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For linear ill-posed problems with nontrivial null spaces, Tikhonov regularization and truncated singular value decomposition (TSVD) typically yield solutions that are close to the minimum norm solution. Such a bias is not always desirable, and we have therefore in a series of papers developed a weighting procedure which produces solutions with a different and controlled bias. This methodology can also conveniently be invoked when sparsity regularization is employed. The purpose of the present work is to study the potential use of this weighting applied to injective operators. The image under a compact operator of the singular vectors/functions associated with very small singular values will be almost zero. Consequently, one may regard these singular vectors/functions to constitute a basis for a fictitious null space that allows us to mimic the previous weighting procedure. It turns out that this regularization by weighting can improve the solution of injective inverse problems compared with more traditional approaches. We present some analysis of this methodology and exemplify it numerically, using sparsity regularization, for three PDE-driven inverse problems: the inverse heat conduction problem, the Cauchy problem for Laplace's equation, and the (linearized) Electrical Impedance Tomography problem with experimental data., Comment: 32 pages

One of the greatest challenges in galaxy evolution over the last decade has been constraining the prevalence of heavily dust-obscured galaxies in the early Universe. At $z>3$, these galaxies are increasingly rare, and difficult to identify as they are interspersed among the more numerous dust-obscured galaxy population at $z=1-3$, making efforts to secure confident spectroscopic redshifts expensive, and sometimes unsuccessful. In this work, we present the Extended Mapping Obscuration to Reionization with ALMA (Ex-MORA) Survey -- a 2mm blank-field survey in the COSMOS-Web field, and the largest ever ALMA blank-field survey to-date covering 577 arcmin$^2$. Ex-MORA is an expansion of the MORA survey designed to identify primarily $z>3$ dusty, star-forming galaxies while simultaneously filtering out the more numerous $z<3$ population by leveraging the very negative $K$-correction at observed-frame 2mm. We identify 37 significant ($>$5$\sigma$) sources, 33 of which are robust thermal dust emitters. We measure a median redshift of $\langle z \rangle = 3.6^{+0.1}_{-0.2}$, with two-thirds of the sample at $z>3$, and just under half at $z>4$, demonstrating the overall success of the 2mm-selection technique. The integrated $z>3$ volume density of Ex-MORA sources is $\sim1-3\times10^{-5}$ Mpc$^{-3}$, consistent with other surveys of infrared luminous galaxies at similar epochs. We also find that techniques using rest-frame optical emission (or lack thereof) to identify $z>3$ heavily dust-obscured galaxies miss at least half of Ex-MORA galaxies. This supports the idea that the dusty galaxy population is heterogeneous, and that synergies across observatories spanning multiple energy regimes are critical to understanding their formation and evolution at $z>3$., Comment: 13 pages, 5 figures, submitted to ApJ; fully reduced mosaic will be shared upon publication

Submillimetre galaxies (SMGs) are some of the most extreme star-forming systems in the Universe, whose place in the framework of galaxy evolution is as yet uncertain. It has been hypothesised that SMGs are progenitors of local early-type galaxies, requiring that SMGs generally reside in galaxy cluster progenitors at high redshift. We test this hypothesis and explore SMG environments using a narrowband VLT/HAWK-I+GRAAL study of H$\alpha$ and [OIII] emitters around an unbiased sample of three ALMA-identified and spectroscopically-confirmed SMGs at $z \sim 2.3$ and $z \sim 3.3$, where these SMGs were selected solely on spectroscopic redshift. Comparing with blank-field observations at similar epochs, we find that one of the three SMGs lies in an overdensity of emission-line sources on the $\sim4$ Mpc scale of the HAWK-I field of view, with overdensity parameter $\delta_{g} = 2.6^{+1.4}_{-1.2}$. A second SMG is significantly overdense only on $\lesssim 1.6$ Mpc scales and the final SMG is consistent with residing in a blank field environment. The total masses of the two overdensities are estimated to be $\log(M_{h}/{\rm M}_{\odot}) =$12.1--14.4, leading to present-day masses of $\log(M_{h,z=0}/{\rm M}_{\odot}) =$12.9--15.9. These results imply that SMGs occupy a range of environments, from overdense protoclusters or protogroups to the blank field, suggesting that while some SMGs are strong candidates for the progenitors of massive elliptical galaxies in clusters, this may not be their only possible evolutionary pathway., Comment: 21 pages, 12 figures. Accepted for publication in MNRAS

This paper introduces a Virtual Immunohistochemistry Multiplex staining (VIMs) model designed to generate multiple immunohistochemistry (IHC) stains from a single hematoxylin and eosin (H&E) stained tissue section. IHC stains are crucial in pathology practice for resolving complex diagnostic questions and guiding patient treatment decisions. While commercial laboratories offer a wide array of up to 400 different antibody-based IHC stains, small biopsies often lack sufficient tissue for multiple stains while preserving material for subsequent molecular testing. This highlights the need for virtual IHC staining. Notably, VIMs is the first model to address this need, leveraging a large vision-language single-step diffusion model for virtual IHC multiplexing through text prompts for each IHC marker. VIMs is trained on uniplex paired H&E and IHC images, employing an adversarial training module. Testing of VIMs includes both paired and unpaired image sets. To enhance computational efficiency, VIMs utilizes a pre-trained large latent diffusion model fine-tuned with small, trainable weights through the Low-Rank Adapter (LoRA) approach. Experiments on nuclear and cytoplasmic IHC markers demonstrate that VIMs outperforms the base diffusion model and achieves performance comparable to Pix2Pix, a standard generative model for paired image translation. Multiple evaluation methods, including assessments by two pathologists, are used to determine the performance of VIMs. Additionally, experiments with different prompts highlight the impact of text conditioning. This paper represents the first attempt to accelerate histopathology research by demonstrating the generation of multiple IHC stains from a single H&E input using a single model trained solely on uniplex data., Comment: Accepted to MICCAI Workshop 2024

Starting from a single layer of NbS$_2$ grown on graphene by molecular beam epitaxy, the single unit cell thick 2D materials Nb$_{5/3}$S$_3$-2D and Nb$_2$S$_3$-2D are created using two different pathways. Either annealing under sulfur-deficient conditions at progressively higher temperatures or deposition of increasing amounts of Nb at elevated temperature result in phase-pure Nb$_{5/3}$S$_3$-2D followed by Nb$_2$S$_3$-2D. The materials are characterized by scanning tunneling microscopy, scanning tunneling spectroscopy and X-ray photoemission spectroscopy. The experimental assessment combined with systematic density functional theory calculations reveals their structure. The 2D materials are covalently bound without any van der Waals gap. Their stacking sequence and structure are at variance with expectations based on corresponding bulk materials highlighting the importance of surface and interface effects in structure formation.

We present JWST NIRCam imaging targeting 13 $z\sim3$ infrared-luminous ($L_{\rm IR}\sim5\times10^{12}L_{\odot}$) galaxies from the ALESS survey with uniquely deep, high-resolution (0.08$''$$-$0.16$''$) ALMA 870$\mu$m imaging. The 2.0$-$4.4$\mu$m (observed frame) NIRCam imaging reveals the rest-frame near-infrared stellar emission in these submillimeter-selected galaxies (SMGs) at the same (sub-)kpc resolution as the 870$\mu$m dust continuum. The newly revealed stellar morphologies show striking similarities with the dust continuum morphologies at 870$\mu$m, with the centers and position angles agreeing for most sources, clearly illustrating that the spatial offsets reported previously between the 870$\mu$m and HST morphologies were due to strong differential dust obscuration. The F444W sizes are 78$\pm$21% larger than those measured at 870$\mu$m, in contrast to recent results from hydrodynamical simulations that predict larger 870$\mu$m sizes. We report evidence for significant dust obscuration in F444W for the highest-redshift sources, emphasizing the importance of longer-wavelength MIRI imaging. The majority of the sources show evidence that they are undergoing mergers/interactions, including tidal tails/plumes -- some of which are also detected at 870$\mu$m. We find a clear correlation between NIRCam colors and 870$\mu$m surface brightness on $\sim$1 kpc scales, indicating that the galaxies are primarily red due to dust -- not stellar age -- and we show that the dust structure on $\sim$kpc-scales is broadly similar to that in nearby galaxies. Finally, we find no strong stellar bars in the rest-frame near-infrared, suggesting the extended bar-like features seen at 870$\mu$m are highly obscured and/or gas-dominated structures that are likely early precursors to significant bulge growth., Comment: 26 pages, 10 figures. Submitted to ApJ. Comments welcome

We here propose a novel hierarchical transformer model that adeptly integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the advanced representational potential of Vision Transformers (ViTs). Addressing the lack of inductive biases and dependence on extensive training datasets in ViTs, our model employs a CNN backbone to generate hierarchical visual representations. These representations are then adapted for transformer input through an innovative patch tokenization. We also introduce a 'scale attention' mechanism that captures cross-scale dependencies, complementing patch attention to enhance spatial understanding and preserve global perception. Our approach significantly outperforms baseline models on small and medium-sized medical datasets, demonstrating its efficiency and generalizability. The components are designed as plug-and-play for different CNN architectures and can be adapted for multiple applications. The code is available at https://github.com/xiaoyatang/DuoFormer.git., Comment: 11 pages, 5 figures

We present Atacama Large Millimetre/submillimetre Array (ALMA) observations of the [O$_{\rm III}$] 88 $\mu$m emission of a sample of thirteen galaxies at $z$ = 6 to 7.6 selected as [C$_{\rm II}$]-emitting companion sources of quasars. To disentangle the origins of the luminous Oxygen line in the $z$ > 6 Universe, we looked at emission-line galaxies that are selected through an excellent star-formation tracer [C$_{\rm II}$] with star-formation rates between 9 and 162 M$_{\odot}$/yr. Direct observations reveal [O$_{\rm III}$] emission in just a single galaxy (L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ = 2.3), and a stacked image shows no [O$_{\rm III}$] detection, providing deep upper limits on the L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ ratios in the $z > 6$ Universe (L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ < 1.2 at 3${\sigma}$). While the fidelity of this sample is high, no obvious optical/near-infrared counterpart is seen in the JWST imaging available for four galaxies. Additionally accounting for low-redshift CO emitters, line stacking shows that our sample-wide result remains robust: The enhanced L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ reported in the first billion years of the Universe is likely due to the selection towards bright, blue Lyman-break galaxies with high surface star-formation rates or young stellar populations. The deep upper limit on the rest-frame 90 $\mu$m continuum emission (< 141 $\mu$Jy at 3${\sigma}$), implies a low average dust temperature (T$_{\rm dust}$ < 30K) and high dust mass (M$_{\rm dust}$ ~ 10$^8$ M$_{\odot}$). As more normal galaxies are explored in the early Universe, synergy between JWST and ALMA is fundamental to further investigate the ISM properties of the a broad range of samples of high-$z$ galaxies., Comment: 20 pages; 13 figures; accepted for publication in MNRAS

We present results from Atacama Large Millimeter/submillimeter Array (ALMA) spectral line-scan observations at 3-mm and 2-mm bands of three near-infrared-dark (NIR-dark) galaxies behind two massive lensing clusters MACS J0417.5-1154 and RXC J0032.1+1808. Each of these three sources is a faint (de-lensed $S_{\text{1.2 mm}}$ $<$ 1 mJy) triply lensed system originally discovered in the ALMA Lensing Cluster Survey. We have successfully detected CO and [C I] emission lines and confirmed that their spectroscopic redshifts are $z=3.652$, 2.391, and 2.985. By utilizing a rich multi-wavelength data set, we find that the NIR-dark galaxies are located on the star formation main sequence in the intrinsic stellar mass range of log ($M_*$/$M_\odot$) = 9.8 - 10.4, which is about one order of magnitude lower than that of typical submillimeter galaxies (SMGs). These NIR-dark galaxies show a variety in gas depletion times and spatial extent of dust emission. One of the three is a normal star-forming galaxy with gas depletion time consistent with a scaling relation, and its infrared surface brightness is an order of magnitude smaller than that of typical SMGs. Since this galaxy has an elongated axis ratio of $\sim 0.17$, we argue that normal star-forming galaxies in an edge-on configuration can be heavily dust-obscured. This implies that existing deep WFC3/F160W surveys may miss a fraction of typical star-forming main-sequence galaxies due to their edge-on orientation., Comment: 23 pages, 10 figures, Submitted to ApJ

Latent Diffusion Models (LDMs) can generate high-fidelity images from noise, offering a promising approach for augmenting histopathology images for training cancer grading models. While previous works successfully generated high-fidelity histopathology images using LDMs, the generation of image tiles to improve prostate cancer grading has not yet been explored. Additionally, LDMs face challenges in accurately generating admixtures of multiple cancer grades in a tile when conditioned by a tile mask. In this study, we train specific LDMs to generate synthetic tiles that contain multiple Gleason Grades (GGs) by leveraging pixel-wise annotations in input tiles. We introduce a novel framework named Self-Distillation from Separated Conditions (DISC) that generates GG patterns guided by GG masks. Finally, we deploy a training framework for pixel-level and slide-level prostate cancer grading, where synthetic tiles are effectively utilized to improve the cancer grading performance of existing models. As a result, this work surpasses previous works in two domains: 1) our LDMs enhanced with DISC produce more accurate tiles in terms of GG patterns, and 2) our training scheme, incorporating synthetic data, significantly improves the generalization of the baseline model for prostate cancer grading, particularly in challenging cases of rare GG5, demonstrating the potential of generative models to enhance cancer grading when data is limited., Comment: Abstract accepted for ISBI 2024. Extended version to be presented at SynData4CV @ CVPR 2024. See more at https://minhmanho.github.io/disc/

The Frozen Section (FS) technique is a rapid and efficient method, taking only 15-30 minutes to prepare slides for pathologists' evaluation during surgery, enabling immediate decisions on further surgical interventions. However, FS process often introduces artifacts and distortions like folds and ice-crystal effects. In contrast, these artifacts and distortions are absent in the higher-quality formalin-fixed paraffin-embedded (FFPE) slides, which require 2-3 days to prepare. While Generative Adversarial Network (GAN)-based methods have been used to translate FS to FFPE images (F2F), they may leave morphological inaccuracies with remaining FS artifacts or introduce new artifacts, reducing the quality of these translations for clinical assessments. In this study, we benchmark recent generative models, focusing on GANs and Latent Diffusion Models (LDMs), to overcome these limitations. We introduce a novel approach that combines LDMs with Histopathology Pre-Trained Embeddings to enhance restoration of FS images. Our framework leverages LDMs conditioned by both text and pre-trained embeddings to learn meaningful features of FS and FFPE histopathology images. Through diffusion and denoising techniques, our approach not only preserves essential diagnostic attributes like color staining and tissue morphology but also proposes an embedding translation mechanism to better predict the targeted FFPE representation of input FS images. As a result, this work achieves a significant improvement in classification performance, with the Area Under the Curve rising from 81.99% to 94.64%, accompanied by an advantageous CaseFD. This work establishes a new benchmark for FS to FFPE image translation quality, promising enhanced reliability and accuracy in histopathology FS image analysis. Our work is available at https://minhmanho.github.io/f2f_ldm/., Comment: Preprint. Our work is available at https://minhmanho.github.io/f2f_ldm/

Controlling gas turbines (GTs) efficiently is vital as GTs are used to balance power in onshore/offshore hybrid power systems with variable renewable energy and energy storage. However, predictive control of GTs is non-trivial when formulated as a dynamic optimisation problem due to the semi-continuous operating regions of GTs, which must be included to ensure complete combustion and high fuel efficiency. This paper studies two approaches for handling the semi-continuous operating regions of GTs in hybrid power systems through predictive control, dynamic optimisation, and complementarity constraints. The proposed solutions are qualitatively investigated and compared with baseline controllers in a case study involving GTs, offshore wind, and batteries. While one of the baseline controllers considers fuel efficiency, it employs a continuous formulation, which results in lower efficiency than the two proposed approaches as it does not account for the semi-continuous operating regions of each GT., Comment: Accepted for presentation at IFAC ADCHEM 2024

Hematoxylin and Eosin (H&E) staining is the most commonly used for disease diagnosis and tumor recurrence tracking. Hematoxylin excels at highlighting nuclei, whereas eosin stains the cytoplasm. However, H&E stain lacks details for differentiating different types of cells relevant to identifying the grade of the disease or response to specific treatment variations. Pathologists require special immunohistochemical (IHC) stains that highlight different cell types. These stains help in accurately identifying different regions of disease growth and their interactions with the cell's microenvironment. The advent of deep learning models has made Image-to-Image (I2I) translation a key research area, reducing the need for expensive physical staining processes. Pix2Pix and CycleGAN are still the most commonly used methods for virtual staining applications. However, both suffer from hallucinations or staining irregularities when H&E stain has less discriminate information about the underlying cells IHC needs to highlight (e.g.,CD3 lymphocytes). Diffusion models are currently the state-of-the-art models for image generation and conditional generation tasks. However, they require extensive and diverse datasets (millions of samples) to converge, which is less feasible for virtual staining applications.Inspired by the success of multitask deep learning models for limited dataset size, we propose StainDiffuser, a novel multitask dual diffusion architecture for virtual staining that converges under a limited training budget. StainDiffuser trains two diffusion processes simultaneously: (a) generation of cell-specific IHC stain from H&E and (b) H&E-based cell segmentation using coarse segmentation only during training. Our results show that StainDiffuser produces high-quality results for easier (CK8/18,epithelial marker) and difficult stains(CD3, Lymphocytes).

As systems for quantum computing keep growing in size and number of qubits, challenges in scaling the control capabilities are becoming increasingly relevant. Efficient schemes to simultaneously mediate coherent interactions between multiple quantum systems and to reduce decoherence errors can minimize the control overhead in next-generation quantum processors. Here, we present a superconducting qubit architecture based on tunable parametric interactions to perform two-qubit gates, reset, leakage recovery and to read out the qubits. In this architecture, parametrically driven multi-element couplers selectively couple qubits to resonators and neighbouring qubits, according to the frequency of the drive. We consider a system with two qubits and one readout resonator interacting via a single coupling circuit and experimentally demonstrate a controlled-Z gate with a fidelity of $98.30\pm 0.23 \%$, a reset operation that unconditionally prepares the qubit ground state with a fidelity of $99.80\pm 0.02 \%$ and a leakage recovery operation with a $98.5\pm 0.3 \%$ success probability. Furthermore, we implement a parametric readout with a single-shot assignment fidelity of $88.0\pm 0.4 \%$. These operations are all realized using a single tunable coupler, demonstrating the experimental feasibility of the proposed architecture and its potential for reducing the system complexity in scalable quantum processors., Comment: 19 pages, 14 figures

We present JWST/NIRCam observations of a strongly-lensed, multiply-imaged galaxy at $z=6.072$, with magnification factors >~20 across the galaxy. We perform a spatially-resolved analysis of the physical properties at scales of ~200 pc, inferred from SED modelling of 5 NIRCam imaging bands on a pixel-by-pixel basis. We find young stars surrounded by extended older stellar populations. By comparing H$\alpha$+[NII] and [OIII]+H$\beta$ maps inferred from the image analysis with our additional NIRSpec IFU data, we find that the spatial distribution and strength of the line maps are in agreement with the IFU measurements. We explore different parametric SFH forms with Bagpipes on the spatially-integrated photometry, finding that a double power-law star formation history retrieves the closest value to the spatially-resolved stellar mass estimate, and other SFH forms suffer from the dominant outshining emission from the youngest stars, thus underestimating the stellar mass - up to ~0.5 dex-. On the other hand, the DPL cannot match the IFU measured emission lines. Additionally, the ionizing photon production efficiency may be overestimated in a spatially-integrated approach by ~0.15 dex, when compared to a spatially-resolved analysis. The agreement with the IFU measurements points towards the pixel-by-pixel approach as a way to mitigate the general degeneracy between the flux excess from emission lines and underlying continuum, especially when lacking photometric medium-band coverage and/or IFU observations. This study stresses the importance of studying galaxies as the complex systems that they are, resolving their stellar populations when possible, or using more flexible SFH parameterisations. This can aid our understanding of the early stages of galaxy evolution by addressing the challenge of inferring robust stellar masses and ionizing photon production efficiencies of high redshift galaxies., Comment: Submitted to A&A; 13 pages. See also the companion papers on arXiv today: Fujimoto+2024 and Valentino+2024

We present a UV to millimeter spectral energy distribution (SED) analysis of 16 hyperluminous, dust-obscured quasars at z $\sim$ 3, selected by the \textit{Wide-field Infrared Survey Explorer}. We aim to investigate the physical properties of these quasars, with a focus on their molecular gas content. We decompose the SEDs into three components: stellar, cold dust, and active galactic nucleus (AGN). By doing so, we are able to derive and analyze the relevant properties of each component. We determine the molecular gas mass from CO line emission based on Atacama Large Millimeter/submillimeter Array (ALMA) observations. By including ALMA observations in the multiwavelength SED analysis, we derive the molecular gas fractions, gas depletion timescales, and star formation efficiencies (SFEs). Their sample median and 16th-84th quartile ranges are $f_{\rm gas}\,\sim\,0.33_{-0.17}^{+0.33}$, $t_{\rm depl}\,\sim$ 39$_{-28}^{+85}$ Myr, SFE $\sim\,$ 297$_{-195}^{+659}$ $\rm K\,\rm km\,\rm s^{-1}\,\rm pc^{-2}$. Compared to main-sequence galaxies, they have a lower molecular gas content and higher SFEs, similar to quasars in the literature. This suggests that the gas in these quasars is rapidly depleted, likely as the result of intense starburst activity and AGN feedback. The observed correlations between these properties and the AGN luminosities further support this scenario. Additionally, we infer the black hole to stellar mass ratio and black hole mass growth rate, which indicate a significant central black hole mass assembly over short timescales. Our results are consistent with the scenario that our sample represents a short transition phase toward unobscured quasars., Comment: 20 pages, 9 figures, in proof

We survey two decades of work on the (sequential) topological complexity of configuration spaces of graphs (ordered and unordered), aiming to give an account that is unifying, elementary, and self-contained. We discuss the traditional approach through cohomology, with its limitations, and the more modern approach through asphericity and the fundamental group, explaining how they are in fact variations on the same core ideas. We close with a list of open problems in the field., Comment: 20 pages, 4 figures. Accepted version of invited contribution to book project "Topology and AI" (working title, ed. Michael Farber and Jes\'us Gonz\'alez)

We study probabilistic variants of the Lusternik--Schnirelmann category and topological complexity, which bound the classical invariants from below. We present a number of computations illustrating both wide agreement and wide disagreement with the classical notions. In the aspherical case, where our invariants are group invariants, we establish a counterpart of the Eilenberg--Ganea theorem in the torsion-free case, as well as a contrasting universal upper bound in the finite case., Comment: 20 pages. To appear in SIAM Journal on Applied Algebra and Geometry. May differ slightly from published version

We aim to investigate if we can improve predictions of stress caused by OCD symptoms using pre-trained models, and present our statistical analysis plan in this paper. With the methods presented in this plan, we aim to avoid bias from data knowledge and thereby strengthen our hypotheses and findings. The Wrist Angel study, which this statistical analysis plan concerns, contains data from nine participants, between 8 and 17 years old, diagnosed with obsessive-compulsive disorder (OCD). The data was obtained by an Empatica E4 wristband, which the participants wore during waking hours for 8 weeks. The purpose of the study is to assess the feasibility of predicting the in-the-wild OCD events captured during this period. In our analysis, we aim to investigate if we can improve predictions of stress caused by OCD symptoms, and to do this we have created a pre-trained model, trained on four open-source data for stress prediction. We intend to apply this pre-trained model to the Wrist Angel data by fine-tuning, thereby utilizing transfer learning. The pre-trained model is a convolutional neural network that uses blood volume pulse, heart rate, electrodermal activity, and skin temperature as time series windows to predict OCD events. Furthermore, using accelerometer data, another model filters physical activity to further improve performance, given that physical activity is physiologically similar to stress. By evaluating various ways of applying our model (fine-tuned, non-fine-tuned, pre-trained, non-pre-trained, and with or without activity classification), we contextualize the problem such that it can be assessed if transfer learning is a viable strategy in this domain., Comment: Statistical Analysis Plan, 11 pages

To control and measure the state of a quantum system it must necessarily be coupled to external degrees of freedom. This inevitably leads to spontaneous emission via the Purcell effect, photon-induced dephasing from measurement back-action, and errors caused by unwanted interactions with nearby quantum systems. To tackle this fundamental challenge, we make use of the design flexibility of superconducting quantum circuits to form a multi-mode element -- an artificial molecule -- with symmetry-protected modes. The proposed circuit consists of three superconducting islands coupled to a central island via Josephson junctions. It exhibits two essential non-linear modes, one of which is flux-insensitive and used as the protected qubit mode. The second mode is flux-tunable and serves via a cross-Kerr type coupling as a mediator to control the dispersive coupling of the qubit mode to the readout resonator. We demonstrate the Purcell protection of the qubit mode by measuring relaxation times that are independent of the mediated dispersive coupling. We show that the coherence of the qubit is not limited by photon-induced dephasing when detuning the mediator mode from the readout resonator and thereby reducing the dispersive coupling. The resulting highly protected qubit with tunable interactions may serve as a basic building block of a scalable quantum processor architecture, in which qubit decoherence is strongly suppressed.

The extent of the effect of active galactic nuclei (AGN) on their host galaxies at high-redshift is not apparent and studying this effect in the distant universe is a difficult process as the mechanisms of tracing AGN activity can often be inaccurately associated with intense star formation and vice versa. Our aim is to better understand the processes governing the interstellar medium (ISM) of the quasar BRI0952-0952 at z = 4.432, specifically with regard to the individual heating processes at work and to place the quasar in an evolutionary context. We analyzed ALMA archival bands 3, 4, and 6 data and combined the results with high-resolution band-7 ALMA observations of the quasar. We detect [C I] (2-1), [C II], CO(5-4), CO(7-6), CO(12-11), OH, H2O, and we report a tentative detection of OH+. We update the lensing model from Kade et al. (2023) and use the radiative transfer code MOLPOP-CEP to constrain the properties of the CO, [CI], and [CII] emission and suggest different possible scenarios for heating mechanisms within the quasar. Modeling from the CO SLED suggests that there are extreme heating mechanisms operating within the quasar in the form of star formation or AGN activity; however, with the current data it remains unclear which of the two is the preferred mechanism. The updated lensing model suggests a velocity gradient across the [C II] line, suggestive of on-going kinematical processes within the quasar. We find that the H2O emission in BRI0952 is likely correlated with star-forming regions of the ISM. We use the molecular gas mass from [C I] to calculate a depletion time for the quasar. We conclude that BRI 0952-0952 is a quasar with a significant AGN contribution while also showing signs of extreme starburst activity, indicating that the quasar could be in a transitional phase between a starburst-dominated stage and an AGN-dominated stage.

We present a new method for modelling the kinematics of galaxies from interferometric observations by performing the optimization of the kinematic model parameters directly in visibility-space instead of the conventional approach of fitting velocity fields produced with the CLEAN algorithm in real-space. We demonstrate our method on ALMA observations of $^{12}$CO (2$-$1), (3$-$2) or (4$-$3) emission lines from an initial sample of 30 massive 850$\mu$m-selected dusty star-forming galaxies with far-infrared luminosities $\gtrsim$$\,10^{12}\,$L$_{\odot}$ in the redshift range $z \sim\,$1.2$-$4.7. Using the results from our modelling analysis for the 12 sources with the highest signal-to-noise emission lines and disk-like kinematics, we conclude the following: (i) Our sample prefers a CO-to-$H_2$ conversion factor, of $\alpha_{\rm CO} = 0.92 \pm 0.36$; (ii) These far-infrared luminous galaxies follow a similar Tully$-$Fisher relation between the circularized velocity, $V_{\rm circ}$, and baryonic mass, $M_{\rm b}$, as more typical star-forming samples at high redshift, but extend this relation to much higher masses $-$ showing that these are some of the most massive disk-like galaxies in the Universe; (iii) Finally, we demonstrate support for an evolutionary link between massive high-redshift dusty star-forming galaxies and the formation of local early-type galaxies using the both the distributions of the baryonic and kinematic masses of these two populations on the $M_{\rm b}\,-\,\sigma$ plane and their relative space densities.

Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring case-level labels from pathology reports. However, patch-level classification is preferable in applications where only a limited number of cases are available or when local prediction accuracy is critical. On the other hand, acquiring extensive datasets with localized labels for training is not feasible. In this paper, we propose a semi-supervised patch-level histopathological image classification model, named CLASS-M, that does not require extensively labeled datasets. CLASS-M is formed by two main parts: a contrastive learning module that uses separated Hematoxylin and Eosin images generated through an adaptive stain separation process, and a module with pseudo-labels using MixUp. We compare our model with other state-of-the-art models on two clear cell renal cell carcinoma datasets. We demonstrate that our CLASS-M model has the best performance on both datasets. Our code is available at github.com/BzhangURU/Paper_CLASS-M/tree/main