pwc-archive/papers-with-abstracts huggingface dataset:
Bitrate Ladder Construction using Visual Information Fidelity
Authors: Krishna Srikar Durbha, Hassene Tmar, Cosmin Stejerean, Ioannis Katsavounidis, Alan C. Bovik
Date: 2023-12-12 00:00:00

Recently proposed perceptually optimized per-title video encoding methods provide better BD-rate savings than fixed bitrate-ladder approaches that have been employed in the past. However, a disadvantage of per-title encoding is that it requires significant time and energy to compute bitrate ladders. Over the past few years, a variety of methods have been proposed to construct optimal bitrate ladders including using low-level features to predict cross-over bitrates, optimal resolutions for each bitrate, predicting visual quality, etc. Here, we deploy features drawn from Visual Information Fidelity (VIF) (VIF features) extracted from uncompressed videos to predict the visual quality (VMAF) of compressed videos. We present multiple VIF feature sets extracted from different scales and subbands of a video to tackle the problem of bitrate ladder construction. Comparisons are made against a fixed bitrate ladder and a bitrate ladder obtained from exhaustive encoding using Bjontegaard delta metrics.
Towards a geometric understanding of Spatio Temporal Graph Convolution Networks
Authors: Pratyusha Das, Sarath Shekkizhar, Antonio Ortega
Date: 2023-12-12 00:00:00
Tasks: Action Recognition, Activity Recognition, Dynamic Time Warping, Human Activity Recognition, Temporal Action Localization

Spatiotemporal graph convolutional networks (STGCNs) have emerged as a desirable model for skeleton-based human action recognition. Despite achieving state-of-the-art performance, there is a limited understanding of the representations learned by these models, which hinders their application in critical and real-world settings. While layerwise analysis of CNN models has been studied in the literature, to the best of our knowledge, there exists no study on the layerwise explainability of the embeddings learned on spatiotemporal data using STGCNs. In this paper, we first propose to use a local Dataset Graph (DS-Graph) obtained from the feature representation of input data at each layer to develop an understanding of the layer-wise embedding geometry of the STGCN. To do so, we develop a window-based dynamic time warping (DTW) method to compute the distance between data sequences with varying temporal lengths. To validate our findings, we have developed a layer-specific Spatiotemporal Graph Gradient-weighted Class Activation Mapping (L-STG-GradCAM) technique tailored for spatiotemporal data. This approach enables us to visually analyze and interpret each layer within the STGCN network. We characterize the functions learned by each layer of the STGCN using the label smoothness of the representation and visualize them using our L-STG-GradCAM approach. Our proposed method is generic and can yield valuable insights for STGCN architectures in different applications. However, this paper focuses on the human activity recognition task as a representative application. Our experiments show that STGCN models learn representations that capture general human motion in their initial layers while discriminating different actions only in later layers. This justifies experimental observations showing that fine-tuning deeper layers works well for transfer between related tasks.
COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images
Authors: Kenan Morani
Date: 2023-12-10 00:00:00
Tasks: Binary Classification, Computed Tomography (CT), COVID-19 Diagnosis, Transfer Learning

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 by 224) were input into an Xception transfer learning model. Leveraging Xception's architecture and pre-trained weights, the modified model achieved binary classification. Promising results on the COV19-CT database showcased higher validation accuracy and macro F1 score at both the slice and patient levels compared to our previous solution and alternatives on the same dataset.
Adversarial Attacks on Graph Neural Networks based Spatial Resource Management in P2P Wireless Communications
Authors: Ahmad Ghasemi, Ehsan Zeraatkar, Majid Moradikia, Seyed, Zekavat
Date: 2023-12-13 00:00:00
Tasks: CPU, Graph Neural Network, Management

This paper introduces adversarial attacks targeting a Graph Neural Network (GNN) based radio resource management system in point to point (P2P) communications. Our focus lies on perturbing the trained GNN model during the test phase, specifically targeting its vertices and edges. To achieve this, four distinct adversarial attacks are proposed, each accounting for different constraints, and aiming to manipulate the behavior of the system. The proposed adversarial attacks are formulated as optimization problems, aiming to minimize the system's communication quality. The efficacy of these attacks is investigated against the number of users, signal-to-noise ratio (SNR), and adversary power budget. Furthermore, we address the detection of such attacks from the perspective of the Central Processing Unit (CPU) of the system. To this end, we formulate an optimization problem that involves analyzing the distribution of channel eigenvalues before and after the attacks are applied. This formulation results in a Min-Max optimization problem, allowing us to detect the presence of attacks. Through extensive simulations, we observe that in the absence of adversarial attacks, the eigenvalues conform to Johnson's SU distribution. However, the attacks significantly alter the characteristics of the eigenvalue distribution, and in the most effective attack, they even change the type of the eigenvalue distribution.
Privacy-Preserving Distributed Optimisation using Stochastic PDMM
Authors: Sebastian O. Jordan, Qiongxiu Li, Richard Heusdens
Date: 2023-12-13 00:00:00
Tasks: Privacy Preserving

Privacy-preserving distributed processing has received considerable attention recently. The main purpose of these algorithms is to solve certain signal processing tasks over a network in a decentralised fashion without revealing private/secret data to the outside world. Because of the iterative nature of these distributed algorithms, computationally complex approaches such as (homomorphic) encryption are undesired. Recently, an information theoretic method called subspace perturbation has been introduced for synchronous update schemes. The main idea is to exploit a certain structure in the update equations for noise insertion such that the private data is protected without compromising the algorithm's accuracy. This structure, however, is absent in asynchronous update schemes. In this paper we will investigate such asynchronous schemes and derive a lower bound on the noise variance after random initialisation of the algorithm. This bound shows that the privacy level of asynchronous schemes is always better than or at least equal to that of synchronous schemes. Computer simulations are conducted to consolidate our theoretical results.
Modeling Sparse Graph Sequences and Signals Using Generalized Graphons
Authors: Feng Ji, Xingchao Jian, Wee Peng Tay
Date: 2023-12-13 00:00:00

Graphons are limit objects of sequences of graphs and are used to analyze the behavior of large graphs. Recently, graphon signal processing has been developed to study signal processing on large graphs. A major limitation of this approach is that any sparse sequence of graphs inevitably converges to the zero graphon, rendering the resulting signal processing theory trivial and inadequate for sparse graph sequences. To overcome this limitation, we propose a new signal processing framework that leverages the concept of generalized graphons and introduces the stretched cut distance as a measure to compare these graphons. Our framework focuses on the sampling of graph sequences from generalized graphons and explores the convergence properties of associated operators, spectra, and signals. Our signal processing framework provides a comprehensive approach to analyzing and processing signals on graph sequences, even if they are sparse. Finally, we discuss the practical implications of our theory for real-world large networks through numerical experiments.
Hierarchical Cognitive Spectrum Sharing in Space-Air-Ground Integrated Networks
Authors: Zizhen Zhou, Qianqian Zhang, Jungang Ge, Ying-Chang Liang
Date: 2023-12-13 00:00:00

In space-air-ground integrated networks (SAGINs), cognitive spectrum sharing has been regarded as a promising solution to improve spectrum efficiency by enabling a secondary network to access the spectrum of a primary network. However, different networks in SAGIN may have different quality of service (QoS) requirements, which can not be well satisfied with the traditional cognitive spectrum sharing architecture. For example, the aerial network typically has high QoS requirements, which however may not be met when it acts as a secondary network. To address this issue, in this paper, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGINs, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, the aerial and terrestrial networks can access the spectrum of the satellite network under the condition that the caused interference to the satellite terminal is below a certain threshold. Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network. Subject to these two constraints, we consider a sum-rate maximization for the terrestrial network by jointly optimizing the transmit beamforming vectors of the aerial and terrestrial base stations. To solve this non-convex problem, we propose a penalty-based iterative beamforming (PIBF) scheme that uses the penalty method and the successive convex approximation technique. Moreover, we also develop three low-complexity schemes by optimizing the normalized beamforming vectors and power control. Finally, we provide extensive numerical simulations to compare the performance of the proposed PIBF scheme and the low-complexity schemes. The results also demonstrate the advantages of the proposed HCSSA compared with the traditional cognitive spectrum sharing architecture.
Unsupervised linear component analysis for a class of probability mixture models
Authors: Marc Castella
Date: 2023-12-13 00:00:00

We deal with a model where a set of observations is obtained by a linear superposition of unknown components called sources. The problem consists in recovering the sources without knowing the linear transform. We extend the well-known Independent Component Analysis (ICA) methodology. Instead of assuming independent source components, we assume that the source vector is a probability mixture of two distributions. Only one distribution satisfies the ICA assumptions, while the other one is concentrated on a specific but unknown support. Sample points from the latter are clustered based on a data-driven distance in a fully unsupervised approach. A theoretical grounding is provided through a link with the Christoffel function. Simulation results validate our approach and illustrate that it is an extension of a formerly proposed method.
IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI
Authors: Rahul Vishwakarma, Rucha Bhalchandra Joshi, Subhankar Mishra
Date: 2023-12-11 00:00:00
Tasks: Graph Neural Network, Indoor Localization

Indoor localization is the process of determining the location of a person or object inside a building. Potential usage of indoor localization includes navigation, personalization, safety and security, and asset tracking. Commonly used technologies for indoor localization include WiFi, Bluetooth, RFID, and Ultra-wideband. Among these, WiFi's Received Signal Strength Indicator (RSSI)-based localization is preferred because of widely available WiFi Access Points (APs). We have two main contributions. First, we develop our method, 'IndoorGNN' which involves using a Graph Neural Network (GNN) based algorithm in a supervised manner to classify a specific location into a particular region based on the RSSI values collected at that location. Most of the ML algorithms that perform this classification require a large number of labeled data points (RSSI vectors with location information). Collecting such data points is a labor-intensive and time-consuming task. To overcome this challenge, as our second contribution, we demonstrate the performance of IndoorGNN on the restricted dataset. It shows a comparable prediction accuracy to that of the complete dataset. We performed experiments on the UJIIndoorLoc and MNAV datasets, which are real-world standard indoor localization datasets. Our experiments show that IndoorGNN gives better location prediction accuracies when compared with state-of-the-art existing conventional as well as GNN-based methods for this same task. It continues to outperform these algorithms even with restricted datasets. It is noteworthy that its performance does not decrease a lot with a decrease in the number of available data points. Our method can be utilized for navigation and wayfinding in complex indoor environments, asset tracking and building management, enhancing mobile applications with location-based services, and improving safety and security during emergencies.
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data
Authors: Christian Kothe, Grant Hanada, Sean Mullen, Tim Mullen
Date: 2023-12-06 00:00:00
Tasks: Transfer Learning

Near-infrared spectroscopy (NIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab. NIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, NIRS research has largely relied on probes with under ten to several hundred channels, although recently a new class of wearable NIRS devices with thousands of channels has emerged. This poses unique challenges for ML classification, as NIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art (SotA) or better performance can be achieved. To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that has not been used in previous NIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches. We show that using the proposed methodology, it is possible to achieve SotA decoding performance with high-resolution NIRS data. We also replicated several SotA approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing the n-Back task and show that these existing methods struggle in the high-channel regime and are largely outperformed by the proposed method. Our approach helps establish high-channel NIRS devices as a viable platform for SotA BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.
Any Stochastic Reaction Network has a Stationary Measure
Authors: Carsten Wiuf, Chuang Xu
Date: 2023-12-11 00:00:00

In this note, we use a result by Harris (1957) to show that there always exists a stationary measure (not necessarily a distribution) on a closed irreducible component of a stochastic reaction network. This measure might not be unique. In particular, any weakly reversible stochastic reaction network has a stationary measure on all closed irreducibe components, irrespective whether it is compelx balanced or not.
Active Inference and Intentional Behaviour
Authors: Karl J. Friston, Tommaso Salvatori, Takuya Isomura, Alexander Tschantz, Alex Kiefer, Tim Verbelen, Magnus Koudahl, Aswin Paul, Thomas Parr, Adeel Razi, Brett Kagan, Christopher L. Buckley, Maxwell J. D. Ramstead
Date: 2023-12-06 00:00:00

Recent advances in theoretical biology suggest that basal cognition and sentient behaviour are emergent properties of in vitro cell cultures and neuronal networks, respectively. Such neuronal networks spontaneously learn structured behaviours in the absence of reward or reinforcement. In this paper, we characterise this kind of self-organisation through the lens of the free energy principle, i.e., as self-evidencing. We do this by first discussing the definitions of reactive and sentient behaviour in the setting of active inference, which describes the behaviour of agents that model the consequences of their actions. We then introduce a formal account of intentional behaviour, that describes agents as driven by a preferred endpoint or goal in latent state-spaces. We then investigate these forms of (reactive, sentient, and intentional) behaviour using simulations. First, we simulate the aforementioned in vitro experiments, in which neuronal cultures spontaneously learn to play Pong, by implementing nested, free energy minimising processes. The simulations are then used to deconstruct the ensuing predictive behaviour, leading to the distinction between merely reactive, sentient, and intentional behaviour, with the latter formalised in terms of inductive planning. This distinction is further studied using simple machine learning benchmarks (navigation in a grid world and the Tower of Hanoi problem), that show how quickly and efficiently adaptive behaviour emerges under an inductive form of active inference.
Statistical model concept to quantify input and output of water, nitrogen and phosphorus for lakes with partly gauged watersheds
Authors: Peter Borgen Sørensen, Anders Nielsen
Date: 2023-12-13 00:00:00

Valid mass load predictions of nutrients, in particular nitrogen (N) and phosphorus (P), are needed for the limnological understanding of single lake ecosystems as well as larger river/lake ecosystems. The mass of N and P that enters a lake will determine the ecological state of the lake, and the mass release from the lake will determine the ecological state of downstream ecosystems. Hence, establishing sound quantifications of the external load is crucial and e.g. contributes to the foundation of assessments of necessary management interventions to improve or preserve the ecological integrity of lakes. The external load of N and P is an integral of several pathways, each having different contributions to the total mass load. Around the world, balances of N and P have been derived for decades to support both lake water quality monitoring and research, but it can be difficult and, thus, costly to make detailed and sufficiently covering measurement campaigns in all tributaries (surface as well as groundwater) in the watershed of the N and P load including seasonality and temporal change from year to year. Thus, load prediction is facing challenge of uncertainty due to unmeasured loads, which can be a consequence of limited resources available for the water flow recordings and water concentration measurements in inlets around the lake, or simply due to invisible water flow taking place through the lake bottom. The lake outlet will typically take place in one single river, so the outlet recording seems easier to measure than inlets, however, the outlet may also have unmeasured parts in cases where water is leaching out though the lake bottom. In this paper, we propose a method that applies incomplete data sets (incomplete in the sense of temporal frequency and percentage of gauged watershed) to generate time series that predict the N and P loads entering and leaving the lake.
Least-Cost Structuring of 24/7 Carbon-Free Electricity Procurements
Authors: Mike Ludkovski, Saad Mouti, Glen Swindle
Date: 2023-12-12 00:00:00

We consider the construction of renewable portfolios targeting specified carbon-free (CFE) hourly performance scores. We work in a probabilistic framework that uses a collection of simulation scenarios and imposes probability constraints on achieving the desired CFE score. In our approach there is a fixed set of available CFE generators and a given load customer who seeks to minimize annual procurement costs. We illustrate results using a realistic dataset of jointly calibrated solar and wind assets, and compare different approaches to handling multiple loads.
Large Human Language Models: A Need and the Challenges
Authors: Nikita Soni, H. Andrew Schwartz, João Sedoc, Niranjan Balasubramanian
Date: 2023-11-09 00:00:00

As research in human-centered NLP advances, there is a growing recognition of the importance of incorporating human and social factors into NLP models. At the same time, our NLP systems have become heavily reliant on LLMs, most of which do not model authors. To build NLP systems that can truly understand human language, we must better integrate human contexts into LLMs. This brings to the fore a range of design considerations and challenges in terms of what human aspects to capture, how to represent them, and what modeling strategies to pursue. To address these, we advocate for three positions toward creating large human language models (LHLMs) using concepts from psychological and behavioral sciences: First, LM training should include the human context. Second, LHLMs should recognize that people are more than their group(s). Third, LHLMs should be able to account for the dynamic and temporally-dependent nature of the human context. We refer to relevant advances and present open challenges that need to be addressed and their possible solutions in realizing these goals.
Benchmarking Distribution Shift in Tabular Data with TableShift
Authors: Josh Gardner, Zoran Popovic, Ludwig Schmidt
Date: 2023-12-10 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Benchmarking, Binary Classification, Domain Generalization

Robustness to distribution shift has become a growing concern for text and image models as they transition from research subjects to deployment in the real world. However, high-quality benchmarks for distribution shift in tabular machine learning tasks are still lacking despite the widespread real-world use of tabular data and differences in the models used for tabular data in comparison to text and images. As a consequence, the robustness of tabular models to distribution shift is poorly understood. To address this issue, we introduce TableShift, a distribution shift benchmark for tabular data. TableShift contains 15 binary classification tasks in total, each with an associated shift, and includes a diverse set of data sources, prediction targets, and distribution shifts. The benchmark covers domains including finance, education, public policy, healthcare, and civic participation, and is accessible using only a few lines of Python code via the TableShift API. We conduct a large-scale study comparing several state-of-the-art tabular data models alongside robust learning and domain generalization methods on the benchmark tasks. Our study demonstrates (1) a linear trend between in-distribution (ID) and out-of-distribution (OOD) accuracy; (2) domain robustness methods can reduce shift gaps but at the cost of reduced ID accuracy; (3) a strong relationship between shift gap (difference between ID and OOD performance) and shifts in the label distribution. The benchmark data, Python package, model implementations, and more information about TableShift are available at https://github.com/mlfoundations/tableshift and https://tableshift.org .