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This article targets at unlocking the potentials of a class of prominent generative artificial intelligence (GAI) method, namely diffusion model (DM), for mobile communications. First, a DM-driven communication architecture is proposed, which introduces two key paradigms, i.e., conditional DM and DM-driven deep reinforcement learning (DRL), for wireless data generation and communication management, respectively. Then, we discuss the key advantages of DM-driven communication paradigms. To elaborate further, we explore DM-driven channel generation mechanisms for channel estimation, extrapolation, and feedback in multiple-input multiple-output (MIMO) systems. We showcase the numerical performance of conditional DM using the accurate DeepMIMO channel datasets, revealing its superiority in generating high-fidelity channels and mitigating unforeseen distribution shifts in sophisticated scenes. Furthermore, several DM-driven communication management designs are conceived, which is promising to deal with imperfect channels and task-oriented communications. To inspire future research developments, we highlight the potential applications and open research challenges of DM-driven communications. Code is available at https://github.com/xiaoxiaxusummer/GAI_COMM/, Comment: This paper has been accepted by IEEE Communications Magzine. Code is available at https://github.com/xiaoxiaxusummer/GAI_COMM/

We develop tight bounds for the covert capacity of slotted asynchronous binary-input Discrete Memoryless Channels (DMCs) and Additive White Gaussian Noise (AWGN) channels, in which a codeword is transmitted in one of several slots with known boundaries, where the number of slots is sub-exponential in the codeword length. Our upper and lower bounds are within a multiplicative factor of $\sqrt{2}$ independent of the channel. This result partially fills a characterization gap between the covert capacity without asynchronism and the covert capacity with exponential asynchronism. Our key technical contributions consist of i) a tight upper bound for the relative entropy characterizing the effect of asynchronism on the covertness constraint in our achievability proof; ii) a careful converse analysis to characterize the maximum allowable weight or power of codewords to meet the covertness constraint. Our results suggest that, unlike the case without asynchronism, the choice of covertness metric does not change the covert capacity in the presence of asynchronism., Comment: 24 pages, 2 figures, submitted to TIT

We report the crystal structures and superconductivity (SC) of LaPt$_{x}$Si$_{2-x}$ ($0.5 \leq x \leq 1.0$) that are solid solutions of LaSi$_{2}$ and LaPtSi with centrosymmetric tetragonal ($I4_{1}/amd$, $D_{4h}^{19}$, \#141) and non-centrosymmetric tetragonal ($I4_{1}md$, $C_{4v}^{11}$, \#109) structures, respectively. It was found that at $0.86 \leq x \leq 1.00$, the non-centrosymmetric tetragonal symmetry is preserved, while partial disorder appears in alternating Pt and Si of the hyper-honeycomb network. The superconducting transition temperature $T_{\rm c}$ was drastically reduced from 3.9 K to 1.5 K as $x$ varies from 1.0 to 0.86. Additionally, a hexagonal phase with an AlB$_{2}$-type structure ($P6/mmm$, $D_{6h}^{1}$, \#191) has been discovered at $0.50 \leq x \leq 0.71$ with a honeycomb network of statistically distributed Pt and Si atoms. The hexagonal phase exhibited SC at $T_{\rm c} = 0.38$ K. This system provides an opportunity to investigate the relationship between topological electronic states, SC, and disorders

Together with EuGa4 and EuAl4, EuAl2Ga2 belongs to the BaAl4 structure type with space group symmetry I4/mmm. EuAl2Ga2 develops an incommensurate charge density wave (CDW) at temperatures below TCDW = 51 K. On the basis of temperature dependent single-crystal X-ray diffraction (SXRD) data, the incommensurately modulated CDW crystal structure of EuAl2Ga2 is determined to possess orthorhombic superspace symmetry Immm(00g)s00. This symmetry is different from the orthorhombic Fmmm based symmetry of the CDW state of EuAl4. Nevertheless, both symmetries Immm(00g)s00 and Fmmm(00g)s00 lead to the same conclusion, that the CDW is supported by the layers of Al1 type atoms, while the Eu and Al2 or Ga atoms are not directly involved in CDW formation. The different symmetries of the CDW states of EuAl4 and EuAl2Ga2, as well as the observation of negative thermal expansion in the CDW state of EuAl2Ga2 might be explained by the effects of Ga substitution in the latter compound., Comment: 24 pages, 6 figures

The continuous aperture array (CAPA) can provide higher degree-of-freedom and spatial resolution than the spatially discrete array (SDPA), where optimizing multi-user current distributions in CAPA systems is crucial but challenging. The challenge arises from solving non-convex functional optimization problems without closed-form objective functions and constraints. In this paper, we propose a deep learning framework called L-CAPA to learn current distribution policies. In the framework, we find finite-dimensional representations of channel functions and current distributions, allowing them to be inputted into and outputted from a deep neural network (DNN) for learning the policy. To address the issue that the integrals in the loss function without closed-form expressions hinder training the DNN in an unsupervised manner, we propose to design another two DNNs for learning the integrals. The DNNs are designed as graph neural networks to incorporate with the permutation properties of the mappings to be learned, thereby improving learning performance. Simulation results show that L-CAPA can achieve the performance upper-bound of optimizing precoding in the SDPA system as the number of antennas approaches infinity, and it is with low inference complexity., Comment: 6 pages, 6 figures

A default assumption in the design of reinforcement-learning algorithms is that a decision-making agent always explores to learn optimal behavior. In sufficiently complex environments that approach the vastness and scale of the real world, however, attaining optimal performance may in fact be an entirely intractable endeavor and an agent may seldom find itself in a position to complete the requisite exploration for identifying an optimal policy. Recent work has leveraged tools from information theory to design agents that deliberately forgo optimal solutions in favor of sufficiently-satisfying or satisficing solutions, obtained through lossy compression. Notably, such agents may employ fundamentally different exploratory decisions to learn satisficing behaviors more efficiently than optimal ones that are more data intensive. While supported by a rigorous corroborating theory, the underlying algorithm relies on model-based planning, drastically limiting the compatibility of these ideas with function approximation and high-dimensional observations. In this work, we remedy this issue by extending an agent that directly represents uncertainty over the optimal value function allowing it to both bypass the need for model-based planning and to learn satisficing policies. We provide simple yet illustrative experiments that demonstrate how our algorithm enables deep reinforcement-learning agents to achieve satisficing behaviors. In keeping with previous work on this setting for multi-armed bandits, we additionally find that our algorithm is capable of synthesizing optimal behaviors, when feasible, more efficiently than its non-information-theoretic counterpart., Comment: Accepted to the Finding the Frame Workshop at RLC 2024

A sequential decision-making agent balances between exploring to gain new knowledge about an environment and exploiting current knowledge to maximize immediate reward. For environments studied in the traditional literature, optimal decisions gravitate over time toward exploitation as the agent accumulates sufficient knowledge and the benefits of further exploration vanish. What if, however, the environment offers an unlimited amount of useful knowledge and there is large benefit to further exploration no matter how much the agent has learned? We offer a simple, quintessential example of such a complex environment. In this environment, rewards are unbounded and an agent can always increase the rate at which rewards accumulate by exploring to learn more. Consequently, an optimal agent forever maintains a propensity to explore., Comment: Accepted to the Finding the Frame Workshop at RLC 2024

A novel accelerated mobile edge generation (MEG) framework is proposed for generating high-resolution images on mobile devices. Exploiting a large-scale latent diffusion model (LDM) distributed across edge server (ES) and user equipment (UE), cost-efficient artificial intelligence generated content (AIGC) is achieved by transmitting low-dimensional features between ES and UE. To reduce overheads of both distributed computations and transmissions, a dynamic diffusion and feature merging scheme is conceived. By jointly optimizing the denoising steps and feature merging ratio, the image generation quality is maximized subject to latency and energy consumption constraints. To address this problem and tailor LDM sub-models, a low-complexity MEG acceleration protocol is developed. Particularly, a backbone meta-architecture is trained via offline distillation. Then, dynamic diffusion and feature merging are determined in online channel environment, which can be viewed as a constrained Markov Decision Process (MDP). A constrained variational policy optimization (CVPO) based MEG algorithm is further proposed for constraint-guaranteed learning, namely MEG-CVPO. Numerical results verify that: 1) The proposed framework can generate 1024$\times$1024 high-quality images over noisy channels while reducing over $40\%$ latency compared to conventional generation schemes. 2) The developed MEG-CVPO effectively mitigates constraint violations, thus flexibly controlling the trade-off between image distortion and generation costs., Comment: 30 pages, 7 figures

The impact of software vulnerabilities on everyday software systems is significant. Despite deep learning models being proposed for vulnerability detection, their reliability is questionable. Prior evaluations show high recall/F1 scores of up to 99%, but these models underperform in practical scenarios, particularly when assessed on entire codebases rather than just the fixing commit. This paper introduces Real-Vul, a comprehensive dataset representing real-world scenarios for evaluating vulnerability detection models. Evaluating DeepWukong, LineVul, ReVeal, and IVDetect shows a significant drop in performance, with precision decreasing by up to 95 percentage points and F1 scores by up to 91 points. Furthermore, Model performance fluctuates based on vulnerability characteristics, with better F1 scores for information leaks or code injection than for path resolution or predictable return values. The results highlight a significant performance gap that needs addressing before deploying deep learning-based vulnerability detection in practical settings. Overfitting is identified as a key issue, and an augmentation technique is proposed, potentially improving performance by up to 30%. Contributions include a dataset creation approach for better model evaluation, Real-Vul dataset, and empirical evidence of deep learning models struggling in real-world settings.

The NOEMA formIng Cluster survEy (NICE) is a large program targeting 69 massive galaxy group candidates at $z>2$ in six deep fields. We report spectroscopic confirmation of eight groups at $1.65\leq z\leq3.61$ in COSMOS. Homogeneously selected as significant overdensities of red IRAC sources with red Herschel colors, four groups are confirmed by CO and [CI] with NOEMA 3mm observations, three are confirmed with ALMA, and one is confirmed by H$\alpha$ from Subaru/FMOS. We constructed the integrated FIR SEDs for the eight groups, obtaining total IR SFR $=260-1300~{\rm M_\odot}$~yr$^{-1}$. We adopted six methods to estimate the dark matter masses, including stellar mass to halo mass relations, overdensity with galaxy bias, and NFW profile fitting to radial stellar mass density. We found the radial stellar mass density are consistent with a NFW profile, supporting that they are collapsed structures hosted by a single dark matter halo. The best halo mass estimates are $\log(M_{\rm h}/{\rm M_\odot})=12.8-13.7$ with uncertainty of 0.3 dex. From halo mass estimates, we derive baryonic accretion rate ${\rm BAR}=(1-8)\times10^{3}\,{\rm M_{\odot}/yr}$ for this sample. We find a quasi-linear correlation between the integrated SFR/BAR and the theoretical halo mass limit for cold streams, $M_{\rm stream}/M_{\rm h}$, with ${\rm SFR/BAR}=10^{-0.46\pm0.22}\left({M_{\rm stream}/M_{\rm h}}\right)^{0.71\pm0.16}$ with a scatter of $0.40\,{\rm dex}$. Further, we compare halo masses and stellar masses with simulations, and find all structures are consistent with being progenitors of $M_{\rm h}(z=0)>10^{14}\,{\rm M_{\odot}}$ galaxy clusters, and the most massive central galaxies have stellar masses consistent with brightest cluster galaxies (BCGs) progenitors in the TNG300 simulation. The results strongly suggest these structures are forming massive galaxy clusters via baryonic and dark matter accretion., Comment: 44 pages (27pp appendix), 32 figures, 18 tables, accepted for publication in A&A

Bug localization refers to the identification of source code files which is in a programming language and also responsible for the unexpected behavior of software using the bug report, which is a natural language. As bug localization is labor-intensive, bug localization models are employed to assist software developers. Due to the domain difference between source code files and bug reports, modern bug-localization systems, based on deep learning models, rely heavily on embedding techniques that project bug reports and source code files into a shared vector space. The creation of an embedding involves several design choices, but the impact of these choices on the quality of embedding and the performance of bug localization models remains unexplained in current research. To address this gap, our study evaluated 14 distinct embedding models to gain insights into the effects of various design choices. Subsequently, we developed bug localization models utilizing these embedding models to assess the influence of these choices on the performance of the localization models. Our findings indicate that the pre-training strategies significantly affect the quality of the embedding. Moreover, we discovered that the familiarity of the embedding models with the data has a notable impact on the bug localization model's performance. Notably, when the training and testing data are collected from different projects, the performance of the bug localization models exhibits substantial fluctuations.

The Schr\"odinger--Newton model is a semi-classical theory in which, in addition to mutual attraction, massive quantum particles interact with their own gravitational fields. While there are many studies on the phenomenology of single particles, correlation dynamics in multipartite systems is largely unexplored. Here, we show that the Schr\"odinger--Newton interactions preserve the product form of the initial state of a many-body system, yet on average agreeing with classical mechanics of continuous mass distributions. This leads to a simple test of the model, based on verifying bipartite gravitational evolution towards non-product states. We show using standard quantum mechanics that, with currently accessible single-particle parameters, two masses released from harmonic traps get correlated well before any observable entanglement is accumulated. Therefore, the Schr\"odinger--Newton model can be tested with setups aimed at observation of gravitational entanglement with significantly relaxed requirements on coherence time. We also present a mixed-state extension of the model that avoids superluminal signaling., Comment: to appear in "Classical and Quantum Gravity"

In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic decoder owned by the base station. This framework supports heterogeneous semantic encoders since it does not require client-side model aggregation. Furthermore, to tackle the semantic imbalance issue arising from heterogeneous datasets across distributed clients, we employ contrastive learning to train a semantic centroid generator (SCG). This generator obtains representative global semantic centroids that exhibit intra-semantic compactness and inter-semantic separability. Consequently, it provides superior supervision for learning discriminative local semantic features. Additionally, we conduct theoretical analysis to quantify the convergence performance of FedCL. Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios., Comment: IEEE Communications Letters

The spin-down law of pulsars is generally perturbed by two types of timing irregularities: glitches and timing noise. Glitches are sudden changes in the rotational frequency of pulsars, while timing noise is a discernible stochastic wandering in the phase, period, or spin-down rate of a pulsar. We present the timing results of a sample of glitching pulsars observed using the Ooty Radio Telescope (ORT) and the upgraded Giant Metrewave Radio Telescope (uGMRT). Our findings include timing noise analysis for 17 pulsars, with seven being reported for the first time. We detected five glitches in four pulsars and a glitch-like event in PSR J1825-0935. The frequency evolution of glitch in pulsars, J0742-2822 and J1740-3015, is presented for the first time. Additionally, we report timing noise results for three glitching pulsars. The timing noise was analyzed separately in the pre-glitch region and post-glitch regions. We observed an increase in the red noise parameters in the post-glitch regions, where exponential recovery was considered in the noise analysis. Timing noise can introduce ambiguities in the correct evaluation of glitch observations. Hence, it is important to consider timing noise in glitch analysis. We propose an innovative glitch verification approach designed to discern between a glitch and strong timing noise. The novel glitch analysis technique is also demonstrated using the observed data., Comment: 27 pages, 12 figures. Accepted for publication in PASA

Dusty star-forming galaxies (DSFGs) contribute significantly to the stellar buildup at cosmic noon. Major mergers and gas accretion are often invoked to explain DSFGs' prodigious star-formation rates (SFRs) and large stellar masses. We conducted a spatially-resolved morphological analysis of the rest-frame UV/NIR emission in three DSFGs at z~2.5. Initially discovered as CO emitters by NOEMA observations of a bright Herschel source, we observed them with the JWST/NIRCam as part of the PEARLS program. The NIRCam data reveal the galaxies' stellar populations and dust distributions on scales of 250 pc. Spatial variations in stellar mass, SFR, and dust extinction are determined in resolved maps obtained through pixel-based SED fitting. The CO emitters are massive, dusty starburst galaxies with SFRs=340-2500 Msun/yr, positioning them among the most active SFGs at 2

Immunotherapy is an effective precision medicine treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability., Comment: Published in Neuro-Oncology Advances 2024

Suppose we have a finite thick generalised quadrangle whose automorphism group $G$ acts primitively on both the set of points and the set of lines. Then $G$ must be almost simple. In this paper, we show that $\operatorname{soc}(G)$ cannot be isomorphic to $\operatorname{Sz}(2^{2m+1})$ or $\operatorname{Ree}(3^{2m+1})$ where $m$ is a positive integer., Comment: 11 pages

A double self-sustainable reconfigurable intelligent surfaces (RISs) assisted multi-user multiple input multiple output (MIMO) system is investigated. Two RISs are equipped with energy harvesting circuit to achieve self-sustainable transmission. The aim is to minimize the transmission power at the base station (BS), while guaranteeing the quality of service (QoS) requirements of the users and meeting the power consumption requirements of the RISs. A block coordinate descent (BCD) algorithm based on the penalty-based method and successive convex approximation (SCA) is employed to alternatively optimize the active beamforming at the BS and the phase shifts, as well as amplitude coefficients of two RISs. Simulation results show that the required power consumption at the BS for the proposed double self-sustainable RISs system is significantly reduced compared to conventional RIS systems.

Strongly correlated electron systems with a kagome lattice can host abundant exotic quantum states such as superconductivity and spin/charge density waves (CDW) due to the complicated interactions between different degrees of freedoms in the framework of a unique two-dimensional geometrically frustrated lattice structure. Recently, successive orders of A-type antiferromagnetism (AFM), $2\times2\times2$ CDW and canted double-cone AFM have been manifested upon cooling in magnetic kagome FeGe. However, the mechanism of the CDW order and its interaction with magnetism are presently enigmatic at best. Here we investigate the evolution of CDW order with temperature across the spin canting transition in FeGe by single-crystal x-ray diffraction. Refinements of its modulated structure are presented using the superspace approach. Interestingly, the superlattice reflections originating from CDW-induced long-range structural modulation become extremely weak after the system enters the canted AFM while a $2\times2$ CDW in the $ab$ plane persists as a long-range order demonstrated by strong electronic modulation in the d$I$/d$V$ map of scanning tunneling spectroscopy. We discovered a novel CDW order without long-range structural modulation in FeGe probably because of the competition between CDW and canted AFM in determining the underlying crystal structure. In addition, occupational modulations of Ge1 atoms located in the kagome plane and displacive modulations of all the atoms were extracted from the refinements, confirming the existence of Ge atom dimerization along the $c$ axis as the major distortion and indicating a dynamic transformation between different CDW domains., Comment: 22 pages, 6 figures. Comments on the manuscript are welcome

The integral quality monitor (IQM) system compares the signal measured with a large volume chamber mounted to the linear accelerator's head to the signal calculated using the patient DICOM RT plan for patient-specific quality assurance (PSQA). A method was developed to reconstruct the dose in patients using the signal measured by IQM chamber and patient planning CT images. A software tool named IQMDose3D was implemented to automate this procedure and integrated into the IQM-based PSQA workflow. IQMDose3D enables the physicists to evaluate PSQA by focusing on the clinical perspective by comparing the delivered plan to the approved clinical plan in terms of the clinical goals, dose-volume histogram (DVH) in addition to the three-dimensional (3D) gamma map and gamma pass rate., Comment: Accepted by ICCR 2024 conference

A software system named AutoMRISimQA was developed to monitor the daily performance of a wide-bore 3T scanner(MRI) which was designed and dedicated to radiotherapy simulation. The system can monitor the performance of the MRI simulator not only by using image quality indices such as signal-to-noise ratio (SNR), uniformity, ghosting and contrast but also performing a quick check of geometry accuracy as well as the external lasers quantitatively. It was implemented into the daily clinically workflow in 2013 and has been used for more than 10 years. It was also seamlessly integrated with QAtrack, allowing continuous monitoring of the consistency of the MRI simulator's performance., Comment: Accepted by conference ICCR 2024

In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations. Thus, an explanation for AI models should be reproducible and tractable to be trustworthy. We present XAIport, a framework of XAI microservices encapsulated into Open APIs to deliver early explanations as observation for learning model quality assurance. XAIport enables configurable XAI operations along with machine learning development. We quantify the operational costs of incorporating XAI with three cloud computer vision services on Microsoft Azure Cognitive Services, Google Cloud Vertex AI, and Amazon Rekognition. Our findings show comparable operational costs between XAI and traditional machine learning, with XAIport significantly improving both cloud AI model performance and explanation stability., Comment: Accepted at the ICSE'24 conference, NIER track

Driven by the ever-increasing requirements of ultra-high spectral efficiency, ultra-low latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next generation multiple access schemes to facilitate provisioning of these stringent demands. This inspires the embrace of non-orthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats, due to the open nature of the air interface, the broadcast characteristic of radio propagation as well as intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security aware design, for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview on the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends., Comment: Invited paper by Proceedings of the IEEE

We report a pressure-induced enhancement of the superconducting transition temperature (Tc) in a non-centrosymmetric (NCS) compound, LaPtGe. With pressure, till 3 GPa, we observed a modest enhancement of the Tc with a rate of 0.071 K/GPa. However, above this pressure, the rate showed a ~2.5 times increase, 0.183 K/GPa. We observed a Tc of 3.94 K at 6 GPa, the highest pressure value used in our transport study. Synchrotron high-pressure x-ray powder diffraction (HP-XRPD) measurements do not reveal any structural phase transition in this system in this pressure range. However, it showed an apparent change of slope in the pressure dependence of lattice parameters above and below 3 GPa. Pressure dependence of the unit-cell volume also followed a distinct trend below and above 3 GPa, with the Birch-Murnaghan equation of state fit providing a bulk modulus (B0) value of ~144 and ~162 GPa, respectively, for two pressure regions. Further, the magnetotransport measurement under pressure up to 2.45 GPa reveals the enhancement of the upper critical field (Hc2(0)) from 0.7 T (0 GPa) to 0.92 T (2.45 GPa). In addition, the upward curvature in Hc2(T) becomes stronger with increasing pressure, suggesting a change of the underlying Fermi surface topology with pressure. The signature for the inducible Hc2(T) with pressure, the distinct enhancement of Tc around 3 GPa, and the noticeable change in lattice parameters around 3 GPa suggests the possibility of multi-gap superconductivity in LaPtGe similar to identical structure NCS compound, LaPtSi. The enhancement of Tc by pressure can be correlated with the possible underlying lattice modulation by compression and the change in the density of states at the Fermi level. Also, the bare change in pressure-dependent activation energy U0 up to 2.45 GPa calculated using the Arrhenius relation clearly shows that the shift in Tc does not arise from grain boundaries., Comment: 16 Pages, 08 figures

Charge density wave (CDW) systems are proposed to exhibit application potential for electronic and optoelectronic devices. Therefore, identifying new materials that exhibit a CDW state at room temperature is crucial for the development of CDW-based devices. Here, we present a non-layered tetragonal polymorph of Gd2Os3Si5, which exhibits a CDW state at room temperature. Gd2Os3Si5 crystallizes in the U2Mn3Si5-type tetragonal crystal structure with the space group P4/mnc. Single-crystal x-ray diffraction (SXRD) analysis shows that Gd2Os3Si5 possesses an incommensurately modulated structure with modulation wave vector q = (0.53, 0, 0), while the modulation reduces the symmetry to orthorhombic Cccm({\sigma}00)0s0. This differs in contrast to isostructural Sm2Ru3Ge5, where the modulated phase has been reported to possess the superspace symmetry Pm({\alpha} 0 {\gamma})0. However, reinvestigation of Sm2Ru3Ge5 suggests that its modulated crystal structure can alternatively be described by Cccm({\sigma}00)0s0, with modulations similar to Gd2Os3Si5. The magnetic susceptibility, \c{hi}(T), exhibits a maximum at low temperatures that indicates an antiferromagnetic transition at TN = 5.5 K. The \c{hi}(T) furthermore shows an anomaly at around 345 K, suggesting a CDW transition at TCDW = 345 K, that corroborates the result from high-temperature SXRD measurements. Interestingly, R2T3X5 compounds are known to crystallize either in the tetragonal Sc2Fe3Si5 type structure or in the orthorhombic U2Co3Si5 structure type. Not all of the compounds in the R2T3X5 series undergo CDW phase transitions. We find that R2T3X5 compounds will exhibit a CDW transition, if the condition : 0.526 < c/sqrt(ab) < 0.543 is satisfied. We suggest the wave vector-dependent electron-phonon coupling to be the dominant mechanism of CDW formation in the tetragonal polymorph of Gd2Os3Si5.

Spaceborne radar remote sensing of the earth system is essential to study natural and man-made changes in the ecosystem, water and energy cycles, weather and air quality, sea level, and surface dynamics. A major challenge with current approaches is the lack of broad spectrum tunability due to narrow band microwave electronics, that limit systems to specific science variable retrievals. This results in a significant limitation in studying dynamic coupled earth system processes such as surface and subsurface hydrology, where broad spectrum radar remote sensing is needed to sense multiple variables simultaneously. Rydberg atomic sensors are highly sensitive broad-spectrum quantum detectors that can be dynamically tuned to cover micro-to-millimeter waves with no requirement for band-specific electronics. Rydberg atomic sensors can use existing transmitted signals such as navigation and communication satellites to enable remote sensing. We demonstrate remote sensing of soil moisture, an important earth system variable, via ground-based radar reflectometry with Rydberg atomic systems. To do this, we sensitize the atoms to XM satellite radio signals and use signal correlations to demonstrate use of these satellite signals for remote sensing of soil moisture. Our approach provides a step towards satellite-based broad-spectrum Rydberg atomic remote sensing.