Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Transfer from Answer Ranking to Answer Generation

Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (termed GenQA). This allows for synthesizing the information from multiple candidates into a concise, natural-sounding answer. However, creating large-scale supervised training data for GenQA models is very challenging. In this paper, we propose to train a GenQA model by transferring knowledge from a trained AS2 model, to overcome the aforementioned issue. First, we use an AS2 model to produce a ranking over answer candidates for a set of questions. Then, we use the top ranked candidate as the generation target, and the next k top ranked candidates as context for training a GenQA model. We also propose to use the AS2 model prediction scores for loss weighting and score-conditioned input/output shaping, to aid the knowledge transfer. Our evaluation on three public and one large industrial datasets demonstrates the superiority of our approach over the AS2 baseline, and GenQA trained using supervised data.
EnDex: Evaluation of Dialogue Engagingness at Scale

We propose EnDex, the first human-reaction based model to evaluate dialogue engagingness. EnDex is trained on 80k Reddit-based Engagement Dataset (RED) curated using a novel distant-supervision framework. Engagingness is a key measure that captures high-level quality of AI dialogue systems and closely reflects actual user experience. However, data shortage, plus the abstract and extensive definition of engagingness makes it challenging to develop an automatic metric. Our work departs from mainstream approaches that use synthetic negative examples to train binary classifiers, and instead, proposes a solution using distant-supervision from human-reaction feedback. To support the soundness of our EnDex metric, we offer a theoretical foundation for engagement, an extensive ablation study, and empirical evidence of high correlation on five engagingness related datasets. We will release code, off-the-shelf EnDex model, and a large-scale dataset upon paper publication to facilitate future research.
MS-DC-UNeXt: An MLP-based Multi-Scale Feature Learning Framework For X-ray Images

: The advancement of deep learning theory and infrastructure is crucial in the progress of automatic segmentation techniques. Compared with traditional segmentation methods, automatic segmentation methods have considerable strengths such as convenience, accuracy, and so on. However, the drawbacks cannot be neglected. In the laboratory environment, most of the segmentation frameworks are based on deep learning at the cost of sacrificing the lightweight network architecture, adding a lot of parameters in the network to trade for excellent segmentation accuracy. In practical clinical applications, the lack of high computing performance (GPU) machines to maintain operational efficiency poses a huge challenge for the migration from laboratory to clinic. Recently, an alternative to the CNN and Transformer frameworks has been enthusiastically touted, with MLP-based network parameters being significantly decreased as all parameters are learned in the linear layer of the MLP and generate striking outcomes similar to both. Inspired by the MLP-based framework, we recommend leveraging the MS-DC-UNeXt as an alternative solution for medical image segmentation, which is mainly composed of Tokenized MLP block, Dual Channel block(DC-block), and Bottleneck (Res-ASPP). Among them, Tokenized MLP block has played a crucial role. On the one hand, adding DC-block and Res-ASPP block in a U-shape network can obtain Multi-Scale features. On the other hand, choosing two linear layers can acquire the self-attention mechanism similar to Transformer, which enables the MS-DC-UNeXt model to obtain local-to-global semantic dependency, and dramatically increase the operational efficiency of the model. We perform extensive validation on a benchmark dataset (ISIC 2018) and conduct medical image segmentation on the other two experimental datasets of X-rays Images. The experimental results show that MS-DC-UNeXt can achieve a better segmentation result on X-rays images than the U-shape model of state-of-the-art and successfully achieves the goal of Accuracy-speed trade-off.
Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources

The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control rules among other options. Such rules enable DERs to respond autonomously in response to time-varying grid loading conditions. The rules comprise affine droop control augmented with a deadband and saturation regions. Nonetheless, selecting the shape of these rules is not an obvious task, and the default options may not be optimal or dynamically stable. To this end, this work develops a novel methodology for customizing Volt/VAR rules on a per-bus basis for a single-phase feeder. The rules are adjusted by the utility every few hours depending on anticipated demand and solar scenarios. Using a projected gradient descent-based algorithm, rules are designed to improve the feeders voltage profile, comply with IEEE 1547 constraints, and guarantee stability of the underlying nonlinear grid dynamics. The stability region is inner approximated by a polytope and the rules are judiciously parameterized so their feasible set is convex. Numerical tests using real-world data on the IEEE 141-bus feeder corroborate the scalability of the methodology and explore the trade-offs of Volt/VAR control with alternatives.
Null test for interactions in the dark sector

Since there is no known symmetry in Nature that prevents a non-minimal coupling between the dark energy (DE) and cold dark matter (CDM) components, such a possibility constitutes an alternative to standard cosmology, with its theoretical and observational consequences being of great interest. In this paper we propose a new null test on the standard evolution of the dark sector based on the time dependence of the ratio between the CDM and DE energy densities which, in the standard $Λ$CDM scenario, scales necessarily as $a^{-3}$. We use the latest measurements of type Ia supernovae, cosmic chronometers and angular baryonic acoustic oscillations to reconstruct the expansion history using model-independent Machine Learning techniques, namely, the Linear Model formalism and Gaussian Processes. We find that while the standard evolution is consistent with the data at $3σ$ level, some deviations from the $Λ$CDM model are found at low redshifts, which may be associated with the current tension between local and global determinations of $H_0$.
Polyaromatic hydrocarbons with an imperfect aromatic system as catalysts of interstellar H2 formation

Although H is the simplest and the most abundant molecule in the Universe, its formation in the interstellar medium, especially in the photodissociation regions is far from being fully understood. According to suggestions, the formation of H is catalysed by polyaromatic hydrocarbons (PAHs) on the surface of interstellar grains. In the present study we have investigated the catalytic effect of small PAHs with an imperfect aromatic system. Quantum chemical computations were performed for the H-atom-abstraction and H-atom-addition reactions of benzene, cyclopentadiene, cycloheptatriene, indene, and 1H-phenalene. Heights of reaction barriers and tunnelling reaction rate constants were computed with density functional theory using the MPWB1K functional. For each molecule, the reaction path and the rate constants were determined at 50 K using ring-polymer instanton theory, and the temperature dependence of the rate constants was investigated for cyclopentadiene and cycloheptatriene. The computational results reveal that defects in the aromatic system compared to benzene can increase the rate of the catalytic H formation at 50 K. GRAPHICAL ABSTRACT
Higher-order regularity of solutions to the large scale moist atmosphere system

In this paper, we study the higher-order regularity of solutions to the large scale moist atmosphere system through the way of p -strong solu- tions. On the basis of the well-posedness results of strong solutions, we ﬁrst improve the regularity of solutions in the vertical direction, and then improve the regularity in the horizontal direction. Thus we obtain the well-posedness of solutions in H 2 space.
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?

The usage of automatic speech recognition (ASR) systems are becoming omnipresent ranging from personal assistant to chatbots, home, and industrial automation systems, etc. Modern robots are also equipped with ASR capabilities for interacting with humans as speech is the most natural interaction modality. However, ASR in robots faces additional challenges as compared to a personal assistant. Being an embodied agent, a robot must recognize the physical entities around it and therefore reliably recognize the speech containing the description of such entities. However, current ASR systems are often unable to do so due to limitations in ASR training, such as generic datasets and open-vocabulary modeling. Also, adverse conditions during inference, such as noise, accented, and far-field speech makes the transcription inaccurate. In this work, we present a method to incorporate a robot’s visual information into an ASR system and improve the recognition of a spoken utterance containing a visible entity. Specifically, we propose a new decoder biasing technique to incorporate the visual context while ensuring the ASR output does not degrade for incorrect context. We achieve a 59% relative reduction in WER from an unmodified ASR system.
Measurement and modeling of electron-cloud-induced betatron tune shifts at CESRTA

We report on extensive measurements at the Cornell Electron-positron Storage Ring of electron-cloud-induced betatron tune shifts for trains of positron bunches at 2.1 and 5.3 GeV with bunch populations ranging between 0.4x10^10 and 9.5x10^10. Measurements using a witness bunch with variable distance from the end of the train and variable bunch population inform the study of cloud decay and cloud pinching during the bunch passage. We employ Monte Carlo simulations of the reflection and absorption of synchrotron radiation photons to determine the pattern of absorption sites around the circumference of the storage ring. The Geant4 simulation toolkit is used to model the interactions of the photons with the beampipe wall and determine the production energy and location distributions of the photoelectrons which seed the electron cloud. An electron cloud buildup model based on these distributions successfully describes the tune shift measurements after determining ring-averaged secondary-yield properties of the vacuum chamber using an iterative parameter optimization procedure.
Chandra Observations of the NLS1 RX J2217.9-5941

We report the results of two Chandra ACIS-S observations from February and August 2003 of the highly X-ray variable Narrow-Line Seyfert 1 galaxy RX J2217.9--5941. Observations spanning the time from the ROSAT All Sky Survey (RASS) through an ASCA observation in 1998 indicate apparently monotonically decreasing flux by a factor of 30. The Chandra observations reveal increased emission over that seen in ASCA, supporting a persistent variability rather than an X-ray outburst event. However, the cause of the strong X-ray variability remains unclear. Our Chandra observations confirm the steep soft X-ray spectrum in the 0.2-2.0 keV band found during the ROSAT All-Sky Survey observation (\ax=2.7). The spectral shape of the source appears to be variable with the spectrum becoming softer when the source becomes fainter. Best fitting models to the data include an absorbed broken power law, a blackbody plus power law, and a power law with partial covering absorption. The latter model suggests a variable partial-covering absorber in the line of sight which can explain in part the variability seen in RX J2217.9--5941. We suggest that there might be a population of Narrow Line Seyfert 1 galaxies which are at least at times highly absorbed.
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis

Large, diachronic datasets of political discourse are hard to come across, especially for resource-lean languages such as Greek. In this paper, we introduce a curated dataset of the Greek Parliament Proceedings that extends chronologically from 1989 up to 2020. It consists of more than 1 million speeches with extensive metadata, extracted from 5,355 parliamentary record files. We explain how it was constructed and the challenges that we had to overcome. The dataset can be used for both computational linguistics and political analysis-ideally, combining the two. We present such an application, showing (i) how the dataset can be used to study the change of word usage through time, (ii) between significant historical events and political parties, (iii) by evaluating and employing algorithms for detecting semantic shifts.
On the Limitations of Reference-Free Evaluations of Generated Text

There is significant interest in developing evaluation metrics which accurately estimate the quality of generated text without the aid of a human-written reference text, which can be time consuming and expensive to collect or entirely unavailable in online applications. However, in this work, we demonstrate that these reference-free metrics are inherently biased and limited in their ability to evaluate generated text, and we argue that they should not be used to measure progress on tasks like machine translation or summarization. We show how reference-free metrics are equivalent to using one generation model to evaluate another, which has several limitations: (1) the metrics can be optimized at test time to find the approximate best-possible output, (2) they are inherently biased toward models which are more similar to their own, and (3) they can be biased against higher-quality outputs, including those written by humans. Therefore, we recommend that reference-free metrics should be used as diagnostic tools for analyzing and understanding model behavior instead of measures of how well models perform a task, in which the goal is to achieve as high of a score as possible.
Combining cosmic shear data with correlated photo-z uncertainties: constraints from DESY1 and HSC-DR1

An accurate calibration of the source redshift distribution p(z) is a key aspect in the analysis of cosmic shear data. This, one way or another, requires the use of spectroscopic or high-quality photometric samples. However, the difficulty to obtain colour-complete spectroscopic samples matching the depth of weak lensing catalogs means that the analyses of different cosmic shear datasets often use the same samples for redshift calibration. This introduces a source of statistical and systematic uncertainty that is highly correlated across different weak lensing datasets, and which must be accurately characterised and propagated in order to obtain robust cosmological constraints from their combination. In this paper we introduce a method to quantify and propagate the uncertainties on the source redshift distribution in two different surveys sharing the same calibrating sample. The method is based on an approximate analytical marginalisation of the p(z) statistical uncertainties and the correlated marginalisation of residual systematics. We apply this method to the combined analysis of cosmic shear data from the DESY1 data release and the HSC-DR1 data, using the COSMOS 30-band catalog as a common redshift calibration sample. We find that, although there is significant correlation in the uncertainties on the redshift distributions of both samples, this does not change the final constraints on cosmological parameters significantly. The same is true also for the impact of residual systematic uncertainties from the errors in the COSMOS 30-band photometric redshifts. Additionally, we show that these effects will still be negligible in Stage-IV datasets. Finally, the combination of DESY1 and HSC-DR1 allows us to constrain the “clumpiness” parameter to S 8 = 0.768+0.021 -0.017. This corresponds to a ∼√(2) improvement in uncertainties with respect to either DES or HSC alone.
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders

Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models (PLM) to encode item text into item representations. Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transferable sequential Recommenders. The main novelty of our approach lies in the new item representation scheme: it first maps item text into a vector of discrete indices (called item code), and then employs these indices to lookup the code embedding table for deriving item representations. Such a scheme can be denoted as “text ⟹ code ⟹ representation”. Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives. Furthermore, we design a new cross-domain fine-tuning method based on a differentiable permutation-based network. Extensive experiments conducted on six public benchmarks demonstrate the effectiveness of the proposed approach, in both cross-domain and cross-platform settings. Code and pre-trained model are available at: https://github.com/RUCAIBox/VQ-Rec.
Bilingual Synchronization: Restoring Translational Relationships with Editing Operations

Machine Translation (MT) is usually viewed as a one-shot process that generates the target language equivalent of some source text from scratch. We consider here a more general setting which assumes an initial target sequence, that must be transformed into a valid translation of the source, thereby restoring parallelism between source and target. For this bilingual synchronization task, we consider several architectures (both autoregressive and non-autoregressive) and training regimes, and experiment with multiple practical settings such as simulated interactive MT, translating with Translation Memory (TM) and TM cleaning. Our results suggest that one single generic edit-based system, once fine-tuned, can compare with, or even outperform, dedicated systems specifically trained for these tasks.
Task-Based Assessment for Neural Networks: Evaluating Undersampled MRI Reconstructions based on Human Observer Signal Detection

Recent research has explored using neural networks to reconstruct undersampled magnetic resonance imaging (MRI) data. Because of the complexity of the artifacts in the reconstructed images, there is a need to develop task-based approaches of image quality. Common metrics for evaluating image quality like the normalized root mean squared error (NRMSE) and structural similarity (SSIM) are global metrics which average out impact of subtle features in the images. Using measures of image quality which incorporate a subtle signal for a specific task allow for image quality assessment which locally evaluates the effect of undersampling on a signal. We used a U-Net to reconstruct under-sampled images with 2x, 3x, 4x and 5x fold 1-D undersampling rates. Cross validation was performed for a 500 and a 4000 image training set with both structural similarity (SSIM) and mean squared error (MSE) losses. A two alternative forced choice (2-AFC) observer study was carried out for detecting a subtle signal (small blurred disk) from images with the 4000 image training set. We found that for both loss functions and training set sizes, the human observer performance on the 2-AFC studies led to a choice of a 2x undersampling but the SSIM and NRMSE led to a choice of a 3x undersampling. For this task, SSIM and NRMSE led to an overestimate of the achievable undersampling using a U-Net before a steep loss of image quality when compared to the performance of human observers in the detection of a subtle lesion.
FCM: Forgetful Causal Masking Makes Causal Language Models Better Zero-Shot Learners

Large language models (LLM) trained using the next-token-prediction objective, such as GPT3 and PaLM, have revolutionized natural language processing in recent years by showing impressive zero-shot and few-shot capabilities across a wide range of tasks. In this work, we propose a simple technique that significantly boosts the performance of LLMs without adding computational cost. Our key observation is that, by performing the next token prediction task with randomly selected past tokens masked out, we can improve the quality of the learned representations for downstream language understanding tasks. We hypothesize that randomly masking past tokens prevents over-attending to recent tokens and encourages attention to tokens in the distant past. By randomly masking input tokens in the PaLM model, we show that we can significantly improve 1B and 8B PaLM’s zero-shot performance on the SuperGLUE benchmark from 55.7 to 59.2 and from 61.6 to 64.0, respectively. Our largest 8B model matches the score of PaLM with an average score of 64, despite the fact that PaLM is trained on a much larger dataset (780B tokens) of high-quality conversation and webpage data, while ours is trained on the smaller C4 dataset (180B tokens). Experimental results show that our method also improves PaLM’s zero and few-shot performance on a diverse suite of tasks, including commonsense reasoning, natural language inference and cloze completion. Moreover, we show that our technique also helps representation learning, significantly improving PaLM’s finetuning results.
Complexity and Ramsey Largeness of Sets of Oracles Separating Complexity Classes

We prove two sets of results concerning computational complexity classes. The first concerns a variation of the random oracle hypothesis posed by Bennett and Gill after they showed that relative to a randomly chosen oracle, P not equal NP with probability 1. This hypothesis was quickly disproven in several ways, most famously in 1992 with the result that IP equals PSPACE, in spite of the classes being shown unequal with probability 1. Here we propose a variation of what it means to be ``large'' using the Ellentuck topology. In this new context, we demonstrate that the set of oracles separating NP and co-NP is not small, and obtain similar results for the separation of PSPACE from PH along with the separation of NP from BQP. We demonstrate that this version of the hypothesis turns it into a sufficient condition for unrelativized relationships, at least in the three cases considered here. Second, we example the descriptive complexity of the classes of oracles providing the separations for these various classes, and determine their exact placement in the Borel hierarchy.