Zefanya Bramantasaputra, Dea Daniella Wangsawijaya, Bharathram Ganapathisubramani
The present study experimentally investigates the recovery of smooth-wall turbulent boundary layers (TBLs) following non-equilibrium pressure gradients (PGs). The imposed pressure gradient history (PGH) comprises favourable-adverse pressure gradient (FAPG) sequences of varying strength, followed by recovery to zero-pressure-gradient (ZPG) conditions. Hot-wire anemometry measurements were obtained at multiple downstream stations in the recovery region, with friction Reynolds numbers $Re_τ$ ranging from 2000 to 6000 depending on downstream development. Comparative analysis at matched $Re_τ$ and Clauser pressure gradient parameter $β$ enables clear assessment of history effects on TBL behaviour. Results show that increasing PGH strength enhances the wake in mean velocity profiles and amplifies turbulence intensities across the boundary layer, including the inner peak, logarithmic region, and outer peak (a signature of APG). Downstream, the mean flow gradually recovers toward a ZPG-like state, but turbulence in the outer region retains a lasting impact of PGH. Spectral analysis indicates that PGH primarily affects outer-layer scales, introducing a distinct PG peak and modifying the VLSM peak - with energy amplification dependent on PGH strength and spatial characteristics governed by history effects. Downstream recovery involves merging of large-scale wavelengths and the reorganisation of turbulence structures toward a ZPG-like state - although the `recovered' VLSM streamwise length becomes shortened due to the mixing of lengthscales with the PG peak. These results demonstrate that even under matched local parameters, TBLs retain a clear imprint of their upstream history, consistent with the findings of Preskett et al. (2025); moreover, this study provides new insights regarding the central role of scale interactions in the recovery mechanism of TBL subjected to complex PGH.
Considering inflationary magnetogenesis induced by time-dependent kinetic and axial couplings of a massless Abelian vector boson field breaking the conformal invariance we show in this article that, surprisingly, the spectral shape of the large-scale primordial magnetic field power spectrum is insensitive to the post-inflationary history, namely the barotropic parameter ($w$) and the gauge coupling functions of the post-inflationary era.
Large Language Models (LLMs) have recently shown a promise and emergence of Theory of Mind (ToM) ability and even outperform humans in certain ToM tasks. To evaluate and extend the boundaries of the ToM reasoning ability of LLMs, we propose a novel concept, taxonomy, and framework, the ToM reasoning with Zero, Finite, and Infinite Belief History and develop a multi-round text-based game, called $\textit{Pick the Right Stuff}$, as a benchmark. We have evaluated six LLMs with this game and found their performance on Zero Belief History is consistently better than on Finite Belief History. In addition, we have found two of the models with small parameter sizes outperform all the evaluated models with large parameter sizes. We expect this work to pave the way for future ToM benchmark development and also for the promotion and development of more complex AI agents or systems which are required to be equipped with more complex ToM reasoning ability.
In this paper, we study how open-source large language models (LLMs) can be effectively deployed for improving query rewriting in conversational search, especially for ambiguous queries. We introduce CHIQ, a two-step method that leverages the capabilities of LLMs to resolve ambiguities in the conversation history before query rewriting. This approach contrasts with prior studies that predominantly use closed-source LLMs to directly generate search queries from conversation history. We demonstrate on five well-established benchmarks that CHIQ leads to state-of-the-art results across most settings, showing highly competitive performances with systems leveraging closed-source LLMs. Our study provides a first step towards leveraging open-source LLMs in conversational search, as a competitive alternative to the prevailing reliance on commercial LLMs. Data, models, and source code will be publicly available upon acceptance at https://github.com/fengranMark/CHIQ.
Using a scale-free $N$-body simulation generated with the ABACUS $N$-body code, we test the robustness of halo mass accretion histories via their convergence to self-similarity. We compare two halo finders, ROCKSTAR and COMPASO. We find superior self-similarity in halo mass accretion histories determined using ROCKSTAR, with convergence to 5% or better between $\sim10^2$ to $10^5$ particles. For COMPASO we find weaker convergence over a similar region, with at least 10% between $\sim10^2$ to $10^4$ particles. Furthermore, we find the convergence to self-similarity improves as the simulation evolves, with the largest and deepest regions of convergence appearing after the scale factor quadrupled from the time at which non-linear structures begin to form. With sufficient time evolution, halo mass accretion histories are converged to self-similarity within 5% with as few as $\sim70$ particles for COMPASO and within 2% for as few as $\sim30$ particles for ROCKSTAR.
Jessica E. Thorne, Aaron S. G. Robotham, Sabine Bellstedt
et al.
Gas-phase metallicities of galaxies are typically measured through auroral or nebular emission lines, but metallicity also leaves an imprint on the overall spectral energy distribution (SED) of a galaxy and can be estimated through SED fitting. We use the ProSpect SED fitting code with a flexible parametric star formation history and an evolving metallicity history to self-consistently measure metallicities, stellar mass, and other galaxy properties for $\sim90\,000$ galaxies from the Deep Extragalactic VIsible Legacy Survey (DEVILS) and Galaxy and Mass Assembly (GAMA) survey. We use these to trace the evolution of the mass-metallicity relation (MZR) and show that the MZR only evolves in normalisation by $\sim0.1\,$dex at stellar mass $M_\star = 10^{10.5}\,M_\odot$. We find no difference in the MZR between galaxies with and without SED evidence of active galactic nuclei emission at low redshifts ($z<0.3$). Our results suggest an anti-correlation between metallicity and star formation activity at fixed stellar mass for galaxies with $M_\star > 10^{10.5}\,M_\odot$ for $z<0.3$. Using the star formation histories extracted using ProSpect we explore higher-order correlations of the MZR with properties of the star formation history including age, width, and shape. We find that at a given stellar mass, galaxies with higher metallicities formed most of their mass over shorter timescales, and before their peak star formation rate. This work highlights the value of exploring the connection of a galaxy's current gas-phase metallicity to its star formation history in order to understand the physical processes shaping the MZR.
Pierre-Louis Guhur, Shizhe Chen, Ricardo Garcia
et al.
In human environments, robots are expected to accomplish a variety of manipulation tasks given simple natural language instructions. Yet, robotic manipulation is extremely challenging as it requires fine-grained motor control, long-term memory as well as generalization to previously unseen tasks and environments. To address these challenges, we propose a unified transformer-based approach that takes into account multiple inputs. In particular, our transformer architecture integrates (i) natural language instructions and (ii) multi-view scene observations while (iii) keeping track of the full history of observations and actions. Such an approach enables learning dependencies between history and instructions and improves manipulation precision using multiple views. We evaluate our method on the challenging RLBench benchmark and on a real-world robot. Notably, our approach scales to 74 diverse RLBench tasks and outperforms the state of the art. We also address instruction-conditioned tasks and demonstrate excellent generalization to previously unseen variations.
Intermediate-mass black holes (IMBHs) have not been detected beyond any reasonable doubt through either dynamical or accretion signatures. Gravitational waves (GWs) represent an unparalleled opportunity to survey the sky and detect mergers of IMBHs, making it possible for the first time to constrain their formation, growth, and merger history across cosmic time. While the current network LIGO-Virgo-KAGRA is significantly limited in detecting mergers of IMBH binaries, the next generation of ground-based observatories and space-based missions promise to shed light on the IMBH population through the detection of several events per year. Here, we asses this possibility by determining the optimal network of next-generation of GW observatories to reconstruct the IMBH merger history across cosmic time. We show that Voyager, the Einstein Telescope, and Cosmic Explorer will be able to constrain the distribution of the primary masses of merging IMBHs up to $\sim 10^3\ M_\odot$ and with mass ratio $\gtrsim 0.1$, while LISA will complementary do so at higher mass and smaller mass ratios. Therefore, a network of next-generation ground-based and space-based observatories will potentially reconstruct the merger history of IMBHs. Moreover, IMBHs with masses $\lesssim 5\times 10^3\ M_\odot$ could be observed in multiband up to a redshift of $z\approx 4$, ushering in a new of era GW astronomy.
Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advantage of both since of the mismatch between natural language and logic-form SQL. In this work, we propose a History Information Enhanced text-to-SQL model (HIE-SQL) to exploit context-dependence information from both history utterances and the last predicted SQL query. In view of the mismatch, we treat natural language and SQL as two modalities and propose a bimodal pre-trained model to bridge the gap between them. Besides, we design a schema-linking graph to enhance connections from utterances and the SQL query to the database schema. We show our history information enhanced methods improve the performance of HIE-SQL by a significant margin, which achieves new state-of-the-art results on the two context-dependent text-to-SQL benchmarks, the SparC and CoSQL datasets, at the writing time.
Product personalization opens the door to price discrimination. A rich product line allows firms to better tailor products to consumers' tastes, but the mere choice of a product carries valuable information about consumers that can be leveraged for price discrimination. We study this trade-off in an upstream-downstream model, where a consumer buys a good of variable quality upstream, followed by an indivisible good downstream. The downstream firm's use of the consumer's purchase history for price discrimination introduces a novel distortion: The upstream firm offers a subset of the products that it would offer if, instead, it could jointly design its product line and downstream pricing. By controlling the degree of product personalization the upstream firm curbs ratcheting forces that result from the consumer facing downstream price discrimination.
Petra N. Tang, J. J. Eldridge, Elizabeth R. Stanway
et al.
We compare the impacts of uncertainties in both binary population synthesis models and the cosmic star formation history on the predicted rates of Gravitational Wave compact binary merger (GW) events. These uncertainties cause the predicted rates of GW events to vary by up to an order of magnitude. Varying the volume-averaged star formation rate density history of the Universe causes the weakest change to our predictions, while varying the metallicity evolution has the strongest effect. Double neutron-star merger rates are more sensitive to assumed neutron-star kick velocity than the cosmic star formation history. Varying certain parameters affects merger rates in different ways depending on the mass of the merging compact objects; thus some of the degeneracy may be broken by looking at all the event rates rather than restricting ourselves to one class of mergers.
Shima Khoshraftar, Sedigheh Mahdavi, Aijun An
et al.
Many real world networks are very large and constantly change over time. These dynamic networks exist in various domains such as social networks, traffic networks and biological interactions. To handle large dynamic networks in downstream applications such as link prediction and anomaly detection, it is essential for such networks to be transferred into a low dimensional space. Recently, network embedding, a technique that converts a large graph into a low-dimensional representation, has become increasingly popular due to its strength in preserving the structure of a network. Efficient dynamic network embedding, however, has not yet been fully explored. In this paper, we present a dynamic network embedding method that integrates the history of nodes over time into the current state of nodes. The key contribution of our work is 1) generating dynamic network embedding by combining both dynamic and static node information 2) tracking history of neighbors of nodes using LSTM 3) significantly decreasing the time and memory by training an autoencoder LSTM model using temporal walks rather than adjacency matrices of graphs which are the common practice. We evaluate our method in multiple applications such as anomaly detection, link prediction and node classification in datasets from various domains.
We explore connections between the generalized multiplicities of square-free monomial ideals and the combinatorial structure of the underlying hypergraphs using methods of commutative algebra and polyhedral geometry. For instance, we show the $j$-multiplicity is multiplicative over the connected components of a hypergraph, and we explicitly relate the $j$-multiplicity of the edge ideal of a properly connected uniform hypergraph to the Hilbert-Samuel multiplicity of its special fiber ring. In addition, we provide general bounds for the generalized multiplicities of the edge ideals and compute these invariants for classes of uniform hypergraphs.
We present a new oblivious RAM that supports variable-sized storage blocks (vORAM), which is the first ORAM to allow varying block sizes without trivial padding. We also present a new history-independent data structure (a HIRB tree) that can be stored within a vORAM. Together, this construction provides an efficient and practical oblivious data structure (ODS) for a key/value map, and goes further to provide an additional privacy guarantee as compared to prior ODS maps: even upon client compromise, deleted data and the history of old operations remain hidden to the attacker. We implement and measure the performance of our system using Amazon Web Services, and the single-operation time for a realistic database (up to $2^{18}$ entries) is less than 1 second. This represents a 100x speed-up compared to the current best oblivious map data structure (which provides neither secure deletion nor history independence) by Wang et al. (CCS 14).
We study merger histories of dark-matter haloes in a suite of N-body simulations that span different cosmological models. The simulated cases include the up-to-date WMAP5 cosmology and other test cases based on the Einstein-deSitter cosmology with different power spectra. We provide a robust fitting function for the conditional mass function (CMF) of progenitor haloes of a given halo. This fit is valid for the different cosmological models and for different halo masses and redshifts, and it is a significant improvement over earlier estimates. Based on this fit, we develop a simple and accurate technique for transforming the merger history of a given simulated halo into haloes of different mass, redshift and cosmology. Other statistics such as main-progenitor history and merger rates are accurately transformed as well. This method can serve as a useful tool for studying galaxy formation. It is less sensitive to the low accuracy of the fit at small time-steps, and it can thus replace the more elaborate task of construction Monte-Carlo realizations. As an alternative approach, we confirm the earlier finding by Neistein & Dekel that the main-progenitor follows a log-normal distribution. This property of merger trees allows us to better capture their behaviour as a function of time and descendant mass, but a broader suite of simulations is required for evaluating the dependence of the log-normal parameters on the cosmological model.
Mach's Principle is usually taken to mean that the mass of a particle as measured locally is determined in some way by the other matter in the universe. This is difficult to formalize in 4D,but is feasible in 5D if the scalar potential of non-compactified Kaluza-Klein theory is interpreted as an inertial field. We therefore review 5D space-time-matter theory, but take the local particle mass to be defined by the integral of a global scalar field. This approach smoothly embeds general relativity, and leads to several new effects which can be tested.