Miracle-dz/UniFAR-FTUs huggingface dataset:
Actigraphic Physical Activity, Pain Intensity, and Polysomnographic Sleep in Fibromyalgia

ABSTRACT Introduction Fibromyalgia involves chronic pain and disrupted physical activity and sleep. Research examining the relationship between pre-bedtime physical activity, pain, and objective sleep is limited. This study examined whether objectively measured physical activity levels (via actigraphy), pain intensity, or their interaction are associated with polysomnographic sleep outcomes. Methods Adults with fibromyalgia and insomnia complaints (n = 134, mean age = 52 yrs, SD = 12 yrs, 94% female) completed 14 days of biaxial, wrist worn actigraphy, pain ratings, and a single night of polysomnography (PSG). Average activity for intervals 9:00–12:00, 12:00–15:00, 15:00–18:00, 18:00–21:00 was computed. Multiple regressions examined whether average activity, average evening pain, or their interaction were associated with PSG outcomes: sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency, %stage1, %stage2, %stage3, and %rapid eye movement. Analyses controlled for age, body mass index, average bedtime, time in bed, and sleep/pain medication use. Results Greater morning actigraphic physical activity from 9:00 to 12:00 was independently associated with greater %stage 1 sleep (B = 0.01, SE = 0.00, p < .01). Greater afternoon activity from 12:00 to 15:00 independently predicted a higher WASO (p < .001). Associations between afternoon physical activity from 12:00 to 15:00 and greater %stage 1 (p < .001) were significant for at higher (~71/100), average (~52/100), but not lowest (~32/100) pain. Conclusion Greater morning and afternoon activity is associated with greater PSG sleep fragmentation and greater %stage 1 sleep in individuals with fibromyalgia and insomnia complaints, and the relationship between higher physical activity and greater %stage 1 is stronger for individuals with higher pain. Further studies examining causal pathways between physical activity, activity pacing, and sleep are warranted in fibromyalgia.
The role of sleep and wakefulness in the recognition of emotional pictures

Sleep has a beneficial effect on memory consolidation. However, its role in emotional memory is currently debated. Here, we investigate the role of sleep and a similar period of wakefulness on the recognition of emotional pictures and subjective emotional reactivity. Forty participants without any major physical, neurological or psychological condition were randomly assigned to the Sleep First Group or Wake First Group. The two groups underwent the encoding phase of an emotional images task with negative and neutral pictures at either 09:00 hours (Wake First Group) or 21:00 hours (Sleep First Group). Then participants performed an immediate recognition test (T1), and two delayed tests 12 hr (T2) and 24 hr (T3) later. Perceived arousal and valence levels were collected for each picture. Sleep parameters were recorded at participants' homes with a portable device. No differences were observed at T1, whereas at T2 the Sleep First Group showed a higher memory performance than the Wake First Group. At T3, performance decreased in the Sleep First Group (who spent the previous 12 hr awake), but not in the Wake First Group (who slept during the previous 12 hr). Overall, negative images were remembered better than neutral ones. We also observed a positive association between memory performance for negative items at the immediate test and the percentage of rapid eye movement sleep the night before the encoding. Our data confirm that negative information is remembered better over time than neutral information, and that sleep benefits the retention of declarative information. However, sleep seems not to preferentially improve emotional memory, although it may affect the encoding of negative information.
Performance evaluation of zero-biased VCSEL for high speed data transmission

In an optical transceiver, the power consumption related to the operation of the laser device takes a significant parcel of the total consumed power. Thus, in optical networks where a large number of transceiver devices are interconnected, e.g. large distributed sensor networks, it is of great importance to reduce this power consumption. In this work an analysis and simulation results are presented regarding the operation of a bias-free vertical-cavity surface-emitting laser (VCSEL) device, which is based on a previously developed model. The impact on bit-error rate (BER) of the increased turn-on jitter due to the bit-pattern and spontaneous emission is considered. A method for mitigating the eye-diagram distortion penalty based on the received signal equalization is also illustrated.
Classification of Metro Facilities with Deep Neural Networks

Metro barrier-detection has been one of the most popular research fields. How to detect obstacles quickly and accurately during metro operation is the key issue in the study of automatic train operation. Intelligent monitoring systems based on computer vision not only complete safeguarding tasks efficiently but also save a great deal of human labor. Deep convolutional neural networks (DCNNs) are the most state-of-the-art technology in computer vision tasks. In this paper, we evaluated the effectiveness in classifying the common facility images in metro tunnels based on Google’s Inception V3 DCNN. The model requires fewer computational resources. The number of parameters and the computational complexity are much smaller than similar DCNNs. We changed its architecture (the last softmax layer and the auxiliary classifier) and used transfer learning technology to retrain the common facility images in the metro tunnel. We use mean average precision (mAP) as the metric for performance evaluation. The results indicate that our recognition model achieved 90.81% mAP. Compared with the existing method, this method is a considerable improvement.
Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making

With the increasing pervasiveness of algorithms across industry and government, a growing body of work has grappled with how to understand their societal impact and ethical implications. Various methods have been used at different stages of algorithm development to encourage researchers and designers to consider the potential societal impact of their research. An understudied yet promising area in this realm is using participatory foresight to anticipate these different societal impacts. We employ crowdsourcing as a means of participatory foresight to uncover four different types of impact areas based on a set of governmental algorithmic decision making tools: (1) perceived valence, (2) societal domains, (3) specific abstract impact types, and (4) ethical algorithm concerns. Our findings suggest that this method is effective at leveraging the cognitive diversity of the crowd to uncover a range of issues. We further analyze the complexities within the interaction of the impact areas identified to demonstrate how crowdsourcing can illuminate patterns around the connections between impacts. Ultimately this work establishes crowdsourcing as an effective means of anticipating algorithmic impact which complements other approaches towards assessing algorithms in society by leveraging participatory foresight and cognitive diversity.
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation

3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to improve model generalization for different sensors and environments. Researchers working on UDA problems in the image domain have shown that sample mixing can mitigate domain shift. We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing. CoSMix consists of a two-branch symmetric network that can process labelled synthetic data (source) and real-world unlabelled point clouds (target) concurrently. Each branch operates on one domain by mixing selected pieces of data from the other one, and by using the semantic information derived from source labels and target pseudo-labels. We evaluate CoSMix on two large-scale datasets, showing that it outperforms state-of-the-art methods by a large margin. Our code is available at https://github.com/saltoricristiano/cosmix-uda.
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis

In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos. An autoregressive over autoregressive generation mechanism is proposed to deal with this variable-size generation task, where a global patch-level autoregressive model considers the dependencies between patches, and a local token-level autoregressive model considers dependencies between visual tokens within each patch. A Nearby Context Pool (NCP) is introduced to cache-related patches already generated as the context for the current patch being generated, which can significantly save computation costs without sacrificing patch-level dependency modeling. An Arbitrary Direction Controller (ADC) is used to decide suitable generation orders for different visual synthesis tasks and learn order-aware positional embeddings. Compared to DALL-E, Imagen and Parti, NUWA-Infinity can generate high-resolution images with arbitrary sizes and support long-duration video generation additionally. Compared to NUWA, which also covers images and videos, NUWA-Infinity has superior visual synthesis capabilities in terms of resolution and variable-size generation. The GitHub link is https://github.com/microsoft/NUWA. The homepage link is https://nuwa-infinity.microsoft. com.
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images

In this paper, we present the results of the HSE-NN team in the 4th competition on Affective Behavior Analysis in-the-wild (ABAW). The novel multi-task EfficientNet model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward neural networks in the multi-task learning challenge. We obtain performance measure 1.3 on the validation set, which is significantly greater when compared to either performance of baseline (0.3) or existing models that are trained only on the s-Aff-Wild2 database. In the learning from synthetic data challenge, the quality of the original synthetic training set is increased by using the super-resolution techniques, such as Real-ESRGAN. Next, the MT-EmotiEffNet is fine-tuned on the new training set. The final prediction is a simple blending ensemble of pre-trained and fine-tuned MT-EmotiEffNets. Our average validation F1 score is 18% greater than the baseline convolutional neural network.
Monocular 3D Object Reconstruction with GAN Inversion

Recovering a textured 3D mesh from a monocular image is highly challenging, particularly for in-the-wild objects that lack 3D ground truths. In this work, we present MeshInversion, a novel framework to improve the reconstruction by exploiting the generative prior of a 3D GAN pre-trained for 3D textured mesh synthesis. Reconstruction is achieved by searching for a latent space in the 3D GAN that best resembles the target mesh in accordance with the single view observation. Since the pre-trained GAN encapsulates rich 3D semantics in terms of mesh geometry and texture, searching within the GAN manifold thus naturally regularizes the realness and fidelity of the reconstruction. Importantly, such regularization is directly applied in the 3D space, providing crucial guidance of mesh parts that are unobserved in the 2D space. Experiments on standard benchmarks show that our framework obtains faithful 3D reconstructions with consistent geometry and texture across both observed and unobserved parts. Moreover, it generalizes well to meshes that are less commonly seen, such as the extended articulation of deformable objects. Code is released at https://github.com/junzhezhang/mesh-inversion
Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models

This article presents a novel online identification algorithm for nonlinear regression models. The online identification problem is challenging due to the presence of nonlinear structure in the models. Previous works usually ignore the special structure of nonlinear regression models, in which the parameters can be partitioned into a linear part and a nonlinear part. In this article, we develop an efficient recursive algorithm for nonlinear regression models based on analyzing the equivalent form of variable projection (VP) algorithm. By introducing the embedded point iteration step, the proposed recursive algorithm can properly exploit the coupling relationship of linear parameters and nonlinear parameters. In addition, we theoretically prove that the proposed algorithm is mean-square bounded. Numerical experiments on synthetic data and real-world time series verify the high efficiency and robustness of the proposed algorithm.
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding

This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Com-pared with the previous ESPnet-SE work, numerous features have been added, including recent state-of-the-art speech enhancement models with their respective training and evaluation recipes. Importantly, a new interface has been designed to flexibly combine speech enhancement front-ends with other tasks, including automatic speech recognition (ASR), speech translation (ST), and spoken language understanding (SLU). To showcase such integration, we performed experiments on care-fully designed synthetic datasets for noisy-reverberant multichannel ST and SLU tasks, which can be used as benchmark corpora for future research. In addition to these new tasks, we also use CHiME-4 and WSJ0-2Mix to benchmark multi-and single-channel SE approaches. Results show that the integration of SE front-ends with back-end tasks is a promising research direction even for tasks besides ASR, especially in the multi-channel scenario. The code is available online at https://github.com/ESPnet/ESPnet . The multichannel ST and SLU datasets, which are another contribution of this work, are released on HuggingFace.
Approximation Power of Deep Neural Networks: an explanatory mathematical survey

The goal of this survey is to present an explanatory review of the approximation properties of deep neural networks. Specifically, we aim at understanding how and why deep neural networks outperform other classical linear and nonlinear approximation methods. This survey consists of three chapters. In Chapter 1 we review the key ideas and concepts underlying deep networks and their compositional nonlinear structure. We formalize the neural network problem by formulating it as an optimization problem when solving regression and classification problems. We briefly discuss the stochastic gradient descent algorithm and the back-propagation formulas used in solving the optimization problem and address a few issues related to the performance of neural networks, including the choice of activation functions, cost functions, overfitting issues, and regularization. In Chapter 2 we shift our focus to the approximation theory of neural networks. We start with an introduction to the concept of density in polynomial approximation and in particular study the Stone-Weierstrass theorem for real-valued continuous functions. Then, within the framework of linear approximation, we review a few classical results on the density and convergence rate of feedforward networks, followed by more recent developments on the complexity of deep networks in approximating Sobolev functions. In Chapter 3, utilizing nonlinear approximation theory, we further elaborate on the power of depth and approximation superiority of deep ReLU networks over other classical methods of nonlinear approximation. keywords: artificial neural networks, approximation theory, power of depth, mathematics of deep learning Intended audience. The material in this survey should be accessible to undergraduate and graduate students and researchers in mathematics, statistics, computer science, and engineering, and to all those who wish to obtain a “deeper” understanding of “deep” neural networks.
LightSolver - A New Quantum-inspired Solver Cracks the 3-Regular 3-XORSAT Challenge

The increasing complexity of required computational tasks alongside the inherent limitations in conventional computing calls for disruptive innovation. LightSolver devised a new quantum-inspired computing paradigm, which utilizes an all-optical platform for solving hard optimization problems. In this work, LightSolver introduces its digital simulator and joins the 3-Regular 3-XORSAT (3R3X) challenge, which aims to map the best available state-of-the-art classical and quantum solvers. So far, the challenge has resulted in a clear exponential barrier in terms of time-to-solution (TTS), preventing the inspected platforms from solving problems larger than a few hundred variables. LightSolver’s simulator is the ﬁrst to break the exponential barrier, outperforming both classical and quantum platforms by several orders-of-magnitude and extending the maximal problem size to more than 16,000 variables.
Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription

Automatic speech recognition (ASR) has progressed significantly in recent years due to the emergence of large-scale datasets and the self-supervised learning (SSL) paradigm. However, as its counterpart problem in the singing domain, the development of automatic lyric transcription (ALT) suffers from limited data and degraded intelligibility of sung lyrics. To fill in the performance gap between ALT and ASR, we attempt to exploit the similarities between speech and singing. In this work, we propose a transfer-learning-based ALT solution that takes advantage of these similarities by adapting wav2vec 2.0, an SSL ASR model, to the singing domain. We maximize the effectiveness of transfer learning by exploring the influence of different transfer starting points. We further enhance the performance by extending the original CTC model to a hybrid CTC/attention model. Our method surpasses previous approaches by a large margin on various ALT benchmark datasets. Further experiments show that, with even a tiny proportion of training data, our method still achieves competitive performance.
Towards Transmission-Friendly and Robust CNN Models over Cloud and Device

Deploying deep convolutional neural network (CNN) models on ubiquitous Internet of Things (IoT) devices has attracted much attention from industry and academia since it greatly facilitates our lives by providing various rapid-response services. Due to the limited resources of IoT devices, cloud-assisted training of CNN models has become the mainstream. However, most existing related works suffer from a large amount of model parameter transmission and weak model robustness. To this end, this paper proposes a cloud-assisted CNN training framework with low model parameter transmission and strong model robustness. In the proposed framework, we first introduce MonoCNN, which contains only a few learnable filters, and other filters are nonlearnable. These nonlearnable filter parameters are generated according to certain rules, i.e., the filter generation function (FGF), and can be saved and reproduced by a few random seeds. Thus, the cloud server only needs to send these learnable filters and a few seeds to the IoT device. Compared to transmitting all model parameters, sending several learnable filter parameters and seeds can significantly reduce parameter transmission. Then, we investigate multiple FGFs and enable the IoT device to use the FGF to generate multiple filters and combine them into MonoCNN. Thus, MonoCNN is affected not only by the training data but also by the FGF. The rules of the FGF play a role in regularizing the MonoCNN, thereby improving its robustness. Experimental results show that compared to state-of-the-art methods, our proposed framework can reduce a large amount of model parameter transfer between the cloud server and the IoT device while improving the performance by approximately 2.2% when dealing with corrupted data.
MeritRank: Sybil Tolerant Reputation for Merit-based Tokenomics

Decentralized reputation schemes present a promising area of experimentation in blockchain applications. These solutions aim to overcome the shortcomings of simple monetary incentive mechanisms of naive tokenomics. However, there is a significant research gap regarding the limitations and benefits of such solutions. We formulate these trade-offs as a conjecture on the irreconcilability of three desirable properties of the reputation system in this context. Such a system can not be simultaneously generalizable, trustless, and Sybil resistant. To handle the limitations of this trilemma, we propose MeritRank: Sybil tolerant feedback aggregation mechanism for reputation. Instead of preventing Sybil attacks, our approach successfully bounds the benefits of these attacks. Using a dataset of participants’ interactions in MakerDAO, we run experiments to demonstrate Sybil tolerance of MeritRank. Decay parameters of reputation in MeritRank: transitivity decay and connectivity decay, allow for a fine-tuning of desirable levels of reputation utility and Sybil tolerance in different use contexts.
Learning Latent Traits for Simulated Cooperative Driving Tasks

To construct effective teaming strategies between humans and AI systems in complex, risky situations requires an understanding of individual preferences and behaviors of humans. Previously this problem has been treated in case-specific or data-agnostic ways. In this paper, we build a framework capable of capturing a compact latent representation of the human in terms of their behavior and preferences based on data from a simulated population of drivers. Our framework leverages, to the extent available, knowledge of individual preferences and types from samples within the population to deploy interaction policies appropriate for specific drivers. We then build a lightweight simulation environment, HMIway-env, for modelling one form of distracted driving behavior, and use it to generate data for different driver types and train intervention policies. We finally use this environment to quantify both the ability to discriminate drivers and the effectiveness of intervention policies.