Abstract

The current study examines the interaction between predis- posed mood, perceptual processing, and induced mood us- ing music. We conducted an experiment in which partici- pants were asked to identify stimuli at global or local (G/L) perceptual levels with four different background music con- ditions, which had different valence and arousal ratings. We used BMIS to assess current mood and PHQ-9 and GAD-7 to assess depression and anxiety, and divided the participants into two groups: distress and no distress (encompassing both disor- ders). We found a main effect of background music on mood. However, the distress group showed an overall low mood. Fur- ther, we observed an overarching effect of predisposed mood, encompassing depression and anxiety, on individuals’ transient mood experience and perceptual task performance. Individuals in the non-distress group showed a larger global-precedence effect. The results are discussed in light of emotional reactiv- ity theories and the theory of positive emotion.

Abstract

Identifying correct human postures is crucial in areas like patient care in hospitals. However, the traditional vision-based methods widely used for this purpose raise privacy concerns for the subject, and the other wearable sensor-based approaches are impractical for real-world scenarios. In this paper, we propose a contactless, privacy-conscious, and memoryefficient posture classification system based on millimeter wave (mmWave) radar. This system utilizes threedimension(3D) point-cloud data captured using Texas Instrument’s IWR1843BOOST Frequency Modulated Continuous Wave (FMCW) radar module to classify the posture of the subject. Two types of datasets are extracted from this radar data: (i) image dataset derived from the isometric view of the point-cloud data, and (ii) spatial coordinates dataset also extracted from the point-cloud data. A low-computational Tiny Machine Learning (TinyML) model is employed on the datasets for efficient implementation on embedded hardware, Raspberry Pi 3 B+. The proposed model’s parameters were quantized to 8 bits (int8), which accurately classify four postures, i.e., standing, sitting, lying, and bending, with an accuracy of 98.97% for the image data. However, to make it more computationally efficient, the int8 quantized TinyML model was trained on the spatial coordinates dataset, giving an accuracy of 96.12%. This highlights the efficiency and effectiveness of our proposed lightweight model that can be deployed on edge devices for real-world applications.

Abstract

Reconfigurable intelligent surfaces (RIS) are emerging as a promising technology for 6G networks due to their ability to shape the radio propagation environment. This ability helps to overcome propagation challenges, such as high path loss, absorption loss, etc., that are particularly needed at millimetre wave (mmWave) bands. Thus, RIS can enhance the capacity of next-generation mmWave communication networks. However, one critical aspect in the design of RIS-aided systems is channel estimation, as it involves estimating two channels (i.e., base stationRIS and RIS-user terminal) separately based on the compound channel observed by the receiver. In this paper, we address this problem by proposing an algorithm that estimates both channels separately by leveraging the advantages of sparsity of the mmWave channel. Specifically, the proposed algorithm uses the parallel factor (PARAFAC) decomposition of the tensor, which is constructed using the received signal matrices observed under different RIS phase shift configurations. Our numerical analysis shows that the proposed algorithm provides significantly smaller normalized mean square error (NMSE) than the widely used alternating least squares (ALS) algorithm, particularly when the number of RIS phase shift configurations is smaller than the number of RIS elements.

Abstract

Automated Sleep stage classification using raw single channel EEG is a critical tool for sleep quality assessment and disorder diagnosis. However, modelling the complexity and variability inherent in this signal is a challenging task, limiting their practicality and effectiveness in clinical settings. To mitigate these challenges, this study presents an end-to-end deep learning (DL) model which integrates squeeze and excitation blocks within the residual network to extract features and stacked Bi-LSTM to understand complex temporal dependencies. A distinctive aspect of this study is the adaptation of GradCam for sleep staging, marking the first instance of an explainable DL model in this domain with alignment of its decision-making with sleep expert's insights. We evaluated our model on the publically available datasets (SleepEDF-20, SleepEDF-78, and SHHS), achieving Macro-F1 scores of 82.5, 78.9, and 81.9, respectively. Additionally, a novel training efficiency enhancement strategy was implemented by increasing stride size, leading to 8x faster training times with minimal impact on performance. Comparative analyses underscore our model outperforms all existing baselines, indicating its potential for clinical usage.

Abstract

This paper studies the contextual multi-armed bandit problem with fairness and privacy guarantees in a federated setting. It proposes a collaborative algorithm, Fed-FairX-LinUCB that achieves sublinear fairness regret and can be adapted to ensure differential privacy. The key challenge is designing a communication protocol that balances privacy and regret. The proposed protocol achieves both sub-linear fairness regret and effective use of privacy budget. Experiments validates the efficacy of both Fed-FairX-LinUCB and its private counterpart, Priv-FairX-LinUCB.

Abstract

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers. We compile a dataset called LimGen, encompassing 4068 research papers and their associated limitations from the ACL anthology. We investigate several approaches to harness large language models (LLMs) for producing suggestive limitations, by thoroughly examining the related challenges, practical insights, and potential opportunities. Our LimGen dataset and code can be accessed at https://github. com/arbmf/LimGen.

Abstract

This paper examines the enhancement of quadrotor efficiency through adaptive control to address the critical need for Fault-Tolerant Control (FTC) in quadrotors amidst operational uncertainties and component inefficiency. State-of-the-art adaptive FTC strategies often assume the uncertainties to be bounded by a constant a priori; however, imprecise knowledge of inertial system parameters lead to state-dependent uncertainties which do not follow such assumption. Remain unattended, statedependent uncertainties can lead to instability, especially under actuator faults. The proposed adaptive FTC offers actuator fault mitigation while tackling unknown (statedependent) uncertainties via suitably designed adaptive laws. Additionally, real-time fault detection and control allocation are used simultaneously to avoid conservative control application. The closed-loop system stability is studied analytically and the effectiveness of the proposed solution is verified on a realistic simulator in comparison to the state of the art.

Abstract

RNA molecules play a significant role in many biological pathways and have diverse functional roles, which is a result of their structural flexibility to fold into diverse conformations. This structural flexibility makes it challenging to obtain the structures of RNAs experimentally. Deep learning can be used to predict the secondary structures of RNA and other properties such as the backbone torsion angles, to be used as restraints for the computational optimization of the tertiary structures of RNA. TorRNA is a transformer encoder-decoder model, that takes an input RNA sequence and predicts the (pseudo)torsion angles of each nucleotide with a pre-trained RNA-FM model as the encoder. TorRNA is able to achieve a performance boost of 2% − 16% over the previous (pseudo)torsion angle prediction method for RNAs. We also demonstrate that TorRNA can used as a tool for model quality assessment of candidate RNA structures

Abstract

In this study, we address the challenge of obstacle avoidance for Unmanned Aerial Vehicles (UAVs) through an innovative composite imitation learning ap- proach that combines Proximal Policy Optimization (PPO) with Behavior Cloning (BC) and Generative Adversarial Imitation Learning (GAIL), enriched by the integration of ray-tracing techniques. Our research underscores the sig- nificant role of ray-tracing in enhancing obstacle detection and avoidance capabilities. Moreover, we demonstrate the effectiveness of incorporating GAIL in coordinating the flight paths of two UAVs, showcasing improved collision avoidance capabilities. Extending our methodology, we apply our combined PPO, BC, GAIL, and ray-tracing framework to scenarios involving four UAVs, illustrating its scalability and adaptability to more complex scenarios. The findings indicate that our approach not only improves the reliability of basic PPO based obstacle avoidance but also paves the way for advanced autonomous UAV operations in crowded or dynamic environments.

Abstract

The paper presents a 0.5 V, ultra-low power current, and voltage reference circuit in a single block, giving reference values of 90.7 pA and 288 mV. The proposed block uses proper addition of CTAT and PTAT curves, resulting in an excellent temperature coefficient of 15.2 ppm/°C and 36.8 ppm/°C for compensated current and voltage values, respectively, at a nominal supply of 0.5 V for a wide temperature range of −55 °C to 100 °C. Usage of gate leakage transistors in place of resistors helps in reducing the power and area of the circuit to 0.087 mm2. The circuit is implemented in 90 nm technology and operates effectively across a wide voltage range of 0.5 V–2.6 V with a supply sensitivity of 0.028 %/V and 0.154 %/V for current and voltage reference, respectively. The entire circuit takes a power of 275.26 pW at nominal conditions.