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This paper presents an optic flow-guided approach for achieving soft landings by resource-constrained unmanned aerial vehicles (UAVs) on dynamic platforms. An offline data-driven linear model based on Koopman operator theory is developed to describe the underlying (nonlinear) dynamics of optic flow output obtained from a single monocular camera that maps to vehicle acceleration as the control input. Moreover, a novel adaptation scheme within the Koopman framework is introduced online to handle uncertainties such as unknown platform motion and ground effect, which exert a significant influence during the terminal stage of the descent process. Further, to minimize computational overhead, an event-based adaptation trigger is incorporated into an event-driven Model Predictive Control (MPC) strategy to regulate optic flow and track a desired reference. A detailed convergence analysis ensures global convergence of the tracking error to a uniform ultimate bound while ensuring Zeno-free behavior. Simulation results demonstrate the algorithm's robustness and effectiveness in landing on dynamic platforms under ground effect and sensor noise, which compares favorably to non-adaptive event-triggered and time-triggered adaptive schemes.

This paper proposes a novel encryption-based access control mechanism for Named Data Networking (NDN). The scheme allows data producers to share their content in encrypted form before transmitting it to consumers. The encryption mechanism incorporates time-based subscription access policies directly into the encrypted content, enabling only consumers with valid subscriptions to decrypt it. This makes the scheme well-suited for real-world, subscription-based applications like Netflix. Additionally, the scheme introduces an anonymous and unlinkable signature-based authentication mechanism that empowers edge routers to block bogus content requests at the network's entry point, thereby mitigating Denial of Service (DoS) attacks. A formal security proof demonstrates the scheme's resistance to Chosen Plaintext Attacks (CPA). Performance analysis, using Mini-NDN-based emulation and a Charm library implementation, further confirms the practicality of the scheme. Moreover, it outperforms closely related works in terms of functionality, security, and communication overhead., Comment: 25 pages, 8 figures

Orthogonal time frequency space (OTFS) modulation is a promising solution to handle doubly-selective fading, but its channel estimation is a nontrivial task in terms of maximizing spectral efficiency. Conventional pilot assignment approaches face challenges: the standard embedded pilot-based scheme suffers from low transmission rates, and the single superimposed pilot (SP)-based scheme experiences inevitable data-pilot interference, leading to coarse channel estimation. To cope with this issue, focusing on the SP-based OTFS system in channel coded scenarios, we propose a novel pilot assignment scheme and an iterative algorithm. The proposed scheme allocates multiple SPs per frame to estimate channel coefficients accurately. Furthermore, the proposed algorithm performs refined interference cancellation, utilizing a replica of data symbols generated from soft-decision outputs provided by a decoder. Assuming fair and unified conditions, we evaluate each pilot assignment scheme in terms of reliability, channel estimation accuracy, effective throughput, and computational complexity. Our numerical simulations demonstrate that the multiple SP-based scheme, which balances the transmission rate and the interference cancellation performance, has the best throughput at the expense of slightly increased complexity. In addition, we confirm that the multiple SP-based scheme achieves further improved throughput due to the proposed interference cancellation algorithm., Comment: 12 pages, 10 figures

In information-theoretic private information retrieval (PIR), a client wants to retrieve one desired file out of $M$ files, stored across $N$ servers, while keeping the index of the desired file private from each $T$-sized subset of servers. A PIR protocol must ideally maximize the rate, which is the ratio of the file size to the total quantum of the download from the servers, while ensuring such privacy. In Weak-PIR (WPIR), the criterion of perfect information-theoretic privacy is relaxed. This enables higher rates to be achieved, while some information about the desired file index leaks to the servers. This leakage is captured by various known privacy metrics. By leveraging the well-established capacity-achieving schemes of Sun and Jafar under non-colluding ($T=1$) and colluding ($1

We show that the holographic Schr\"odinger equation of light-front chiral QCD, together with the 't Hooft equation of (1+1)-dimensional QCD in the large $N_c$ limit, can simultaneously describe the $\phi$-meson mass spectroscopy as well as diffractive cross-section. We compute the $\phi$-meson diffractive cross-section by utilizing its resulting light-front wave functions (LFWFs), in conjunction with the color glass condensate (CGC) dipole scattering amplitude. Our predictions for the diffractive cross sections show good agreement with the existing experimental data from HERA at various energies from H1 and ZEUS collaborations. Additionally, we show that the obtained $\phi$-meson LFWFs effectively describe its various properties, including the decay constant, distribution amplitudes, electromagnetic form factors, charge radius, and magnetic and quadrupole moments., Comment: 17 pages, 9 figures, 3 tables

We calculate all leading-twist gluon generalized parton distributions (GPDs) inside the proton at nonzero skewness using the basis light-front quantization framework. The proton's light-front wave functions are derived from a light-front quantized Hamiltonian incorporating Quantum Chromodynamics inputs. Our results show that the qualitative behaviors of the GPDs are consistent with those from other theoretical calculations. Additionally, we analyze the GPDs in the boost-invariant longitudinal coordinate, $\sigma=\frac{1}{2} b^- P^+$, which serves as the Fourier conjugate of the skewness. The GPDs in $\sigma$-space exhibit diffraction patterns, reminiscent of optical wave diffraction., Comment: 11 pages, 3 figures, and 1 table

Recent ground- and space-based surveys have shown that planets between Earth and Neptune in size, known as "super-Earths," are among the most frequently found planets in the Galaxy. Although the JWST era has provided high-quality atmospheric data on several such super-Earths, modeling tools are crucial for understanding their unobservable interiors. Consequently, interior studies represent the next essential step in gaining a comprehensive understanding of this class of exoplanets. This study investigates the interior structure, thermal evolution, and atmospheric dynamics of the super-Earth GJ 486b using SERPINT, a 1-D self-consistent coupled interior structure and evolution model, aiming to understand the planet's thermal evolution based on an Earth-like structure. Our results indicate that GJ 486b's core is approximately 1.34 times larger than Earth's, with a core pressure of about 1171 GPa. The thermal evolution model predicts that the planet's mantle cools and solidifies over approximately 0.93 million years. As the magma ocean cools, water is released from the melt, forming a water-rich atmosphere during early solidification. Photolysis of water vapor and subsequent hydrogen escape lead to oxygen accumulation, forming a water- and oxygen-rich secondary atmosphere. Future high-sensitivity JWST observations, with improved wavelength coverage and the detection of additional trace gases, will enable a detailed analysis of the planet's atmospheric composition, providing crucial insights into the interior, surface, and subsurface properties of GJ 486b., Comment: Accepted for publication in the Astrophysical Journal; 24 Pages, 7 Figures

Bandwidth constraints limit LoRa implementations. Contemporary IoT applications require higher throughput than that provided by LoRa. This work introduces a LoRa Multiple Input Multiple Output (MIMO) system and a spatial multiplexing algorithm to address LoRa's bandwidth limitation. The transceivers in the proposed approach modulate the signals on distinct frequencies of the same LoRa band. A Frequency Division Multiplexing (FDM) method is used at the transmitters to provide a wider MIMO channel. Unlike conventional Orthogonal Frequency Division Multiplexing (OFDM) techniques, this work exploits the orthogonality of the LoRa signals facilitated by its proprietary Chirp Spread Spectrum (CSS) modulation to perform an OFDM in the proposed LoRa MIMO system. By varying the Spreading Factor (SF) and bandwidth of LoRa signals, orthogonal signals can transmit on the same frequency irrespective of the FDM. Even though the channel correlation is minimal for different spreading factors and bandwidths, different Carrier Frequencies (CF) ensure the signals do not overlap and provide additional degrees of freedom. This work assesses the proposed model's performance and conducts an extensive analysis to provide an overview of resources consumed by the proposed system. Finally, this work provides the detailed results of a thorough evaluation of the model on test hardware., Comment: 8 pages, 7 figures

Satellite Networks (SN) have traditionally been instrumental in providing two key services: communications and sensing. Communications satellites enable global connectivity, while sensing satellites facilitate applications such as Earth observation, navigation, and disaster management. However, the emergence of novel use cases and the exponential growth in service demands make the independent evolution of communication and sensing payloads increasingly impractical. Addressing this challenge requires innovative approaches to optimize satellite resources. Joint Communications and Sensing (JCAS) technology represents a transformative paradigm for SN. By integrating communication and sensing functionalities into unified hardware platforms, JCAS enhances spectral efficiency, reduces operational costs, and minimizes hardware redundancies. This paper explores the potential of JCAS in advancing the next-generation space era, highlighting its role in emerging applications. Furthermore, it identifies critical challenges, such as waveform design, Doppler effect mitigation, and multi-target detection, that remain open for future research. Through these discussions, we aim to stimulate further research into the transformative potential of JCAS in addressing the demands of 6G and beyond SN.

Misfit-layered compounds, naturally occurring bulk heterostructures, present a compelling alternative to artificially engineered ones, offering a unique platform for exploring correlated phases and quantum phenomena. This study investigates the magnetotransport and superconducting properties of the misfit compound (PbS)$_{1.13}$TaS$_2$, comprising alternating PbS and 1$H$-TaS$_2$ layers. It exhibits distinctive transport properties, including a prominent planar Hall effect and a four-fold oscillatory Butterfly-shaped anisotropic magnetoresistance (AMR). Moreover, it shows multigap two-dimensional superconductivity with an exceptionally high in-plane upper critical field, exceeding the Pauli limit. The coexistence of unconventional superconductivity and anomalous transport - two distinct quantum phenomena, within the same material, suggests that misfit compounds provide an ideal platform for realizing quantum effects in the two-dimensional limit of bulk crystals. This opens the door to the development of simpler and more efficient quantum devices., Comment: 10 pages, 5 figures

We obtain the mass spectra of strange mesons using a light-front quantized Hamiltonian with Quantum Chromodynamics (QCD) input, incorporating quark-antiquark and quark-antiquark-gluon Fock components, along with a three-dimensional confinement. We work within the basis light-front quantization framework. The resulting eigenvectors can simultaneously describe the kaon's electromagnetic form factor, decay constant, distribution amplitude, and quark and gluon distribution functions under QCD scale evolution. Using the obtained kaon parton distribution functions (PDFs), supplemented by established nuclear PDFs, we also predict the kaon-nucleus-induced Drell-Yan cross section, which is expected to be measured soon by COMPASS++/AMBER at CERN., Comment: 9 pages, 5 figures

Objective: Automatic sleep scoring is crucial for diagnosing sleep disorders. Existing frameworks based on Polysomnography often rely on long sequences of input signals to predict sleep stages, which can introduce complexity. Moreover, there is limited exploration of simplifying representation learning in sleep scoring methods. Methods: In this study, we propose NeuroSleepNet, an automatic sleep scoring method designed to classify the current sleep stage using only the microevents in the current input signal, without the need for past inputs. Our model employs supervised spatial and multi-scale temporal context learning and incorporates a transformer encoder to enhance representation learning. Additionally, NeuroSleepNet is optimized for balanced performance across five sleep stages by introducing a logarithmic scale-based weighting technique as a loss function. Results: NeuroSleepNet achieved similar and comparable performance with current state-of-the-art results. The best accuracy, macro-F1 score, and Cohen's kappa were 86.1 percent, 80.8 percent, and 0.805 for Sleep-EDF expanded; 82.0 percent, 76.3 percent, and 0.753 for MESA; 80.5 percent, 76.8 percent, and 0.738 for Physio2018; and 86.7 percent, 80.9 percent, and 0.804 for the SHHS database. Conclusion: NeuroSleepNet demonstrates that even with a focus on computational efficiency and a purely supervised learning approach, it is possible to achieve performance that is comparable to state-of-the-art methods. Significance: Our study simplifies automatic sleep scoring by focusing solely on microevents in the current input signal while maintaining remarkable performance. This offers a streamlined alternative for sleep diagnosis applications.

In this paper, we introduce and apply Operations Research Question Answering (ORQA), a new benchmark designed to assess the generalization capabilities of Large Language Models (LLMs) in the specialized technical domain of Operations Research (OR). This benchmark evaluates whether LLMs can emulate the knowledge and reasoning skills of OR experts when confronted with diverse and complex optimization problems. The dataset, developed by OR experts, features real-world optimization problems that demand multistep reasoning to construct their mathematical models. Our evaluations of various open source LLMs, such as LLaMA 3.1, DeepSeek, and Mixtral, reveal their modest performance, highlighting a gap in their ability to generalize to specialized technical domains. This work contributes to the ongoing discourse on LLMs generalization capabilities, offering valuable insights for future research in this area. The dataset and evaluation code are publicly available., Comment: 12 pages, 10 figures. Accepted and to be published in AAAI25

Lepton-number-violating interactions occur in the Standard Model Effective Field Theory (SMEFT) at odd dimensions starting from the dimension-5 Weinberg operator. Although the operators at dimension-7 and higher are more suppressed by the heavy new scale, they can be crucial when traditional seesaw mechanisms leading to tree-level dimension-5 contributions are absent. We identify all minimal tree-level UV-completions for dimension-7 $\Delta L=2$ SMEFT operators without covariant derivatives and propose a new simplified approach for estimating the radiative neutrino masses arising from such operators. This dimensional-regularisation-based approach provides a more accurate estimate for the loop neutrino masses when the new physics fields are hierarchical in mass, as compared to the cut-off-regularisation-based approach often employed in the literature. This allows us to identify viable regions of parameter space in the full list of relevant simplified models close to the current limits set by neutrinoless double beta decay and the LHC that would previously have been thought to be excluded by neutrino-mass constraints., Comment: 36 pages, 9 figures, 6 tables

Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significant advances in automating aspects of scientific reasoning, simulation, and experimentation, we still lack integrated AI systems capable of performing autonomous long-term scientific research and discovery. This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery, including the need for science-focused AI agents, improved benchmarks and evaluation metrics, multimodal scientific representations, and unified frameworks combining reasoning, theorem proving, and data-driven modeling. Addressing these challenges could lead to transformative AI tools to accelerate progress across disciplines towards scientific discovery., Comment: AAAI 2025

Recent studies have shown that generative language models often reflect and amplify societal biases in their outputs. However, these studies frequently conflate observed biases with other task-specific shortcomings, such as comprehension failure. For example, when a model misinterprets a text and produces a response that reinforces a stereotype, it becomes difficult to determine whether the issue arises from inherent bias or from a misunderstanding of the given content. In this paper, we conduct a multi-faceted evaluation that distinctly disentangles bias from flaws within the reading comprehension task. We propose a targeted stereotype mitigation framework that implicitly mitigates observed stereotypes in generative models through instruction-tuning on general-purpose datasets. We reduce stereotypical outputs by over 60% across multiple dimensions -- including nationality, age, gender, disability, and physical appearance -- by addressing comprehension-based failures, and without relying on explicit debiasing techniques. We evaluate several state-of-the-art generative models to demonstrate the effectiveness of our approach while maintaining the overall utility. Our findings highlight the need to critically disentangle the concept of `bias' from other types of errors to build more targeted and effective mitigation strategies. CONTENT WARNING: Some examples contain offensive stereotypes.

Two-dimensional (2D) Janus materials hold a great importance in spintronic and valleytronic applications due to their unique lattice structures and emergent properties. They intrinsically exhibit both an in-plane inversion and out-of-plane mirror symmetry breakings, which offer a new degree of freedom to electrons in the material. One of the main limitations in the multifunctional applications of these materials is, however, that, they are usually non-magnetic in nature. Here, using first-principles calculations, we propose to induce magnetic degree of freedom in non-magnetic WSTe via doping with transition metal (TM) elements -- Fe, Mn and Co. Further, we comprehensively probe the electronic, spintronic and valleytronic properties in these systems. Our simulations predict intrinsic Rashba and Zeeman-type spin splitting in pristine WSTe. The obtained Rashba parameter is $\sim$ 422 meV\AA\; along the $\Gamma - K$ direction. Our study shows a strong dependence on uniaxial and biaxial strains where we observe an enhancement of $\sim$ 2.1\% with 3\% biaxial compressive strain. The electronic structure of TM-substituted WSTe reveals half-metallic nature for 6.25 and 18.75\% of Fe, 25\% of Mn, and 18.75 and 25\% of Co structures, which leads to 100\% spin polarization. The obtained values of valley polarization 65, 54.4 and 46.3 meV for 6.25\% of Fe, Mn and Co, respectively, are consistent with the literature data for other Janus materials. Further, our calculations show a strain dependent tunability of valley polarization, where we find an increasing (decreasing) trend with uniaxial and biaxial tensile (compressive) strains. We observed a maximum enhancement of $\sim$ 1.72\% for 6.25\% of Fe on application of 3\% biaxial tensile strain., Comment: 13 figure, 3 tables

Large language models (LLMs) are powerful but resource intensive, limiting accessibility. HITgram addresses this gap by offering a lightweight platform for n-gram model experimentation, ideal for resource-constrained environments. It supports unigrams to 4-grams and incorporates features like context sensitive weighting, Laplace smoothing, and dynamic corpus management to e-hance prediction accuracy, even for unseen word sequences. Experiments demonstrate HITgram's efficiency, achieving 50,000 tokens/second and generating 2-grams from a 320MB corpus in 62 seconds. HITgram scales efficiently, constructing 4-grams from a 1GB file in under 298 seconds on an 8 GB RAM system. Planned enhancements include multilingual support, advanced smoothing, parallel processing, and model saving, further broadening its utility.

Predictive maintenance (PdM) is increasingly pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically scheduling maintenance. However, the remoteness of wind farms often renders current methodologies ineffective, as they fail to provide a sufficiently reliable advance time window for maintenance planning, limiting PdM's practicality. This study introduces a novel deep learning (DL) methodology for future RUL forecasting. By employing a multi-parametric attention-based DL approach that bypasses feature engineering, thereby minimizing the risk of human error, two models: ForeNet-2d and ForeNet-3d are proposed. These models successfully forecast the RUL for seven multifaceted wind turbine (WT) failures with a 2-week forecast window. The most precise forecast deviated by only 10 minutes from the actual RUL, while the least accurate prediction deviated by 1.8 days, with most predictions being off by only a few hours. This methodology offers a substantial time frame to access remote WTs and perform necessary maintenance, thereby enabling the practical implementation of PdM., Comment: 19 pages, 16 figures, Journal Paper

We investigated the magnetic transitions in BiFeO$_3$ at low temperature (5-300 K) and observed nearly 90$^o$ rotation of magnetic domains (imaged by vertical magnetic force microscopy) across 150 K in an epitaxial thin film of thickness $\sim$36 nm. It offers a clear evidence of spin reorientation transition. It also corroborates the transition observed below $\sim$150 K in the zero-field-cooled and field-cooled magnetization versus temperature data. The field-driven 180$^o$ domain switching at room temperature, on the other hand, signifies presence of ferromagnetism. Since bulk antiferromagnetic BiFeO$_3$ does not exhibit such a transition, this observation in ferromagnetic thin film of BiFeO$_3$ indicates a radical effect because of epitaxial strain. Density functional theory based first-principles calculations too reveal that combined in- and out-of-plane epitaxial strain induces magnetic transition from G- to C-type structure in BiFeO$_3$., Comment: 12 pages, 9 figures, published in Phys. Rev. B

We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies close to the inner boundary of the habitable zone. We find the magma ocean stage can last up to 50 Myr, remove 8 Earth-oceans of water, and leave up to 2000 bars of oxygen in the atmosphere. However, if the planet forms with a hydrogen envelope as small as 0.1 Earth-masses, no water will be lost during the star's pre-main sequence phase from thermal escape processes. We find that the planets are unlikely to be in a 3:1 mean motion resonance and that both planets tidally circularize within 0.5 Gyr when tidal dissipation is held constant. However, if tidal dissipation is a function of mantle temperature and rheology, then we find that planet c's orbit may require more than 7 Gyr to circularize, during which time tidal heating may reach hundreds of terawatts. We thus conclude that the habitability of planet c depends most strongly on the initial volatile content and internal properties, but no data yet preclude the viability of an active biosphere on the planet., Comment: 16 pages, 9 figures, accepted to PSJ

In topological materials, Berry curvature leads to intrinsic Hall responses. Focusing on time-reversal symmetric systems with broken inversion symmetry, a spontaneoous (zero magnetic field) Hall effect is expected to develop under an applied electric field. Motivated by recent developments in Weyl-Kondo semimetals, here we advance a fully nonequilibrium (FNE) Hall response due to the Berry curvature. In particular, we show that, while the spontaneous Hall current is quadratic in the previously described regime of weak electric field, due to the contribution from the dipole moment of the Berry curvature, the FNE Hall response for non-perturbative electric fields is not controlled by the Berry curvature dipole. Remarkably, the FNE Hall response resembles what happens in systems that break the microscopic time-reversal symmetry. We illustrate the universality of these results by comparing them with their counterparts in systems with any higher-multipole of the Berry curvature. The implications of our results for the understanding of strongly correlated topological semimetals are discussed., Comment: 6 + 8 pages, 3 + 4 figures. v2) fixed equation numbering in the appendix; some clarifications added to main text

In this paper, we extend the class of admissible functions for the trace formula of the second order in the self-adjoint, unitary, and contraction cases for a perturbation in the Hilbert-Schmidt class $\mathcal{S}^2(\mathcal{H})$ by assuming a certain factorization of the divided difference $f^{[2]}$. This class is the natural one to ensure that the second order Taylor remainder is a trace class operator. It encompasses all the classes of functions for which the trace formula was previously known. Secondly, for a Schatten $\mathcal{S}^p$-perturbation, $1

We uncover the dynamics of particles with Gaussian core interactions across melting in pure and disordered two-dimensional (2D) systems. Intriguing signatures of cooperative motion of particles in string-like paths are found at low temperatures. Such a motion, while common to glasses and supercooled liquids, are realized here in traditional equilibrium phases, including in pure systems. We explore the interplay of such motion and impurities and report their repercussions on spatiotemporal correlations. In particular, cooperative motion seems to cause a departure from the diffusive dynamics, causing slow relaxation., Comment: 13 pages, 8 figures

We consider a one-dimensional system comprising of $N$ run-and-tumble particles confined in a harmonic trap interacting via a repulsive inverse-square power-law interaction. This is the ``active" version of the Calogero-Moser system where the particles are associated with telegraphic noise with two possible states $\pm v_0$. We numerically compute the global density profile in the steady state which shows interesting crossovers between three different regimes: as the activity increases, we observe a change from a density with sharp peaks characteristic of a crystal region to a smooth bell-shaped density profile, passing through the intermediate stage of a smooth Wigner semi-circle characteristic of a liquid phase. We also investigate analytically the crossover between the crystal and the liquid regions by computing the covariance of the positions of these particles in the steady state in the weak noise limit. It is achieved by using the method introduced in Touzo {\it et al.} [Phys. Rev. E {\bf 109}, 014136 (2024)] to study the active Dyson Brownian motion. Our analytical results are corroborated by thorough numerical simulations.

We report on the heterostructure and interfacial engineering of metalorganic chemical vapor deposition (MOCVD) grown reverse-graded contacts to ultra-wide bandgap AlGaN. A record low contact resistivity of 1.4 x 10-6 Ohm.cm2 was reported on an Al0.82Ga0.18N metal semiconductor field effect transistor (MESFET) by compositionally grading the contact layer from Al0.85Ga0.15N to Al0.14Ga0.86N with degenerate doping and proper interfacial engineering considering bandgap-narrowing-induced band offset between channel and contact layer. This represents orders-of-magnitude of lower contact resistivity than that obtained in similar MOCVD-grown structures. A detailed, layer-by-layer analysis of the reverse graded contact and TCAD simulation of the bandgap narrowing effect highlighted that the reverse graded contact layer itself is extremely conductive and interfacial resistance due to bandgap-narrowing-induced barrier between contact and channel dominates the contact resistance.

We propose to measure the beam normal single spin asymmetry in elastic scattering of transversely polarized electron from target nuclei with 12 $\leq Z \leq$ 90 at Q$^2$ = 0.0092 GeV$^2$ to study its nuclear dependence. While the theoretical calculations based on two-photon exchange suggest no nuclear dependence at this kinematics, the results of 208Pb from Jefferson Lab show a striking disagreement from both theoretical predictions and light nuclei measurements. The proposed measurements will provide new data for intermediate to heavy nuclei where no data exists for $Z \geq$ 20 in the kinematics of previous high-energy experiments. It will allow one to investigate the missing contributions that are not accounted in the current theoretical models., Comment: Submitted to Jefferson Lab PAC52

In this article, we propose the integration of the Holographic Multiple Input Multiple Output (HMIMO) as a transformative solution for next generation Non-Terrestrial Networks (NTNs), addressing key challenges, such as high hardware costs, launch expenses, and energy inefficiency. Traditional NTNs are constrained by the financial and operational limitations posed by bulky, costly antenna systems, alongside the complexities of maintaining effective communications in space. HMIMO offers a novel approach utilizing compact and lightweight arrays of densely packed radiating elements with real-time reconfiguration capabilities, thus, capable of optimizing system performance under dynamic conditions such as varying orbital dynamics and Doppler shifts. By replacing conventional antenna systems with HMIMO, the complexity and cost of satellite manufacturing and launch can be substantially reduced, enabling more streamlined and cost-effective satellite designs. This advancement holds significant potential to democratize space communications, making them accessible to a broader range of stakeholders, including smaller nations and commercial enterprises. Moreover, the inherent capabilities of HMIMO in enhancing energy efficiency, scalability, and adaptability position this technology as a key enabler of new use cases and sustainable satellite operations.

Rare-earth magnets with parent cubic symmetry exhibit unique topological properties. However, the origin of these behaviors remains presently unclear. Here, we develop minimal models for Dirac semimetals (DSMs) with accidental band crossings and higher-order topology in cubic systems, incorporating candidate magnetic order to analyze bulk, surface, and hinge state characteristics. In certain cubic-symmetric DSMs, we identify an effective Z2 chiral symmetry which significantly impacts surface and hinge-localized states. Our results highlight distinct features in surface state dispersions, Fermi arcs, polarization dependence, and band splitting that correlate with photoemission data in rare-earth monopnictides. We also suggest candidate materials and experimental tests for further validation. These findings advance our understanding of surface states in rare-earth magnets with parent cubic symmetries and illuminate the role of DSM physics in these systems., Comment: 13 pages, 7 figures

We consider the adjoint action of the symplectic Lie group $\mathrm{Sp}(2n,\mathbb{C})$ on its Lie algebra $\mathfrak{sp}(2n,\mathbb{C})$. An element $X \in \mathfrak{sp}(2n,\mathbb{C})$ is called $\mathrm{Ad}_{\mathrm{Sp}(2n,\mathbb{C})}$-real if $ -X = \mathrm{Ad}(g)X$ for some $g \in \mathrm{Sp}(2n,\mathbb{C})$. Moreover, if $ -X = \mathrm{Ad}(h)X $ for some involution $h \in \mathrm{Sp}(2n,\mathbb{C})$, then $X \in \mathfrak{sp}(2n,\mathbb{C})$ is called strongly $\mathrm{Ad}_{\mathrm{Sp}(2n,\mathbb{C})}$-real. In this paper, we prove that for every element $X \in \mathfrak{sp}(2n,\mathbb{C})$, there exists a skew-involution $g \in \mathrm{Sp}(2n,\mathbb{C})$ such that $-X =\mathrm{Ad}(g)X$. Furthermore, we classify the strongly $\mathrm{Ad}_{\mathrm{Sp}(2n,\mathbb{C})}$-real elements in $\mathfrak{sp}(2n,\mathbb{C})$. We also classify skew-Hamiltonian matrices that are similar to their negatives via a symplectic involution., Comment: 9 pages, Final version, To appear in Linear Algebra and its Applications

The emergence of superconductivity in the octahedrally coordinated (1T) phase of TaS2 is preceded by the intriguing loss of long-range order in the charge density wave (CDW). Such decoherence, attainable by different methods, results in the formation of nm-sized coherent CDW domains bound by a two-dimensional network of domain walls (DW) - mosaic phase -, which has been proposed as the spatial origin of the superconductivity. Here, we report the atomic-scale characterization of the superconducting state of 1T-TaSSe, a model 1T compound exhibiting the CDW mosaic phase. We use high-resolution scanning tunneling spectroscopy and Andreev spectroscopy to probe the microscopic nature of the superconducting state in unambiguous connection with the electronic structure of the mosaic phase. Spatially resolved conductance maps at the Fermi level at the onset of superconductivity reveal that the density of states is mostly localized on the CDW domains compared to the domain walls, which suggests their dominant role in the formation of superconductivity. This scenario is confirmed within the superconducting dome at 340 mK, where superconductivity is fully developed, and the subtle spatial inhomogeneity of the superconducting gap remains unlinked to the domain wall network. Our results provide key new insights into the fundamental interplay between superconductivity and CDW in these relevant strongly correlated systems.

Agricultural landscapes are quite complex, especially in the Global South where fields are smaller, and agricultural practices are more varied. In this paper we report on our progress in digitizing the agricultural landscape (natural and man-made) in our study region of India. We use high resolution imagery and a UNet style segmentation model to generate the first of its kind national-scale multi-class panoptic segmentation output. Through this work we have been able to identify individual fields across 151.7M hectares, and delineating key features such as water resources and vegetation. We share how this output was validated by our team and externally by downstream users, including some sample use cases that can lead to targeted data driven decision making. We believe this dataset will contribute towards digitizing agriculture by generating the foundational baselayer., Comment: 34 pages, 7 tables, 15 figs

The increasing growth of social media provides us with an instant opportunity to be informed of the opinions of a large number of politically active individuals in real-time. We can get an overall idea of the ideologies of these individuals on governmental issues by analyzing the social media texts. Nowadays, different kinds of news websites and popular social media such as Facebook, YouTube, Instagram, etc. are the most popular means of communication for the mass population. So the political perception of the users toward different parties in the country is reflected in the data collected from these social sites. In this work, we have extracted three types of features, such as the stylometric feature, the word-embedding feature, and the TF-IDF feature. Traditional machine learning classifiers and deep learning models are employed to identify political ideology from the text. We have compared our methodology with the research work in different languages. Among them, the word embedding feature with LSTM outperforms all other models with 88.28% accuracy., Comment: 8 Pages !

The Gender Identification (GI) problem is concerned with determining the gender of the author from a given text. It has numerous applications in different fields like forensics, literature, security, marketing, trade, etc. Due to its importance, researchers have put extensive efforts into identifying gender from the text for different languages. Unfortunately, the same statement is not true for the Bangla language despite its being the 7th most spoken language in the world. In this work, we explore Gender Identification from Social media Bangla Text. Specially, we consider two approaches for feature extraction. The first one is Bag-Of-Words(BOW) approach and another one is based on computing features from sentiment and emotions. There is a common stereotype that female authors write in a more emotional way than male authors. One goal of this work is to validate this stereotype for the Bangla language., Comment: 7 pages

In this work, we explore the entanglement structure of two $N = Z$ nuclei, $^{20}$Ne and $^{22}$Na using single-orbital entanglement entropy within the No-Core Shell Model (NCSM) framework for two realistic interactions, INOY and N$^3$LO. We begin with the determination of the optimal frequencies based on the variation of ground-state (g.s.) binding energy with NCSM parameters, $N_{max}$ and $\hbar \Omega$, followed by an analysis of the total single-orbital entanglement entropy, $S_{tot}$, for the g.s. of $^{20}$Ne and $^{22}$Na. Our results show that $S_{tot}$ increases with $N_{max}$ and decreases with $\hbar \Omega$ after reaching a maximum. We use $S_{tot}$ to guide the selection of an additional set of optimal frequencies that can enhance electromagnetic transition strengths. We also calculate the low-energy spectra and $S_{tot}$ for four low-lying states of $^{20}$Ne and six low-lying states of $^{22}$Na. Finally, we calculate a few $E2$ and one $M1$ transition strengths, finding that N$^3$LO provides better results for $B(E2; 5^+_1 \to 3^+_1$) and INOY performs well for the $B(M1; 0_1^+ \to 1_1^+)$ transition in the $^{22}$Na nucleus while considering the first set of optimal frequencies. We also observe that the second set of optimal frequencies enhances electromagnetic transition strengths, particularly for the states with large and comparable $S_{tot}$. Also, for both nuclei, the $S_{tot}$ for INOY and N$^3$LO are close while considering the second set of optimal frequencies, suggesting that the calculated $S_{tot}$ are more dependent on $\hbar \Omega$ than the interactions employed for the same model space defined by the $N_{max}$ parameter., Comment: 15 pages, 5 figures

Multimodal device operations are essential to advancing the integration of 2D semiconductors in electronics, photonics, information and quantum technology. Precise control over carrier dynamics, particularly exciton generation and transport, is crucial for finetuning the functionality of optoelectronic devices based on 2D semiconductor heterostructure. However, the traditional exciton engineering methods in 2D semiconductors are mainly restricted to the artificially assembled vertical pn heterostructures with electrical or strain induced confinements. In this study, we utilized bilayer 2D lateral npn multijunction heterostructures with intrinsically spatially separated energy landscapes to achieve preferential exciton generation and manipulation without external confinement. In lateral npn FET geometry, we uncover unique and nontrivial properties, including dynamic tuning of channel photoresponsivity from positive to negative. The multimodal operation of these 2D FETs is achieved by carefully adjusting electrical bias and the impinging photon energy, enabling precise control over the trions generation and transport. Cryogenic photoluminescence measurement revealed the presence of trions in bilayer MoSe2 and intrinsic trap states in WSe2. Measurements in different FET device geometries show the multifunctionality of 2D lateral heterostructure phototransistors for efficient tuning and electrical manipulation of excitonic characteristics. Our findings pave the way for developing practical exciton-based transistors, sensors, multimodal optoelectronic and quantum technologies

Recent large language models (LLMs) have excelled across a wide range of tasks, but their use in high-stakes and compute-limited settings has intensified the demand for interpretability and efficiency. We address this need by proposing Induction-head ngram models (Induction-Gram), a method that builds an efficient, interpretable LM by bolstering modern ngram models with a hand-engineered "induction head". This induction head uses a custom neural similarity metric to efficiently search the model's input context for potential next-word completions. This process enables Induction-Gram to provide ngram-level grounding for each generated token. Moreover, experiments show that this simple method significantly improves next-word prediction over baseline interpretable models (up to 26%p) and can be used to speed up LLM inference for large models through speculative decoding. We further study Induction-Gram in a natural-language neuroscience setting, where the goal is to predict the next fMRI response in a sequence. It again provides a significant improvement over interpretable models (20% relative increase in the correlation of predicted fMRI responses), potentially enabling deeper scientific investigation of language selectivity in the brain. The code is available at https://github.com/ejkim47/induction-gram.

Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the relationships between users and items, leading to inaccurate user profiles and ineffective recommendations. In this work, we explore the utility of Knowledge Graphs (KGs), which contain extensive and reliable relationships between users and items, for recommendation. Our key insight is that the paths in a KG can capture complex relationships between users and items, eliciting the underlying reasons for user preferences and enriching user profiles. Leveraging this insight, we propose Knowledge Graph Enhanced Language Agents(KGLA), a framework that unifies language agents and KG for recommendation systems. In the simulated recommendation scenario, we position the user and item within the KG and integrate KG paths as natural language descriptions into the simulation. This allows language agents to interact with each other and discover sufficient rationale behind their interactions, making the simulation more accurate and aligned with real-world cases, thus improving recommendation performance. Our experimental results show that KGLA significantly improves recommendation performance (with a 33%-95% boost in NDCG@1 among three widely used benchmarks) compared to the previous best baseline method.

We present the discovery of an extended and faint tail observed in the isolated environment associated with galaxy NGC 3785. This study additionally provides observational evidence supporting the formation of ultra-diffuse galaxies at the end of the tail. We utilized the Gnuastro software to detect and analyse the low surface brightness structures in the optical $g$, and $r$ bands using data from the Dark Energy Camera Legacy Survey. We created a detection map to identify the faint tail, and measured its length using cubic spline fitting. Additionally, we found 84 star-forming clumps along the tail and performed photometric analysis on the tail portion after applying a significance threshold on the signal-to-noise ratio. We have measured the projected length of the tail, which is $\sim$390 kpc. We propose that this tail arises from the interaction of the NGC 3785 with a gas-rich galaxy, which ends up as ultra-diffuse galaxy at the end of the tail., Comment: 6 pages, 4 figures, 1 table, Accepted for publication in A&A Letters

Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-box and black-box models, aiming to achieve interpretability without sacrificing accuracy. The standard training procedure for CBMs is to predefine a candidate set of human-interpretable concepts, extract their values from the training data, and identify a sparse subset as inputs to a transparent prediction model. However, such approaches are often hampered by the tradeoff between enumerating a sufficiently large set of concepts to include those that are truly relevant versus controlling the cost of obtaining concept extractions. This work investigates a novel approach that sidesteps these challenges: BC-LLM iteratively searches over a potentially infinite set of concepts within a Bayesian framework, in which Large Language Models (LLMs) serve as both a concept extraction mechanism and prior. BC-LLM is broadly applicable and multi-modal. Despite imperfections in LLMs, we prove that BC-LLM can provide rigorous statistical inference and uncertainty quantification. In experiments, it outperforms comparator methods including black-box models, converges more rapidly towards relevant concepts and away from spuriously correlated ones, and is more robust to out-of-distribution samples.

It is shown that van Suijlekom's technique of imposing a set of conditions on operator system spectral triples ensures Gromov-Hausdorff convergence of sequences of sets of unital completely positive maps (equipped with the BW-topology which is metrizable). This implies that even when only a part of the spectrum of the Dirac operator is available together with a certain truncation of the $C^*$-algebra, information about the geometry can be extracted.

An $s$-sparse polynomial has at most $s$ monomials with nonzero coefficients. The Equivalence Testing problem for sparse polynomials (ETsparse) asks to decide if a given polynomial $f$ is equivalent to (i.e., in the orbit of) some $s$-sparse polynomial. In other words, given $f \in \mathbb{F}[\mathbf{x}]$ and $s \in \mathbb{N}$, ETsparse asks to check if there exist $A \in \mathrm{GL}(|\mathbf{x}|, \mathbb{F})$ and $\mathbf{b} \in \mathbb{F}^{|\mathbf{x}|}$ such that $f(A\mathbf{x} + \mathbf{b})$ is $s$-sparse. We show that ETsparse is NP-hard over any field $\mathbb{F}$, if $f$ is given in the sparse representation, i.e., as a list of nonzero coefficients and exponent vectors. This answers a question posed in [Gupta-Saha-Thankey, SODA'23] and [Baraskar-Dewan-Saha, STACS'24]. The result implies that the Minimum Circuit Size Problem (MCSP) is NP-hard for a dense subclass of depth-$3$ arithmetic circuits if the input is given in sparse representation. We also show that approximating the smallest $s_0$ such that a given $s$-sparse polynomial $f$ is in the orbit of some $s_0$-sparse polynomial to within a factor of $s^{\frac{1}{3} - \epsilon}$ is NP-hard for any $\epsilon > 0$; observe that $s$-factor approximation is trivial as the input is $s$-sparse. Finally, we show that for any constant $\sigma \geq 5$, checking if a polynomial (given in sparse representation) is in the orbit of some support-$\sigma$ polynomial is NP-hard. Support of a polynomial $f$ is the maximum number of variables present in any monomial of $f$. These results are obtained via direct reductions from the $3$-SAT problem., Comment: A preliminary version of the paper appeared in the proceedings of ICALP 2024. This version is slightly stronger than the ECCC one: a) We show it is NP-hard to test equivalence to support-5 polynomials (instead of support-6). b) We adapt the proof technique to show it is NP-hard to test equivalence to sparse polynomials under translations only; A suitable gap version of the result is also shown

Within the basis light-front quantization framework, we systematically investigate the unpolarized and longitudinally polarized double parton distributions (DPDs) of quarks inside the proton. We utilize the light-front wave functions of the proton derived in the valence sector from a Hamiltonian quantized on the light-front. The interaction terms of the Hamiltonian consist of a one-gluon exchange interaction at fixed coupling and a three-dimensional confinement potential. Our current analysis yields significant correlations of the quarks' longitudinal momenta with their transverse separation. We also demonstrate that our calculations do not support the commonly used $x-\vec{k}_\perp$ factorization of the DPDs in $x$ and $k_\perp$. Our results are qualitatively consistent with those of other phenomenological models., Comment: 14 pages, 16 figures

Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vectors from diverse domains, obtained from black-box pretrained encoders. By aligning input data with an LLM's embedding space through lightweight projectors, we observe that LLMs can effectively process and learn from these projected vectors, which we term Vector-ICL. In particular, we find that pretraining projectors with general language modeling objectives enables Vector-ICL, while task-specific finetuning further enhances performance. In our experiments across various tasks and modalities, including text reconstruction, numerical function regression, text classification, summarization, molecule captioning, time-series classification, graph classification, and fMRI decoding, Vector-ICL often surpasses both few-shot ICL and domain-specific model or tuning. We further conduct analyses and case studies, indicating the potential of LLMs to process vector representations beyond traditional token-based paradigms.

An element of a group is called reversible if it is conjugate to its own inverse. Reversible elements are closely related to strongly reversible elements, which can be expressed as a product of two involutions. In this paper, we classify the reversible and strongly reversible elements in the quaternionic special linear group $\mathrm{SL}(n,\mathbb{H})$ and quaternionic projective linear group $ \mathrm{PSL}(n,\mathbb{H})$. We prove that an element of $ \mathrm{SL}(n,\mathbb{H})$ (resp. $ \mathrm{PSL}(n,\mathbb{H})$) is reversible if and only if it is a product of two skew-involutions (resp. involutions)., Comment: 15 pages, To appear in Canadian Mathematical Bulletin. arXiv admin note: text overlap with arXiv:2307.07967

In this study, we developed and tested two different isospin symmetry-breaking (ISB) versions of the microscopic DJ16A interaction. Starting with the isospin symmetric DJ16A interaction, we introduced two different Coulomb interactions- Coulomb-CD and Coulomb-w/SRC- along with phenomenological charge symmetry breaking (CSB) and charge independence breaking (CIB) effects. Then, we employed these interactions to calculate $b$- and $c$-parameters of the isobaric multiplet mass equation for $|T_z| = 1/2$ and $|T_z| = 1$ nuclei across the $sd$-shell. Our results indicate that the DJ16A$^\dagger$ interaction provides the most accurate $b$-parameter predictions between the two DJ16A-based interactions. Additionally, we explored mirror energy differences (MEDs) in low-energy spectra around $A = 20$ and demonstrated that large MEDs are primarily associated with high occupancies of the $1s_{1/2}$ orbital. Furthermore, $E2$ transition strengths were calculated using both DJ16A-based ISB interactions agreed with the experimental data, with minimal ISB effects observed on these transitions. Overall, the DJ16A$^\dagger$ interaction serves as a complementary set to the newly developed USD-family interactions, USDC, and USDCm and can be further tested for other mirror nuclei across the $sd$-shell to study nuclear structure properties and ISB effects in nuclear $\beta$-decay., Comment: 17 pages, 14 figures

Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is unclear what features of the language stimulus drive the response in each brain area. We present generative explanation-mediated validation, a framework for generating concise explanations of language selectivity in the brain and then validating those explanations in follow-up experiments that use synthetic stimuli. This approach is successful at explaining selectivity both in individual voxels and cortical regions of interest (ROIs).We show that explanatory accuracy is closely related to the predictive power and stability of the underlying statistical models. These results demonstrate that LLMs can be used to bridge the widening gap between data-driven models and formal scientific theories.

Large language models (LLMs) often show unwarranted preference for certain choice options when responding to multiple-choice questions, posing significant reliability concerns in LLM-automated systems. To mitigate this selection bias problem, previous solutions utilized debiasing methods to adjust the model's input and/or output. Our work, in contrast, investigates the model's internal representation of the selection bias. Specifically, we introduce a novel debiasing approach, Bias Node Pruning (BNP), which eliminates the linear layer parameters that contribute to the bias. Furthermore, we present Auxiliary Option Injection (AOI), a simple yet effective input modification technique for debiasing, which is compatible even with black-box LLMs. To provide a more systematic evaluation of selection bias, we review existing metrics and introduce Choice Kullback-Leibler Divergence (CKLD), which addresses the insensitivity of the commonly used metrics to label imbalance. Experiments show that our methods are robust and adaptable across various datasets when applied to three LLMs.

Low latency models are critical for real-time speech enhancement applications, such as hearing aids and hearables. However, the sub-millisecond latency space for resource-constrained hearables remains underexplored. We demonstrate speech enhancement using a computationally efficient minimum-phase FIR filter, enabling sample-by-sample processing to achieve mean algorithmic latency of 0.32 ms to 1.25 ms. With a single microphone, we observe a mean SI-SDRi of 4.1 dB. The approach shows generalization with a DNSMOS increase of 0.2 on unseen audio recordings. We use a lightweight LSTM-based model of 644k parameters to generate FIR taps. We benchmark that our system can run on low-power DSP with 388 MIPS and mean end-to-end latency of 3.35 ms. We provide a comparison with baseline low-latency spectral masking techniques. We hope this work will enable a better understanding of latency and can be used to improve the comfort and usability of hearables.