Miracle-dz/UniFAR-FTUs huggingface dataset:
Ask To The Point: Open-Domain Entity-Centric Question Generation

We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. The task aims to generate questions from an entity perspective. To solve ECQG, we propose a coherent PLM-based framework GenCONE with two novel modules: content focusing and question verification. The content focusing module first identifies a focus as"what to ask"to form draft questions, and the question verification module refines the questions afterwards by verifying the answerability. We also construct a large-scale open-domain dataset from SQuAD to support this task. Our extensive experiments demonstrate that GenCONE significantly and consistently outperforms various baselines, and two modules are effective and complementary in generating high-quality questions.
Discovery of a Collimated Jet from the Low-luminosity Protostar IRAS 16253−2429 in a Quiescent Accretion Phase with the JWST

Investigating Protostellar Accretion (IPA) is a JWST Cycle 1 GO program that uses NIRSpec integral field units and MIRI Medium Resolution Spectrograph to obtain 2.9–28 μm spectral cubes of young, deeply embedded protostars with luminosities of 0.2–10,000 L ⊙ and central masses of 0.15–12 M ⊙. In this Letter, we report the discovery of a highly collimated atomic jet from the Class 0 protostar IRAS 16253−2429, the lowest-luminosity source (L bol = 0.2 L ⊙) in the IPA program. The collimated jet is detected in multiple [Fe ii] lines and [Ne ii], [Ni ii], and H i lines but not in molecular emission. The atomic jet has a velocity of about 169 ± 15 km s−1, after correcting for inclination. The width of the jet increases with distance from the central protostar from 23 to 60 au, corresponding to an opening angle of 2. °6 ± 0. °5. By comparing the measured flux ratios of various fine-structure lines to those predicted by simple shock models, we derive a shock speed of 54 km s−1 and a preshock density of 2.0 × 103 cm−3 at the base of the jet. From these quantities and using a suite of jet models and extinction laws, we compute a mass-loss rate between 0.4 and 1.1 ×10−10 M ⊙ yr −1. The low mass-loss rate is consistent with simultaneous measurements of low mass accretion rate (2.4 ± 0.8 × 10−9 M ⊙ yr−1) for IRAS 16253−2429 from JWST observations, indicating that the protostar is in a quiescent accretion phase. Our results demonstrate that very low-mass protostars can drive highly collimated, atomic jets, even during the quiescent phase.
Extending Input Contexts of Language Models through Training on Segmented Sequences

Effectively training language models on long inputs poses many technical challenges. As a cost consideration, languages models are pretrained on a fixed sequence length before being adapted to longer sequences. We explore various methods for adapting models to longer inputs by training on segmented sequences and an interpolation-based method for extending absolute positional embeddings. We develop a training procedure to extend the input context size of pretrained models with no architectural changes and no additional memory costs than training on the original input lengths. By sub-sampling segments from long inputs while maintaining their original position the model is able to learn new positional interactions. Our method benefits both models trained with absolute positional embeddings, by extending their input contexts, as well as popular relative positional embedding methods showing a reduced perplexity on sequences longer than they were trained on. We demonstrate our method can extend input contexts by a factor of 4x while improving perplexity.
Structural generalization in COGS: Supertagging is (almost) all you need

In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples. In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work, we extend a neural graph-based semantic parsing framework in several ways to alleviate this issue. Notably, we propose: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) a reduction of the graph prediction problem to the maximum matching problem; (3) the design of an incremental early-stopping training strategy to prevent overfitting. Experimentally, our approach significantly improves results on examples that require structural generalization in the COGS dataset, a known challenging benchmark for compositional generalization. Overall, our results confirm that structural constraints are important for generalization in semantic parsing.
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts

As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a great complementarity to each other on math reasoning tasks. In this work, we propose XoT, an integrated problem solving framework by prompting LLMs with diverse reasoning thoughts. For each question, XoT always begins with selecting the most suitable method then executes each method iteratively. Within each iteration, XoT actively checks the validity of the generated answer and incorporates the feedback from external executors, allowing it to dynamically switch among different prompting methods. Through extensive experiments on 10 popular math reasoning datasets, we demonstrate the effectiveness of our proposed approach and thoroughly analyze the strengths of each module. Moreover, empirical results suggest that our framework is orthogonal to recent work that makes improvements on single reasoning methods and can further generalise to logical reasoning domain. By allowing method switching, XoT provides a fresh perspective on the collaborative integration of diverse reasoning thoughts in a unified framework. The code is available at https://github.com/tengxiaoliu/XoT.
Tightening QC Relaxations of AC Optimal Power Flow through Improved Linear Convex Envelopes

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed a variety of convex relaxations that bound the optimal objective values of AC OPF problems. The well-known QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms (trigonometric functions and products) within convex envelopes. The accuracy of this method strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a convex envelope around this nonlinear function that takes the form of a polytope and then use projections of this envelope to obtain convex expressions for the nonlinear terms. Second, we use certain characteristics of the sine and cosine expressions along with the changes in their curvature to tighten this convex envelope. We also propose a coordinate transformation that rotates the power flow equations by an angle specific to each bus in order to obtain a tighter envelope. We demonstrate these improvements relative to a state-of-the-art QC relaxation implementation using the PGLib-OPF test cases. The results show improved optimality gaps in 68% of these cases.
SOUL at LBT: commissioning results, science and future

The SOUL systems at the Large Bincoular Telescope can be seen such as precursor for the ELT SCAO systems, combining together key technologies such as EMCCD, Pyramid WFS and adaptive telescopes. After the first light of the first upgraded system on September 2018, going through COVID and technical stops, we now have all the 4 systems working on-sky. Here, we report about some key control improvements and the system performance characterized during the commissioning. The upgrade allows us to correct more modes (500) in the bright end and increases the sky coverage providing SR(K)>20% with reference stars G$_{RP}$<17, opening to extragalcatic targets with NGS systems. Finally, we review the first astrophysical results, looking forward to the next generation instruments (SHARK-NIR, SHARK-Vis and iLocater), to be fed by the SOUL AO correction.
Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception

Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited. Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. However, the challenge lies not only in establishing the efficacy of LLMs but also in discerning the nuances of interaction between learners and these models, which impact learners' engagement and results. We conducted a formative study in an undergraduate computer science classroom (N=145) and a controlled experiment on Prolific (N=356) to explore the impact of four pedagogically informed guidance strategies on the learners' performance, confidence and trust in LLMs. Direct LLM answers marginally improved performance, while refining student solutions fostered trust. Structured guidance reduced random queries as well as instances of students copy-pasting assignment questions to the LLM. Our work highlights the role that teachers can play in shaping LLM-supported learning environments.
Caching Connections in Matchings

Motivated by the desire to utilize a limited number of configurable optical switches by recent advances in Software Defined Networks (SDNs), we define an online problem which we call the Caching in Matchings problem. This problem has a natural combinatorial structure and therefore may find additional applications in theory and practice. In the Caching in Matchings problem our cache consists of $k$ matchings of connections between servers that form a bipartite graph. To cache a connection we insert it into one of the $k$ matchings possibly evicting at most two other connections from this matching. This problem resembles the problem known as Connection Caching, where we also cache connections but our only restriction is that they form a graph with bounded degree $k$. Our results show a somewhat surprising qualitative separation between the problems: The competitive ratio of any online algorithm for caching in matchings must depend on the size of the graph. Specifically, we give a deterministic $O(nk)$ competitive and randomized $O(n \log k)$ competitive algorithms for caching in matchings, where $n$ is the number of servers and $k$ is the number of matchings. We also show that the competitive ratio of any deterministic algorithm is $\Omega(\max(\frac{n}{k},k))$ and of any randomized algorithm is $\Omega(\log \frac{n}{k^2 \log k} \cdot \log k)$. In particular, the lower bound for randomized algorithms is $\Omega(\log n)$ regardless of $k$, and can be as high as $\Omega(\log^2 n)$ if $k=n^{1/3}$, for example. We also show that if we allow the algorithm to use at least $2k-1$ matchings compared to $k$ used by the optimum then we match the competitive ratios of connection catching which are independent of $n$. Interestingly, we also show that even a single extra matching for the algorithm allows to get substantially better bounds.
Revisiting Implicit Differentiation for Learning Problems in Optimal Control

This paper proposes a new method for differentiating through optimal trajectories arising from non-convex, constrained discrete-time optimal control (COC) problems using the implicit function theorem (IFT). Previous works solve a differential Karush-Kuhn-Tucker (KKT) system for the trajectory derivative, and achieve this efficiently by solving an auxiliary Linear Quadratic Regulator (LQR) problem. In contrast, we directly evaluate the matrix equations which arise from applying variable elimination on the Lagrange multiplier terms in the (differential) KKT system. By appropriately accounting for the structure of the terms within the resulting equations, we show that the trajectory derivatives scale linearly with the number of timesteps. Furthermore, our approach allows for easy parallelization, significantly improved scalability with model size, direct computation of vector-Jacobian products and improved numerical stability compared to prior works. As an additional contribution, we unify prior works, addressing claims that computing trajectory derivatives using IFT scales quadratically with the number of timesteps. We evaluate our method on a both synthetic benchmark and four challenging, learning from demonstration benchmarks including a 6-DoF maneuvering quadrotor and 6-DoF rocket powered landing.
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond

Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions. However, we have identified two dataset biases that models can exploit as shortcuts to resolve various VL tasks correctly without proper understanding. The first type of dataset bias is \emph{Unbalanced Matching} bias, where the correct answer overlaps the question and image more than the incorrect answers. The second type of dataset bias is \emph{Distractor Similarity} bias, where incorrect answers are overly dissimilar to the correct answer but significantly similar to other incorrect answers within the same sample. To address these dataset biases, we first propose Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data. We then introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing the synthesized training data, particularly the counterfactual data, via focusing on intra-sample differentiation. Extensive experiments demonstrate the effectiveness of ADS and ICT in consistently improving model performance across different benchmarks, even in domain-shifted scenarios.
Evaluating Large Language Models on Controlled Generation Tasks

While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large language models on generation tasks. We present an extensive analysis of various benchmarks including a sentence planning benchmark with different granularities. After comparing large language models against state-of-the-start finetuned smaller models, we present a spectrum showing large language models falling behind, are comparable, or exceed the ability of smaller models. We conclude that **large language models struggle at meeting fine-grained hard constraints**.
An infrared, Raman, and X-ray database of battery interphase components

Further technological advancement of both lithium-ion and emerging battery technologies can be catalyzed by an improved understanding of the chemistry and working mechanisms of the solid electrolyte interphases (SEIs) that form at electrochemically active battery interfaces. However, collecting and interpreting spectroscopy results of SEIs is difficult for several reasons, including the chemically diverse composition of SEIs. To address this challenge, we herein present a vibrational spectroscopy and X-ray diffraction data library of ten suggested SEI chemical constituents relevant to both lithium-ion and emerging battery chemistries. The data library includes attenuated total reflectance Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray diffraction data, collected in inert atmospheres afforded by custom designed sample holders. The data library presented in this work (and online repository) alleviates challenges with locating related work that is either diffusely spread throughout the literature, or is non-existent, and provides energy storage researchers streamlined access to vital SEI-relevant data that can catalyse future battery research efforts.
FlipDyn with Control: Resource Takeover Games with Dynamics

We present the FlipDyn, a dynamic game in which two opponents (a defender and an adversary) choose strategies to optimally takeover a resource that involves a dynamical system. At any time instant, each player can take over the resource and thereby control the dynamical system after incurring a state-dependent and a control-dependent costs. The resulting model becomes a hybrid dynamical system where the discrete state (FlipDyn state) determines which player is in control of the resource. Our objective is to compute the Nash equilibria of this dynamic zero-sum game. Our contributions are four-fold. First, for any non-negative costs, we present analytical expressions for the saddle-point value of the FlipDyn game, along with the corresponding Nash equilibrium (NE) takeover strategies. Second, for continuous state, linear dynamical systems with quadratic costs, we establish sufficient conditions under which the game admits a NE in the space of linear state-feedback policies. Third, for scalar dynamical systems with quadratic costs, we derive the NE takeover strategies and saddle-point values independent of the continuous state of the dynamical system. Fourth and finally, for higher dimensional linear dynamical systems with quadratic costs, we derive approximate NE takeover strategies and control policies which enable the computation of bounds on the value functions of the game in each takeover state. We illustrate our findings through a numerical study involving the control of a linear dynamical system in the presence of an adversary.
Radiometrically correct sharpening of multispectral images using a panchromatic image

With the advent of a panchromatic band on the enhanced thematic mapper (ETM) sensor, the interest in sharpening techniques for multispectral imagery has substantially increased. Previous work has been performed to increase the spatial resolution of multispectral imagery by combining it with a higher spatial resolution broad band panchromatic source. While these techniques resulted in a sharper multispectral image, they limited the extent to which the spectral information could be used. In this paper, we describe a technique that employs pseudoinverse estimation to generate a sharpened multispectral image. This technique provides the minimum mean squared error estimate of the sharpened spectral signatures given the information available from the panchromatic and multispectral sensors. The resulting radiometrically correct sharpened image is useful for various types of spectral analysis such as spectral classification, demixing, and detection. We then present the results of a quantitative evaluation of the processing technique in various operating modes as well as the results of a qualitative evaluation from analysts using MSI for mapping purposes.
Kiwifruit Respiration Rates, Storage Temperatures and Harvest Maturity

Kiwifruit (Actinidia deliciosa var. deliciosa 'Hayward' (A. Chev.) C.F. Liang et A.R. Ferguson) are stored commercially at ca. 0°C for extended periods prior to commercial sale and there is a slow decline in their respiration rate during storage. Respiration rates are being considered by the postharvest sector of the kiwifruit industry as one of a number of possible indicators of storage potential. Fruit respiration rates are known to be strongly dependent on temperature but the precise relationship for kiwifruit from different growing regions and at different times of the season have not been previously studied. 'Hayward' kiwifruit were picked from orchards in the Nelson, Bay of Plenty and Northland growing regions of New Zealand at three times during the harvest season in 2009: mid-April, mid-May and late May (the end of the commercial season). Eighty fruits per grower were couriered to Massey University within 24 h of sampling, and equilibrated for 2-4 d at 1, 5, 10 or 20°C. Respiration rates were then measured at those temperatures. Fruit weights were recorded and soluble solids were measured. The mean fruit weights in the samples were ca. 120 g and there were no consistent differences in mean weight between the regions. The mean soluble solids content increased during the season (6.8, 8.7 and 10.8 °Brix from early, mid and late season fruit respectively). Respiration rates were closely correlated with equilibration temperature. In addition there was a significant positive correlation between respiration rate and soluble solids content at 10 or 20°C but not at lower temperatures. There were no consistent differences in respiration rate that were attributable to growing region. One batch of fruit was affected by a late season hail event a week before harvest which left small corky scars on the fruit surface but this did not affect fruit respiration at the time of measurement.
The Sun within: active processes from two-temperature models

We propose an embedding of standard active particle models in terms of two-temperature processes. One temperature refers to an ambient thermal bath, and the other temperature effectively describes “hot spots,” i.e., systems with few degrees of freedom showing important population homogenization or even inversion of energy levels as a result of activation. As a result, the effective Carnot efficiency would get much higher than for our standard macroscopic thermal engines, making connection with the recent conundrum of hot mitochondria. Moreover, that setup allows to quantitatively specify the resulting nonequilibrium driving, useful in particular for bringing the notion of heat into play, and making easy contact with thermodynamic features. Finally, we observe that the shape transition in the steady low-temperature behavior of run-and-tumble particles (with the interesting emergence of edge states at high persistence) is stable and occurs for all temperature differences, including close-to-equilibrium.
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz

Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates. However, the current convergence analysis of sign-based methods relies on the strong assumptions of first-order gradient Lipschitz and second-order gradient Lipschitz, which may not hold in practical tasks like deep neural network training that involve high non-smoothness. In this paper, we revisit sign-based methods and analyze their convergence under more realistic assumptions of first- and second-order smoothness. We first establish the convergence of the sign-based method under weak first-order Lipschitz. Motivated by the weak first-order Lipschitz, we propose a relaxed second-order condition that still allows for nonconvex acceleration in sign-based methods. Based on our theoretical results, we gain insights into the computational advantages of the recently developed LION algorithm. In distributed settings, we prove that this nonconvex acceleration persists with linear speedup in the number of nodes, when utilizing fast communication compression gossip protocols. The novelty of our theoretical results lies in that they are derived under much weaker assumptions, thereby expanding the provable applicability of sign-based algorithms to a wider range of problems.