any computable function over the real numbers can be computed by a CRN over a finite set of formal molecular species using at most bimolecular reactions with mass action law kinetics. The proof uses a previous result of Turing completeness for functions defined by polynomial ordinary differential equations (PODE), the dualrail encoding of real variables by the difference of concentration between two molecular species, and a back-end quadratization transformation to restrict to elementary reactions with at most two reactants. In this paper, we present a polynomialization algorithm of quadratic time complexity to transform a system of elementary differential equations in PODE. This algorithm is used as a front-end transformation to compile any elementary mathematical function, either of time or of some input species, into a finite CRN. We illustrate the performance of our compiler on a benchmark of elementary functions relevant to CRN design problems in synthetic biology specified by mathematical functions. In particular, the abstract CRN obtained by compilation of the Hill function of order 5 is compared to the natural CRN structure of MAPK signalling networks.
Harnessing the Benefits of Open Electronics in Science
Authors: Michael Oellermann, Jolle W. Jolles, Diego Ortiz, Rui Seabra, Tobias Wenzel, Hannah Wilson, Richelle Tanner
Date: 2021-06-30 00:00:00

Freely and openly shared low-cost electronic applications, known as open electronics, have sparked a new open-source movement, with much un-tapped potential to advance scientific research. Initially designed to appeal to electronic hobbyists, open electronics have formed a global community of "makers" and inventors and are increasingly used in science and industry. Here, we review the current benefits of open electronics for scientific research and guide academics to enter this emerging field. We discuss how electronic applications, from the experimental to the theoretical sciences, can help (I) individual researchers by increasing the customization, efficiency, and scalability of experiments, while improving data quantity and quality; (II) scientific institutions by improving access and maintenance of high-end technologies, visibility and interdisciplinary collaboration potential; and (III) the scientific community by improving transparency and reproducibility, helping decouple research capacity from funding, increasing innovation, and improving collaboration potential among researchers and the public. Open electronics are powerful tools to increase creativity, democratization, and reproducibility of research and thus offer practical solutions to overcome significant barriers in science.
Decision making with dynamic probabilistic forecasts
Authors: Peter Tankov, Laura Tinsi
Date: 2021-06-30 00:00:00
Tasks: Decision Making, energy trading, Scheduling, Sequential Decision Making

We consider a sequential decision making process, such as renewable energy trading or electrical production scheduling, whose outcome depends on the future realization of a random factor, such as a meteorological variable. We assume that the decision maker disposes of a dynamically updated probabilistic forecast (predictive distribution) of the random factor. We propose several stochastic models for the evolution of the probabilistic forecast, and show how these models may be calibrated from ensemble forecasts, commonly provided by weather centers. We then show how these stochastic models can be used to determine optimal decision making strategies depending on the forecast updates. Applications to wind energy trading are given.
LED2-Net: Monocular 360deg Layout Estimation via Differentiable Depth Rendering
Authors: Fu-En Wang, Yu-Hsuan Yeh, Min Sun, Wei-Chen Chiu, Yi-Hsuan Tsai
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Depth Estimation, Depth Prediction, Room Layout Estimation

    Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360 layout estimation as a problem of predicting depth on the horizon line of a panorama. Specifically, we propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable, thus making our proposed model end-to-end trainable while leveraging the 3D geometric information, without the need of providing the ground truth depth. Our method achieves state-of-the-art performance on numerous 360 layout benchmark datasets. Moreover, our formulation enables a pre-training step on the depth dataset, which further improves the generalizability of our layout estimation model.
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
Authors: Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, Wenqi Wei
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Diversity, Ensemble Learning, Ensemble Pruning, Image Classification

    Neural network ensembles are gaining popularity by harnessing the complementary wisdom of multiple base models. Ensemble teams with high diversity promote high failure independence, which is effective for boosting the overall ensemble accuracy. This paper provides an in-depth study on how to design and compute ensemble diversity, which can capture the complementary decision capacity of ensemble member models. We make three original contributions. First, we revisit the ensemble diversity metrics in the literature and analyze the inherent problems of poor correlation between ensemble diversity and ensemble accuracy, which leads to the low quality ensemble selection using such diversity metrics. Second, instead of computing diversity scores for ensemble teams of different sizes using the same criteria, we introduce focal model based ensemble diversity metrics, coined as FQ-diversity metrics. Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy. Third, we introduce a diversity fusion method, coined as the EQ-diversity metric, by integrating the top three most representative FQ-diversity metrics. Comprehensive experiments on two benchmark datasets (CIFAR-10 and ImageNet) show that our FQ and EQ diversity metrics are effective for selecting high diversity ensemble teams to boost overall ensemble accuracy.
LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity
Authors: Cheng-Fu Yang, Wan-Cyuan Fan, Fu-En Yang, Yu-Chiang Frank Wang
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Decoder, Diversity, Layout Generation

    When translating text inputs into layouts or images, existing works typically require explicit descriptions of each object in a scene, including their spatial information or the associated relationships. To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper. Given a scene-graph input, our LT-Net uniquely encodes the semantic features for exploiting their co-occurrences and implicit relationships. This allows one to manipulate conceptually diverse yet plausible layout outputs. Moreover, the decoder of our LT-Net translates the encoded contextual features into bounding boxes with self-supervised relation consistency preserved. By fitting their distributions to Gaussian mixture models, spatially-diverse layouts can be additionally produced by LT-Net. We conduct extensive experiments on the datasets of MS-COCO and Visual Genome, and confirm the effectiveness and plausibility of our LT-Net over recent layout generation models.
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution
Authors: Xin Deng, Hao Wang, Mai Xu, Yichen Guo, Yuhang Song, Li Yang
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Deep Reinforcement Learning, Image Super-Resolution, Super-Resolution

    The omnidirectional images (ODIs) are usually at low-resolution, due to the constraints of collection, storage and transmission. The traditional two-dimensional (2D) image super-resolution methods are not effective for spherical ODIs, because ODIs tend to have non-uniformly distributed pixel density and varying texture complexity across latitudes. In this work, we propose a novel latitude adaptive upscaling network (LAU-Net) for ODI super-resolution, which allows pixels at different latitudes to adopt distinct upscaling factors. Specifically, we introduce a Laplacian multi-level separation architecture to split an ODI into different latitude bands, and hierarchically upscale them with different factors. In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands. To the best of our knowledge, LAU-Net is the first attempt to consider the latitude difference for ODI super-resolution. Extensive results demonstrate that our LAU-Net significantly advances the super-resolution performance for ODIs.
Exploring Heterogeneous Clues for Weakly-Supervised Audio-Visual Video Parsing
Authors: Yu Wu, Yi Yang
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Contrastive Learning

    We investigate the weakly-supervised audio-visual video parsing task, which aims to parse a video into temporal event segments and predict the audible or visible event categories. The task is challenging since there only exist video-level event labels for training, without indicating the temporal boundaries and modalities. Previous works take the overall event labels to supervise both audio and visual model predictions. However, we argue that such overall labels harm the model training due to the audio-visual asynchrony. For example, commentators speak in a basketball video, but we cannot visually find the speakers. In this paper, we tackle this issue by leveraging the cross-modal correspondence of audio and visual signals. We generate reliable event labels individually for each modality by swapping audio and visual tracks with other unrelated videos. If the original visual/audio data contain event clues, the event prediction from the newly assembled data would still be highly confident. In this way, we could protect our models from being misled by ambiguous event labels. In addition, we propose the cross-modal audio-visual contrastive learning to induce temporal difference on attention models within videos, i.e., urging the model to pick the current temporal segment from all context candidates. Experiments show we outperform state-of-the-art methods by a large margin.
Image De-Raining via Continual Learning
Authors: Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, Zheng-Jun Zha
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Continual Learning

    While deep convolutional neural networks (CNNs) have achieved great success on image de-raining task, most existing methods can only learn fixed mapping rules between paired rainy/clean images on a single dataset. This limits their applications in practical situations with multiple and incremental datasets where the mapping rules may change for different types of rain streaks. However, the catastrophic forgetting of traditional deep CNN model challenges the design of generalized framework for multiple and incremental datasets. A strategy of sharing the network structure but independently updating and storing the network parameters on each dataset has been developed as a potential solution. Nevertheless, this strategy is not applicable to compact systems as it dramatically increases the overall training time and parameter space. To alleviate such limitation, in this study, we propose a parameter importance guided weights modification approach, named PIGWM. Specifically, with new dataset (e.g. new rain dataset), the well-trained network weights are updated according to their importance evaluated on previous training dataset. With extensive experimental validation, we demonstrate that a single network with a single parameter set of our proposed method can process multiple rain datasets almost without performance degradation. The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks. The results indicate that our proposed method has great potential for other computer vision tasks with dynamic learning environments.
Learning Student Networks in the Wild
Authors: Hanting Chen, Tianyu Guo, Chang Xu, Wenshuo Li, Chunjing Xu, Chao Xu, Yunhe Wang
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Knowledge Distillation, Model Compression

    Data-free learning for student networks is a new paradigm for solving users' anxiety caused by the privacy problem of using original training data. Since the architectures of modern convolutional neural networks (CNNs) are compact and sophisticated, the alternative images or meta-data generated from the teacher network are often broken. Thus, the student network cannot achieve the comparable performance to that of the pre-trained teacher network especially on the large-scale image dataset. Different to previous works, we present to maximally utilize the massive available unlabeled data in the wild. Specifically, we first thoroughly analyze the output differences between teacher and student network on the original data and develop a data collection method. Then, a noisy knowledge distillation algorithm is proposed for achieving the performance of the student network. In practice, an adaptation matrix is learned with the student network for correcting the label noise produced by the teacher network on the collected unlabeled images. The effectiveness of our DFND (Data-Free Noisy Distillation) method is then verified on several benchmarks to demonstrate its superiority over state-of-the-art data-free distillation methods. Experiments on various datasets demonstrate that the student networks learned by the proposed method can achieve comparable performance with those using the original dataset. Code is available at https://github.com/huawei-noah/Data-Efficient-Model-Compression
Person30K: A Dual-Meta Generalization Network for Person Re-Identification
Authors: Yan Bai, Jile Jiao, Wang Ce, Jun Liu, Yihang Lou, Xuetao Feng, Ling-Yu Duan
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Domain Generalization, Meta-Learning, Person Re-Identification

    Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods. However, these methods are still not reliable enough for real-world deployments, due to the insufficient generalization capability of the models learned on existing benchmarks that have limitations in multiple aspects, including limited data scale, capture condition variations, and appearance diversities. To this end, we collect a new dataset named Person30K with the following distinct features: 1) a very large scale containing 1.38 million images of 30K identities, 2) a large capture system containing 6,497 cameras deployed at 89 different sites, 3) abundant sample diversities including varied backgrounds and diverse person poses. Furthermore, we propose a domain generalization ReID method, dual-meta generalization network (DMG-Net), to exploit the merits of meta-learning in both the training procedure and the metric space learning. Concretely, we design a "learning then generalization evaluation" meta-training procedure and a meta-discrimination loss to enhance model generalization and discrimination capabilities. Comprehensive experiments validate the effectiveness of our DMG-Net. (Dataset and code will be released.)