pwc-archive/papers-with-abstracts huggingface dataset:
Large Array Antenna Spectrum Sensing in Cognitive Radio Networks
Authors: Amirhossein Taherpour, Abbas Taherpour, Tamer Khattab
Date: 2024-02-22 00:00:00

We investigate the problem of spectrum sensing in cognitive radios (CRs) when the receivers are equipped with a large array of antennas. We propose and derive three detectors based on the concept of linear spectral statistics (LSS) in the field of random matrix theory (RMT). These detectors correspond to the generalized likelihood ratio (GLR), Frobenius norm, and Rao tests employed in conventional multiple antenna spectrum sensing (MASS). Subsequently, we compute the Gaussian distribution of the proposed detectors under the noise-only hypothesis, leveraging the central limit theorem (CLT) applied to high-dimensional random matrices. We evaluate the performance of the proposed detectors and analyze the impact of the number of antennas and samples on their efficacy. Furthermore, we assess the accuracy of the theoretical results by comparing them with simulation outcomes. The simulation results provide evidence that the proposed detectors exhibit efficient performance in wireless networks featuring large array antennas. These detectors find practical applications in diverse domains, including massive MIMO wireless communications, radar systems, and astronomical applications.
Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?
Authors: Tara Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian
Date: 2024-02-21 00:00:00
Tasks: Integrated sensing and communication, ISAC

Reconfigurable intelligent surface (RIS) have introduced unprecedented flexibility and adaptability toward smart wireless channels. Recent research on integrated sensing and communication (ISAC) systems has demonstrated that RIS platforms enable enhanced signal quality, coverage, and link capacity. In this paper, we explore the application of fully-connected beyond diagonal RIS (BD-RIS) to ISAC systems. BD-RIS introduces additional degrees of freedom by allowing non-zero off-diagonal elements for the scattering matrix, potentially enabling further functionalities and performance enhancements. In particular, we consider the joint design objective of maximizing the weighted sum of the signal-to-noise ratio (SNR) at the radar receiver and communication users by leveraging the extra degrees-of-freedom offered in the BD-RIS setting. These degrees-of-freedom are unleashed by formulating an alternating optimization process over known and auxiliary (latent) variables of such systems. Our numerical results reveal the advantages of deploying BD-RIS in the context of ISAC and the effectiveness of the proposed algorithm by improving the SNR values for both radar and communication users by several orders of magnitude.
A Note on Optimal Liquidation with Linear Price Impact
Authors: Yan Dolinsky, Doron Greenstein
Date: 2024-02-21 00:00:00

In this note we consider the maximization of the expected terminal wealth for the setup of quadratic transaction costs. First, we provide a very simple probabilistic solution to the problem. Although the problem was largely studied, as far as we know up to date this simple and probabilistic form of the solution has not appeared in the literature. Next, we apply the general result for the numerical study of the case where the risky asset is given by a fractional Brownian Motion and the information flow of the investor can be diversified.
Mechanism and Prevention of Hyperbaric Oxygen Convulsions
Authors: Ondrej Groborz, Ludek Sefc, Petr Marsalek
Date: 2024-02-22 00:00:00

Hyperbaric oxygen therapy (HBOT) proves vital in saving lives by elevating the partial pressure of oxygen (pO2). However, HBOT may also have toxic effects, including lung and retinal damage (peripheral HBOT toxicity), muscle spasms and violent myoclonic convulsions (CNS HBOT toxicity), which may even lead to death if left untreated. Despite the severity of the toxic effects of HBOT, their mechanism is only poorly understood to date. This lack of understanding the underlying mechanism hinders the development of new, effective therapies and preventive strategies to supress HBOT toxicity. Herein, we provide evidence that (1) increased pO2 increases the content of reactive oxygen species (ROS) in tissues, which causes peripheral HBOT toxicity and contributes to CNS toxicity by irreversibly altering cell receptors. Moreover, (2) increased ROS concentration in brain lowers activity of glutamic decarboxylase (GD), which lowers concentrations of inhibitory neurotransmitter gamma-aminobutyric acid (GABA), thereby contributing to the onset of HBOT-derived convulsions. At last, we provide long overlooked evidence that (3) elevated ambient pressure directly inhibits GABA(A) and glycine receptors, thereby leading to the rapid onset of HBOT-derived convulsions. We show that only a combination of these three mechanisms (1 + 2 + 3) are needed to explain most phenomena seen in HBOT toxicity (especially in CNS toxicity). Based on these proposed intertwined mechanisms, we propose administering antioxidants (lowering ROS concentrations), pyridoxine (restoring GD activity), and low doses of sedatives/ anaesthetics (reversing inhibitory effects of pressure on GABA(A) and glycine receptors) before routine hyperbaric oxygen therapies and deep-sea diving to prevent the HBOT toxicity.
Evaluating Cognitive and Neuropsychological Assessments -- A Comprehensive Review
Authors: Chuang Li, Rubing Lin, Yantong Liu, Yichen Wei
Date: 2024-02-22 00:00:00
Tasks: Diagnostic, Management

Cognitive impairments in older adults represent a significant public health concern, necessitating accurate diagnostic and monitoring strategies. In this study, the principal cognitive and neuropsychological evaluations employed for the diagnosis and longitudinal observation of cognitive deficits in the elderly are investigated. An analytical review of instruments including the Mini-Mental State Examination (MMSE), Digit Symbol Substitution Test (DSST), Montreal Cognitive Assessment (MoCA), and Trail Making Test (TMT) is conducted. This examination encompasses an assessment of each instrument's methodology, efficacy, advantages, and limitations. The objective is to enhance comprehension of these assessments for the early identification and effective management of conditions such as dementia and mild cognitive impairment, thereby contributing to the advancement of cognitive health within the geriatric population.
Object permanence in newborn chicks is robust against opposing evidence
Authors: Justin N. Wood, Tomer D. Ullman, Brian W. Wood, Elizabeth S. Spelke, Samantha M. W. Wood
Date: 2024-02-22 00:00:00
Tasks: Object

Newborn animals have advanced perceptual skills at birth, but the nature of this initial knowledge is unknown. Is initial knowledge flexible, continuously adapting to the statistics of experience? Or can initial knowledge be rigid and robust to change, even in the face of opposing evidence? We address this question through controlled-rearing experiments on newborn chicks. First, we reared chicks in an impoverished virtual world, where objects never occluded one another, and found that chicks still succeed on object permanence tasks. Second, we reared chicks in a virtual world in which objects teleported from one location to another while out of view: an unnatural event that violates the continuity of object motion. Despite seeing thousands of these violations of object permanence, and not a single non-violation, the chicks behaved as if object permanence were true, exhibiting the same behavior as chicks reared with natural object permanence events. We conclude that object permanence develops prenatally and is robust to change from opposing evidence.
The role of gap junctions and clustered connectivity in emergent synchronisation patterns of inhibitory neuronal networks
Authors: Hélène Todd, Mathieu Desroches, Alex Cayco-Gajic, Boris Gutkin
Date: 2024-02-22 00:00:00

Inhibitory interneurons, ubiquitous in the central nervous system, form networks connected through both chemical synapses and gap junctions. These networks are essential for regulating the activity of principal neurons, especially by inducing temporally patterned dynamic states. We aim to understand the dynamic mechanisms for synchronisation in networks of electrically and chemically coupled interneurons. We use the exact mean-field reduction to derive a neural mass model for both homogeneous and clustered networks. We first analyse a single population of neurons to understand how the two couplings interact with one another. We demonstrate that the network transitions from an asynchronous to a synchronous regime either by increasing the strength of the gap junction connectivity or the strength of the background input current. Conversely, the strength of inhibitory synapses affects the population firing rate, suggesting that electrical and chemical coupling strengths act as complementary mechanisms by which networks can tune synchronous oscillatory behavior. In line with previous work, we confirm that the depolarizing spikelet is crucial for the emergence of synchrony. Furthermore, find that the fast frequency component of the spikelet ensures robustness to heterogeneity. Next, inspired by the existence of multiple interconnected interneuron subtypes in the cerebellum, we analyse networks consisting of two clusters of cell types defined by differing chemical versus electrical coupling strengths. We show that breaking the electrical and chemical coupling symmetry between these clusters induces bistability, so that a transient external input can switch the network between synchronous and asynchronous firing. Together, our results shows the variety of cell-intrinsic and network properties that contribute to synchronisation of interneuronal networks with multiple types of coupling.
On the Arrow of Inference
Authors: Xin Li
Date: 2024-02-22 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, multimodal interaction, Quantization, Representation Learning, Survey

Just as the arrow of time structures physics, the arrow of inference organizes cognition, directing the flow of information in perception, action, and memory. The Context-Content Uncertainty Principle (CCUP) formalizes this asymmetry, between high-entropy context and low-entropy content, and frames inference as a cycle that aligns the two through selective, bidirectional interaction. Cycle formation resolves the Information Bottleneck (IB) in Optimal Transport (OT) by coordinating bottom-up contextual disambiguation with top-down content reconstruction, a process neurobiologically mirrored in the cyclical interplay between dorsal (context) and ventral (content) streams. Local inference cycles extend into memory chains that simulate goals, support counterfactual reasoning, and scaffold internal model refinement across time. By operating on delta-seeded goal manifolds, each level of the hierarchy circumvents the curse of dimensionality through structured diffusion guided by priors and context. This mechanism generalizes across timescales, from perception-action loops to the sleep-wake cycle-and scales socially through language, which externalizes inference by transmitting latent content across minds. Thus, CCUP provides a unifying framework for understanding cognition as cycle-consistent inference, anchoring both individual thought and collective intelligence.
The affinity-efficacy problem: an essential part of pharmacology education
Authors: James P Higham, David Colquhoun
Date: 2024-02-21 00:00:00

A fundamental mistake in receptor theory has led to an enduring misunderstanding of how to estimate the affinity and efficacy of an agonist. These properties are inextricably linked and cannot be easily separated in any case where the binding of a ligand induces a conformation change in its receptor. Consequently, binding curves and concentration-response relationships for receptor agonists have no straightforward interpretation. This problem, the affinity-efficacy problem, remains overlooked and misunderstood despite it being recognised in 1987. To avoid the further propagation of this misunderstanding, we propose that the affinity-efficacy problem should be included in the core curricula for pharmacology undergraduates proposed by the British Pharmacological Society and IUPHAR.
The Riccati Tontine: How to Satisfy Regulators on Average
Authors: Moshe A. Milevsky, Thomas S. Salisbury
Date: 2024-02-22 00:00:00

This paper presents a new type of modern accumulation-based tontine, called the Riccati tontine, named after two Italians: mathematician Jacobo Riccati (b. 1676, d. 1754) and financier Lorenzo di Tonti (b. 1602, d. 1684). The Riccati tontine is yet another way of pooling and sharing longevity risk, but is different from competing designs in two key ways. The first is that in the Riccati tontine, the representative investor is expected -- although not guaranteed -- to receive their money back if they die, or when the tontine lapses. The second is that the underlying funds within the tontine are deliberately {\em not} indexed to the stock market. Instead, the risky assets or underlying investments are selected so that return shocks are negatively correlated with stochastic mortality, which will maximize the expected payout to survivors. This means that during a pandemic, for example, the Riccati tontine fund's performance will be impaired relative to the market index, but will not be expected to lose money for participants. In addition to describing and explaining the rationale for this non-traditional asset allocation, the paper provides a mathematical proof that the recovery schedule that generates this financial outcome satisfies a first-order ODE that is quadratic in the unknown function, which (yes) is known as a Riccati equation.
Short-maturity asymptotics for option prices with interest rates effects
Authors: Dan Pirjol, Lingjiong Zhu
Date: 2024-02-21 00:00:00

We derive the short-maturity asymptotics for option prices in the local volatility model in a new short-maturity limit $T\to 0$ at fixed $\rho = (r-q) T$, where $r$ is the interest rate and $q$ is the dividend yield. In cases of practical relevance $\rho$ is small, however our result holds for any fixed $\rho$. The result is a generalization of the Berestycki-Busca-Florent formula for the short-maturity asymptotics of the implied volatility which includes interest rates and dividend yield effects of $O(((r-q) T)^n)$ to all orders in $n$. We obtain analytical results for the ATM volatility and skew in this asymptotic limit. Explicit results are derived for the CEV model. The asymptotic result is tested numerically against exact evaluation in the square-root model model $\sigma(S)=\sigma/\sqrt{S}$, which demonstrates that the new asymptotic result is in very good agreement with exact evaluation in a wide range of model parameters relevant for practical applications.
Quantifying neural network uncertainty under volatility clustering
Authors: Steven Y. K. Wong, Jennifer S. K. Chan, Lamiae Azizi
Date: 2024-02-22 00:00:00
Tasks: Clustering, regression, Time Series, Uncertainty Quantification

Time-series with volatility clustering pose a unique challenge to uncertainty quantification (UQ) for returns forecasts. Methods for UQ such as Deep Evidential regression offer a simple way of quantifying return forecast uncertainty without the costs of a full Bayesian treatment. However, the Normal-Inverse-Gamma (NIG) prior adopted by Deep Evidential regression is prone to miscalibration as the NIG prior is assigned to latent mean and variance parameters in a hierarchical structure. Moreover, it also overparameterizes the marginal data distribution. These limitations may affect the accurate delineation of epistemic (model) and aleatoric (data) uncertainties. We propose a Scale Mixture Distribution as a simpler alternative which can provide favorable complexity-accuracy trade-off and assign separate subnetworks to each model parameter. To illustrate the performance of our proposed method, we apply it to two sets of financial time-series exhibiting volatility clustering: cryptocurrencies and U.S. equities and test the performance in some ablation studies.
Statistical validation of a deep learning algorithm for dental anomaly detection in intraoral radiographs using paired data
Authors: Pieter Van Leemput, Johannes Keustermans, Wouter Mollemans
Date: 2024-02-01 00:00:00
Tasks: Anomaly Detection, Deep Learning, Diagnostic, Sensitivity, Specificity

This article describes the clinical validation study setup, statistical analysis and results for a deep learning algorithm which detects dental anomalies in intraoral radiographic images, more specifically caries, apical lesions, root canal treatment defects, marginal defects at crown restorations, periodontal bone loss and calculus. The study compares the detection performance of dentists using the deep learning algorithm to the prior performance of these dentists evaluating the images without algorithmic assistance. Calculating the marginal profit and loss of performance from the annotated paired image data allows for a quantification of the hypothesized change in sensitivity and specificity. The statistical significance of these results is extensively proven using both McNemar's test and the binomial hypothesis test. The average sensitivity increases from $60.7\%$ to $85.9\%$, while the average specificity slightly decreases from $94.5\%$ to $92.7\%$. We prove that the increase of the area under the localization ROC curve (AUC) is significant (from $0.60$ to $0.86$ on average), while the average AUC is bounded by the $95\%$ confidence intervals ${[}0.54, 0.65{]}$ and ${[}0.82, 0.90{]}$. When using the deep learning algorithm for diagnostic guidance, the dentist can be $95\%$ confident that the average true population sensitivity is bounded by the range $79.6\%$ to $91.9\%$. The proposed paired data setup and statistical analysis can be used as a blueprint to thoroughly test the effect of a modality change, like a deep learning based detection and/or segmentation, on radiographic images.
Text Role Classification in Scientific Charts Using Multimodal Transformers
Authors: Hye Jin Kim, Nicolas Lell, Ansgar Scherp
Date: 2024-02-08 00:00:00
Tasks: Data Augmentation, Document Layout Analysis

Text role classification involves classifying the semantic role of textual elements within scientific charts. For this task, we propose to finetune two pretrained multimodal document layout analysis models, LayoutLMv3 and UDOP, on chart datasets. The transformers utilize the three modalities of text, image, and layout as input. We further investigate whether data augmentation and balancing methods help the performance of the models. The models are evaluated on various chart datasets, and results show that LayoutLMv3 outperforms UDOP in all experiments. LayoutLMv3 achieves the highest F1-macro score of 82.87 on the ICPR22 test dataset, beating the best-performing model from the ICPR22 CHART-Infographics challenge. Moreover, the robustness of the models is tested on a synthetic noisy dataset ICPR22-N. Finally, the generalizability of the models is evaluated on three chart datasets, CHIME-R, DeGruyter, and EconBiz, for which we added labels for the text roles. Findings indicate that even in cases where there is limited training data, transformers can be used with the help of data augmentation and balancing methods. The source code and datasets are available on GitHub under https://github.com/hjkimk/text-role-classification
Scaling Up LLM Reviews for Google Ads Content Moderation
Authors: Wei Qiao, Tushar Dogra, Otilia Stretcu, Yu-Han Lyu, Tiantian Fang, Dongjin Kwon, Chun-Ta Lu, Enming Luo, YuAn Wang, Chih-Chun Chia, Ariel Fuxman, Fangzhou Wang, Ranjay Krishna, Mehmet Tek
Date: 2024-02-07 00:00:00

Large language models (LLMs) are powerful tools for content moderation, but their inference costs and latency make them prohibitive for casual use on large datasets, such as the Google Ads repository. This study proposes a method for scaling up LLM reviews for content moderation in Google Ads. First, we use heuristics to select candidates via filtering and duplicate removal, and create clusters of ads for which we select one representative ad per cluster. We then use LLMs to review only the representative ads. Finally, we propagate the LLM decisions for the representative ads back to their clusters. This method reduces the number of reviews by more than 3 orders of magnitude while achieving a 2x recall compared to a baseline non-LLM model. The success of this approach is a strong function of the representations used in clustering and label propagation; we found that cross-modal similarity representations yield better results than uni-modal representations.
Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation
Authors: Zifei, Han, Jionghao Lin, Ashish Gurung, Danielle R. Thomas, Eason Chen, Conrad Borchers, Shivang Gupta, Kenneth R. Koedinger
Date: 2024-02-04 00:00:00
Tasks: Hallucination, Math, RAG, Retrieval, Retrieval-augmented Generation

One-on-one tutoring is an effective instructional method for enhancing learning, yet its efficacy hinges on tutor competencies. Novice math tutors often prioritize content-specific guidance, neglecting aspects such as social-emotional learning. Social-emotional learning promotes equity and inclusion and nurturing relationships with students, which is crucial for holistic student development. Assessing the competencies of tutors accurately and efficiently can drive the development of tailored tutor training programs. However, evaluating novice tutor ability during real-time tutoring remains challenging as it typically requires experts-in-the-loop. To address this challenge, this preliminary study aims to harness Generative Pre-trained Transformers (GPT), such as GPT-3.5 and GPT-4 models, to automatically assess tutors' ability of using social-emotional tutoring strategies. Moreover, this study also reports on the financial dimensions and considerations of employing these models in real-time and at scale for automated assessment. The current study examined four prompting strategies: two basic Zero-shot prompt strategies, Tree of Thought prompt, and Retrieval-Augmented Generator (RAG) based prompt. The results indicate that the RAG prompt demonstrated more accurate performance (assessed by the level of hallucination and correctness in the generated assessment texts) and lower financial costs than the other strategies evaluated. These findings inform the development of personalized tutor training interventions to enhance the the educational effectiveness of tutored learning.
Learning causation event conjunction sequences
Authors: Thomas E. Portegys
Date: 2024-02-17 00:00:00

This is an examination of some methods that learn causations in event sequences. A causation is defined as a conjunction of one or more cause events occurring in an arbitrary order, with possible intervening non-causal events, that lead to an effect. The methods include recurrent and non-recurrent artificial neural networks (ANNs), as well as a histogram-based algorithm. An attention recurrent ANN performed the best of the ANNs, while the histogram algorithm was significantly superior to all the ANNs.