pwc-archive/papers-with-abstracts huggingface dataset:
Complex dynamics of a heterogeneous network of Hindmarsh-Rose neurons
Authors: Zeric Tabekoueng Njitacke, Sishu Shankar Muni, Soumyajit Seth, Jan Awrejcewicz, Jacques Kengne
Date: 2022-05-03 00:00:00

In this contribution, we have considered the collective behavior of the two as well as the network of heterogeneous coupled Hindmarsh Rose (HR) neurons. The heterogeneous models were made of a memristive 2D (HR) and the traditional 3D HR neurons. Investigating a model of two coupled neurons through an electrical synapse reveals dissipative properties. When control parameters are varied, the coupled neuron model exhibits rich dynamics, such as the periodic, quasi-periodic, and chaotic dynamics involving either bursting or spiking oscillations. For weak electrical coupling strength, non-synchronized motion is observed. But in the case of higher coupling strength, synchronized cluster states are observed. Besides, ring-star networks of up to 100 under three different heterogeneous topologies are being investigated, and various spatiotemporal patterns are explored. It is found that the spatiotemporal patterns depend on the topology of the heterogeneous network considered. A new clustered chimera state is revealed qualitatively via the recurrence plot. The cluster states are indicated in the ring and star configurations of the heterogeneous network. Single and double-well chimera states have been revealed in the ring and ring-star structures. Finally, an equivalent electronic circuit for the two coupled heterogeneous is designed and investigated in the PSIM simulation environment. A perfect match is observed between the results obtained from the designed analog circuit and the mathematical model of the two coupled neurons, which supports the fact that our obtained results are not related to an artifact.
The effect of time delay for synchronisation suppression in neuronal networks
Authors: Matheus Hansen, Paulo R. Protachevicz, Kelly C. Iarosz, Ibere L. Caldas, Antonio M. Batista, Elbert E. N. Macau
Date: 2022-04-30 00:00:00

We study the time delay in the synaptic conductance for suppression of spike synchronisation in a random network of Hodgkin Huxley neurons coupled by means of chemical synapses. In the first part, we examine in detail how the time delay acts over the network during the synchronised and desynchronised neuronal activities. We observe a relation between the neuronal dynamics and the syaptic conductance distributions. We find parameter values in which the time delay has high effectiveness in promoting the suppression of spike synchronisation. In the second part, we analyse how the delayed neuronal networks react when pulsed inputs with different profiles (periodic, random, and mixed) are applied on the neurons. We show the main parameters responsible for inducing or not synchronous neuronal oscillations in delayed networks.
Approximating Choice Data by Discrete Choice Models
Authors: Haoge Chang, Yusuke Narita, Kota Saito
Date: 2022-05-04 00:00:00
Tasks: Discrete Choice Models

We obtain a necessary and sufficient condition under which random-coefficient discrete choice models, such as mixed-logit models, are rich enough to approximate any nonparametric random utility models arbitrarily well across choice sets. The condition turns out to be the affine-independence of the set of characteristic vectors. When the condition fails, resulting in some random utility models that cannot be closely approximated, we identify preferences and substitution patterns that are challenging to approximate accurately. We also propose algorithms to quantify the magnitude of approximation errors.
Utilitarianism on the front lines: COVID-19, public ethics, and the "hidden assumption" problem
Authors: Charles Shaw, Silvio Vanadia
Date: 2022-05-04 00:00:00
Tasks: Ethics

How should we think of the preferences of citizens? Whereas self-optimal policy is relatively straightforward to produce, socially optimal policy often requires a more detailed examination. In this paper, we identify an issue that has received far too little attention in welfarist modelling of public policy, which we name the "hidden assumptions" problem. Hidden assumptions can be deceptive because they are not expressed explicitly and the social planner (e.g. a policy maker, a regulator, a legislator) may not give them the critical attention they need. We argue that ethical expertise has a direct role to play in public discourse because it is hard to adopt a position on major issues like public health policy or healthcare prioritisation without making contentious assumptions about population ethics. We then postulate that ethicists are best situated to critically evaluate these hidden assumptions, and can therefore play a vital role in public policy debates.
Knowledge is non-fungible
Authors: César A. Hidalgo
Date: 2022-05-04 00:00:00

What would you do if you were asked to "add" knowledge? Would you say that "one plus one knowledge" is two "knowledges"? Less than that? More? Or something in between? Adding knowledge sounds strange, but it brings to the forefront questions that are as fundamental as they are eclectic. These are questions about the nature of knowledge and about the use of mathematics to model reality. In this chapter, I explore the mathematics of adding knowledge starting from what I believe is an overlooked but key observation: the idea that knowledge is non-fungible.
A counter example to the theorems of social preference transitivity and social choice set existence under the majority rule
Authors: Fujun Hou
Date: 2022-05-04 00:00:00

I present an example in which the individuals' preferences are strict orderings, and under the majority rule, a transitive social ordering can be obtained and thus a non-empty choice set can also be obtained. However, the individuals' preferences in that example do not satisfy any conditions (restrictions) of which at least one is required by Inada (1969) for social preference transitivity under the majority rule. Moreover, the considered individuals' preferences satisfy none of the conditions of value restriction (VR), extremal restriction (ER) or limited agreement (LA), some of which is required by Sen and Pattanaik (1969) for the existence of a non-empty social choice set. Therefore, the example is an exception to a number of theorems of social preference transitivity and social choice set existence under the majority rule. This observation indicates that the collection of the conditions listed by Inada (1969) is not as complete as might be supposed. This is also the case for the collection of conditions VR, ER and LA considered by Sen and Pattanaik (1969). This observation is a challenge to some necessary conditions in the current social choice theory. In addition to seeking new conditions, one possible way to deal with this challenge may be, from a theoretical prospective, to represent the identified conditions (such as the VR, ER and LA) in terms of a common mathematical tool, and then, people may find more.
Intelligent Reflecting Surface Aided Mobile Edge Computing With Binary Offloading: Energy Minimization for IoT Devices
Authors: Yizhen Yang, Yi Gong, Yik-Chung Wu
Date: 2022-05-04 00:00:00
Tasks: CPU, Edge-computing

Mobile edge computing (MEC) is envisioned as a promising technique to support computation-intensive and timecritical applications in future Internet of Things (IoT) era. However, the uplink transmission performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Recently, intelligent reflecting surface (IRS) has drawn much attention because of its capability to control the wireless environments so as to enhance the spectrum and energy efficiencies of wireless communications. In this paper, we consider an IRS-aided multidevice MEC system where each IoT device follows the binary offloading policy, i.e., a task has to be computed as a whole either locally or remotely at the edge server. We aim to minimize the total energy consumption of devices by jointly optimizing the binary offloading modes, the CPU frequencies, the offloading powers, the offloading times and the IRS phase shifts for all devices. Two algorithms, which are greedy-based and penalty-based, are proposed to solve the challenging nonconvex and discontinuous problem. It is found that the penalty-based method has only linear complexity with respect to the number of devices, but it performs close to the greedy-based method with cubic complexity with respect to number of devices. Furthermore, binary offloading via IRS indeed saves more energy compared to the case without IRS.
On the Diversity and Coded Modulation Design of Fluid Antenna Systems
Authors: Constantinos Psomas, Ghassan M. Kraidy, Kai-Kit Wong, Ioannis Krikidis
Date: 2022-05-04 00:00:00
Tasks: Diversity, Scheduling

Reconfigurability is a desired characteristic of future communication networks. From a transceiver's standpoint, this can be materialized through the implementation of fluid antennas (FAs). An FA consists of a dielectric holder, in which a radiating liquid moves between pre-defined locations (called ports) that serve as the transceiver's antennas. Due to the nature of liquids, FAs can practically take any size and shape, making them both flexible and reconfigurable. In this paper, we deal with the outage probability of FAs under general fading channels, where a port is scheduled based on selection combining. An analytical framework is provided for the performance with and without estimation errors, as a result of post-scheduling delays. We show that although FAs achieve maximum diversity, this cannot be realized in the presence of delays. Hence, a linear prediction scheme is proposed that overcomes delays and restores the lost diversity by predicting the next scheduled port. Moreover, we design space-time coded modulations that exploit the FA's sequential operation with space-time rotations and code diversity. The derived expressions for the pairwise error probability and average word error rate give an accurate estimate of the performance. We illustrate that the proposed design attains maximum diversity, while keeping a low-complexity receiver, thereby confirming the feasibility of FAs.
Explicit and implicit measures of emotions: Data-science might help to account for data complexity and heterogeneity
Authors: M. Moranges, C. Rouby, M. Plantevit, M. Bensafi
Date: 2022-05-04 00:00:00

Measuring emotions is a real challenge for fundamental and applied research, especially in ecological contexts. de Wijk and Noldus propose combining two types of measures-explicit to characterize a specific food, and implicit-physiological-to capture the whole experience of a meal in real-life situations. This raises several challenges including development of new and miniaturized sensors and devices but also developing new ways of data analysis. We suggest a path to follow for future studies regarding data analysis: to include Data Science in the game. This field of research may enable developing predictive but also explicative models that link subjective experience of emotions and physiological responses in real-life contexts. We suggest that food scientists should go out of their comfort zone by collaborating with computer scientists and then be trained with the new tools of Data Science, which will undoubtedly enable them 1/ to better manage complex and heterogeneous data sets, 2/ to extract knowledge that will be essential to this field of research.
Learning, fast and slow
Authors: Markus Meister
Date: 2022-04-06 00:00:00

Animals can learn efficiently from a single experience and change their future behavior in response. However, in other instances, animals learn very slowly, requiring thousands of experiences. Here I survey tasks involving fast and slow learning and consider some hypotheses for what differentiates the underlying neural mechanisms. It has been proposed that fast learning relies on neural representations that favor efficient Hebbian modification of synapses. These efficient representations may be encoded in the genome, resulting in a repertoire of fast learning that differs across species. Alternatively, the required neural representations may be acquired from experience through a slow process of unsupervised learning from the environment.
A model for malaria treatment evaluation in the presence of multiple species
Authors: Camelia R. Walker, Roslyn I. Hickson, Edmond Chang, Pengby Ngor, Siv Sovannaroth, Julie A. Simpson, David J. Price, James M. McCaw, Ric N. Price, Jennifer A. Flegg, Angela Devine
Date: 2022-05-04 00:00:00

Plasmodium (P.) falciparum and P. vivax are the two most common causes of malaria. While the majority of deaths and severe morbidity are due to P. falciparum, P. vivax poses a greater challenge to eliminating malaria outside of Africa due to its ability to form latent liver stage parasites (hypnozoites), which can cause relapsing episodes within an individual patient. In areas where P. falciparum and P. vivax are co-endemic, individuals can carry parasites of both species simultaneously. These mixed infections complicate dynamics in several ways; treatment of mixed infections will simultaneously affect both species, P. falciparum can mask the detection of P. vivax, and it has been hypothesised that clearing P. falciparum may trigger a relapse of dormant P. vivax. When mixed infections are treated for only blood-stage parasites, patients are at risk of relapse infections due to P. vivax hypnozoites. We present a stochastic mathematical model that captures interactions between P. falciparum and P. vivax, and incorporates both standard schizontocidal treatment (which targets blood-stage parasites) and radical treatment (which additionally targets liver-stage parasites). We apply this model to assess the implications of different treatment coverage of radical cure for mixed and P. vivax infections and a so-called "unified radical cure" treatment strategy for P. falciparum, P. vivax and mixed infections. We find that a unified radical cure strategy, with G6PD screening, leads to a substantially lower incidence of malaria cases and deaths overall. We perform a one-way sensitivity analysis to highlight important model parameters.
Eine empirische Analyse der Skalierung von Value-at-Risk Schaetzungen
Authors: Marita Kuhlmann
Date: 2022-05-03 00:00:00

In practice, the value-at-risk (VaR) for a longer holding period is often scaled using the 'square root of time rule'. The VaR is determined for a shorter holding period and then scaled up according to the desired holding period. For example, the Basel rules allow banks to scale up the 1-day VaR by the square root of ten to determine the 10-day VaR. It can be seen from the results of this thesis that scaling can also provide good and accurate estimates of VaR. However, it is probably much more important to consider that, depending on the methods or data set involved, there may also be significant consequences for risk provisioning. Particularly, since scaling does not always avoid the occurrence of losses that exceed the VaR estimate on a frequent basis over a period of time. Overall, the permission to use the square root of time rule in the regulatory framework should be reconsidered.
A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure
Authors: Yinan Yu, Samuel Scheidegger, John-Fredrik Grönvall, Magnus Palm, Erik Svanberg, Johan Amoruso Wennerby, Jörg Bakker
Date: 2022-04-17 00:00:00
Tasks: Management, object-detection, Object Detection

Single-vehicle accidents are the most common type of fatal accidents in Sweden, where a car drives off the road and runs into hazardous roadside objects. Proper installation and maintenance of protective objects, such as crash cushions and guard rails, may reduce the chance and severity of such accidents. Moreover, efficient detection and management of hazardous roadside objects also plays an important role in improving road safety. To better understand the state-of-the-art and system requirements, in this pre-study, we investigate the feasibility, implementation, limitations and scaling up of data processing pipelines for roadside object detection. In particular, we divide our investigation into three parts: the target of interest, the sensors of choice and the algorithm design. The data sources we consider in this study cover two common setups: 1) road surveying fleet - annual scans conducted by Trafikverket, the Swedish Transport Administration, and 2) consumer vehicle - data collected using a research vehicle from the laboratory of Resource for vehicle research at Chalmers (REVERE). The goal of this report is to investigate how to implement a scalable roadside object detection system towards safe road infrastructure and Sweden's Vision Zero.
Group Activity Recognition Using Joint Learning of Individual Action Recognition and People Grouping
Authors: Chihiro Nakatani, Kohei Sendo, Norimichi Ukita
Date: 2021-07-17 00:00:00
Proceeding: MVA 2021 7
Tasks: Action Recognition, Activity Recognition, Group Activity Recognition

This paper proposes joint learning of individual action recognition and people grouping for improving group activity recognition. By sharing the information between two similar tasks (i.e., individual action recognition and people grouping) through joint learning, errors of these two tasks are mutually corrected. This joint learning also improves the accuracy of group activity recognition. Our proposed method is designed to consist of any individual action recognition methods as a component. The effectiveness is validated with various IAR methods. By employing existing group activity recognition methods for ensembling with the proposed method, we achieved the best performance compared to the similar SOTA group activity recognition methods.
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
Authors: Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, Xiaowei Zhou
Date: 2022-05-05 00:00:00
Proceeding: CVPR 2022 1
Tasks: 2D Semantic Segmentation, 3D Reconstruction, 3D Scene Reconstruction, Semantic Segmentation

This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes. An approach to solving this issue is to incorporate planer constraints into the depth map estimation in multi-view stereo-based methods, but the per-view plane estimation and depth optimization lack both efficiency and multi-view consistency. In this work, we show that the planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. Specifically, we use an MLP network to represent the signed distance function as the scene geometry. Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network. To resolve the inaccurate segmentation, we encode the semantics of 3D points with another MLP and design a novel loss that jointly optimizes the scene geometry and semantics in 3D space. Experiments on ScanNet and 7-Scenes datasets show that the proposed method outperforms previous methods by a large margin on 3D reconstruction quality. The code is available at https://zju3dv.github.io/manhattan_sdf.
Region-to-region kernel interpolation of acoustic transfer function with directional weighting
Authors: Juliano G. C. Ribeiro, Shoichi Koyama, Hiroshi Saruwatari
Date: 2022-05-05 00:00:00
Tasks: Hyperparameter Optimization

A method of interpolating the acoustic transfer function (ATF) between regions that takes into account both the physical properties of the ATF and the directionality of region configurations is proposed. Most spatial ATF interpolation methods are limited to estimation in the region of receivers. A kernel method for region-to-region ATF interpolation makes it possible to estimate the ATFs for both source and receiver regions from a discrete set of ATF measurements. We newly formulate the reproducing kernel Hilbert space and associated kernel function incorporating directional weight to enhance the interpolation accuracy. We also investigate hyperparameter optimization methods for this kernel function. Numerical experiments indicate that the proposed method outperforms the method without the use of directional weighting.
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
Authors: Xiang Chen, Ningyu Zhang, Lei LI, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen
Date: 2022-05-04 00:00:00
Tasks: Information Retrieval, Knowledge Graph Completion, Knowledge Graphs, Link Prediction, NER, Question Answering, Relation Extraction, Retrieval

Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering, and recommendation system. Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction. However, different tasks and modalities require changes to the model architecture, and not all images/objects are relevant to text input, which hinders the applicability to diverse real-world scenarios. In this paper, we propose a hybrid transformer with multi-level fusion to address those issues. Specifically, we leverage a hybrid transformer architecture with unified input-output for diverse multimodal knowledge graph completion tasks. Moreover, we propose multi-level fusion, which integrates visual and text representation via coarse-grained prefix-guided interaction and fine-grained correlation-aware fusion modules. We conduct extensive experiments to validate that our MKGformer can obtain SOTA performance on four datasets of multimodal link prediction, multimodal RE, and multimodal NER. Code is available in https://github.com/zjunlp/MKGformer.
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
Authors: Xiang Chen, Lei LI, Ningyu Zhang, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
Date: 2022-05-04 00:00:00
Tasks: Few-Shot Learning, Memorization, Relation, Relation Extraction, Retrieval

Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relation extraction may still fail to generalize to those rare or hard patterns. Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test. Those long-tailed or hard patterns can hardly be memorized in parameters given few-shot instances. To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. We construct an open-book datastore for retrieval regarding prompt-based instance representations and corresponding relation labels as memorized key-value pairs. During inference, the model can infer relations by linearly interpolating the base output of PLM with the non-parametric nearest neighbor distribution over the datastore. In this way, our model not only infers relation through knowledge stored in the weights during training but also assists decision-making by unwinding and querying examples in the open-book datastore. Extensive experiments on benchmark datasets show that our method can achieve state-of-the-art in both standard supervised and few-shot settings. Code are available in https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE.