KDDI Research (Japan)

Quantum walk-based hash functions have attracted a lot of attention in recent years because of its faster execution time and robust resistance against attacks compared to classical hash functions. It has been observed that the underlying graph and the way message controls the quantum walk iteration steps play a crucial role for the robustness of the hash function. We propose a quantum hash function based on the discrete-time quantum walk on a Hanoi network—a one-dimensional periodic lattice with extra long-range edges of a specific form—which is highly collision resistant. The message bits of our scheme control the flow of probability amplitude through the extra long-range edges and the conditional shift operators. Our method even works for messages with small bit-lengths, contrary to most of the quantum walk based hash functions defined on a cycle, which usually work for messages with bit-lengths more than the length of the cycle.

This paper explores a compression method for the phase component of computer-generated hologram (CGH) data. The proposed method utilizes the framework of JPEG-LS which is an international standard for lossless image compression. The phase component can be regarded as a signal distributed on the unit circle. However, its image representation introduces discontinuities around values of 0 and 2π, even though these values are adjacent on the circle. Such discontinuities significantly degrade coding efficiency when conventional image compression techniques are applied. To address this issue, we modify the MED predictor used in the JPEG-LS standard to accommodate the discontinuous nature of the phase component.

To keep users safe online, current protections frequently employ blocklists of known malware and phishing websites. However, such defenses suffer from an inherent gap between malicious content creation and its detection, leaving a window where users are left vulnerable. To address this limitation, earlier research has shown that one could use individual user web browsing behavior to identify imminent exposure to malicious content. While existing methods frequently rely on temporal proximity (e.g., aggregating browsing patterns over the recent past), they do not leverage temporal ordering in user browsing, which results in suboptimal performance and is, in practice, inadequate given the low base rates of malware incidence. We introduce network and browser-level features (e.g., page rank, tab browsing time) and a temporal model that captures user behavior through a time-series representation. This not only improves classification performance by a significant margin (between 93% and 145% F1-score improvements) over previous models, but also maintains strong robustness across completely disparate sets of users. More importantly, our method shows strong resilience to concept drift, as performance holds steady over multiple years of testing. We discuss how this method is capable of anticipating future exposure. We also assess the relative importance of each feature to the performance, as well as their impact on false positive rates—whose minimization is critical to foster adoption. Finally, we discuss use cases for such behavior-based models.

We introduce a hologram compression method that selectively preserves phase information in high-intensity regions using amplitude-guided masking. This approach improves coding efficiency while maintaining compatibility with existing image codecs, requiring no changes to encoders or decoders. Experiments demonstrate significant bitrate reductions and improved visual quality in shallow-depth scenes.

This paper presents a full-stack testbed for evaluating inter-site central processing unit (CPU) cooperation in large-scale cell-free massive multiple-input multiple-output (CF-mMIMO) systems. In such systems, multiple CPUs are distributed across different sites to address computational and fronthaul capacity limitations. However, this distributed architecture introduces inter-site interference that degrades radio quality at site edges. To mitigate this issue, two inter-site CPU cooperation schemes, tight CPU cooperation (TCC) and loose CPU cooperation (LCC), have been proposed. Despite their theoretical promise, their practical feasibility in an end-to-end system and their effectiveness under real outdoor conditions remain unclear. To address these gaps, we have developed a full-stack 5G testbed to experimentally validate the feasibility and performance of TCC and LCC. Measurements conducted over a 5400 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> outdoor area demonstrate that both methods significantly improve uplink throughput by suppressing inter-site interference. We further analyze the relationship between throughput, signal power, and interference power on a per-user basis. Finally, we discuss key challenges for scaling CF-mMIMO deployments.

Background: Problematic smartphone use (PSU) involves excessive smartphone engagement that disrupts daily functioning and is linked to attentional control deficits and altered reward processing. The nucleus accumbens (NAcc), a key structure in the reward system, may contribute to difficulty disengaging from rewarding digital content. This study examined relationships between NAcc volume, attentional switching, and objectively measured nighttime screen time in individuals with PSU. Methods: Fifty-three participants (aged ≥ 13 years) from an outpatient internet dependency clinic completed psychological assessments, brain MRI, and smartphone logging. PSU was diagnosed by two psychiatrists. Attentional switching was measured via the Autism Spectrum Quotient subscale. Nighttime screen time (00:00–06:00) was recorded via smartphone. MRI-derived NAcc volumes were normalized to total gray matter volume. Correlations, multiple regression (controlling for ASD and ADHD), and mediation analyses were conducted. Results: Difficulty in attention switching correlated with larger right NAcc volume (r = 0.45, p = 0.012) and increased nighttime screen time (r = 0.44, p = 0.014). Right NAcc volume also correlated with nighttime screen time (r = 0.46, p = 0.012). Regression showed right NAcc volume predicted nighttime screen time (β = 0.33, p = 0.022), whereas attentional switching was not significant. Mediation was unsupported. Sensitivity analyses confirmed associations. Conclusions: Larger right NAcc volume independently predicts prolonged nighttime smartphone use and is associated with impaired attentional switching in PSU. Structural variations in reward-related regions may underlie difficulty disengaging from digital content. Integrating neurobiological, cognitive, and behavioral measures offers a framework for understanding PSU.

We present the first demonstration of coherent communication at the C-band using an InP-based photonic-crystal surface-emitting laser (PCSEL), achieving 1-Gb/s free-space optical (FSO) transmission with a link budget exceeding 67 dB. This highlights the potential of InP-based PCSELs for fibre-amplifier-free standard C-band space communication. ©2025 The Author(s)