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The cold ($\sim 10^{4}\,{\rm K}$) component of the circumgalactic medium (CGM) accounts for a significant fraction of all galactic baryons. However, using current galaxy-scale simulations to determine the origin and evolution of cold CGM gas poses a significant challenge, since it is computationally infeasible to directly simulate a galactic halo alongside the sub-pc scales that are crucial for understanding the interactions between cold CGM gas and the surrounding ''hot'' medium. In this work, we introduce a new approach: the Cold Gas Subgrid Model (CGSM), which models unresolved cold gas as a second fluid in addition to the standard ''normal'' gas fluid. The CGSM tracks the total mass density and bulk momentum of unresolved cold gas, deriving the properties of its unresolved cloudlets from the resolved gas phase. The interactions between the subgrid cold fluid and the resolved fluid are modeled by prescriptions from high-resolution simulations of ''cloud crushing'' and thermal instability. Through a series of idealized tests, we demonstrate the CGSM's ability to overcome the resolution limitations of traditional hydrodynamics simulations, successfully capturing the correct cold g

The generation of cold molecules is a core topic in the field of cold atoms and molecules, which has advanced relevant research like ultracold chemistry, quantum computation, and quantum metrology. With high atomic phase space density, optical dipole trap has been widely performed to prepare and trap cold molecules, and can also be further developed for multiple cold molecule formation and dynamics study. In this work, Rb2 molecules are photoassociated in the magneto-optical trap to obtain precise rovibrational spectroscopy, which provides accurate numerical references for multiple photoassociations. By achieving the harsh requirements of photoassociation in the optical dipole trap, the cold molecule photoassociation process is well explored, and different rovibrational cold molecules are formed in the optical dipole trap for the first time. This method can be universally extended to simultaneously photoassociate various molecules with different internal states or atomic species in just one optical dipole trap, and then advance generous cold molecule research such as cold molecule collision dynamics.

Large language models (LLMs) and cross-encoder rerankers have gained attention for improving recommender systems, particularly in cold-start scenarios where user interaction history is limited. However, practical deployment reveals significant performance gaps between LLM-based approaches and simple baselines. This paper presents a systematic diagnostic study of cross-encoder rerankers in cold-start movie recommendation using the Serendipity-2018 dataset. Through controlled experiments with 500 users across multiple random seeds, we identify three critical failure modes: (1) low retrieval coverage in candidate generation (recall@200 = 0.109 vs. 0.609 for baselines), (2) severe exposure bias with rerankers concentrating recommendations on 3 unique items versus 497 for random baseline, and (3) minimal score discrimination between relevant and irrelevant items (mean difference = 0.098, Cohen's d = 0.13). We demonstrate that popularity-based ranking substantially outperforms LLM reranking (HR@10: 0.268 vs. 0.008, p < 0.001), with the performance gap primarily attributable to retrieval stage limitations rather than reranker capacity. Based on these findings, we provide actionable rec

Activation steering methods enable inference-time control of large language model (LLM) behavior without retraining, but current approaches face a fundamental trade-off: sample-efficient methods suboptimally capture steering signals from labeled examples, while methods that better extract these signals require hundreds to thousands of examples. We introduce COLD-Steer, a training-free framework that steers LLM activations by approximating the representational changes that would result from gradient descent on in-context examples. Our key insight is that the effect of fine-tuning on a small set of examples can be efficiently approximated at inference time without actual parameter updates. We formalize this through two complementary approaches: (i) a unit kernel approximation method that updates the activations directly using gradients with respect to them, normalized across examples, and (ii) a finite-difference approximation requiring only two forward passes regardless of example count. Experiments across a variety of steering tasks and benchmarks demonstrate that COLD-Steer achieves upto 95% steering effectiveness while using 50 times fewer samples compared to the best baseline. C

The presence of cold ($T \lesssim 10^4$ K) gas in the circumgalactic medium (CGM) of galaxies has been confirmed both in observations and high-resolution simulations, but its origin still represents a puzzle. Possible mechanisms are cold accretion from the intergalactic medium (IGM), clumps embedded in outflows and transported from the disk, gas detaching from the hot CGM phase via thermal instabilities. In this work, we aim at characterizing the history of cold CGM gas, in order to identify the dominant origin channels at different evolutionary stages of the main galaxy. To this goal, we track gas particles in different snapshots of the SPH cosmological zoom-in simulation Eris2k. We perform a backward tracking of cold gas, starting from different redshifts, until we identify one of the followings origins for the particle: cold inflow, ejected from the disk, cooling down in-situ or stripped from a satellite. We also perform a forward tracking of gas in different components of the galaxy (such as the disk and outflows). We find a clear transition between two epochs. For $z>2$, most cold gas (up to 80%) in the CGM comes from cold accretion streams as the galaxy is accreting in the

Cold-start recommendation is one of the major challenges faced by recommender systems (RS). Herein, we focus on the user cold-start problem. Recently, methods utilizing side information or meta-learning have been used to model cold-start users. However, it is difficult to deploy these methods to industrial RS. There has not been much research that pays attention to the user cold-start problem in the matching stage. In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. A gate network is applied to incorporate the results from two experts. Furthermore, dynamic knowledge distillation acting as a teacher selector is introduced to assist experts in better learning user representation. With comprehensive mutual information, features highly relevant to user behavior are selected for the bias net which explicitly models user behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in comparison to models commonly applied in the matching stage and it outperforms other models on all user types. The proposed model has also been deployed on an industrial short video platform and ach

The correlation between close-in super Earths and distant cold Jupiters in planetary systems has important implications for their formation and evolution. In contrary to some earlier findings, a recent study conducted by Bonomo et al.\ suggests that the occurrence of cold Jupiter companions is not excessive in super Earth systems. Here we show that this discrepancy can be seen as a Simpson's paradox and is resolved once the metallicity dependence of the super Earth--cold Jupiter relation is taken into account. A common feature is noticed that almost all the cold Jupiter detections with inner super Earth companions are found around metal-rich stars. Focusing on the Sun-like hosts with super-solar metallicities, we show that the frequency of cold Jupiters conditioned on the presence of inner super Earths is $39_{-11}^{+12}\%$, whereas the frequency of cold Jupiters in the same metallicity range is no more than $20\%$. Therefore, the occurrences of close-in super Earths and distant cold Jupiters appear correlated around metal-rich hosts. The relation between the two types of planets remains unclear for stars with metal-poor hosts due to the limited sample size and the much lower occur

We present the results of a study of the amount and distribution of cold atomic gas, as well its correlation with recent star formation in a sample of extremely faint dwarf irregular galaxies. Our sample is drawn from the Faint Irregular Galaxy GMRT Survey (FIGGS) and its extension, FIGGS2. We use two different methods to identify cold atomic gas. In the first method, line-of-sight HI spectra were decomposed into multiple Gaussian components and narrow Gaussian components were identified as cold HI. In the second method, the brightness temperature (T_B) is used as a tracer of cold HI. We find that the amount of cold gas identified using the T_B method is significantly larger than the amount of gas identified using Gaussian decomposition. We also find that a large fraction of the cold gas identified using the T_B method is spatially coincident with regions of recent star formation, although the converse is not true. That is only a small fraction of the regions with recent star formation are also covered by cold gas. For regions where the star formation and the cold gas overlap, we study the relationship between the star formation rate density and the cold \HI column density. We find

We report relations between inner ($<1$ au) super Earths (planets with mass/radius between Earth and Neptune) and outer ($>1$ au) giant planets (mass $>0.3~M_{\rm J}$, or cold Jupiters) around Sun-like stars, based on data from both ground-based radial velocity (RV) observations and the Kepler mission. We find that cold Jupiters appear three times more often around hosts of super Earths than they do around field stars. Given the prevalence of the super Earth systems, their cold Jupiters can account for nearly all cold Jupiters. In other words, cold Jupiters are almost certainly ($\sim90\%$) companied by super Earths. A few corollaries follow: (1) around metal-rich ([Fe/H]$>0.1$) stars, the fraction of super Earths with cold Jupiters can rise to $60\%$ or higher; (2) the inner architecture can be strongly impacted by the outer giant and we report some observational evidence for this; (3) planetary systems like our own, with cold Jupiters but no super Earths, should be rare ($\sim1\%$). The strong correlation between super Earths and cold Jupiters establish that super Earths and cold Jupiters do not compete for solid material, rather, they share similar origins, with the

The growth of recommender systems (RecSys) is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. The content in these systems often changes rapidly and therefore they constantly face the ongoing cold-start problem, where new items lack interaction data and are hard to value. Existing solutions for the cold-start problem, such as content-based recommenders and hybrid methods, leverage item metadata to determine item similarities. The main challenge with these methods is their reliance on structured and informative metadata to capture detailed item similarities, which may not always be available. This paper introduces a novel approach for cold-start item recommendation that utilizes the language model (LM) to estimate item similarities, which are further integrated as a Bayesian prior with classic recommender systems. This approach is generic and able to boost the performance of various recommenders. Specifically, our experiments integrate it with both sequential and collaborative filtering-based recommender and evaluate it on two real-world datasets, demonstrating the enhanced performance of the proposed approach.

We report an experimental demonstration of optical 2DCS in cold atoms. The experiment integrates a collinear 2DCS setup with a magneto-optical trap (MOT), in which cold rubidium (Rb) atoms are prepared at a temperature of about 200 $μ$K and a number density of $10^{10}$ cm$^{-3}$. With a sequence of femtosecond laser pulses, we first obtained one-dimensional second- and fourth-order nonlinear signals and then acquired both one-quantum and zero-quantum 2D spectra of cold Rb atoms. The capability of performing optical 2DCS in cold atoms is an important step toward optical 2DCS study of many-body physics in cold atoms and ultimately in atom arrays and trapped ions. Optical 2DCS in cold atoms/molecules can also be a new avenue to probe chemical reaction dynamics in cold molecules.

We summarize the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observa

Table of contents (abridged): COLD FRONTS Origin and evolution of merger cold fronts Cold fronts in cluster cool cores . . . Simulations of gas sloshing. Origin of density discontinuity. . . . Effect of sloshing on cluster mass estimates and cooling flows. Zoology of cold fronts COLD FRONTS AS EXPERIMENTAL TOOL Velocities of gas flows Thermal conduction and diffusion across cold fronts Stability of cold fronts . . . Rayleigh-Taylor instability. Kelvin-Helmholtz instability. Possible future measurements using cold fronts . . . Plasma depletion layer and magnetic field. Effective viscosity of ICM. SHOCK FRONTS AS EXPERIMENTAL TOOL Cluster merger shocks Mach number determination Front width Mach cone and reverse shock? Test of electron-ion equilibrium . . . Comparison with other astrophysical plasmas Shocks and cluster cosmic ray population . . . Shock acceleration. Compression of fossil electrons. . . . Yet another method to measure intracluster magnetic field.

This article presents a review of the current state of the art in the research field of cold and ultracold molecules. It serves as an introduction to the Special Issue of the New Journal of Physics on Cold and Ultracold Molecules and describes new prospects for fundamental research and technological development. Cold and ultracold molecules may revolutionize physical chemistry and few body physics, provide techniques for probing new states of quantum matter, allow for precision measurements of both fundamental and applied interest, and enable quantum simulations of condensed-matter phenomena. Ultracold molecules offer promising applications such as new platforms for quantum computing, precise control of molecular dynamics, nanolithography, and Bose-enhanced chemistry. The discussion is based on recent experimental and theoretical work and concludes with a summary of anticipated future directions and open questions in this rapidly expanding research field.

We present the first results of Vz-GAL, a high-redshift CO(J=1-0) large survey with the Karl G. Jansky Very Large Array, targeting 92 Herschel-selected, infrared-luminous, dusty star-forming galaxies (DSFGs) at redshifts 1 to 6. These sources are selected based on having redshifts and mid/high-J CO transitions from the NOrthern Extended Millimeter Array z-GAL survey. We successfully detect CO(J=1-0) emission in 90/92 galaxies at the expected positions and redshifts, including 9 tentative detections at $2σ- 3σ$ significance, and CO(J=2-1) emission in 10 of these galaxies. The CO(J=1-0) luminosities suggest apparent gas masses in the range $μ{M}_{\rm H_2}$ = $(2-20) \times {10}^{11}~(α_{CO}/{4.0})~\mathrm{M_{\odot}}$, which implies gas depletion times of $(50-600)$ Myr. These timescales show similar spread as local ULIRGs, suggesting a self-regulatory mechanism that maintains a consistent SFR per unit gas mass in starbursts across redshifts. To quantify the contribution of "excitation correction" factors to gas mass estimates, we calculate median CO line brightness temperature ratios of $r_{21}=0.88\pm0.25$, $r_{31}=0.61\pm0.22$, $r_{41}=0.49\pm0.15$, $r_{51}=0.47\pm0.13$, and $r_{61

Water ice is thought to be trapped in large permanently shadowed regions (PSRs) in the Moon's polar regions, due to their extremely low temperatures. Here, we show that many unmapped cold traps exist on small spatial scales, substantially augmenting the areas where ice may accumulate. Using theoretical models and data from the Lunar Reconnaissance Orbiter, we estimate the contribution of shadows on scales from 1 km down to 1 cm, the smallest distance over which we find cold-trapping to be effective for water ice. Approximately 10-20\% of the permanent cold trap area for water is found to be contained in these "micro cold traps," which are the most numerous cold traps on the Moon. Consideration of all spatial scales therefore substantially increases the number of cold traps over previous estimates, for a total area of ~40,000 km^2. A majority of cold traps for water ice is found at latitudes >80° because permanent shadows equatorward of 80° are typically too warm to support ice accumulation. Our results show that water trapped at the lunar poles may be more accessible as a resource for future missions than previously thought.

We report the results of the 2dF-VST ATLAS Cold Spot galaxy redshift survey (2CSz) based on imaging from VST ATLAS and spectroscopy from 2dF AAOmega over the core of the CMB Cold Spot. We sparsely surveyed the inner 5$^{\circ}$ radius of the Cold Spot to a limit of $i_{AB} \le 19.2$, sampling $\sim7000$ galaxies at $z<0.4$. We have found voids at $z=$ 0.14, 0.26 and 0.30 but they are interspersed with small over-densities and the scale of these voids is insufficient to explain the Cold Spot through the $Λ$CDM ISW effect. Combining with previous data out to $z\sim1$, we conclude that the CMB Cold Spot could not have been imprinted by a void confined to the inner core of the Cold Spot. Additionally we find that our 'control' field GAMA G23 shows a similarity in its galaxy redshift distribution to the Cold Spot. Since the GAMA G23 line-of-sight shows no evidence of a CMB temperature decrement we conclude that the Cold Spot may have a primordial origin rather than being due to line-of-sight effects.

Routing questions in Community Question Answer services (CQAs) such as Stack Exchange sites is a well-studied problem. Yet, cold-start -- a phenomena observed when a new question is posted is not well addressed by existing approaches. Additionally, cold questions posted by new askers present significant challenges to state-of-the-art approaches. We propose ColdRoute to address these challenges. ColdRoute is able to handle the task of routing cold questions posted by new or existing askers to matching experts. Specifically, we use Factorization Machines on the one-hot encoding of critical features such as question tags and compare our approach to well-studied techniques such as CQARank and semantic matching (LDA, BoW, and Doc2Vec). Using data from eight stack exchange sites, we are able to improve upon the routing metrics (Precision$@1$, Accuracy, MRR) over the state-of-the-art models such as semantic matching by $159.5\%$,$31.84\%$, and $40.36\%$ for cold questions posted by existing askers, and $123.1\%$, $27.03\%$, and $34.81\%$ for cold questions posted by new askers respectively.

This paper presents a novel teachable conversation interaction system that is capable of learning users preferences from cold start by gradually adapting to personal preferences. In particular, the TAI system is able to automatically identify and label user preference in live interactions, manage dialogue flows for interactive teaching sessions, and reuse learned preference for preference elicitation. We develop the TAI system by leveraging BERT encoder models to encode both dialogue and relevant context information, and build action prediction (AP), argument filling (AF) and named entity recognition (NER) models to understand the teaching session. We adopt a seeker-provider interaction loop mechanism to generate diverse dialogues from cold-start. TAI is capable of learning user preference, which achieves 0.9122 turn level accuracy on out-of-sample dataset, and has been successfully adopted in production.