Contrastive Learning of Sentence Embeddings from Scratch

Contrastive learning has been the dominant approach to train state-of-the-art sentence embeddings. Previous studies have typically learned sentence embeddings either through the use of human-annotated natural language inference (NLI) data or via large-scale unlabeled sentences in an unsupervised manner. However, even in the case of unlabeled data, their acquisition presents challenges in certain domains due to various reasons. To address these issues, we present SynCSE, a contrastive learning framework that trains sentence embeddings with synthesized data. Specifically, we explore utilizing large language models to synthesize the required data samples for contrastive learning, including (1) producing positive and negative annotations given unlabeled sentences (SynCSE-partial), and (2) generating sentences along with their corresponding annotations from scratch (SynCSE-scratch). Experimental results on sentence similarity and reranking tasks indicate that both SynCSE-partial and SynCSE-scratch greatly outperform unsupervised baselines, and SynCSE-partial even achieves comparable performance to the supervised models in most settings.Comment: Preprin

System Structure Risk Metric Method Based on Information Flow

Part 5: Modelling and SimulationInternational audienceThe measurement of structure risk aims to analysis and evaluate the not occurred, potential, and the objectively exist risk in system structure. It is an essential way to validate system function and system quality. This paper proposes the risk metric model and algorithm based on information flow and analysis risk trend between traditional tree structure and network-centric structure

A Splicing Approach to Best Subset of Groups Selection

Best subset of groups selection (BSGS) is the process of selecting a small part of non-overlapping groups to achieve the best interpretability on the response variable. It has attracted increasing attention and has far-reaching applications in practice. However, due to the computational intractability of BSGS in high-dimensional settings, developing efficient algorithms for solving BSGS remains a research hotspot. In this paper,we propose a group-splicing algorithm that iteratively detects the relevant groups and excludes the irrelevant ones. Moreover, coupled with a novel group information criterion, we develop an adaptive algorithm to determine the optimal model size. Under mild conditions, it is certifiable that our algorithm can identify the optimal subset of groups in polynomial time with high probability. Finally, we demonstrate the efficiency and accuracy of our methods by comparing them with several state-of-the-art algorithms on both synthetic and real-world datasets.Comment: 49 pages, 7 figure

Correlations between stacked structures and weak itinerant magnetic properties of La2−x_{2-x} Yx_x Ni7_7 compounds

Hexagonal La$_2$Ni$_7$ and rhombohedral Y$_2$Ni$_7$ are weak itinerant antiferromagnet (wAFM) and ferromagnet (wFM), respectively. The crystal structure and magnetic properties of $A_2B_7$ intermetallic compounds ($A$ = La, Y, $B$ = Ni) have been investigated combining X-ray powder diffraction and magnetic measurements. The La$_{2-x}$Y$_x$Ni$_7$ intermetallic compounds with $0 \leq x \leq 1$ crystallize in the Ce$_{2}$Ni$_7$-type hexagonal structure with Y preferentially located in the [$AB_2$] units. The compounds with larger Y content ($1.2 \leq x < 2$) crystallize in both hexagonal and rhombohedral (Gd$_2$Co$_7$-type) structures with a progressive substitution of Y for La in the $A$ sites belonging to the [$AB_5$] units. Y$_2$Ni$_7$ crystallizes in the rhombohedral structure only. The average cell volume decreases linearly versus Y content, whereas the c/a ratio presents a minimum at $x = 1$ due to geometric constrains. The magnetic properties are strongly dependent on the structure type and the Y content. La$_{2}$Ni$_7$ displays a complex metamagnetic behavior with split AFM peaks. Compounds with x = 0.25 and 0.5 display a wAFM ground state and two metamagnetic transitions, the first one towards an intermediate wAFM state and the second one towards a FM state.T$_N$ and the second critical field increase with the Y content, indicating a stabilization of the AFM state. LaYNi$_7$, which is as the boundary between the two structure types, presents a very wFM state at low field and an AFM state as the applied field increases. All the compounds with $x > 1$ and containing a rhombohedral phase are wFM with $T_C$ = 53(2) K. In addition to the experimental studies, first principles calculations using spin polarization have been performed to interpret the evolution of both structural phase stability and magnetic ordering for $0 \leq x < 2$.Comment: 26 pages (7 for supplementary material), 4 tables, 9 main figures and 8 figures in supplementary materia

A SIMPLE Approach to Provably Reconstruct Ising Model with Global Optimality

Reconstruction of interaction network between random events is a critical problem arising from statistical physics and politics to sociology, biology, and psychology, and beyond. The Ising model lays the foundation for this reconstruction process, but finding the underlying Ising model from the least amount of observed samples in a computationally efficient manner has been historically challenging for half a century. By using the idea of sparsity learning, we present a approach named SIMPLE that has a dominant sample complexity from theoretical limit. Furthermore, a tuning-free algorithm is developed to give a statistically consistent solution of SIMPLE in polynomial time with high probability. On extensive benchmarked cases, the SIMPLE approach provably reconstructs underlying Ising models with global optimality. The application on the U.S. senators voting in the last six congresses reveals that both the Republicans and Democrats noticeably assemble in each congresses; interestingly, the assembling of Democrats is particularly pronounced in the latest congress

Solid-state Li-ion batteries operating at room temperature using new borohydride argyrodite electrolytes

Using a new class of (BH4)- substituted argyrodite Li6PS5Z0.83(BH4)0.17, (Z = Cl, I) solid electrolyte, Li-metal solid-state batteries operating at room temperature have been developed. The cells were made by combining the modified argyrodite with an In-Li anode and two types of cathode: an oxide, LixMO2 (M = 1/3Ni, 1/3Mn, 1/3Co; so called NMC) and a titanium disulfide, TiS2. The performance of the cells was evaluated through galvanostatic cycling and Alternating Current AC electrochemical impedance measurements. Reversible capacities were observed for both cathodes for at least tens of cycles. However, the high-voltage oxide cathode cell shows lower reversible capacity and larger fading upon cycling than the sulfide one. The AC impedance measurements revealed an increasing interfacial resistance at the cathode side for the oxide cathode inducing the capacity fading. This resistance was attributed to the intrinsic poor conductivity of NMC and interfacial reactions between the oxide material and the argyrodite electrolyte. On the contrary, the low interfacial resistance of the TiS2 cell during cycling evidences a better chemical compatibility between this active material and substituted argyrodites, allowing full cycling of the cathode material, 240 mAhg-1, for at least 35 cycles with a coulombic efficiency above 97%