pwc-archive/papers-with-abstracts huggingface dataset:
Kernel-CF: Collaborative filtering done right with social network analysis and kernel smoothing
Authors: Hao Wang
Date: 2023-03-08 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative filtering is the simplest but oldest machine learning algorithm in the field of recommender systems. In spite of its long history, it remains a discussion topic in research venues. Usually people use users/items whose similarity scores with the target customer greater than 0 to compute the algorithms. However, this might not be the optimal solution after careful scrutiny. In this paper, we transform the recommender system input data into a 2-D social network, and apply kernel smoothing to compute preferences for unknown values in the user item rating matrix. We unifies the theoretical framework of recommender system and non-parametric statistics and provides an algorithmic procedure with optimal parameter selection method to achieve the goal.
Achievable Rates and Low-Complexity Encoding of Posterior Matching for the BSC
Authors: Amaael Antonini, Rita Gimelshein, Richard Wesel
Date: 2023-03-08 00:00:00
Tasks: Decoder

Horstein, Burnashev, Shayevitz and Feder, Naghshvar et al. and others have studied sequential transmission of a K-bit message over the binary symmetric channel (BSC) with full, noiseless feedback using posterior matching. Yang et al. provide an improved lower bound on the achievable rate using martingale analysis that relies on the small-enough difference (SED) partitioning introduced by Naghshvar et al. SED requires a relatively complex encoder and decoder. To reduce complexity, this paper replaces SED with relaxed constraints that admit the small enough absolute difference (SEAD) partitioning rule. The main analytical results show that achievable-rate bounds higher than those found by Yang et al. are possible even under the new constraints, which are less restrictive than SED. The new analysis does not use martingale theory for the confirmation phase and applies a surrogate channel technique to tighten the results. An initial systematic transmission further increases the achievable rate bound. The simplified encoder associated with SEAD has a complexity below order O(K^2) and allows simulations for message sizes of at least 1000 bits. For example, simulations achieve 99% of of the channel's 0.50-bit capacity with an average block size of 200 bits for a target codeword error rate of 10^(-3).
GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation
Authors: Song Yang, Jiamou Liu, Kaiqi Zhao
Date: 2023-03-03 00:00:00
Tasks: Prediction

Next POI recommendation intends to forecast users' immediate future movements given their current status and historical information, yielding great values for both users and service providers. However, this problem is perceptibly complex because various data trends need to be considered together. This includes the spatial locations, temporal contexts, user's preferences, etc. Most existing studies view the next POI recommendation as a sequence prediction problem while omitting the collaborative signals from other users. Instead, we propose a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) to better exploit the extensive collaborative signals for a more accurate next POI prediction, and alleviate the cold start problem in the meantime. GETNext incorporates the global transition patterns, user's general preference, spatio-temporal context, and time-aware category embeddings together into a transformer model to make the prediction of user's future moves. With this design, our model outperforms the state-of-the-art methods with a large margin and also sheds light on the cold start challenges within the spatio-temporal involved recommendation problems.
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank
Authors: Haitao Li, Jia Chen, Weihang Su, Qingyao Ai, Yiqun Liu
Date: 2023-02-28 00:00:00
Tasks: Learning-To-Rank

This paper describes the approach of the THUIR team at the WSDM Cup 2023 Pre-training for Web Search task. This task requires the participant to rank the relevant documents for each query. We propose a new data pre-processing method and conduct pre-training and fine-tuning with the processed data. Moreover, we extract statistical, axiomatic, and semantic features to enhance the ranking performance. After the feature extraction, diverse learning-to-rank models are employed to merge those features. The experimental results show the superiority of our proposal. We finally achieve second place in this competition.
LaSER: Language-Specific Event Recommendation
Authors: Sara Abdollahi, Simon Gottschalk, Elena Demidova
Date: 2023-02-24 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Retrieval

While societal events often impact people worldwide, a significant fraction of events has a local focus that primarily affects specific language communities. Examples include national elections, the development of the Coronavirus pandemic in different countries, and local film festivals such as the C\'esar Awards in France and the Moscow International Film Festival in Russia. However, existing entity recommendation approaches do not sufficiently address the language context of recommendation. This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context. This task can support essential information retrieval activities, including web navigation and exploratory search, considering the language context of user information needs. We propose LaSER, a novel approach toward language-specific event recommendation. LaSER blends the language-specific latent representations (embeddings) of entities and events and spatio-temporal event features in a learning to rank model. This model is trained on publicly available Wikipedia Clickstream data. The results of our user study demonstrate that LaSER outperforms state-of-the-art recommendation baselines by up to 33 percentage points in MAP@5 concerning the language-specific relevance of recommended events.
Form 10-K Itemization
Authors: Yanci Zhang, Mengjia Xia, Mingyang Li, Haitao Mao, Yutong Lu, Yupeng Lan, Jinlin Ye, Rui Dai
Date: 2023-02-18 00:00:00
Tasks: Financial Analysis, Form, Retrieval

Form 10-K report is a financial report disclosing the annual financial state of a public company. It is an important evidence to conduct financial analysis, i.e., asset pricing, corporate finance. Practitioners and researchers are constantly designing algorithms to better conduct analysis on information in the Form 10-K report. The vast majority of previous works focus on quantitative data. With recent advancement on natural language processing (NLP), textual data in financial filing attracts more attention. However, to incorporate textual data for analyzing, Form 10-K Itemization is a necessary pre-process step. It aims to segment the whole document into several Item sections, where each Item section focuses on a specific financial aspect of the company. With the segmented Item sections, NLP techniques can directly apply on those Item sections related to downstream tasks. In this paper, we develop a Form 10-K Itemization system which can automatically segment all the Item sections in 10-K documents. The system is both effective and efficient. It reaches a retrieval rate of 93%.
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction
Authors: Albert Sayapin, Gleb Balitskiy, Daniel Bershatsky, Aleksandr Katrutsa, Evgeny Frolov, Alexey Frolov, Ivan Oseledets, Vitaliy Kharin
Date: 2023-02-05 00:00:00
Tasks: Collaborative Filtering, Federated Learning, Privacy Preserving

In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it requires proper modification since the data are sequential, the user feedback is distributed among devices and the transmission of users' data to aggregate common patterns must be protected against leakage. According to such requirements, we modify the structure of the classical matrix factorization model and update the training procedure to sequential learning. Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model. One more ingredient of the proposed approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server. To demonstrate the efficiency of the proposed model we use publicly available mobile user behavior data. We compare our model with sequential rules and models based on the frequency of app launches. The comparison is conducted in static and dynamic environments. The static environment evaluates how our model processes sequential data compared to competitors. Therefore, the standard train-validation-test evaluation procedure is used. The dynamic environment emulates the real-world scenario, where users generate new data by running apps on devices, and evaluates our model in this case. Our experiments show that the proposed model provides comparable quality with other methods in the static environment. However, more importantly, our method achieves a better privacy-utility trade-off than competitors in the dynamic environment, which provides more accurate simulations of real-world usage.
Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico-Nanophytoplankton Responses in Coastal Oligotrophic Waters
Authors: R. Fuchs, V. Rossi, C. Caille, N. Bensoussan, C. Pinazo, O. Grosso, M. Thyssen
Date: 2023-03-02 00:00:00
Proceeding: Geophysical Research Letters 2023 3
Tasks: PICO

Pico-nanophytoplankton organisms are dominant in oceanic oligotrophic areas but their adaptive growth rates make their contribution to the carbon cycle difficult to estimate. Here we address their response capacities after sporadic wind gusts causing upwelling events in a coastal Mediterranean station. When the water column is stratified, corresponding to oligotrophic conditions, these events generate intense short-lived nutrient pulses and seawater temperature drops lasting 6 days on average with decreases up to 10°C. Using an automated flow cytometer and statistical rupture-detection methods, we characterize the responses of five pico-nanophytoplankton functional groups at a two-hour frequency from September 2019 to November 2021. These events trigger delayed increases in both abundances and biomasses following similar patterns for most groups that can overpass spring bloom values, and are immediately followed by an overall decrease, suggesting a clear physical driver. These submesoscale events, due to their short duration, are poorly represented in coastal carbon budgets.
Turbulent Velocity Fields in SPH--simulated Galaxy Clusters
Authors: F. Vazza, G. Tormen, R. Cassano, G. Brunetti, K. Dolag
Date: 2006-02-10 00:00:00

We present a study of the turbulent velocity fields in the Intra Cluster Medium of a sample of 21 galaxy clusters simulated by the SPH--code Gadget2, using a new numerical scheme where the artificial viscosity is suppressed outside shocks. The turbulent motions in the ICM of our simulated clusters are detected with a novel method devised to better disentangle laminar bulk motions from chaotic ones. We focus on the scaling law between the turbulent energy content of the gas particles and the total mass, and find that the energy in the form of turbulence scales approximatively with the thermal energy of clusters. We follow the evolution with time of the scaling laws and discuss the physical origin of the observed trends. The simulated data are in agreement with independent semi--analytical calculations, and the combination between the two methods allows to constrain the scaling law over more than two decades in cluster mass.
Optimal scheduling of park-level integrated energy system considering ladder-type carbon trading mechanism and flexible load
Authors: Hongbin Sun, Xinmei Sun, Lei Kou, Benfa Zhang, Xiaodan Zhu
Date: 2023-03-03 00:00:00
Tasks: Scheduling

In an attempt to improve the utilization efficiency of multi-energy coupling in park-level integrated energy system (PIES), promote wind power consumption and reduce carbon emissions, a low-carbon economic operation optimization model of PIES integrating flexible load and carbon trading mechanism is constructed. Firstly, according to the characteristics of load response, the demand response is divided into four types: which can be shifted, transferred, reduced and replaced. Secondly, the PIES basic architecture is given by considering the combined heat and power generation coupling equipment, new energy and flexible load in the park. Finally, introducing the ladder-type carbon trading mechanism into the system and minimize the total operating cost, the low-carbon economic operation optimization model of PIES is established. The YALMIP toolbox and CPLEX solver are used to solve the example, the simulation results show that the participation of electrical and thermal coupled scheduling and flexible electric or thermal loads can significantly reduce the system operating cost, reduce the load peak-to-valley difference and relieve peak power consumption pressure.
The evolution of cooperation and diversity by integrated indirect reciprocity
Authors: Tatsuya Sasaki, Satoshi Uchida, Isamu Okada, Hitoshi Yamamoto
Date: 2023-03-08 00:00:00
Tasks: Diversity

Indirect reciprocity is one of the major mechanisms for the evolution of cooperation in human societies. There are two types of indirect reciprocity: upstream and downstream. Cooperation in downstream reciprocity follows the pattern, 'You helped someone, and I will help you'. The direction of cooperation is reversed in upstream reciprocity, which instead follows the pattern, 'You helped me, and I will help someone else'. In reality, these two types of indirect reciprocity often occur in combination. However, upstream and downstream reciprocity have mostly been studied theoretically in isolation. Here, we propose a new model that integrates both types. We apply the standard giving-game framework of indirect reciprocity and analyze the model by means of evolutionary game theory. We show that the model can result in the stable coexistence of altruistic reciprocators and free riders in well-mixed populations. We also found that considering inattention in the assessment rule can strengthen the stability of this mixed equilibrium, even resulting in a global attractor. Our results indicate that the cycles of forwarding help and rewarding help need to be established for creating and maintaining diversity and inclusion in a society.
A Feasibility Study on Opportunistic Rainfall Measurement From Satellite TV Broadcasts
Authors: Fabiola Sapienza, Giacomo Bacci, Filippo Giannetti, Vincenzo Lottici, Attilio Vaccaro, Giovanni Serafino, Alberto Ortolani, Francesca Caparrini, Andrea Antonini, Samantha Melani, Antonio Mazza, Luca Baldini, Elisa Adirosi, Luca Facheris
Date: 2023-03-08 00:00:00

Rainfall precipitation maps are usually derived based on the measurements collected by classical weather devices, such as rain gauges and weather stations. This article aims to show the benefits obtained by opportunistic rainfall measurements based on signal strength measurements provided by commercial-grade satellite terminals (e.g., used in TV broadcasting). To assess not only the feasibility of this approach, with significant advantages in terms of capital and operational expenditure, but also improvements in terms of accuracy, we focus on a case study for agricultural applications using a Gaussian-modeled synthetic rain over a specific, real-world test area.
MU-Massive MIMO with Multiple RISs: SINR Maximization and Asymptotic Analysis
Authors: Somayeh Aghashahi, Zolfa Zeinalpour-Yazdi, Aliakbar Tadaion, Mahdi Boloursaz Mashhadi, Ahmed Elzanaty
Date: 2023-03-07 00:00:00
Tasks: Management

In this letter, we investigate the signal-to-interference-plus-noise-ratio (SINR) maximization problem in a multi-user massive multiple-input-multiple-output (massive MIMO) system enabled with multiple reconfigurable intelligent surfaces (RISs). We examine two zero-forcing (ZF) beamforming approaches for interference management namely BS-UE-ZF and BS-RIS-ZF that enforce the interference to zero at the users (UEs) and the RISs, respectively.Then, for each case, we resolve the SINR maximization problem to find the optimal phase shifts of the elements of the RISs. Also, we evaluate the asymptotic expressions for the optimal phase shifts and the maximum SINRs when the number of the base station (BS) antennas tends to infinity. We show that if the channels of the RIS elements are independent and the number of the BS antennas tends to infinity, random phase shifts achieve the maximum SINR using the BS-UE-ZF beamforming approach. The simulation results illustrate that by employing the BS-RIS-ZF beamforming approach, the asymptotic expressions of the phase shifts and maximum SINRs achieve the rate obtained by the optimal phase shifts even for a small number of the BS antennas.
Converting the Suggested Upper Merged Ontology to Typed First-order Form
Authors: Adam Pease
Date: 2023-03-02 00:00:00
Tasks: Form, Translation

We describe the translation of the Suggested Upper Merged Ontology (SUMO) to Typed First-order Form (TFF) with level 0 polymorphism. Building on our prior work to create a TPTP FOF translation of SUMO for use in the E and Vampire theorem provers, we detail the transformations required to handle an explicitly typed logic, and express SUMO's type hierarchy for numbers in a manner consistent with its intended semantics and the three numerical classes allowed in TFF. We provide description of the open source code and an example proof in Vampire on the resulting theory.
A Multi-Scale Simulation of Retinal Physiology
Authors: Belal Abuelnasr, Adam R. Stinchcombe
Date: 2023-03-08 00:00:00

We present a detailed physiological model of the retina that includes the biochemistry and electrophysiology of phototransduction, neuronal electrical coupling, and the spherical geometry of the eye. The model is a parabolic-elliptic system of partial differential equations based on the mathematical framework of the bi-domain equations, which we have generalized to account for multiple cell-types. We discretize in space with non-uniform finite differences and step through time with a custom adaptive time-stepper that employs a backward differentiation formula and an inexact Newton method. A refinement study confirms the accuracy and efficiency of our numerical method. Numerical simulations using the model compare favorably with experimental findings, such as desensitization to light stimuli and calcium buffering in photoreceptors. Other numerical simulations suggest an interplay between photoreceptor gap junctions and inner segment, but not outer segment, calcium concentration. Applications of this model and simulation include analysis of retinal calcium imaging experiments, the design of electroretinograms, the design of visual prosthetics, and studies of ephaptic coupling within the retina.
Synthetic aperture imaging of dispersive targets
Authors: Arnold D. Kim, Chrysoula Tsogka
Date: 2023-03-06 00:00:00

We introduce a dispersive point target model based on scattering by a particle in the far-field. The synthetic aperture imaging problem is then expanded to identify these targets and recover their locations and frequency dependent reflectivities. We show that Kirchhoff migration (KM) is able to identify dispersive point targets in an imaging region. However, KM predicts target locations that are shifted in range from their true locations. We derive an estimate for this range shift for a single target. We also show that because of this range shift we cannot recover the complex-valued frequency dependent reflectivity, but we can recover its absolute value and hence the radar cross-section (RCS) of the target. Simulation results show that we can detect, recover the approximate location, and recover the RCS for dispersive point targets thereby opening opportunities to classifying important differences between multiple targets such as their sizes or material compositions.
An Efficient Game Theory-Based Power Control Algorithm for D2D Communication in 5G Networks
Authors: Abdu Saif, Kamarul Ariffin Bin Noordin, Kaharudin Dimyati, Nor Shahida Mohd Shah, Yousef Ali Al-Gumaei, Qazwan Abdullah, Kamal Ali Alezabi
Date: 2023-03-08 00:00:00

Device-to-Device (D2D) communication is one of the enabling technologies for 5G networks that support proximity-based service (ProSe) for wireless network communications. This paper proposes a power control algorithm based on the Nash equilibrium and game theory to eliminate the interference between the cellular user device and D2D links. This leads to reliable connectivity with minimal power consumption in wireless communication. The power control in D2D is modeled as a non-cooperative game. Each device is allowed to independently select and transmit its power to maximize (or minimize) user utility. The aim is to guide user devices to converge with the Nash equilibrium by establishing connectivity with network resources. The proposed algorithm with pricing factors is used for power consumption and reduces overall interference of D2Ds communication. The proposed algorithm is evaluated in terms of the energy efficiency of the average power consumption, the number of D2D communication, and the number of iterations. Besides, the algorithm has a relatively fast convergence with the Nash Equilibrium rate. It guarantees that the user devices can achieve their required Quality of Service (QoS) by adjusting the residual cost coefficient and residual energy factor. Simulation results show that the power control shows a significant reduction in power consumption that has been achieved by approximately 20% compared with algorithms in [11].
One-4-All: Neural Potential Fields for Embodied Navigation
Authors: Sacha Morin, Miguel Saavedra-Ruiz, Liam Paull
Date: 2023-03-07 00:00:00
Tasks: All, Navigate

A fundamental task in robotics is to navigate between two locations. In particular, real-world navigation can require long-horizon planning using high-dimensional RGB images, which poses a substantial challenge for end-to-end learning-based approaches. Current semi-parametric methods instead achieve long-horizon navigation by combining learned modules with a topological memory of the environment, often represented as a graph over previously collected images. However, using these graphs in practice requires tuning a number of pruning heuristics. These heuristics are necessary to avoid spurious edges, limit runtime memory usage and maintain reasonably fast graph queries in large environments. In this work, we present One-4-All (O4A), a method leveraging self-supervised and manifold learning to obtain a graph-free, end-to-end navigation pipeline in which the goal is specified as an image. Navigation is achieved by greedily minimizing a potential function defined continuously over image embeddings. Our system is trained offline on non-expert exploration sequences of RGB data and controls, and does not require any depth or pose measurements. We show that O4A can reach long-range goals in 8 simulated Gibson indoor environments and that resulting embeddings are topologically similar to ground truth maps, even if no pose is observed. We further demonstrate successful real-world navigation using a Jackal UGV platform.