InterDigital (United States)

Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood detection to overcome the limitations of the currently deployed linear detectors. This paper presents X-ResQ, a QA-based MIMO detector system featuring flexible parallelism that is uniquely enabled by quantum Reverse Annealing (RA). Unlike prior designs, X-ResQ has many desirable parallel QA system properties and has effectively improved detection performance as more qubits are assigned. In our evaluations on a state-of-the-art quantum annealer, fully parallel X-ResQ achieves near-optimal throughput for 4 × 6 MIMO with 16-QAM using approx. 240 qubits achieving 2.5–5× gains compared against other classical and quantum detectors. We also implement and evaluate X-ResQ in the non-quantum digital setting for more comprehensive evaluations. This classical X-ResQ showcases the potential to realize ultra-large 1024 × 1024 MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.

While neural lossy compression techniques have markedly advanced the efficiency of Channel State Information (CSI) compression and reconstruction for feedback in MIMO communications, efficient algorithms for more challenging and practical tasks—such as CSI compression for future channel prediction and reconstruction with relevant side information—remain underexplored, often resulting in suboptimal performance when existing methods are extended to these scenarios. To that end, we propose a novel framework for compression with side information, featuring an encoding process with fixed-rate compression using a trainable codebook for codeword quantization, and a decoding procedure modeled as a backward diffusion process conditioned on both the codeword and the side information. Experimental results show that our method significantly outperforms existing CSI compression algorithms, often yielding over twofold performance improvement by achieving comparable distortion at less than half the data rate of competing methods in certain scenarios. These findings underscore the potential of diffusion-based compression for practical deployment in communication systems.

Risk management in banks' investment activities is increasingly relevant amid market volatility and systemic uncertainty. Traditional approaches often ignore the interaction between risks, limiting management effectiveness. This study aims to enhance risk management by considering the cumulative impact of transformation, credit, interest rate, and default risks. A refined classification and quantitative framework are proposed. Findings highlight the key role of transformation risk in interest rate exposure and the importance of balanced resource strategies. Empirical evidence shows that rising default risk, if uncompensated by returns, reduces investment efficiency. Quantitative models help identify critical risk points, while decision-making quality proves vital for financial resilience and stability.

ObjectiveTo evaluate learners' acceptance of multiuser virtual reality (VR) simulation platform while practicing the Furlow repair technique for cleft palate reconstruction.DesignNonrandomized with pre- and postsurveys.SettingTertiary care institution's Department of Surgery September 2023 through August 2024.ParticipantsTwenty plastic surgery residents from level PGY1 to PGY6 from a single institution.InterventionsMultiuser Meta Quest 2 VR simulation-based workshop with an expert surgeon demonstrating a Furlow cleft palate repair. Feedback included audio and visual.Main outcome measuresLearner confidence in using VR as a tool for surgical education, understanding of the Furlow technique, and opinion regarding the incorporation of VR into surgical and medical education. Satisfaction as measured by the Student Evaluation of Educational Quality questionnaire survey with a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree).Results20 residents participated in this study, of which 65% had previously participated in a cleft palate repair and 40% had used VR. After the simulation, trainees' confidence in using VR as a tool for surgical education, understanding of the Furlow technique, and opinion regarding the incorporation of VR into surgical and medical education significantly increased (P < .05). Trainees found the simulation stimulating (4.85 ± 0.67), interesting (4.45 ± 0.83), clear (4.60 ± 0.82), an effective teaching tool (4.75 ± 0.44) and would recommend it to others (4.90 ± 0.31). Participants reported that they would feel somewhat comfortable repeating the simulation alone (3.95 ± 1.05).ConclusionMultiuser VR-based simulation workshops can significantly increase learners' confidence and skills in the Furlow technique and promote positive opinions regarding VR as an educational tool. Learners considered this platform effective and stimulating and would recommend it as an educational tool.

The introduction of massive MIMO (Multiple Input Multiple Output) communication systems enables base stations (BS) to perform beamforming for enhancing communication reliability. A typical key assumption, however, is the availability of accurate downlink channel state information (CSI). In practice, CSI estimation and reporting delays coupled with the process of channel aging result in the BS receiving outdated CSI information, which in turn impacts the system's spectral efficiency. To combat this latency, this paper develops efficient methods of CSI prediction that preemptively predict future downlink CSI based on historical data. We leverage the spatial and temporal correlation properties of the channel and use explicit feature extraction frameworks for both dimensions to accurately predict future CSI. We analyze combinations of spatial and temporal feature extractors in terms of a tradeoff between performance and latency. We evaluate the performance of the proposed prediction model in terms of proximity to the ground truth, prediction latency, and model footprint. Our experiments show that our method outperforms classical statistical methods as well as existing CSI prediction baselines.

To assess the performance of Apneal®, an application that only requires the patient to tape a smartphone on the thorax for one night, compared with polysomnography. <bold>Methods:</bold> We concomitantly recorded polysomnography (PSG, following AASM standards for recording and scoring) and smartphone signals (microphone, accelerometer, and gyroscope) with the Apneal® v0.1 software for one night in patients referred to a sleep center. PSG were scored manually by trained sleep specialists. Apneal recordings were scored by non-specialist scorers who did a first manual scoring based on various signals extracted from the smartphone sensors, like the probability of snoring, or the filtered acceleration. We also used a deep learning model to produce an automatic scoring, using as input features extracted from the smartphone signals. We compared the Apnea Hypopnea Indexes (AHI) obtained by these different scorings with the PSG as a reference. <bold>Results:</bold> We included 44 patients (mean age 57.5, mean BMI 28.7, men 66%) with suspected OSA. Pearson correlation was 0.90 (p-value=1.25 x 10-17) for manual scoring, and 0.87 (p-value = 1.7 x 10-12) for automatic scoring. Using a cut-off of AHI≥ 15, we found a sensitivity of 0.91 (95% CI [0.8, 1]) and a positive predictive value (PPV) of 0.89 (CI 95% [0.76, 0.97]) for manual scoring, and sensitivity of 1 (95% CI [0.95, 1]) and PPV 0.9 (95% CI [0.8, 0.9]) for automatic scoring. <bold>Conclusions:</bold> The first results obtained by this new minimally-invasive method to diagnose sleep apnea are promising and offer a proof of concept for further larger studies.

Gir det egentlig mening å skille mellom «danning» og «digital danning»? Er «barndom» og «digital barndom» to sider av samme sak? Når skillet mellom livet i og utenfor det digitale er i ferd med å viskes ut, gir kanskje svaret seg selv. Men konsekvensene for barn og unges danning er ikke like åpenbare. Denne vitenskapelige antologien fokuserer på digital danning i barnehage, skole, hjem og fritid. Forfatterne utforsker sentrale aspekter ved digital danning gjennom å studere møtepunktene mellom forskjellige fag- og profesjonspraksiser og vår stadig mer digitaliserte hverdag. Digital danning viser til en helhetlig forståelse av hvordan man lærer, tilegner seg verdier og holdninger, og utvikler identitet og selvforståelse i en digital kontekst. Barn og unges danningsprosess angår oss alle. Samtidig varer den personlige danningen livet ut. Bevisstgjøring og kunnskap om forholdet mellom danningen og det digitale er derfor et allment behov i vår medialiserte samtid. Denne antologien vil være en viktig ressurs og inspirasjonskilde for lærerutdannere, medieforskere, barnehage- og grunnskolelærere, studenter, foreldre, samt barne- og ungdomsarbeidere på ulike arenaer. Redaktørene Margunn Serigstad Dahle (førstelektor) og Grete Skjeggestad Meyer (dosent) er begge ansatt ved NLA Høgskolen. De leder forskergruppen Barn, medier og livssyn, som denne antologien springer ut fra. Antologien åpner med et essay om Mats «Ibelin», skrevet av hans far Robert Steen eksklusivt for denne utgivelsen. Gjennom sin dypt personlige tekst viser han hvor viktig det er å søke å forstå barn og unges digitale liv – ikke minst i forhold til vennskap og opplevelse av mening.