Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination without sacrificing calibration.

Modeling the dynamics of financial Limit Order Books (LOB) at the message level is challenging due to irregular event timing, rapid regime shifts, and the reactions of high-frequency traders to visible order flow. Previous LOB models require cumbersome data representations and lack adaptability outside their original tasks, leading us to introduce LOBERT, a general-purpose encoder-only foundation model for LOB data suitable for downstream fine-tuning. LOBERT adapts the original BERT architecture for LOB data by using a novel tokenization scheme that treats complete multi-dimensional messages as single tokens while retaining continuous representations of price, volume, and time. With these methods, LOBERT achieves leading performance in tasks such as predicting mid-price movements and next messages, while reducing the required context length compared to previous methods.

Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination without sacrificing calibration.

Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransformer, a sequence model that augments a vanilla GPT-style architecture with (i) a dedicated time encoder that embeds either absolute timestamps or inter-event values, and (ii) a learned positional encoder that preserves relative order. Experiments on a large industrial dataset -- tens of millions of transactions and auxiliary events -- show that FraudTransformer surpasses four strong classical baselines (Logistic Regression, XGBoost and LightGBM) as well as transformer ablations that omit either the time or positional component. On the held-out test set it delivers the highest AUROC and PRAUC.

Accurate localization is essential for a wide range of applications, including asset tracking, smart agriculture, and animal monitoring. While traditional localization methods, such as Global Navigation Satellite System (GNSS), Wi-Fi, and Bluetooth Low Energy (BLE), offer varying levels of accuracy and coverage, they have drawbacks regarding power consumption, infrastructure requirements, and deployment flexibility. Ultra-Wideband (UWB) is emerging as an alternative, offering centimeter-level accuracy and energy efficiency, especially suitable for medium to large field monitoring with capabilities to work indoors and outdoors. However, existing UWB localization systems require infrastructure with mains power to supply the anchors, which impedes their scalability and ease of deployment. This underscores the need for a fully battery-powered and energy-efficient localization system. This paper presents an energy-optimized, battery-operated UWB localization system that leverages Long Range Wide Area Network (LoRaWAN) for data transmission to a server backend. By employing single-sided two-way ranging (SS-TWR) in a time-slotted localization approach, the power consumption both on the anchor and the tag is reduced, while maintaining high accuracy. With a low average power consumption of 20.44 mW per anchor and 7.19 mW per tag, the system allows fully battery-powered operation for up to 25 days, achieving average accuracy of 13.96 cm with self-localizing anchors on a 600 m2 testing ground. To validate its effectiveness and ease of installation in a challenging application scenario, ten anchors and two tags were successfully deployed in a tropical zoological biome where they could be used to track Aldabra Giant Tortoises (Aldabrachelys gigantea).

-- Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where precise identification of stance and swing phases is essential. This study evaluated the performance of seven kinematics-based methods and a Long Short-T erm Memory (LSTM) model for detecting heel strike and toe-off events across 4363 gait cycles from 588 able-bodied subjects. The results indicated that while the Zeni et al. method achieved the highest accuracy among kinematics-based approaches, other methods exhibited systematic biases or required dataset-specific tuning. The LSTM model performed comparably to Zeni et al., providing a data-driven alternative without systematic bias. Future research will explore the generalizability of these methods in pathological populations, such as individuals with post-stroke conditions and knee osteoarthritis, as well as their robustness across varied gait conditions and data collection settings to enhance their applicability in rehabilitation and exoskeleton control.

PET is a functional imaging method that visualizes metabolic processes. TOF information can be derived from coincident detector signals and incorporated into image reconstruction to enhance the SNR. PET detectors are typically assessed by their CTR, but timing performance is degraded by various factors. Research on timing calibration seeks to mitigate these degradations and restore accurate timing information. While many calibration methods use analytical approaches, machine learning techniques have recently gained attention due to their flexibility. We developed a residual physics-based calibration approach that combines prior domain knowledge with the power of machine learning models. This approach begins with an initial analytical calibration addressing first-order skews. The remaining deviations, regarded as residual effects, are used to train machine learning models to eliminate higher-order skews. The key advantage is that the experimenter guides the learning process through the definition of timing residuals. In earlier studies, we developed models that directly predicted the expected time difference, which offered corrections only implicitly (implicit correction models). In this study, we introduce a new definition for timing residuals, enabling us to train models that directly predict correction values (explicit correction models). The explicit correction approach significantly simplifies data acquisition, improves linearity, and enhances timing performance from $371 \pm 6$ ps to $281 \pm 5$ ps for coincidences from 430 keV to 590 keV. Additionally, the new definition reduces model size, making it suitable for high-throughput applications like PET scanners. Experiments were conducted using two detector stacks composed of $4 \times 4$ LYSO:Ce,Ca crystals ($3.8\times 3.8\times 20$ mm$^{3}$) coupled to $4 \times 4$ Broadcom NUV-MT SiPMs and digitized with the TOFPET2 ASIC.

Accurate calibration of acoustic sensing systems made of multiple asynchronous microphone arrays is essential for satisfactory performance in sound source localization and tracking. State-of-the-art calibration methods for this type of system rely on the time difference of arrival and direction of arrival measurements among the microphone arrays (denoted as TDOA-M and DOA, respectively). In this paper, to enhance calibration accuracy, we propose to incorporate the time difference of arrival measurements between adjacent sound events (TDOAS) with respect to the microphone arrays. More specifically, we propose a two-stage calibration approach, including an initial value estimation (IVE) procedure and the final joint optimization step. The IVE stage first initializes all parameters except for microphone array orientations, using hybrid TDOA (i.e., TDOAM and TDOA-S), odometer data from a moving robot carrying a speaker, and DOA. Subsequently, microphone orientations are estimated through the iterative closest point method. The final joint optimization step estimates multiple microphone array locations, orientations, time offsets, clock drift rates, and sound source locations simultaneously. Both simulation and experiment results show that for scenarios with low or moderate TDOA noise levels, our approach outperforms existing methods in terms of accuracy. All code and data are available at https://github.com/AISLABsustech/Hybrid-TDOA-Multi-Calib.

--Egomotion estimation is crucial for applications such as autonomous navigation and robotics, where accurate and real-time motion tracking is required. However, traditional methods relying on inertial sensors are highly sensitive to external conditions, and suffer from drifts leading to large inaccuracies over long distances. Vision-based methods, particularly those util-ising event-based vision sensors, provide an efficient alternative by capturing data only when changes are perceived in the scene. In this work, we propose a fully event-based pipeline for egomotion estimation that processes the event stream directly within the event-based domain. This method eliminates the need for frame-based intermediaries, allowing for low-latency and energy-efficient motion estimation. We construct a shallow spiking neural network using a synaptic gating mechanism to convert precise event timing into bursts of spikes. These spikes encode local optical flow velocities, and the network provides an event-based readout of egomotion. We evaluate the network's performance on a dedicated chip, demonstrating strong potential for low-latency, low-power motion estimation. Additionally, simulations of larger networks show that the system achieves state-of-the-art accuracy in egomotion estimation tasks with event-based cameras, making it a promising solution for real-time, power-constrained robotics applications. The estimation of egomotion plays an important role in applications such as autonomous navigation, robotics and Augmented Reality (AR).

--The advent of blockchain technology has facilitated the widespread adoption of smart contracts in the financial sector . However, current fraud detection methodologies exhibit limitations in capturing both global structural patterns within transaction networks and local semantic relationships embedded in transaction data. Most existing models focus on either structural information or semantic features individually, leading to suboptimal performance in detecting complex fraud patterns.In this paper, we propose a dynamic feature fusion model that combines graph-based representation learning and semantic feature extraction for blockchain fraud detection. Specifically, we construct global graph representations to model account relationships and extract local contextual features from transaction data. A dynamic multimodal fusion mechanism is introduced to adaptively integrate these features, enabling the model to capture both structural and semantic fraud patterns effectively. We further develop a comprehensive data processing pipeline, including graph construction, temporal feature enhancement, and text preprocessing. Experimental results on large-scale real-world blockchain datasets demonstrate that our method outperforms existing benchmarks across accuracy, F1 score, and recall metrics. This work highlights the importance of integrating structural relationships and semantic similarities for robust fraud detection and offers a scalable solution for securing blockchain systems. LOCKCHAIN technology has developed rapidly in recent years and has triggered far-reaching changes in several fields, especially in the financial industry [1]. However, as the popularity of blockchain applications grows, so does the significant increase in fraudulent behaviors it has brought about, with serious implications for society [2]. Blockchain technology, due to its decentralization and transparency, has become a tool for unscrupulous individuals to exploit, although it provides greater security and efficiency in financial transactions [3]. For example, the application of blockchain technology in the supply chain is seen as an effective means to enhance transparency and traceability, but it also faces a crisis of social trust due to fraudulent behavior [4].