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Two-dimensional ferroelectric materials are beneficial for power-efficient memory devices and transistor applications. Here, we predict out-of-plane ferroelectricity in a new family of buckled metal oxide (MO; M: Ge, Sn, Pb) monolayers with significant spontaneous polarization. Additionally, these monolayers have a narrow valence band, which is energetically separated from the rest of the low-lying valence bands. Such a unique band structure limits the long thermal tail of the hot carriers, mitigating subthreshold thermionic leakage and allowing field-effect transistors (FETs) to function beyond the bounds imposed on conventional FETs by thermodynamics. Our quantum transport simulations reveal that the FETs based on these MO monolayers exhibit a large ON/OFF ratio with an average subthreshold swing of less than 60 mV/decade at room temperature, even for short gate lengths. Our work motivates further exploration of the MO monolayers for developing advanced, high-performance memory and logic devices.

Multilingual LLMs support a variety of languages; however, their performance is suboptimal for low-resource languages. In this work, we emphasize the importance of continued pre-training of multilingual LLMs and the use of translation-based synthetic pre-training corpora for improving LLMs in low-resource languages. We conduct our study in the context of the low-resource Indic language Hindi. We introduce Nemotron-Mini-Hindi 4B, a bilingual SLM supporting both Hindi and English, based on Nemotron-Mini 4B. The model is trained using a mix of real and synthetic Hindi + English tokens, with continuous pre-training performed on 400B tokens. We demonstrate that both the base and instruct models achieve state-of-the-art results on Hindi benchmarks while remaining competitive on English tasks. Additionally, we observe that the continued pre-training approach enhances the model's overall factual accuracy.

This paper addresses the challenge of estimating high-dimensional parameters in non-standard data environments, where traditional methods often falter due to issues such as heavy-tailed distributions, data contamination, and dependent observations. We propose robust subsampling techniques, specifically Adaptive Importance Sampling (AIS) and Stratified Subsampling, designed to enhance the reliability and efficiency of parameter estimation. Under some clearly outlined conditions, we establish consistency and asymptotic normality for the proposed estimators, providing non-asymptotic error bounds that quantify their performance. Our theoretical foundations are complemented by controlled experiments demonstrating the superiority of our methods over conventional approaches. By bridging the gap between theory and practice, this work offers significant contributions to robust statistical estimation, paving the way for advancements in various applied domains.

Ternary content addressable memories (TCAMs) are useful for certain computing tasks since they allow us to compare a search query with a whole dataset stored in the memory array. They can also unlock unique advantages for cryogenic applications like quantum computing, high-performance computing, and space exploration by improving speed and energy efficiency through parallel searching. This paper explores the design and implementation of a cryogenic ternary content addressable memory based on ferroelectric superconducting quantum interference devices (FeSQUIDs). The use of FeSQUID for designing the TCAM provides several unique advantages. First, we can get binary decisions (zero or non-zero voltage) for matching and mismatching conditions without using any peripheral circuitry. Moreover, the proposed TCAM needs ultra-low energy (1.36 aJ and 26.5 aJ average energy consumption for 1-bit binary and ternary search, respectively), thanks to the use of energy-efficient SQUIDs. Finally, we show the efficiency of FeSQUID through the brain-inspired application of Hyperdimensional Computing (HDC). Here, the FeSQUID-based TCAM implements the associative memory to support the highly parallel search needed in the inference step. We estimate an energy consumption of 89.4 fJ per vector comparison using a vector size of 10,000 bits. We also compare the FeSQUID-based TCAM array with the 5nm FinFET-based cryogenic SRAM-based TCAM array and observe that the proposed FeSQUID-based TCAM array consumes over one order of magnitude lower energy while performing the same task., Comment: 6 figures

Previous text-to-SQL datasets and systems have primarily focused on user questions with clear intentions that can be answered. However, real user questions can often be ambiguous with multiple interpretations or unanswerable due to a lack of relevant data. In this work, we construct a practical conversational text-to-SQL dataset called PRACTIQ, consisting of ambiguous and unanswerable questions inspired by real-world user questions. We first identified four categories of ambiguous questions and four categories of unanswerable questions by studying existing text-to-SQL datasets. Then, we generate conversations with four turns: the initial user question, an assistant response seeking clarification, the user's clarification, and the assistant's clarified SQL response with the natural language explanation of the execution results. For some ambiguous queries, we also directly generate helpful SQL responses, that consider multiple aspects of ambiguity, instead of requesting user clarification. To benchmark the performance on ambiguous, unanswerable, and answerable questions, we implemented large language model (LLM)-based baselines using various LLMs. Our approach involves two steps: question category classification and clarification SQL prediction. Our experiments reveal that state-of-the-art systems struggle to handle ambiguous and unanswerable questions effectively. We will release our code for data generation and experiments on GitHub.

While large language models (LLMs) have rapidly improved their performance on a broad number of tasks, they still often fall short on reasoning tasks. As LLMs become more integrated in diverse real-world tasks, advancing their reasoning capabilities is crucial to their effectiveness in nuanced, complex problems. Wang et al's self-consistency framework reveals that sampling multiple rationales before taking a majority vote reliably improves model performance across various closed-answer reasoning tasks. Standard methods based on this framework aggregate the final decisions of these rationales but fail to utilize the detailed step-by-step reasoning paths applied by these paths. Our work enhances this approach by incorporating and analyzing both the reasoning paths of these rationales in addition to their final decisions before taking a majority vote. These methods not only improve the reliability of reasoning paths but also cause more robust performance on complex reasoning tasks., Comment: Accepted to MATH-AI at NeurIPS 2024

Optical pumping of barium monofluoride (BaF) within a cryogenic neon matrix is demonstrated. Interestingly, with an applied magnetic field of 2~G, optical pumping is found to be considerably more efficient for a laser beam with right-circular polarization compared to left-circular polarization. Calculations show that the higher efficiency is due to a constructive versus destructive interference and is dependent on the orientation of the BaF molecule relative to the magnetic field direction. The effect leads to orientation-dependent optical pumping within the matrix. As optical pumping is the first step used in our planned electron electric-dipole moment (eEDM) measurement, we intend to exploit this property to obtain the selection of molecular orientations that is required for an eEDM measurement., Comment: 9 pages, 5 figures

A cryogenic neon solid doped with barium monofluoride (BaF) is created on a cryogenic substrate using a stream of Ne gas and a high-intensity beam of BaF molecules produced in a cryogenic helium-buffer-gas laser-ablation source. The apparatus is designed for eventual use in a measurement of the electron electric dipole moment (eEDM). Laser-induced fluorescence is observed from transitions up to the $B\,^2\Sigma_{1/2}$ state. The number of BaF molecules found to be present in the solid and addressable with this laser transition is approximately 10$^{10}$ per mm$^3$, which is of the same order as the total number of BaF molecules that impact the substrate during the hour of growth time for the solid. As a result, an eventual eEDM measurement could have continual access to an accumulation of an hour's worth of molecules (all of which are contained within a 1-mm$^3$ volume and are thermalized into the ground state), compared to beam experiments which study the molecules from a single ablation during the millisecond-timescale of their passage through a much larger-scale measurement apparatus. The number of BaF molecules observed in the doped solid matches the target value for our planned eEDM measurement., Comment: 9 pages, 6 figures