pwc-archive/papers-with-abstracts huggingface dataset:
Only Encode Once: Making Content-based News Recommender Greener
Authors: Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiao-Ming Wu
Date: 2023-08-27 00:00:00
Tasks: News Recommendation, Recommendation Systems, Representation Learning

Large pretrained language models (PLM) have become de facto news encoders in modern news recommender systems, due to their strong ability in comprehending textual content. These huge Transformer-based architectures, when finetuned on recommendation tasks, can greatly improve news recommendation performance. However, the PLM-based pretrain-finetune framework incurs high computational cost and energy consumption, primarily due to the extensive redundant processing of news encoding during each training epoch. In this paper, we propose the ``Only Encode Once'' framework for news recommendation (OLEO), by decoupling news representation learning from downstream recommendation task learning. The decoupled design makes content-based news recommender as green and efficient as id-based ones, leading to great reduction in computational cost and training resources. Extensive experiments show that our OLEO framework can reduce carbon emissions by up to 13 times compared with the state-of-the-art pretrain-finetune framework and maintain a competitive or even superior performance level. The source code is released for reproducibility.
CTR is not Enough: a Novel Reinforcement Learning based Ranking Approach for Optimizing Session Clicks
Authors: Shaowei Liu, Yangjun Liu
Date: 2023-08-27 00:00:00
Tasks: Recommendation Systems

Ranking is a crucial module using in the recommender system. In particular, the ranking module using in our YoungTao recommendation scenario is to provide an ordered list of items to users, to maximize the click number throughout the recommendation session for each user. However, we found that the traditional ranking method for optimizing Click-Through rate(CTR) cannot address our ranking scenario well, since it completely ignores user leaving, and CTR is the optimization goal for the one-step recommendation. To effectively undertake the purpose of our ranking module, we propose a long-term optimization goal, named as CTE (Click-Through quantity expectation), for explicitly taking the behavior of user leaving into account. Based on CTE, we propose an effective model trained by reinforcement learning. Moreover, we build a simulation environment from offline log data for estimating PBR and CTR. We conduct extensive experiments on offline datasets and an online e-commerce scenario in TaoBao. Experimental results show that our method can boost performance effectively
Video and Audio are Images: A Cross-Modal Mixer for Original Data on Video-Audio Retrieval
Authors: Zichen Yuan, Qi Shen, Bingyi Zheng, YuTing Liu, Linying Jiang, Guibing Guo
Date: 2023-08-26 00:00:00
Tasks: Cross-Modal Retrieval, Decoder, Retrieval

Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia. Generally, the information from each modality exhibits distinct representations and semantic information, which makes feature tends to be in separate latent spaces encoded with dual-tower architecture and makes it difficult to establish semantic relationships between modalities, resulting in poor retrieval performance. To address this issue, we propose a novel framework for cross-modal retrieval which consists of a cross-modal mixer, a masked autoencoder for pre-training, and a cross-modal retriever for downstream tasks.In specific, we first adopt cross-modal mixer and mask modeling to fuse the original modality and eliminate redundancy. Then, an encoder-decoder architecture is applied to achieve a fuse-then-separate task in the pre-training phase.We feed masked fused representations into the encoder and reconstruct them with the decoder, ultimately separating the original data of two modalities. In downstream tasks, we use the pre-trained encoder to build the cross-modal retrieval method. Extensive experiments on 2 real-world datasets show that our approach outperforms previous state-of-the-art methods in video-audio matching tasks, improving retrieval accuracy by up to 2 times. Furthermore, we prove our model performance by transferring it to other downstream tasks as a universal model.
Adversarial Collaborative Filtering for Free
Authors: Huiyuan Chen, Xiaoting Li, Vivian Lai, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Mahashweta Das, Hao Yang
Date: 2023-08-20 00:00:00
Tasks: Collaborative Filtering

Collaborative Filtering (CF) has been successfully used to help users discover the items of interest. Nevertheless, existing CF methods suffer from noisy data issue, which negatively impacts the quality of recommendation. To tackle this problem, many prior studies leverage adversarial learning to regularize the representations of users/items, which improves both generalizability and robustness. Those methods often learn adversarial perturbations and model parameters under min-max optimization framework. However, there still have two major drawbacks: 1) Existing methods lack theoretical guarantees of why adding perturbations improve the model generalizability and robustness; 2) Solving min-max optimization is time-consuming. In addition to updating the model parameters, each iteration requires additional computations to update the perturbations, making them not scalable for industry-scale datasets. In this paper, we present Sharpness-aware Collaborative Filtering (SharpCF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer. To achieve this goal, we first revisit the existing adversarial collaborative filtering and discuss its connection with recent Sharpness-aware Minimization. This analysis shows that adversarial training actually seeks model parameters that lie in neighborhoods around the optimal model parameters having uniformly low loss values, resulting in better generalizability. To reduce the computational overhead, SharpCF introduces a novel trajectory loss to measure the alignment between current weights and past weights. Experimental results on real-world datasets demonstrate that our SharpCF achieves superior performance with almost zero additional computational cost comparing to adversarial training.
A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System
Authors: M Charity, Yash Bhartia, Daniel Zhang, Ahmed Khalifa, Julian Togelius
Date: 2023-08-16 00:00:00
Tasks: Game Design, Word Embeddings

This paper introduces a system used to generate game feature suggestions based on a text prompt. Trained on the game descriptions of almost 60k games, it uses the word embeddings of a small GLoVe model to extract features and entities found in thematically similar games which are then passed through a generator model to generate new features for a user's prompt. We perform a short user study comparing the features generated from a fine-tuned GPT-2 model, a model using the ConceptNet, and human-authored game features. Although human suggestions won the overall majority of votes, the GPT-2 model outperformed the human suggestions in certain games. This system is part of a larger game design assistant tool that is able to collaborate with users at a conceptual level.
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Authors: Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, Jie Jiang
Date: 2023-08-16 00:00:00
Tasks: Multi-Task Learning, Recommendation Systems, Transfer Learning

Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negative transfer, but existing studies explored negative transfer on all samples, overlooking the inherent complexities within them. We split the samples according to the relative amount of positive feedback among tasks. Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks. Existing work commonly employs a shared-embedding paradigm, limiting the ability of modeling diverse user preferences on different tasks. In this paper, we introduce a novel Shared and Task-specific EMbeddings (STEM) paradigm that aims to incorporate both shared and task-specific embeddings to effectively capture task-specific user preferences. Under this paradigm, we propose a simple model STEM-Net, which is equipped with an All Forward Task-specific Backward gating network to facilitate the learning of task-specific embeddings and direct knowledge transfer across tasks. Remarkably, STEM-Net demonstrates exceptional performance on comparable samples, achieving positive transfer. Comprehensive evaluation on three public MTL recommendation datasets demonstrates that STEM-Net outperforms state-of-the-art models by a substantial margin. Our code is released at https://github.com/LiangcaiSu/STEM.
Implicit ZCA Whitening Effects of Linear Autoencoders for Recommendation
Authors: Katsuhiko Hayashi, Kazuma Onishi
Date: 2023-08-15 00:00:00
Tasks: Recommendation Systems, regression

Recently, in the field of recommendation systems, linear regression (autoencoder) models have been investigated as a way to learn item similarity. In this paper, we show a connection between a linear autoencoder model and ZCA whitening for recommendation data. In particular, we show that the dual form solution of a linear autoencoder model actually has ZCA whitening effects on feature vectors of items, while items are considered as input features in the primal problem of the autoencoder/regression model. We also show the correctness of applying a linear autoencoder to low-dimensional item vectors obtained using embedding methods such as Item2vec to estimate item-item similarities. Our experiments provide preliminary results indicating the effectiveness of whitening low-dimensional item embeddings.
Markus Thill Temporal convolutional autoencoder for unsupervised anomaly detection in time series
Authors: Markus Thill, Wolfgang Konen, Hao Wang, Thomas Bäck
Date: 2021-11-01 00:00:00
Proceeding: Applied Soft Computing 2021 11
Tasks: Anomaly Detection, Time Series, Unsupervised Anomaly Detection

Learning temporal patterns in time series remains a challenging task up until today. Particularly for anomaly detection in time series, it is essential to learn the underlying structure of a system’s normal behavior. Periodic or quasiperiodic signals with complex temporal patterns make the problem even more challenging: Anomalies may be a hard-to-detect deviation from the normal recurring pattern. In this paper, we present TCN-AE, a temporal convolutional network autoencoder based on dilated convolutions. Contrary to many other anomaly detection algorithms, TCN-AE is trained in an unsupervised manner. The algorithm demonstrates its efficacy on a comprehensive real-world anomaly benchmark comprising electrocardiogram (ECG) recordings of patients with cardiac arrhythmia. TCN-AE significantly outperforms several other unsupervised state-of-the-art anomaly detection algorithms. Moreover, we investigate the contribution of the individual enhancements and show that each new ingredient improves the overall performance on the investigated benchmark.
Towards a Holodeck-style Simulation Game
Authors: Ahad Shams, Douglas Summers-Stay, Arpan Tripathi, Vsevolod Metelsky, Alexandros Titonis, Karan Malhotra
Date: 2023-08-22 00:00:00
Tasks: Unity

We introduce Infinitia, a simulation game system that uses generative image and language models at play time to reshape all aspects of the setting and NPCs based on a short description from the player, in a way similar to how settings are created on the fictional Holodeck. Building off the ideas of the Generative Agents paper, our system introduces gameplay elements, such as infinite generated fantasy worlds, controllability of NPC behavior, humorous dialogue, cost & time efficiency, collaboration between players and elements of non-determinism among in-game events. Infinitia is implemented in the Unity engine with a server-client architecture, facilitating the addition of exciting features by community developers in the future. Furthermore, it uses a multiplayer framework to allow humans to be present and interact in the simulation. The simulation will be available in open-alpha shortly at https://infinitia.ai/ and we are looking forward to building upon it with the community.
SMART: A Decision-Making Framework with Multi-modality Fusion for Autonomous Driving Based on Reinforcement Learning
Authors: Yuyang Xia; Shuncheng Liu; Rui Hu; Quanlin Yu; Xiushi Feng; Kai Zheng; Han Su
Date: 2023-04-23 00:00:00
Proceeding: International Conference on Database Systems for Advanced Applications (DASFAA 2023) 2023 4
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Graph Attention

Decision-making in autonomous driving is an emerging technology that has rapid progress over the last decade. In single-lane scenarios, autonomous vehicles should simultaneously optimize their velocity decisions and steering angle decisions to achieve safety, efficiency, comfort, small impacts on rear vehicles, and small offsets to the lane center line. Previous studies, however, have typically optimized these two decisions separately, ignoring the potential relationship between them. In this work, we propose a decision-making framework, named SMART (deciSion-Making frAmework based on ReinforcemenT learning), to optimize the velocity and steering angle of the autonomous vehicle in parallel. In order for the autonomous vehicle to effectively perceive the curvature of the lane and interactions with other vehicles, we adopt a graph attention mechanism to extract and fuse the features from different modalities (i.e., sensor-collected vehicle states and camera-collected lane information). Then a hybrid reward function takes into account aspects of safety, efficiency, comfort, impact, and lane centering to instruct the autonomous vehicle to make optimal decisions. Furthermore, our framework enables the autonomous vehicle to adaptively choose the duration of an action, which helps the autonomous vehicle pursue higher reward values. Extensive experiments evidence that SMART significantly outperforms the existing methods in multiple metrics.
Channel Modeling and Parameter Optimization for Hovering UAV-Based Free-Space Optical Links
Authors: Mohammad Taghi Dabiri, Seyed Mohammad Sajad Sadough, and Mohammad Ali Khalighi
Date: 2018-09-01 00:00:00
Proceeding: IEEE Journal on Selected Areas in Communications 2018 9
Tasks: Position

Recently, the use of multi-rotor (MR) unmanned
aerial vehicles (UAVs) has emerged as a promising solution
for establishing ﬂexible free-space optical communication links.
We address, in this paper, the accurate channel modeling to
assess the beneﬁts of MR UAV-based deployment for such
links. In particular, in the absence of active tracking sub-
systems, we derive statistical models for ground-to-UAV, UAV-
to-UAV, and UAV-to-ground links over both Gamma–Gamma
and log-normal atmospheric turbulence models. Unlike previous
works on this topic, our proposed model considers the joint
effect of atmospheric turbulence along with position and angle-of-
arrival ﬂuctuations. The high accuracy of the proposed analytical
models is veriﬁed by comparing numerically solved and Monte
Carlo simulation results in terms of link outage probability.
We further discuss the impact of different transmitter/receiver
parameters an
The Other Side of Compression: Measuring Bias in Pruned Transformers
Authors: Irina Proskurina, Guillaume Metzler, Julien Velcin
Date: 2023-04-01 00:00:00
Proceeding: Advances in Intelligent Data Analysis XXI 2023 4
Tasks: Hate Speech Detection, Network Pruning

Social media platforms have become popular worldwide. Online discussion forums attract users because of their easy access, speech freedom, and ease of communication. Yet there are also possible negative aspects of such communication, including hostile and hate language.
While fast and effective solutions for detecting inappropriate language online are constantly being developed, there is little research focusing on the bias of compressed language models that are commonly used nowadays. In this work, we evaluate bias in compressed models trained on Gab and Twitter hatespeech data and estimate to which extent these pruned models capture the relevant context when classifying the input text as hateful, offensive or neutral. Results of our experiments show that transformer-based encoders with 70% or fewer preserved weights are prone to gender, racial, and religious identity-based bias, even if the performance loss is insignificant. We suggest a supervised attention mechanism to counter bias amplification using ground truth per-token hate speech annotation. The proposed method allows pruning BERT, RoBERTa and their distilled versions up to 50% while preserving 90% of their initial performance according to bias and plausibility scores.
Towards Ubiquitous Intelligent Hand Interaction
Authors: Chen Liang
Date: 2023-08-21 00:00:00
Tasks: Mixed Reality

The development of ubiquitous computing and sensing devices has brought about novel interaction scenarios such as mixed reality and IoT (e.g., smart home), which pose new demands for the next generation of natural user interfaces (NUI). Human hand, benefit for the large degree-of-freedom, serves as a medium through which people interact with the external world in their daily lives, thus also being regarded as the main entry of NUI. Unfortunately, current hand tracking system is largely confined on first perspective vision-based solutions, which suffer from optical artifacts and are not practical in ubiquitous environments. In my thesis, I rethink this problem by analyzing the underlying logic in terms of sensor, behavior, and semantics, constituting a research framework for achieving ubiquitous intelligent hand interaction. Then I summarize my previous research topics and illustrated the future research directions based on my research framework.
CryoET reveals organelle phenotypes in huntington disease patient iPSC-derived and mouse primary neurons
Authors: cite Wu GH, Smith-Geater C, Galaz-Montoya JG, Gu Y, Gupte SR, Aviner R, Mitchell PG, Hsu J, Miramontes R, Wang KQ, Geller NR, Hou C, Danita C, Joubert LM, Schmid MF, Yeung S, Frydman J, Mobley W, Wu C, Thompson LM, Chiu W.
Date: 2023-02-08 00:00:00
Proceeding: Nature Communications 2023 2
Tasks: Cryogenic Electron Tomography, Electron Tomography

Huntington’s disease (HD) is caused by an expanded CAG repeat in the huntingtin gene, yielding a Huntingtin protein with an expanded polyglutamine tract. While experiments with patient-derived induced pluripotent stem cells (iPSCs) can help understand disease, defining pathological biomarkers remains challenging. Here, we used cryogenic electron tomography to visualize neurites in HD patient iPSC-derived neurons with varying CAG repeats, and primary cortical neurons from BACHD, deltaN17-BACHD, and wild-type mice. In HD models, we discovered sheet aggregates in double membrane-bound organelles, and mitochondria with distorted cristae and enlarged granules, likely mitochondrial RNA granules. We used artificial intelligence to quantify mitochondrial granules, and proteomics experiments reveal differential protein content in isolated HD mitochondria. Knockdown of Protein Inhibitor of Activated STAT1 ameliorated aberrant phenotypes in iPSC- and BACHD neurons. We show that integrated ultrastructural and proteomic approaches may uncover early HD phenotypes to accelerate diagnostics and the development of targeted therapeutics for HD.
Slimmed optical neural networks with multiplexed neuron sets and a corresponding backpropagation training algorithm
Authors: Yi-Feng Liu, Rui-Yao Ren, Dai-Bao Hou, Hai-Zhong Weng, Bo-Wen Wang, Ke-Jie Huang, Xing Lin, Feng Liu, Chen-Hui Li, Chao-Yuan Jin
Date: 2023-08-27 00:00:00

Due to their intrinsic capabilities on parallel signal processing, optical neural networks (ONNs) have attracted extensive interests recently as a potential alternative to electronic artificial neural networks (ANNs) with reduced power consumption and low latency. Preliminary confirmation of the parallelism in optical computing has been widely done by applying the technology of wavelength division multiplexing (WDM) in the linear transformation part of neural networks. However, inter-channel crosstalk has obstructed WDM technologies to be deployed in nonlinear activation in ONNs. Here, we propose a universal WDM structure called multiplexed neuron sets (MNS) which apply WDM technologies to optical neurons and enable ONNs to be further compressed. A corresponding back-propagation (BP) training algorithm is proposed to alleviate or even cancel the influence of inter-channel crosstalk on MNS-based WDM-ONNs. For simplicity, semiconductor optical amplifiers (SOAs) are employed as an example of MNS to construct a WDM-ONN trained with the new algorithm. The result shows that the combination of MNS and the corresponding BP training algorithm significantly downsize the system and improve the energy efficiency to tens of times while giving similar performance to traditional ONNs.
Impact of geolocation data on augmented reality usability: A comparative user test
Authors: Julien Mercier, N. Chabloz, G. Dozot, C. Audrin, O. Ertz, E. Bocher, D. Rappo
Date: 2023-08-21 00:00:00

Abstract. While the use of location-based augmented reality (AR) for education has demonstrated benefits on participants' motivation, engagement, and on their physical activity, geolocation data inaccuracy causes augmented objects to jitter or drift, which is a factor in downgrading user experience. We developed a free and open source web AR application and conducted a comparative user test (n = 54) in order to assess the impact of geolocation data on usability, exploration, and focus. A control group explored biodiversity in nature using the system in combination with embedded GNSS data, and an experimental group used an external module for RTK data. During the test, eye tracking data, geolocated traces, and in-app user-triggered events were recorded. Participants answered usability questionnaires (SUS, UEQ, HARUS).We found that the geolocation data the RTK group was exposed to was less accurate in average than that of the control group. The RTK group reported lower usability scores on all scales, of which 5 out of 9 were significant, indicating that inaccurate data negatively predicts usability. The GNSS group walked more than the RTK group, indicating a partial effect on exploration. We found no significant effect on interaction time with the screen, indicating no specific relation between data accuracy and focus. While RTK data did not allow us to better the usability of location-based AR interfaces, results allow us to assess our system's overall usability as excellent, and to define optimal operating conditions for future use with pupils.
Complementarities in childcare allocation under priorities
Authors: Ata Atay, Antonio Romero-Medina
Date: 2023-08-28 00:00:00

We investigate the allocation of children to childcare facilities and propose solutions to overcome limitations in the current allocation mechanism. We introduce a natural preference domain and a priority structure that address these setbacks, aiming to enhance the allocation process. To achieve this, we present an adaptation of the Deferred Acceptance mechanism to our problem, which ensures strategy-proofness within our preference domain and yields the student-optimal stable matching. Finally, we provide a maximal domain for the existence of stable matchings using the properties that define our natural preference domain. Our results have practical implications for allocating indivisible bundles with complementarities.