Miracle-dz/UniFAR-FTUs huggingface dataset:
Lyα at Cosmic Dawn with a Simulated Roman Grism Deep Field

The slitless grism on the Nancy Grace Roman Space Telescope will enable deep near-infrared spectroscopy over a wide field of view. We demonstrate Roman's capability to detect Lyα galaxies at z > 7 using a multiple position angle (PA) observational strategy. We simulate Roman grism data using a realistic foreground scene from the COSMOS field. We also input fake Lyα galaxies spanning redshift z = 7.5–10.5 and a line-flux range of interest. We show how a novel data-cube search technique—CUBGRISM—originally developed for the Galaxy Evolution Explorer can be applied to Roman grism data to produce a Lyα flux-limited sample without the need for continuum detections. We investigate the impact of altering the number of independent PAs and exposure time. A deep Roman grism survey with 25 PAs and a total exposure time of 70 hr can achieve Lyα line depths comparable to the deepest z = 7 narrowband surveys (L Lyα ≳ 1043 erg s−1). Assuming a null result, where the opacity of the intergalactic medium (IGM) remains unchanged from z ∼ 7, this level of sensitivity will detect ∼400 deg−2 Lyα emitters from z = 7.25 to 8.75. A decline from this expected number density is the signature of an increasing neutral hydrogen fraction and the onset of reionization. Our simulations indicate that a deep Roman grism survey has the ability to measure the timing and magnitude of this decline, allowing us to infer the ionization state of the IGM and helping us to distinguish between models of reionization.
The Unexpected Efficiency of Bin Packing Algorithms for Dynamic Storage Allocation in the Wild: An Intellectual Abstract

Two-dimensional rectangular bin packing (2DBP) is a known abstraction of dynamic storage allocation (DSA). We argue that such abstractions can aid practical purposes. 2DBP algorithms optimize their placements’ makespan, i.e., the size of the used address range. At first glance modern virtual memory systems with demand paging render makespan irrelevant as an optimization criterion: allocators commonly employ sparse addressing and need worry only about fragmentation caused within page boundaries. But in the embedded domain, where portions of memory are statically pre-allocated, makespan remains a reasonable metric. Recent work has shown that viewing allocators as black-box 2DBP solvers bears meaning. For instance, there exists a 2DBP-based fragmentation metric which often correlates monotonically with maximum resident set size (RSS). Given the field’s indeterminacy with respect to fragmentation definitions, as well as the immense value of physical memory savings, we are motivated to set allocator-generated placements against their 2DBP-devised, makespan-optimizing counterparts. Of course, allocators must operate online while 2DBP algorithms work on complete request traces; but since both sides optimize criteria related to minimizing memory wastage, the idea of studying their relationship preserves its intellectual–and practical–interest. Unfortunately no implementations of 2DBP algorithms for DSA are available. This paper presents a first, though partial, implementation of the state-of-the-art. We validate its functionality by comparing its outputs’ makespan to the theoretical upper bound provided by the original authors. Along the way, we identify and document key details to assist analogous future efforts. Our experiments comprise 4 modern allocators and 8 real application workloads. We make several notable observations on our empirical evidence: in terms of makespan, allocators outperform Robson’s worst-case lower bound 93.75% of the time. In 87.5% of cases, GNU’s malloc implementation demonstrates equivalent or superior performance to the 2DBP state-of-the-art, despite the second operating offline. Most surprisingly, the 2DBP algorithm proves competent in terms of fragmentation, producing up to 2.46x better solutions. Future research can leverage such insights towards memory-targeting optimizations.
On Strong Secrecy for Multiple Access Channel with States and causal CSI

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i. d. channel states and the channel state information (CSI) is revealed to the encoders in a causal manner. An inner bound of the capacity is provided. To establish the inner bound, we investigate coding schemes incorporating wiretap coding and secret key agreement between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are studied. The first one uses backward decoding and Wyner-Ziv coding and the secret key is constructed from a lossy reproduction of the CSI. The other one is an extended version of the existing coding scheme for point-to-point wiretap channels with causal CSI. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases.
Distributed Optimization for Power Systems with Radial Partitioning

This paper proposes group-based distributed optimization algorithms on top of intelligent partitioning for the optimal power flow (OPF) problem. Radial partitioning of the graph of a network is introduced as a systematic way to split a large-scale problem into more tractable sub-problems, which can potentially be solved efficiently with methods such as convex relaxations. The simple implementation of a Distributed Consensus Algorithm (DiCA) with very few parameters makes it viable for different parameter selection methods, which are crucial for the fast convergence of the distributed algorithms. The DiCA algorithm returns more accurate solutions to the tested problems with fewer iterations than component-based algorithms. Our numerical results show the performance of the algorithms for different power network instances and the effect of parameter selection. A software package DiCARP is created, which is implemented in Python using the Pyomo optimization package.
Computationally Relaxed Locally Decodable Codes, Revisited

We revisit computationally relaxed locally decodable codes (crLDCs) (Blocki et al., Trans. Inf. Theory ’21) and give two new constructions. Our first construction is a Hamming crLDC that is conceptually simpler than prior constructions, leveraging digital signature schemes and an appropriately chosen Hamming code. Our second construction is an extension of our Hamming crLDC to handle insertion-deletion (InsDel) errors, yielding an InsDel crLDC. This extension crucially relies on the noisy binary search techniques of Block et al. (FSTTCS ’20) to handle InsDel errors. Both crLDC constructions have binary codeword alphabets, are resilient to a constant fraction of Hamming and InsDel errors, respectively, and under suitable parameter choices have poly-logarithmic locality and encoding length linear in the message length and polynomial in the security parameter. These parameters compare favorably to prior constructions in the poly-logarithmic locality regime.
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement

Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable due to overconfidence. In this paper, we estimate a model's reliability by measuring \emph{the agreement between its latent space, and the latent space of a foundation model}. However, it is challenging to measure the agreement between two different latent spaces due to their incoherence, \eg, arbitrary rotations and different dimensionality. To overcome this incoherence issue, we design a \emph{neighborhood agreement measure} between latent spaces and find that this agreement is surprisingly well-correlated with the reliability of a model's predictions. Further, we show that fusing neighborhood agreement into a model's predictive confidence in a post-hoc way significantly improves its reliability. Theoretical analysis and extensive experiments on failure detection across various datasets verify the effectiveness of our method on both in-distribution and out-of-distribution settings.
A study on Poynting effect in brain white matter: A hyperelastic 3D micromechanical model

A novel 3D micromechanical Finite Element Model (FEM) has been developed to depict the Poynting effect in bi-phasic Representative volume element (RVE) with axons embedded in surrounding extra-cellular matrix (ECM) for simulating the brain white matter response under simple and pure shear. In the proposed 3D FEM, nonlinear Ogden hyper-elastic material model describes axons and ECM materials. The modeled bi-phasic RVEs have axons tied to surrounding matrix. In this proof-of-concept (POC) FEM, three simple shear loading configurations and a pure shear scenario were simulated. Root mean square deviation (RMSD) were computed for stress and deformation response plots to depict role of axon-ECM orientations&loading condition on the Poynting effect. Variations in normal stresses (S11, S22, or S33) perpendicular to the shear plane emphasized role of fiber-matrix interactions. At high strains, the stress-strain% plots also indicated modest strain stiffening effects and bending stresses in purely sheared axons.
Non-Markovian quantum interconnect formed by a surface plasmon polariton waveguide

Allowing the generation of effective interactions between distant quantum emitters (QEs) via flying photons, quantum interconnect (QI) is essentially a light-matter interface and acts as a building block in quantum technologies. A surface plasmon polariton (SPP) supported by a metallic waveguide provides an ideal interface to explore strong light-matter couplings and to realize QI. However, the loss of SPP in metal makes the mediated entanglement of the QEs damp with the increase of the distance and time, which hinders its applications. We propose a scheme of non-Markovian QI formed by the SPP of a metallic nanowire. A mechanism to make the generated entanglement of the QEs persistent is discovered. We find that, as long as bound states are formed in the energy spectrum of total QE-SPP system, the damping of the SPP-mediated entanglement is overcome even in the presence of the metal absorption to the SPP. Our finding enriches our understanding of light-matter couplings in absorptive medium and paves the way for using the SPP in designing QI.
Complex Logical Reasoning over Knowledge Graphs using Large Language Models

Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in vector space for logical query operations, but they suffer from subpar performance on complex queries and dataset-specific representations. In this paper, we propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and logical query reasoning, to leverage the strengths of graph extraction algorithms and large language models (LLM), respectively. Our experiments demonstrate that the proposed approach outperforms state-of-the-art KG reasoning methods on standard benchmark datasets across several logical query constructs, with significant performance gain for queries of higher complexity. Furthermore, we show that the performance of our approach improves proportionally to the increase in size of the underlying LLM, enabling the integration of the latest advancements in LLMs for logical reasoning over KGs. Our work presents a new direction for addressing the challenges of complex KG reasoning and paves the way for future research in this area.
Conditional Graph Information Bottleneck for Molecular Relational Learning

Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. Recently, graph neural networks have recently shown great success in molecular relational learning by modeling a molecule as a graph structure, and considering atom-level interactions between two molecules. Despite their success, existing molecular relational learning methods tend to overlook the nature of chemistry, i.e., a chemical compound is composed of multiple substructures such as functional groups that cause distinctive chemical reactions. In this work, we propose a novel relational learning framework, called CGIB, that predicts the interaction behavior between a pair of graphs by detecting core subgraphs therein. The main idea is, given a pair of graphs, to find a subgraph from a graph that contains the minimal sufficient information regarding the task at hand conditioned on the paired graph based on the principle of conditional graph information bottleneck. We argue that our proposed method mimics the nature of chemical reactions, i.e., the core substructure of a molecule varies depending on which other molecule it interacts with. Extensive experiments on various tasks with real-world datasets demonstrate the superiority of CGIB over state-of-the-art baselines. Our code is available at https://github.com/Namkyeong/CGIB.
Model-agnostic Measure of Generalization Difficulty

The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models. However, quantifying the generalization difficulty of machine learning benchmarks has remained challenging. We propose what is to our knowledge the first model-agnostic measure of the inherent generalization difficulty of tasks. Our inductive bias complexity measure quantifies the total information required to generalize well on a task minus the information provided by the data. It does so by measuring the fractional volume occupied by hypotheses that generalize on a task given that they fit the training data. It scales exponentially with the intrinsic dimensionality of the space over which the model must generalize but only polynomially in resolution per dimension, showing that tasks which require generalizing over many dimensions are drastically more difficult than tasks involving more detail in fewer dimensions. Our measure can be applied to compute and compare supervised learning, reinforcement learning and meta-learning generalization difficulties against each other. We show that applied empirically, it formally quantifies intuitively expected trends, e.g. that in terms of required inductive bias, MNIST<CIFAR10<Imagenet and fully observable Markov decision processes (MDPs)<partially observable MDPs. Further, we show that classification of complex images<few-shot meta-learning with simple images. Our measure provides a quantitative metric to guide the construction of more complex tasks requiring greater inductive bias, and thereby encourages the development of more sophisticated architectures and learning algorithms with more powerful generalization capabilities.
Logion: Machine Learning for Greek Philology

This paper presents machine-learning methods to address various problems in Greek philology. After training a BERT model on the largest premodern Greek dataset used for this purpose to date, we identify and correct previously undetected errors made by scribes in the process of textual transmission, in what is, to our knowledge, the first successful identification of such errors via machine learning. Additionally, we demonstrate the model's capacity to fill gaps caused by material deterioration of premodern manuscripts and compare the model's performance to that of a domain expert. We find that best performance is achieved when the domain expert is provided with model suggestions for inspiration. With such human-computer collaborations in mind, we explore the model's interpretability and find that certain attention heads appear to encode select grammatical features of premodern Greek.
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models

Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions. Knowledge distillation has been shown to be a practical solution to reduce inference costs, but the distillation process itself requires significant computational resources. Rather than buying or renting GPUs to fine-tune, then distill a large model, an NLP practitioner might instead choose to allocate the available budget to hire annotators and manually label additional fine-tuning data. In this paper, we investigate how to most efficiently use a fixed budget to build a compact model. Through extensive experiments on six diverse tasks, we show that distilling from T5-XXL (11B) to T5-Small (60M) is almost always a cost-efficient strategy compared to annotating more data to directly train a compact model (T5-Small). We further investigate how the optimal budget allocated towards computation varies across scenarios. We will make our code, datasets, annotation cost estimates, and baseline models available as a benchmark to support further work on cost-efficient training of compact models.
Geometric Latent Diffusion Models for 3D Molecule Generation

Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as molecule design. Inspired by the recent huge success of Stable (latent) Diffusion models, we propose a novel and principled method for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM). GeoLDM is the first latent DM model for the molecular geometry domain, composed of autoencoders encoding structures into continuous latent codes and DMs operating in the latent space. Our key innovation is that for modeling the 3D molecular geometries, we capture its critical roto-translational equivariance constraints by building a point-structured latent space with both invariant scalars and equivariant tensors. Extensive experiments demonstrate that GeoLDM can consistently achieve better performance on multiple molecule generation benchmarks, with up to 7\% improvement for the valid percentage of large biomolecules. Results also demonstrate GeoLDM's higher capacity for controllable generation thanks to the latent modeling. Code is provided at \url{https://github.com/MinkaiXu/GeoLDM}.
Towards a fundamental calibration of the cosmic distance scale by Cepheid variable stars

This paper updates the calibration of the cosmic distance scale by Cepheid variable stars making reference to a ‘geometric’ Baade–Wesselink (BW) distance indicator which adopts pulsation linear radii and photometric angular diameters from period–radius and surface brightness relations, respectively, calibrated by fundamental data of Galactic Cepheids. The large number of fundamental-like BW distances (pulsation parallaxes) predictable for Cepheids by using the period and the ( V , I ) intensity mean magnitudes from HIPP photometry are then applied to calibrate the Galactic period–luminosity relations and the reddening-free Wesenheit relation in the ( V , I ) passbands well suited to obtain metallicity-corrected distance moduli μ O = (18.486 ± 0.047) mag or D = (49.80 ± 1.10) kpc from Large Magellanic Cloud Cepheids of the Optical Gravitational Lensing Experiment map and μ O = (29.281 ± 0.051) mag or D = (7.18 ± 0.17) Mpc from Hubble Space Telescope ( HST ) Cepheids in the inner ﬁeld of the maser host galaxy NGC 4258, this value resulting in fair good agreement with the high-weight geometric water maser distance modulus. The Cepheid distance moduli of galaxies from the HST SN Ia Calibration Programme are found to be affected by signiﬁcant systematic errors. Removing these errors leads to an upward revision of the Hubble constant at the value H O = (72.4 ± 6.0) km s − 1 Mpc − 1 , in fair good agreement now with the result of H O = (72 ± 8) km s − 1 Mpc − 1 originally derived in the HST Key Project Programme as well as with the most recent accurate determination of H O = (73.8 ± 2.4) km s − 1 Mpc − 1 obtained by HST near-infrared observations of Cepheids.
Multiphasic stochastic epidemic models

At the onset of the Covid-19 pandemic, a number of non-pharmaceutical interventions have been implemented in order to reduce transmission, thus leading to multiple phases of transmission. The disease reproduction number $R_t$, a way of quantifying transmissibility, has been a key part in assessing the impact of such interventions. We discuss the distinct types of transmission models used and how they are linked. We consider a hierarchical stochastic epidemic model with piece-wise constant $R_t$, appropriate for modelling the distinct phases of the epidemic and quantifying the true disease magnitude. The location and scale of $R_t$ changes are inferred directly from data while the number of transmissibility phases is allowed to vary. We determine the model complexity via appropriate Poisson point process and Dirichlet process-type modelling components. The models are evaluated using synthetic data sets and the methods are applied to freely available data from California and New York states as well as the United Kingdom and Greece. We estimate the true infected cases and the corresponding $R_t$, among other quantities, and independently validate the proposed approach using a large seroprevalence study.
NELoRa-Bench: A Benchmark for Neural-enhanced LoRa Demodulation

Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. The standard LoRa demodulation method accumulates the chirp power of the whole chirp into an energy peak in the frequency domain. In this way, it can support communication even when SNR is lower than -15 dB. Beyond that, we proposed NELoRa, a neural-enhanced decoder that exploits multi-dimensional information to achieve significant SNR gain. This paper presents the dataset used to train/test NELoRa, which includes 27,329 LoRa symbols with spreading factors from 7 to 10, for further improvement of neural-enhanced LoRa demodulation. The dataset shows that NELoRa can achieve 1.84-2.35 dB SNR gain over the standard LoRa decoder. The dataset and codes can be found at https://github.com/daibiaoxuwu/NeLoRa_Dataset.
Graded character sheaves, HOMFLY-PT homology, and Hilbert schemes of points on $\mathbb{C}^2$

Using a geometric argument building on our new theory of graded sheaves, we compute the categorical trace and Drinfel'd center of the (graded) finite Hecke category $\mathsf{H}_W^\mathsf{gr} = \mathsf{Ch}^b(\mathsf{SBim}_W)$ in terms of the category of (graded) unipotent character sheaves, upgrading results of Ben-Zvi-Nadler and Bezrukavninov-Finkelberg-Ostrik. In type $A$, we relate the categorical trace to the category of $2$-periodic coherent sheaves on the Hilbert schemes $\mathsf{Hilb}_n(\mathbb{C}^2)$ of points on $\mathbb{C}^2$ (equivariant with respect to the natural $\mathbb{C}^* \times \mathbb{C}^*$ action), yielding a proof of a conjecture of Gorsky-Negut-Rasmussen which relates HOMFLY-PT link homology and the spaces of global sections of certain coherent sheaves on $\mathsf{Hilb}_n(\mathbb{C}^2)$. As an important computational input, we also establish a conjecture of Gorsky-Hogancamp-Wedrich on the formality of the Hochschild homology of $\mathsf{H}_W^\mathsf{gr}$.