Miracle-dz/UniFAR-FTUs huggingface dataset:
Deformation and failure mechanisms of electrochemically lithiated silicon thin films

A fundamental understanding of mechanical behavior of a Li–Si system is necessary to address the poor mechanical integrity of amorphous silicon (a-Si) electrodes, in order to utilize their enormous capacity in Li-ion batteries. In this work, deformation and failure mechanisms of electrochemically lithiated a-Si thin films were investigated using nanoindentation and molecular dynamics simulation techniques. The cracking observed in the a-Si thin films after the initial lithiation–delithiation cycle is associated with the tension stress developed when constrained by the substrates. The MD simulations provide an atomistic insight on the origin of plasticity and transition of fracture mechanisms with increasing lithium concentration in the electrode. Both experiment and the MD simulations indicate reduced strength, elastic modulus but increased ductility in the a-Si films after the full lithiation–delithiation cycle, as a result of increased disorder in the microstructures. Also, the mapping of void nucleation and growth indicates different failure modes in pristine and delithiated a-Si.
Stellar kinematics in the nuclear regions of nearby LIRGs with VLT-SINFONI

Context. Nearby luminous infrared galaxies (LIRGs) are often considered to be the local counterpart of the star forming galaxy (SFG) population at z > 1. Therefore, local LIRGs are ideal systems with which to perform spatially resolved studies on the physical processes that govern these objects and to validate assumptions made in high-z studies because of a lack of sensitivity and/or spatial resolution. Aims. In this work we analyse the spatially resolved kinematics of the stellar component in the inner r < 1–2 kpc of ten nearby (mean z = 0.014) LIRGs, establishing the dynamical state of the stars and estimating their dynamical masses (Mdyn). We compare the stellar kinematics with those for different gas phases, and analyse the relative effects of using different tracers when estimating dynamical masses. Methods. We use seeing-limited SINFONI H- and K-band spectroscopy in combination with ancillary infrared (IR) imaging from various instruments (NICMOS/F160W, NACO/Ks and IRAC/3.6 μm). The stellar kinematics are extracted in both near-IR bands by fitting the continuum emission using pPXF. The velocity maps are then modelled as rotating discs and used to extract the geometrical parameters (i.e. centre, PA, and inclination), which are compared with their photometric counterparts extracted from the near-IR images. We use the stellar and the previously extracted gas velocity and velocity dispersion maps to estimate the dynamical mass using the different tracers. Results. We find that the different gas phases have similar kinematics, whereas the stellar component is rotating with slightly lower velocities (i.e. V* ∼ 0.8Vg) but in significantly warmer orbits (i.e. σ* ∼ 2σg) than the gas phases, resulting in significantly lower V/σ for the stars (i.e. ∼1.5–2) than for the gas (i.e. ∼4–6). These ratios can be understood if the stars are rotating in thick discs while the gas phases are confined in dynamically cooler (i.e. thinner) rotating discs. However, these differences do not lead to significant discrepancies between the dynamical mass estimations based on the stellar and gas kinematics. This result suggests that the gas kinematics can be used to estimate Mdyn also in z ∼ 2 SFGs, a galaxy population that shares many structural and kinematic properties with local LIRGs.
Diversity of nuclear star cluster formation mechanisms revealed by their star formation histories

Nuclear star clusters (NSCs) are the densest stellar systems in the Universe and are found in the centres of all types of galaxies. They are thought to form via mergers of star clusters such as ancient globular clusters (GCs) that spiral to the centre as a result of dynamical friction or through in situ star formation directly at the galaxy centre. There is evidence that both paths occur, but the relative contribution of either channel and their correlation with galaxy properties are not yet constrained observationally. Our aim was to derive the dominant NSC formation channel for a sample of 25 nucleated galaxies, mostly in the Fornax galaxy cluster, with stellar masses between Mgal ∼ 108 and 1010.5 M⊙ and NSC masses between MNSC ∼ 105 and 108.5 M⊙. Using Multi-Unit Spectroscopic Explorer data from the Fornax 3D survey and the ESO archive, we derived star formation histories, mean ages, and metallicities of NSCs, and compared them to the host galaxies. In many low-mass galaxies, the NSCs are significantly more metal poor than their hosts, with properties similar to GCs. In contrast, in the massive galaxies we find diverse star formation histories and cases of ongoing or recent in situ star formation. Massive NSCs (> 107 M⊙) occupy a different region in the mass–metallicity diagram than lower-mass NSCs and GCs, indicating a different enrichment history. We find a clear transition of the dominant NSC formation channel with both galaxy and NSC mass. We hypothesise that while GC accretion forms the NSCs of the dwarf galaxies, central star formation is responsible for the efficient mass build up in the most massive NSCs in our sample. At intermediate masses both channels can contribute. The transition between these formation channels seems to occur at galaxy masses Mgal ∼ 109 M⊙ and NSC masses MNSC ∼ 107 M⊙.
Should Semantic Vector Composition be Explicit? Can it be Linear?

Vector representations have become a central element in semantic language modelling, leading to mathematical overlaps with many fields including quantum theory. Compositionality is a core goal for such representations: given representations for ‘wet’ and ‘fish’, how should the concept ‘wet fish’ be represented? This position paper surveys this question from two points of view. The first considers the question of whether an explicit mathematical representation can be successful using only tools from within linear algebra, or whether other mathematical tools are needed. The second considers whether semantic vector composition should be explicitly described mathematically, or whether it can be a model-internal side-effect of training a neural network. A third and newer question is whether a compositional model can be implemented on a quantum computer. Given the fundamentally linear nature of quantum mechanics, we propose that these questions are related, and that this survey may help to highlight candidate operations for future quantum implementation.
Large-Scale Contextualised Language Modelling for Norwegian

We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training. This paper introduces the first large-scale monolingual language models for Norwegian, based on both the ELMo and BERT frameworks. In addition to detailing the training process, we present contrastive benchmark results on a suite of NLP tasks for Norwegian. For additional background and access to the data, models, and software, please see: http://norlm.nlpl. eu
Can Active Learning Preemptively Mitigate Fairness Issues?

Dataset bias is one of the prevailing causes of unfairness in machine learning. Addressing fairness at the data collection and dataset preparation stages therefore becomes an essential part of training fairer algorithms. In particular, active learning (AL) algorithms show promise for the task by drawing importance to the most informative training samples. However, the effect and interaction between existing AL algorithms and algorithmic fairness remain under-explored. In this paper, we study whether models trained with uncertainty-based AL heuristics such as BALD are fairer in their decisions with respect to a protected class than those trained with identically independently distributed (i.i.d.) sampling. We found a significant improvement on predictive parity when using BALD, while also improving accuracy compared to i.i. d. sampling. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD. We found that, while addressing different fairness issues, their interaction further improves the results on most benchmarks and metrics we explored.
Deep Nonparametric Regression on Approximately Low-dimensional Manifolds

In this paper, we study the properties of nonparametric least squares regression using deep neural networks. We derive non-asymptotic upper bounds for the prediction error of the empirical risk minimizer for feedforward deep neural regression. Our error bounds achieve the minimax optimal rate and significantly improve over the existing ones in the sense that they depend linearly or quadratically on the dimension d of the predictor, instead of exponentially on d. We show that the neural regression estimator can circumvent the curse of dimensionality under the assumption that the predictor is supported on an approximate low-dimensional manifold. This assumption differs from the structural condition imposed on the target regression function and is weaker and more realistic than the exact low-dimensional manifold support assumption in the existing literature. We investigate how the prediction error of the neural regression estimator depends on the structure of neural networks and propose a notion of network relative efficiency between two types of neural networks, which provides a quantitative measure for evaluating the relative merits of different network structures. Our results are derived under weaker assumptions on the data distribution, the target regression function and the neural network structure than those in the existing literature.
Optimal Rates of Teaching and Learning Under Uncertainty

In this paper, we consider a recently-proposed model of teaching and learning under uncertainty, in which a teacher receives independent observations of a single bit corrupted by binary symmetric noise, and sequentially transmits to a student through another binary symmetric channel based on the bits observed so far. After a given number <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> of transmissions, the student outputs an estimate of the unknown bit, and we are interested in the exponential decay rate of the error probability as <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> increases. We propose a novel block-structured teaching strategy in which the teacher encodes the number of 1s received in each block, and show that the resulting error exponent is the binary relative entropy <inline-formula> <tex-math notation="LaTeX">$D\left({\frac {1}{2}\|\max (p,q)}\right)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> are the noise parameters. This matches a trivial converse result based on the data processing inequality, and settles two conjectures of [Jog and Loh, 2021] and [Huleihel <italic>et al.</italic>, 2019]. In addition, we show that the computation time required by the teacher and student is linear in <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. We also study a more general setting in which the binary symmetric channels are replaced by general binary-input discrete memoryless channels. We provide an achievability bound and a converse bound, and show that the two coincide in certain cases, including (i) when the two channels are identical, and (ii) when the student-teacher channel is a binary symmetric channel. More generally, we give sufficient conditions under which our learning rate is the best possible for block-structured protocols.
On algebraic bi-Lipschitz homeomorphisms

Let X ⊂ C;Y ⊂ C be closed affine varieties and let φ : X → Y be an algebraic bi-Lipschitz homeomorphism. Then deg X = deg Y. Similarly, let (X, 0) ⊂ (C, 0), (Y, 0) ⊂ (C, 0) be germs of analytic sets and let f : (X, 0) → (Y, 0) be a cholomorphic and bi-Lipschitz homeomorphism. Then mult0 X = mult0 Y. Finally we show that the normality is not a bi-Lipschitz invariant.
MSˆ2: Multi-Document Summarization of Medical Studies

To assess the effectiveness of any medical intervention, researchers must conduct a time-intensive and manual literature review. NLP systems can help to automate or assist in parts of this expensive process. In support of this goal, we release MSˆ2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20K summaries derived from the scientific literature. This dataset facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies, and is the first large-scale, publicly available multi-document summarization dataset in the biomedical domain. We experiment with a summarization system based on BART, with promising early results, though significant work remains to achieve higher summarization quality. We formulate our summarization inputs and targets in both free text and structured forms and modify a recently proposed metric to assess the quality of our system’s generated summaries. Data and models are available at https://github.com/allenai/ms2.
An Integrated Optimization-Learning Framework for Online Combinatorial Computation Offloading in MEC Networks

Mobile edge computing (MEC) is a promising paradigm to accommodate the increasingly prosperous delay-sensitive and computation-intensive applications in 5G systems. To achieve optimum computation performance in a dynamic MEC environment, mobile devices often need to make online decisions on whether to offload the computation tasks to nearby edge terminals under the uncertainty of future system information (e.g., random wireless channel gain and task arrivals). The design of an efficient online offloading algorithm is challenging. On one hand, the fast-varying edge environment requires frequently solving a hard combinatorial optimization problem where the integer offloading decision and continuous resource allocation variables are strongly coupled. On the other hand, the uncertainty of future system parameters makes it hard for the online decisions to satisfy long-term system constraints. To address these challenges, this article overviews the existing methods and introduces a novel framework that efficiently integrates model-based optimization and model-free learning techniques. We suggest some promising future research directions for online computation offloading control in MEC networks.
New results for MaxCut in H$H$ ‐free graphs

The MaxCut problem asks for the size mc(G)${\rm mc}(G)$ of a largest cut in a graph G$G$ . It is well known that mc(G)⩾m/2${\rm mc}(G)\geqslant m/2$ for any m$m$ ‐edge graph G$G$ , and the difference mc(G)−m/2${\rm mc}(G)-m/2$ is called the surplus of G$G$ . The study of the surplus of H$H$ ‐free graphs was initiated by Erdős and Lovász in the 70s, who, in particular, asked what happens for triangle‐free graphs. This was famously resolved by Alon, who showed that in the triangle‐free case the surplus is Ω(m4/5)$\Omega (m^{4/5})$ , and found constructions matching this bound. We prove several new results in this area. (i)Confirming a conjecture of Alon, Krivelevich and Sudakov, we show that for every fixed odd r⩾3$r\geqslant 3$ , any Cr$C_r$ ‐free graph with m$m$ edges has surplus Ωr(mr+1r+2)$\Omega _r(m^{\frac{r+1}{r+2}})$ . This is tight, as is shown by a construction of pseudo‐random Cr$C_r$ ‐free graphs due to Alon and Kahale. It improves previous results of several researchers, and complements a result of Alon, Krivelevich and Sudakov which is the same bound when r$r$ is even. (ii)Generalising the result of Alon, we allow the graph to have triangles, and show that if the number of triangles is a bit less than in a random graph with the same density, then the graph has large surplus. For regular graphs, our bounds on the surplus are sharp. (iii)We prove that an n$n$ ‐vertex graph with few copies of Kr$K_r$ and average degree d$d$ has surplus Ωr(dr−1/nr−3)$\Omega _r(d^{r-1}/n^{r-3})$ , which is tight when d$d$ is close to n$n$ provided that a conjectured dense pseudo‐random Kr$K_r$ ‐free graph exists. This result is used to improve the best known lower bound (as a function of m$m$ ) on the surplus of Kr$K_r$ ‐free graphs. Our proofs combine techniques from semi‐definite programming, probabilistic reasoning, as well as combinatorial and spectral arguments.
BERT Embeddings Can Track Context in Conversational Search

The use of conversational assistants to search for information is becoming increasingly more popular among the general public, pushing the research towards more advanced and sophisticated techniques. In the last few years, in particular, the interest in conversational search is increasing, not only because of the generalization of conversational assistants but also because conversational search is a step forward in allowing a more natural interaction with the system. In this work, the focus is on exploring the context present of the conversation via the historical utterances and respective embeddings with the aim of developing a conversational search system that helps people search for information in a natural way. In particular, this system must be able to understand the context where the question is posed, tracking the current state of the conversation and detecting mentions to previous questions and answers. We achieve this by using a context-tracking component based on neural query-rewriting models. Another crucial aspect of the system is to provide the most relevant answers given the question and the conversational history. To achieve this objective, we used a Transformer-based re-ranking method and expanded this architecture to use the conversational context. The results obtained with the system developed showed the advantages of using the context present in the natural language utterances and in the neural embeddings generated throughout the conversation.
Existence of the first magic angle for the chiral model of bilayer graphene

We consider the chiral model of twisted bilayer graphene introduced by TarnopolskyKruchkov-Vishwanath (TKV). TKV have proved that for inverse twist angles α such that the effective Fermi velocity at the moiré K point vanishes, the chiral model has a perfectly flat band at zero energy over the whole Brillouin zone. By a formal expansion, TKV found that the Fermi velocity vanishes at α ≈ . 586. In this work, we give a proof that the Fermi velocity vanishes for at least one α between . 57 and . 61 by rigorously justifying TKV’s formal expansion of the Fermi velocity over a sufficiently large interval of α values. The idea of the proof is to project the TKV Hamiltonian onto a finite dimensional subspace, and then expand the Fermi velocity in terms of explicitly computable linear combinations of modes in the subspace, while controlling the error. The proof relies on two propositions whose proofs are computer-assisted, i.e., numerical computation together with worst-case estimates on the accumulation of round-off error which show that round-off error cannot possibly change the conclusion of the computation. The propositions give a bound below on the spectral gap of the projected Hamiltonian, an Hermitian 80 × 80 matrix whose spectrum is symmetric about 0, and verify that two real 18th order polynomials, which approximate the numerator of the Fermi velocity, take values with definite sign when evaluated at specific values of α. Together with TKV’s work our result proves existence of at least one perfectly flat band of the chiral model.
S-Wave Superconductivity in Kagome Metal CsV3Sb5 Revealed by 121/123Sb NQR and 51V NMR Measurements

Chao Mu(牟超), Qiangwei Yin(殷蔷薇), Zhijun Tu(涂志俊), Chunsheng Gong(龚春生), Hechang Lei(雷和畅), Zheng Li(李政)1,2*, and Jianlin Luo(雒建林)1,2,4* Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices, Renmin University of China, Beijing 100872, China Songshan Lake Materials Laboratory, Dongguan 523808, China
X-ray-induced chemistry of water and related molecules in low-mass protostellar envelopes

Context. Water is a key molecule in star- and planet-forming regions. Recent water line observations toward several low-mass protostars suggest low water gas fractional abundances (<10−6 with respect to total hydrogen density) in the inner warm envelopes (r < 102 au). Water destruction by X-rays is thought to influence the water abundances in these regions, but the detailed chemistry, including the nature of alternative oxygen carriers, is not yet understood. Aims. Our aim is to understand the impact of X-rays on the composition of low-mass protostellar envelopes, focusing specifically on water and related oxygen-bearing species. Methods. We computed the chemical composition of two proto-typical low-mass protostellar envelopes using a 1D gas-grain chemical reaction network. We varied the X-ray luminosities of the central protostars, and thus the X-ray ionization rates in the protostellar envelopes. Results. The protostellar X-ray luminosity has a strong effect on the water gas abundances, both within and outside the H2O snowline (Tgas ~ 102 K, r ~ 102 au). Outside, the water gas abundance increases with LX, from ~10−10 for low LX to ~10−8–10−7 at LX > 1030 erg s−1. Inside, water maintains a high abundance of ~10−4 for LX ≲ 1029–1030 erg s−1, with water and CO being the dominant oxygen carriers. For LX ≳ 1030–1031 erg s−1, the water gas abundances significantly decrease just inside the water snowline (down to ~10−8–10−7) and in the innermost regions with Tgas ≳ 250 K (~10−6). For these cases, the fractional abundances of O2 and O gas reach ~10−4 within the water snowline, and they become the dominant oxygen carriers. In addition, the fractional abundances of HCO+ and CH3OH, which have been used as tracers of the water snowline, significantly increase and decrease, respectively, within the water snowline as the X-ray fluxes become larger. The fractional abundances of some other dominant molecules, such as CO2, OH, CH4, HCN, and NH3, are also affected by strong X-ray fields, especially within their own snowlines. These X-ray effects are larger in lower-density envelope models. Conclusions. X-ray-induced chemistry strongly affects the abundances of water and related molecules including O, O2, HCO+, and CH3OH, and can explain the observed low water gas abundances in the inner protostellar envelopes. In the presence of strong X-ray fields, gas-phase water molecules within the water snowline are mainly destroyed with ion-molecule reactions and X-ray-induced photodissociation. Future observations of water and related molecules (using, e.g., ALMA and ngVLA) will access the regions around protostars where such X-ray-induced chemistry is effective.