Predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties remains a critical bottleneck in drug discovery. While molecular fingerprints effectively capture local structural features, they struggle to represent higher-order correlations among molecular substructures. We present a quantum-inspired feature extraction method that encodes molecular fingerprints into a parameterized Hamiltonian, using mutual information (MI) to guide entanglement structure. By simulating quantum evolution on GPU-accelerated backends, we extract expectation values that capture pairwise and triadic correlations among fingerprint bits. On ten Therapeutic Data Commons (TDC) ADMET benchmarks, our method achieves state-of-the-art performance on CYP3A4 substrate prediction (AUROC 0.673 0.004) and improves over classical baselines on 8/10 tasks. SHAP (SHapley Additive exPlanations) analysis reveals that quantum-derived features contribute up to 33% of model importance despite comprising only 1.6% of features, demonstrating that Hamiltonian encoding concentrates predictive signal. This simulation study establishes the foundation for hardware validation on near-term quantum devices.

Secure aggregation is a foundational building block of privacy-preserving learning, yet achieving robustness under adversarial behavior remains challenging. Modern systems increasingly adopt the shuffle model of differential privacy (Shuffle-DP) to locally perturb client updates and globally anonymize them via shuffling for enhanced privacy protection. However, these perturbations and anonymization distort gradient geometry and remove identity linkage, leaving systems vulnerable to adversarial poisoning attacks. Moreover, the shuffler, typically a third party, can be compromised, undermining security against malicious adversaries. To address these challenges, we present Robust Aggregation in Noise (RAIN), a unified framework that reconciles privacy, robustness, and verifiability under Shuffle-DP. At its core, RAIN adopts sign-space aggregation to robustly measure update consistency and limit malicious influence under noise and anonymization. Specifically, we design two novel secret-shared protocols for shuffling and aggregation that operate directly on additive shares and preserve Shuffle-DP's tight privacy guarantee. In each round, the aggregated result is verified to ensure correct aggregation and detect any selective dropping, achieving malicious security with minimal overhead. Extensive experiments across comprehensive benchmarks show that RAIN maintains strong privacy guarantees under Shuffle-DP and remains robust to poisoning attacks with negligible degradation in accuracy and convergence. It further provides real-time integrity verification with complete tampering detection, while achieving up to 90x lower communication cost and 10x faster aggregation compared with prior work.

Robust principal component analysis is an important representative method in data analysis. It is usually viewed as an optimization problem involving the rank and $\ell_0$-norm of matrices. In this paper, we study the rank and $\ell_0$ regularized optimization problem and its matrix factorization problem. We establish their equivalences on global minimizers and stationary points, respectively. Furthermore, we construct a broadly applicable equivalent nonconvex relaxation framework for the constrained factorization model in the sense of global minimizers and stationary points with strong optimality conditions (called strong stationary points). For the general factorization problem with lower semicontinuous regularizers and a loss function whose gradient is locally Lipschitz, we propose a novel proximal gradient-based algorithm based on joint and alternating calculation with convergence to its limiting-critical points. The algorithm can attain the stationary points of the original problem and its adaptive counterpart can attain the strong stationary points of the factorization problem.

In nanostructures with two-dimensional (2D) electrons and very low defect densities, a hydrodynamic transport regime has recently been realized. In this regime, 2D electrons form a viscous fluid due to frequent electron-electron collisions. Many unusual and mysterious magnetotransport and high-frequency effects have been observed in these systems. Their understanding is crucial for a general comprehending the formation of hydrodynamic transport. Two-component electronic systems, where there are two types of carriers with different concentrations and relaxation times, are of particularly interest. These systems can be realized by filling two lower subbands in a quantum well with electrons, or by filling one subband and applying a strong magnetic field in the well plane, leading to a Zeeman splitting of the subband. In this work, we construct the hydrodynamic equations for a viscous two-component electronic fluid for a Zeeman-type two-component 2D electronic system. By solving the kinetic equation, we calculate the relaxation rates of the first and second harmonics of the two-component distribution function. The resulting balance hydrodynamic equations take into account the effect of shear viscosity in each component and the effect of the friction between the two components. The lastleads to the alignment of the velocities of the two components of the fluid. The obtained equations can be used to explain magnetotransport measurements in ultra-pure nanostructures in an inclined magnetic field, where two-component electronic fluid is formed.

For positive integers $a$, $b$, and $c$ which have no common divisor, the Frobenius number of $a$, $b$ and $c$ is defined to be the largest integer that cannot be expressed as a linear combination of $a$, $b$ and $c$ with non-negative integer coefficients. In 2017, Tripathi gave an algorithmic formula for the Frobenius number in three variables, however there were some minor inconsistencies in the formula. In this paper, we settle these inconsistencies.

This research focuses on the design of Ka-band mobile antennas for satellite communication operating at 29 GHz. Starting from a single element and progressing to an 8x8 array, the antennas achieved a gain of up to 21 dB and return losses as low as -30 dB. The design process involves mathematical calculations and software implementation, utilizing parameters like patch dimensions, substrate properties, and effective permittivity. The chosen Ka-band frequency range, known for higher data transfer rates, addresses the demand for swift communication. Challenges in Ka-band mobile antenna design, including signal attenuation, directional accuracy, circular polarization, and impedance matching, are addressed through various configurations, including phased-array and electronically steerable antennas. This research focuses on the design of Ka-band mobile antennas for satellite communication at 29GHz, progressing from a single element to an 8x8 array. The antennas achieved a gain of up to 21 dB and return losses as low as -30 dB through mathematical calculations and software implementation using CST. Challenges in Ka-band antenna design, such as signal attenuation and impedance matching, are addressed through various configurations, including phased-array and electronically steerable antennas. Integration of machine learning techniques aids in optimization. In conclusion, this research advances high-frequency transmission technology, meeting the demands of modern satellite-based communication systems for applications like high-speed internet access and multimedia streaming. Keywords: Ka-band, mobile antennas, satellite communication, 29 GHz, antenna design, CST, high-speedinternet access, multimedia streaming

Given a function $b$, holomorphic on the disc and bounded by 1, one can construct an associated reproducing kernel Hilbert space called the de Branges--Rovnyak space $H(b)$. We explore representations of such spaces via descriptions of the corresponding families of orthogonal polynomials. We find relevant structures in the linear systems involved in a diversity of cases when $b$ is rational. We also establish a form of invariance under some composition operators on $H(b)$ spaces.

Side surfaces of cuprate superconductors are expected to display a suppressed $d$-wave order parameter and zero-energy topological flat bands with a large density of states, making them susceptible to symmetry broken orders. Yet such surfaces have never been investigated with momentum-resolved, surface-sensitive probes, because high-temperature superconductors rarely cleave along them. Using focused-ion-beam milling to define a controlled breaking point, we expose pristine (110) side surfaces of overdoped La$_{2-x}$Sr$_x$CuO$_4$ ($x=0.22$) suitable for angle-resolved photoemission. We observe the suppression of the superconducting spectral gap within our energy resolution ($\sim 4~\mathrm{meV}$), and surprisingly, the expected zero-energy flat band peak is also suppressed, despite the high topographic quality of the surface. Self-consistent Bogoliubov--de~Gennes calculations show that the measured geometric roughness of the cleaved surface is too weak to eliminate these modes. The calculations further demonstrate that bulk inhomogeneities characteristic of high-temperature superconductors, modelled as moderate Anderson-type disorder, can broaden the flat-band states beyond detectability. Our results provide the first momentum-resolved view of the electronic structure on a cuprate side surface and reveal disorder as the key factor currently preventing appearance of flat bands and their associated correlated orders.

We study the market design of keep-alive caching policies applicable in serverless computing. Prior work has assumed that the cost of a cache miss (cold start) is uniform across all customer applications. However, the cost of a cache miss depends on the customer's application. We investigate the market design where the customers submit a bid for their cost of a cache miss. We design a cache allocation policy based on online learning from a mixture of fixed allocation experts. We show that our custom cache allocation policy is asymptotically efficient and monotonically non-increasing with respect to the submitted bid. We examine two ways of charging customers to achieve good incentives. In the first payment scheme the customers are charged based on Myerson's theory, whereas in the second payment scheme the customers are charged their externality. We show via a mix of simulations and theory that both schemes have desirable revenue and incentive properties.

The MeerKAT Observations of the Saraswati Supercluster (MOSS) is an ongoing project attempting to study the radio and optical properties of the core region of the Saraswati supercluster which will eventually entail a full survey of the entire supercluster region. We have used MeerKAT L-band (1.28 GHz) images at an angular resolution of 8 arcsec from previous deep (central RMS noise of 11 - 16 uJy beam-1) pilot observations of the core region (z ~ 0.28) of the Saraswati supercluster containing the two most massive galaxy clusters: Abell 2631 and ZwCl2341. These cluster fields cover an area of 1.6 deg2 and the radio catalogs produced from each cluster region contain 1999 and 2611 sources (5sigma limit) for Abell 2631 and ZwCL2341, respectively. For each catalogue, we investigated the noise properties, astrometry, flux density scale accuracy, spectral properties, etc of the radio sources. The catalogs were then corrected for various observational biases before derivation of the radio source counts. In agreement with previous studies, we find that at the sub-mJy level our counts show the characteristic flattening, indicating the increased dominance of the star-forming galaxy (SFG) population over the active galactic nuclei (AGN). Furthermore, in this sub-mJy regime the counts lie slightly higher (a 'bump' feature) compared to other deep MeerKAT data and recent radio-sky simulations. We suggest that this feature could be attributed to an enhanced population of intermediate SFG and/or AGNs associated with these galaxy cluster fields. In addition cosmic variance could represent an important source of uncertainty in the source counts.

Serverless computing and stream processing represent two dominant paradigms for event-driven data processing, yet both make assumptions that render them inefficient for short-running, lightweight, and unpredictable streams that require stateful processing. We propose stream functions as a novel extension of the Function-as-a-Serivce model that treat short streams as the unit of execution, state, and scaling. Stream functions process streams via an iterator-based interface, enabling seamless inter-event logic while retaining the elasticity and scale-to-zero capabilities offered by serverless platforms. Our evaluation shows that stream functions reduce the processing overhead by ~99 % compared to a mature stream process- ing engine in a video-processing use case. By providing comparable performance to serverless functions with stream semantics, stream functions provide an effective and efficient abstractions for a class of workloads underserved by existing models.

Context. Technical Debt (TD) refers to short-term beneficial software solutions that impede future changes, making TD management essential. However, establishing a TD management (TDM) process is one of the most pressing concerns in practice. Goal. We plan to identify which previously researched TDM approaches are feasible in practice and what typical challenges emerge to create a guideline for establishing a TDM process. Method. We replicated our previously published action research study by conducting five workshops introducing TDM with two teams from different companies. To determine the feasibility of TDM approaches, we presented the teams with approaches for various TD activities and let them decide which to adopt. Overall, we conducted 19 workshops and retrospectives, analyzing 108 meetings (96 hours) over a 30-month period. Results. The adopted TD prevention strategies and documentation were similar in all teams. The teams utilized their respective backlogs and created a new backlog item type for TD, incorporating similar attributes such as interest, contagiousness, a resubmission date, and reminders to discuss drawbacks and risks. However, they used different prioritization approaches and deviating repayment methods. The teams had to overcome similar challenges during the establishment, which we list in this paper. Conclusions. We identified the TDM approaches used by all teams as a starting point for best practices. For challenges, we provided solutions or identified them as research gaps. Issue tracking system vendors should implement TD issue types employing the identified attributes. Finally, we created a white paper for practitioners to establish a TDM process based on our results.

Craig's Interpolation theorem has a wide range of applications, from mathematical logic to computer science. Proof-theoretic techniques for establishing interpolation usually follow a method first introduced by Maehara for the Sequent Calculus and then adapted by Prawitz to Natural Deduction. The result can be strengthened to a proof-relevant version, taking proof terms into account: this was first established by Čubrić in the simply-typed lambda-calculus with sums and more recently in linear, classical and intuitionistic sequent calculi. We give a new proof of Čubrić's proof-relevant interpolation theorem by building on principles of bidirectional typing, and formalise it in Rocq.

We evaluate the forecasting performance of a deep learning model, originally introduced as a pattern-extraction framework, that operates on the spatiotemporal evolution of seismic b-values in a short-term forecasting context. Model output is rescaled to account for training on balanced datasets and evaluated relative to a spatial base-rate model using the Brier Skill Score (BSS). Absolute skill values are small, but mean BSS values are consistently positive, including at locations where Mw geq 5 earthquakes occurred during the test period, indicating information content beyond historical seismicity alone. Alarm-based evaluation using Molchan diagrams shows elevated event capture rates at low alarm fractions (5.88 percent of events captured at 1 percent area under alarm), indicating discrimination exceeding random and purely spatial reference models under constrained alarm conditions. Comparison with ETAS-derived triggered probabilities further reveals a weak positive correlation, suggesting partial sensitivity of the model output to seismic regimes characterized by enhanced clustering and recent activity, while remaining distinct from classical aftershock-based descriptions. Together, these results indicate that spatiotemporal variations in b-values contain a persistent, though limited, signal relevant to probabilistic earthquake forecasting, yielding marginal but consistent improvements over baseline models across complementary evaluation frameworks.

Recent observations have identified hub-filament systems (HFSs) as the primary formation sites of massive stars and star clusters. Some HFSs are characterized by multiple filaments aligned radially toward a central high-density hub. However, the physical origin of radially aligned filaments remains unknown. Here, we propose a new formation mechanism of HFSs driven by the interaction of a fast magnetohydrodynamic shock with a molecular cloud characterized by an hourglass-shaped magnetic field and density inhomogeneity. Our three-dimensional magnetohydrodynamic simulations show that the shock propagation leads to the formation of radially aligned filamentary structures with line masses slightly above the thermally critical line mass and lengths of $1$-$3\,\rm{pc}$, and widths of $0.06$-$0.08\,\rm{pc}$. High-density filamentary gas ($n_{\rm{H_2}} \sim 10^4 \, \rm{cm^{-3}}$) selectively exhibits inward velocities of $1-4\, \rm{km \, s^{-1}}$ that increase toward the hub center, while the ambient low-density inter-filament gas retains low velocities regardless of the radius. Mass accretion onto the hub is channeled through dense filaments. The filament formation is driven by oblique shocks generated at the bent magnetic field lines. The resulting post-shock amplification of the tangential magnetic field induces a magnetically guided inflow. The shock-interface interaction amplifies density perturbations, resembling Richtmyer--Meshkov instability modes, which promotes the fragmentation of the shocked layer into multiple filaments. The process studied in this Letter explains both the morphology of radially aligned filaments and the selective mass accretion observed in HFSs. In our simulation, the resulting star formation efficiency is $\sim4\%$, suggesting that the shock-driven evolution limits the SFE to only a few percent.

We present DLIOS, a Large Language Model (LLM)-augmented real-time multi-modal interactive enhancement overlay system for Douyin (TikTok) live streaming. DLIOS employs a three-layer transparent window architecture for independent rendering of danmaku (scrolling text), gift and like particle effects, and VIP entrance animations, built around an event-driven WebView2 capture pipeline and a thread-safe event bus. On top of this foundation we contribute an LLM broadcast automation framework comprising: (1) a per-song four-segment prompt scheduling system (T1 opening/transition, T2 empathy, T3 era story/production notes, T4 closing) that generates emotionally coherent radio-style commentary from lyric metadata; (2) a JSON-serializable RadioPersonaConfig schema supporting hot-swap multi-persona broadcasting; (3) a real-time danmaku quick-reaction engine with keyword routing to static urgent speech or LLM-generated empathetic responses; and (4) the Suwan Li AI singer-songwriter persona case study -- over 100 AI-generated songs produced with Suno. A 36-hour stress test demonstrates: zero danmaku overlap, zero deadlock crashes, gift effect P95 latency <= 180 ms, LLM-to-TTS segment P95 latency <= 2.1 s, and TTS integrated loudness gain of 9.5 LUFS. live streaming; danmaku; large language model; prompt engineering; virtual persona; WebView2; WINMM; TTS; Suno; loudness normalization; real-time scheduling

Generative AI (genAI) is increasingly influencing architectural design practice and is expected to affect, or even transform, the profession, even though its benefits and costs remain unresolved. In response, design schools are increasingly integrating genAI into their curricula. Yet this integration creates a paradox: critical engagement with genAI often requires increased use of the tools in question, despite limited methods for estimating their environmental cost in teaching contexts. In this paper, we argue that HCI offers a useful methodological lens for addressing this tension. We propose three HCI-informed directions for more sustainable genAI integration in architectural education: contextual eco-feedback, participatory stakeholder scoping, and reframing data centres as an interdisciplinary focus. We therefore argue that genAI should be understood not only as a new architectural design tool, but also as a socio-technical process that architectural education, and design education in general, must engage with critically.

We establish universal approximation theorems for infinite-dimensional geometric rough paths, i.e., we show that continuous functions on the space of infinite-dimensional weakly geometric Hölder continuous rough paths can be approximated by functions that are linear in the signature of the path. The underlying topology determining continuity and compactness can be either the norm topology or the weak$^*$ topology. Whereas considerably more effort is required to obtain the universal approximation theorem with respect to the weak$^*$ topology, this setting ensures uniform approximation on norm-bounded sets. The motivation for establishing universal approximation theorems lies in the desire to approximate quantities derived from the solution of a stochastic partial differential equation. More specifically, our universal approximation theorems form the foundations of a novel approach to e.g. pricing of forward rates within the Heath--Jarrow--Morton--Musiela framework.

We consider a two-dimensional electron gas (2DEG) formed at a near-ferroelectric interface and strongly coupled to polar phonons. Through a self-consistent microscopic many-body calculation, we show that the coupled system stabilizes a composite electron-lattice ordered state in which the lattice polarization spontaneously forms a polarization density wave (PDW), accompanied by an electronic stripe order in the 2DEG. This intertwined order partially reconstructs the electronic spectrum and generates a twofold quasiparticle anisotropy, giving rise to electronic nematicity at the single-particle level. However, under strong external electric fields, the nematic response becomes dominated by the collective sliding dynamics of the composite order: the sliding motion overwhelms the quasiparticle anisotropy and produces a strongly enhanced nematic signal with higher-order angular harmonics. The theory offers a natural explanation for several anomalous transport and anisotropic responses recently observed at the KTaO$_3$ (111) interface. We also estimate the mean-field transition temperature of this emergent ordered state, obtaining good agreement with experiments, and analyze its evolution with several tuning parameters. The proposed composite order, along with the field-induced crossover from quasiparticle-driven to sliding-dominated nematicity, provides a distinct mechanism of nematicity arising from many-body effects and collective dynamics in critical electron-boson systems, with applicability beyond ferroelectric platforms.

The debris disk surrounding the young star TWA 7 exhibits morphological features that tightly constrain its planetary architecture. JWST/MIRI observations have recently revealed a directly imaged outer planet at large separation. The disk also displays a sharply defined inner edge near 23 au and an extended asymmetric structure that may trace a horseshoe-like distribution of material indicative of gravitational interactions between planets and planetesimals. We investigate whether the observed disk morphology and the possible co-orbital material can be explained by the combined gravitational influence of the known outer planet and an undetected inner companion. We aim to identify planetary configurations consistent with both the disk structure and the long-term stability of the system. We combined N-body simulations and secular perturbation theory to explore how an undetected inner planet could shape the inner edge of the disk while maintaining the dynamical coldness required for stable co-orbital structures around the outer planet. The analytical framework quantifies the secular coupling between the two planets and delineates dynamically viable configurations. The inner edge of the disk near 23 au can be reproduced by a sub-Jovian planet orbiting between 13 and 23 au. Secular interactions further restrict this companion to nearly circular orbits, as higher eccentricities would excite the outer planet and destabilize the co-orbital material. Together, these constraints confine the system to a narrow region of parameter space. The TWA 7 system appears dynamically cold, with all components, including the planets and the debris disk, sharing nearly circular and coplanar orbits. Such a quiescent configuration likely reflects the weak dynamical stirring, making it a promising laboratory to study the early interplay between planet formation, co-orbital dynamics, and debris-disk evolution.

The path to fault-tolerant quantum computing hinges on hardware that scales while remaining compatible with quantum error correction (QEC). Silicon spin qubits are a leading hardware candidate because they combine industrial fabrication compatibility with a nanoscale footprint that could accommodate millions of qubits on a chip. However, their suitability for QEC remains uncertain since spatially correlated noise naturally emerges from the resulting close proximity of qubits. These correlations increase the likelihood of simultaneous errors and erode the redundancy that QEC depends on. Here we quantify the spatial extent of noise correlations in a five-qubit silicon array and assess their impact on QEC. We identify two distinct sources of correlated noise: global magnetic field drifts that generate perfectly correlated fluctuations, and charge noise from two-level fluctuators that produces short-range correlations decaying within neighboring qubits. While magnetic drifts represent a critical correlated noise source that can compromise QEC, they can be mitigated. In contrast, the measured charge noise correlations are moderate, electrically tunable, and compatible with fault-tolerant operation with minimal qubit overhead. Our results establish quantitative benchmarks for correlated noise and clarify how such correlations impact the viability of quantum error correction in scalable qubit arrays.

We employ a quantum computer to simulate the effect of spin impurities on nitrogen-vacancy (NV) centers in diamond. As these defects operate as nanoscale quantum sensors, modeling quantum noise is crucial to identify limitations in precision. The analysis is performed by means of quantum state tomography on two transmon qubits, representing respectively the NV center and a single spin impurity, modeling either a nuclear spin or an additional NV center. We demonstrate a versatile platform to simulate benchmark protocols such as Ramsey or Hahn-echo. Although we focus on a two-spin system, the same approach opens the door to using quantum processors as scalable simulators of many-spin environments, intractable in classical simulation due to the rapid exponential growth of the Hilbert space. The results reveal the effect different spin-sensor coupling regimes have on coherence, helping to identify detection schemes that maximize the sensitivity under the effect of impurities. Moreover, the role of entanglement generation is analyzed using the Peres-Horodecki criterion and CHSH inequalities. Although no violation of the latter is observed, the presence of entanglement is confirmed.

We theoretically investigate the chaotic behavior of spin-torque ferromagnetic resonance in magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy under thermal fluctuations. By calculating the Lyapunov exponent based on the Landau-Lifshitz-Gilbert equation, we demonstrate that an MTJ characterized by a double-well potential, composed of uniaxial magnetic anisotropy and an external magnetic field, exhibits chaotic magnetization dynamics that can be controlled by means of the DC current bias. Furthermore, we find that thermal fluctuations help to induce these chaotic magnetization dynamics, which can be regarded as noise-induced chaos. This research provides a basis for brain-inspired computing using spintronic devices and advances the understanding of the interplay between thermal fluctuations and chaos in magnetization dynamics.

Dual quasars separated at kiloparsec scale are widely regarded as precursors to binary supermassive black holes and offer a key insight into the dynamical evolution of galaxy mergers. Our series of studies focus on searching for dual quasars by using a selection strategy of zero proper motion and zero parallax to isolate quasar candidates near known ones and by follow-up spectroscopy of the candidates. This paper, the third in the series, reports the spectroscopic confirmations of our quasar pair candidates primarily based on the data of the DESI DR1. We newly identified 16 dual quasars and 36 projected quasars. The redshifts of the 16 dual quasars range from 0.609 to 2.758, with a median of 1.46. One notable system, J0023+0417, exhibits nearly identical spectral features in the two members and shows evidence of a potential foreground galaxy, making it a high-confidence strong gravitational lensing system. The redshift of the 36 projected quasars are from 0.377 to 3.399, with a median of 1.663. Among them, four have projected distances below 30 kpc, offering valuable opportunities to probe the circumgalactic medium (CGM) of the foreground host galaxy through absorption lines.

We compute the coregularity of del Pezzo surfaces with du Val singularities. To this aim, we study the relation between del Pezzo surfaces of degree $1$ and elliptic fibrations. It turns out that del Pezzo surfaces with positive coregularity correspond to isotrivial elliptic fibrations with some special properties. We also prove results about coregularity of del Pezzo surfaces over non-algebraically closed fields of characteristic $0$. Our results confirm the expectation that "most" del Pezzo surfaces have coregularity $0$, while del Pezzo surfaces with positive coregularity enjoy some special properties.

In recent years, the Third Generation Partnership Project (3GPP) has developed the new radio-vehicle-to-everything (NR-V2X) sidelink standard, to enable direct communication between connected and autonomous vehicles (CAVs). Users can autonomously select radio resources for their transmissions with the Mode 2 channel access scheme, which can also operate under out-of-coverage conditions. However, Mode 2 performance is hindered by interference and packet collisions arising from dynamic mobile environments and limitations in assessing radio resource availability. The 3GPP specifications allow transmitting multiple copies of the same packet to improve reliability, though at the cost of increased channel congestion. This paper proposes to leverage receivers equipped with successive interference cancellation (SIC) capabilities, to exploit packet repetitions. Specifically, once a packet is successfully decoded the interfering contribution carried by repetitions can be cancelled from future or past received signals, enabling the decoding of new packets. Extensive highway scenario simulations demonstrate that the proposed solution significantly outperforms the legacy Mode 2 scheme, especially under high interference conditions, achieving improvements exceeding 100% in some cases.

Perovskite nanocrystals are a convenient model system for optical spin orientation and manipulation. However, its real potential might be underestimated due to the incomplete knowledge on spin relaxation times, which are obscured by the limited sensitivity of measurement techniques as well as by the insufficient understanding of the spin relaxation mechanisms in perovskites. In this work, we study the spin relaxation of charge carriers in perovskite nanocrystals both experimentally and theoretically. We address the electron and hole spins in CsPbI$_3$ nanocrystals embedded in a glass matrix by the resonant spin inertia technique based on optically detected magnetic resonance. It allows us to determine the longitudinal spin relaxation time $T_1$ separately for electrons and holes, the $g$ factors, and the effective Overhauser field of the nuclear spin bath. At a temperature of 1.6 K, the $T_1$ time for electrons can be as long as 0.9 ms. We reveal the effect of the time-varying nuclear field fluctuations, which enhances the electron spin relaxation at low magnetic fields, and measure a rather long nuclear spin correlation time of about 60 $μ$s. We develop a model of the spin relaxation in nanocrystals based on a two-LO-phonon Raman process, which explains the observed temperature dependence of the time $T_1$.

We use topological data analysis to study neural population activity in the Sensorium 2023 dataset, which records responses from thousands of mouse visual cortex neurons to diverse video stimuli. For each video, we build frame-by-frame cubical complexes from neuronal activity and apply zigzag persistent homology to capture how topological structure evolves over time. These dynamics are summarized with persistence landscapes, providing a compact vectorized representation of temporal features. We focus on one-dimensional topological features-loops in the data-that reflect coordinated, cyclical patterns of neural co-activation. To test their informativeness, we compare repeated trials of different videos by clustering their resulting topological neural representations. Our results show that these topological descriptors reliably distinguish neural responses to distinct stimuli. This work highlights a connection between evolving neuronal activity and interpretable topological signatures, advancing the use of topological data analysis for uncovering neural coding in complex dynamical systems.

We propose to combine Bose-Einstein condensation in higher Bloch bands and a driven-dissipative cavity-BEC system into a hybrid light-matter platform. Specifically, the condensate is trapped in a bipartite $s$-$p_x$-$p_y$-lattice, with a tunable energy offset. This enables a controlled population transfer from the $s$-orbital to the nearly degenerate $p_x$ and $p_y$ orbitals. The system forms a chiral ground state with $p_x \pm i p_y$ symmetry, with staggered orbital currents. By increasing the transverse pump strength, we drive the system into the superradiant phase, resulting in a self-organized, density checkerboard, which rectifies the staggered chiral order into a topological superfluid state. Using truncated Wigner simulations and complementary mean-field analysis, we determine the phase transition into this state as first order. Our results show that higher-band condensates coupled to a cavity provide a promising platform for engineering non-trivial orbital order and topological superfluid phases in quantum optical many-body systems.

In this note, we clarify the relationship between the two-point Carrollian correlator and massless scattering in black hole background. It turns out that there are two kinds of Carrollian correlators at the null boundaries of each asymptotically flat spacetime. The correlator from $\mathscr I^-$ to $\mathscr I^+$ should be regularized by subtracting the flat space analog, and it is the position space version of the reflection amplitude of massless scattering. On the other hand, the correlator from $\mathscr I^-$ to the future horizon $\mathcal H^+$ is absent in flat space, and it is the position space version of the transmission amplitude. The poles of the Carrollian correlators are governed by the null geodesics from $\mathscr I^-$ to $\mathscr I^+$ or $\mathcal H^+$, and they define two kinds of classical equations in Carrollian space. These equations establish the relationship between the Shapiro time delay and the deflection angle for light rays and should be understood as the dual descriptions of the quasinormal modes (QNMs) and the branch cut of the Green's function. We find that the time delay contains a logarithmic/quadratic behavior for the correlator from $\mathscr I^-$ to $\mathscr I^+/\mathcal H^+$ for small deflection angles. On the other hand, the time delay is always increasing linearly for both correlators when the deflection angle is large.