Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating How Fine-tuning on Bimodal Data Effects Code Generation

Despite the increase in popularity of language models for code generation, it is still unknown how training on bimodal coding forums affects a model's code generation performance and reliability. We, therefore, collect a dataset of over 2.2M StackOverflow questions with answers for finetuning. These fine-tuned models have average $pass@k$ improvements of 54.64% and 85.35% on the HumanEval (Chen et al., 2021) and Mostly Basic Program Problems (Austin et al., 2021) tasks, respectively. This regime further decreases the number of generated programs with both syntax and runtime errors. However, we find that at higher temperatures, there are significant decreases to the model's ability to generate runnable programs despite higher $pass@k$ scores, underscoring the need for better methods of incorporating such data that mitigate these side effects. The code can be found https://github.com/gabeorlanski/bimodalcode-generation
Exploiting Contrastive Learning and Numerical Evidence for Improving Confusing Legal Judgment Prediction

Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case’s charge, law article and penalty term. A core problem of LJP is how to distinguish confusing legal cases, where only subtle text differences exist. Previous studies fail to distinguish different classiﬁcation errors with a standard cross-entropy classiﬁcation loss, and ignore the numbers in the fact description for predicting the term of penalty. To tackle these issues, in this work, ﬁrst, we propose a moco-based supervised contrastive learning to learn distinguishable representations, and explore the best strategy to construct positive example pairs to beneﬁt all three subtasks of LJP simultaneously. Second, in order to exploit the numbers in legal cases for predicting the penalty terms of certain cases, we further enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model. Extensive experiments on public benchmarks show that the proposed method achieves new state-of-the-art results, especially on confusing legal cases. Ablation studies also demonstrate the effectiveness of each component.
Heatmap-based Out-of-Distribution Detection

Our work investigates out-of-distribution (OOD) detection as a neural network output explanation problem. We learn a heatmap representation for detecting OOD images while visualizing in- and out-of-distribution image regions at the same time. Given a trained and fixed classifier, we train a decoder neural network to produce heatmaps with zero response for in-distribution samples and high response heatmaps for OOD samples, based on the classifier features and the class prediction. Our main innovation lies in the heatmap definition for an OOD sample, as the normalized difference from the closest in-distribution sample. The heatmap serves as a margin to distinguish between in- and out-of-distribution samples. Our approach generates the heatmaps not only for OOD detection, but also to indicates in- and out-of-distribution regions of the input image. In our evaluations, our approach mostly outperforms the prior work on fixed classifiers, trained on CIFAR-10, CIFAR-100 and Tiny ImageNet. The code is publicly available at: https://github.com/jhornauer/heatmap_ood.
QAmeleon: Multilingual QA with Only 5 Examples

Abstract The availability of large, high-quality datasets has been a major driver of recent progress in question answering (QA). Such annotated datasets, however, are difficult and costly to collect, and rarely exist in languages other than English, rendering QA technology inaccessible to underrepresented languages. An alternative to building large monolingual training datasets is to leverage pre-trained language models (PLMs) under a few-shot learning setting. Our approach, QAmeleon, uses a PLM to automatically generate multilingual data upon which QA models are fine-tuned, thus avoiding costly annotation. Prompt tuning the PLM with only five examples per language delivers accuracy superior to translation-based baselines; it bridges nearly 60% of the gap between an English-only baseline and a fully-supervised upper bound fine-tuned on almost 50,000 hand-labeled examples; and consistently leads to improvements compared to directly fine-tuning a QA model on labeled examples in low resource settings. Experiments on the TyDiqa-GoldP and MLQA benchmarks show that few-shot prompt tuning for data synthesis scales across languages and is a viable alternative to large-scale annotation.1
MEAL: Stable and Active Learning for Few-Shot Prompting

Few-shot classification has made great strides due to foundation models that, through priming and prompting, are highly effective few-shot learners. However, this approach has high variance both across different sets of few shots (data selection) and across different finetuning runs (run variability). This is problematic not only because it impedes the fair comparison of different approaches, but especially because it makes few-shot learning too unreliable for many real-world applications. To alleviate these issues, we make two contributions for more stable and effective few-shot learning: First, we propose novel ensembling methods and show that they substantially reduce run variability. Second, we introduce a new active learning (AL) criterion for data selection and present the first AL-based approach specifically tailored towards prompt-based learning. In our experiments, we show that our combined method, MEAL (Multiprompt finetuning and prediction Ensembling with Active Learning), improves overall performance of prompt-based finetuning by 2.3 points on five diverse tasks. We publicly share our code and data splits in https://github.com/akoksal/MEAL.
The Image Size of Iterated Rational Maps over Finite Fields

Let $\varphi : {{\mathbb{P}}}^1( {{\mathbb{F}}}_q)\to{{\mathbb{P}}}^1( {{\mathbb{F}}}_q)$ be a rational map of degree $d>1$ on a fixed finite field. We give asymptotic formulas for the size of image sets $\varphi ^n( {{\mathbb{P}}}^1( {{\mathbb{F}}}_q))$ as a function of $n$. This is done using properties of Galois groups of iterated maps, whose connection to the size of image sets is established via the Chebotarev Density Theorem. We apply our results in the following setting. For a rational map defined over a number field, consider the reduction of the map modulo each prime of the number field. We use our results to give explicit bounds on the proportion of periodic points in the residue fields.
On the rate of convergence of Bregman proximal methods in constrained variational inequalities

We examine the last-iterate convergence rate of Bregman proximal methods – from mirror descent to mirror-prox – in constrained variational inequalities. Our analysis shows that the convergence speed of a given method depends sharply on the Legendre exponent of the underlying Bregman regularizer (Euclidean, entropic, or other), a notion that measures the growth rate of said regularizer near a solution. In particular, we show that boundary solutions exhibit a clear separation of regimes between methods with a zero and non-zero Legendre exponent respectively, with linear convergence for the former versus sublinear for the latter. This dichotomy becomes even more pronounced in linearly constrained problems where, specifically, Euclidean methods converge along sharp directions in a finite number of steps, compared to a linear rate for entropic methods.
Topological transitions with an imaginary Aubry-André-Harper potential

We study one-dimensional lattices with imaginary-valued Aubry-Andre-Harper (AAH) potentials. Such lattices can host edge states with purely imaginary eigenenergies, which differ from the edge states of the Hermitian AAH model and are stabilized by a non-Hermitian particle-hole symmetry. The edge states arise when the period of the imaginary potential is a multiple of four lattice constants. They are topological in origin, and can manifest on domain walls between lattices with different modulation periods and phases, as predicted by a bulk polarization invariant. Interestingly, the edge states persist and remain localized even if the real line gap closes. These features can be used in laser arrays to select topological lasing modes under spatially extended pumping.
EIGER. I. A Large Sample of [O iii]-emitting Galaxies at 5.3 < z < 6.9 and Direct Evidence for Local Reionization by Galaxies

We present a first sample of 117 [O iii] λλ4960, 5008–selected star-forming galaxies at 5.33 < z < 6.93 detected in JWST/NIRCam 3.5 μm slitless spectroscopy of a 6. ′5×3. ′4 field centered on the hyperluminous quasar SDSS J0100+2802, obtained as part of the Emission-line galaxies and Intergalactic Gas in the Epoch of Reionization (EIGER) survey. Three prominent galaxy overdensities are observed, one of them at the redshift of the quasar. Galaxies are found within 200 pkpc and 105 km s−1 of four known metal absorption-line systems. We focus on the role of the galaxies in ionizing the intergalactic medium (IGM) during the later stages of cosmic reionization and construct the mean Lyα and Lyβ transmission as a function of distance from the galaxies. At the lowest redshifts in our study, 5.3 < z < 5.7, the IGM transmission rises monotonically with distance from the galaxies, as seen previously at lower redshifts. In contrast, at 5.7 < z < 6.14, the transmission of both Lyα and Lyβ first increases with distance but then peaks at a distance of 5 cMpc before declining. Finally, in the region 6.15 < z < 6.26, where the additional ionizing radiation from the quasar dominates, the monotonic increase in transmission with distance is reestablished. This result is interpreted to represent evidence that the transmission of the IGM at z ∼ 5.9 toward J0100+2802 results from the “local” ionizing radiation of galaxies that dominates over the much-reduced cosmic background.
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase. However, the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks, limiting the real-world deployment of these methods. This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it. The benchmark includes 13 publicly available datasets for OOD testing, and evaluations are conducted on 8 classic NLP tasks over 21 popularly used PLMs, including GPT-3 and GPT-3.5. Our findings confirm the need for improved OOD accuracy in NLP tasks, as significant performance degradation was observed in all settings compared to in-distribution (ID) accuracy.
Resolution of the symmetric algebra of a finite base locus

We provide a locally free resolution of the projectivized symmetric algebra of the ideal sheaf of a zero-dimensional scheme defined by n + 1 equations in an n-dimensional variety. The resolution is given in terms of the resolution of the ideal itself and of the Eagon-Northcott complex of the Koszul hull.
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization

Deep Q-learning based algorithms have been applied successfully in many decision making problems, while their theoretical foundations are not as well understood. In this paper, we study a Fitted Q-Iteration with two-layer ReLU neural network parameterization, and find the sample complexity guarantees for the algorithm. Our approach estimates the Q-function in each iteration using a convex optimization problem. We show that this approach achieves a sample complexity of $\tilde{\mathcal{O}}(1/\epsilon^{2})$, which is order-optimal. This result holds for a countable state-spaces and does not require any assumptions such as a linear or low rank structure on the MDP.
Monocular BEV Perception of Road Scenes via Front-to-Top View Projection

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.
Direct observation of the leakage current in epitaxial diamond Schottky barrier devices by conductive-probe atomic force microscopy and Raman imaging

The origin of the high leakage current measured in several vertical-type diamond Schottky devices is conjointly investigated by conducting probe atomic force microscopy and confocal micro-Raman/photoluminescence imaging analysis. Local areas characterized by a strong decrease of the local resistance (5–6 orders of magnitude drop) with respect to their close surrounding have been identified in several different regions of the sample surface. The same local areas, also referenced as electrical hot-spots, reveal a slightly constrained diamond lattice and three dominant Raman bands in the low-wavenumber region (590, 914 and 1040 cm−1). These latter bands are usually assigned to the vibrational modes involving boron impurities and its possible complexes that can electrically act as traps for charge carriers. Local current–voltage measurements performed at the hot-spots point out a trap-filled-limited current as the main conduction mechanism favouring the leakage current in the Schottky devices.
Norms on complex matrices induced by random vectors

Abstract We introduce a family of norms on the $n \times n$ complex matrices. These norms arise from a probabilistic framework, and their construction and validation involve probability theory, partition combinatorics, and trace polynomials in noncommuting variables. As a consequence, we obtain a generalization of Hunter’s positivity theorem for the complete homogeneous symmetric polynomials.
The low wind expansion velocity of metal-poor carbon stars in the Halo and the Sagittarius stream

We report the detection, from observations using the James Clerk Maxwell Telescope, of CO J = 3 -> 2 transition lines in six carbon stars, selected as members of the Galactic Halo and having similar infrared colours. Just one Halo star had been detected in CO before this study. Infrared observations show that these stars are red (J - K > 3), due to the presence of large dusty circumstellar envelopes. Radiative transfer models indicate that these stars are losing mass with rather large dust mass-loss rates in the range 1-3.3 x 10-8 M-circle dot yr-1, similar to what can be observed in the Galactic disc. We show that two of these stars are effectively in the Halo, one is likely linked to the stream of the Sagittarius Dwarf Spheroidal galaxy (Sgr dSph), and the other three stars certainly belong to the thick disc. The wind expansion velocities of the observed stars are lower compared to carbon stars in the thin disc and are lower for the stars in the Halo and the Sgr dSph stream than in the thick disc. We discuss the possibility that the low expansion velocities result from the low metallicity of the Halo carbon stars. This implies that metal-poor carbon stars lose mass at a rate similar to metal-rich carbon stars, but with lower expansion velocities, as predicted by recent theoretical models. This result implies that the current estimates of mass-loss rates from carbon stars in Local Group galaxies will have to be reconsidered.
MEASUREMENTS OF ABSOLUTE ABUNDANCES IN SOLAR FLARES

We present measurements of elemental abundances in solar flares with the EUV Variability Experiment (EVE) on the Solar Dynamics Observatory. EVE observes both high temperature Fe emission lines (Fe xv–Fe xxiv) and continuum emission from thermal bremsstrahlung that is proportional to the abundance of H. By comparing the relative intensities of line and continuum emission it is possible to determine the enrichment of the flare plasma relative to the composition of the photosphere. This is the first ionization potential or FIP bias (f). Since thermal bremsstrahlung at EUV wavelengths is relatively insensitive to the electron temperature, it is important to account for the distribution of electron temperatures in the emitting plasma. We accomplish this by using the observed spectra to infer the differential emission measure distribution and FIP bias simultaneously. In each of the 21 flares that we analyze we find that the observed composition is close to photospheric. The mean FIP bias in our sample is f = 1.17 ± 0.22. This analysis suggests that the bulk of the plasma evaporated during a flare comes from deep in the chromosphere, below the region where elemental fractionation occurs.
Shell-model half-lives for r-process waiting point nuclei including first-forbidden contributions

We have performed large-scale shell-model calculations of the half-lives and neutron-branching probabilities of the r-process waiting point nuclei at the magic neutron numbers N=50, 82, and 126. The calculations include contributions from allowed Gamow-Teller and first-forbidden transitions. We find good agreement with the measured half-lives for the N=50 nuclei with charge numbers Z=28-32 and for the N=82 nuclei 129Ag and 130Cd. The contribution of forbidden transitions reduce the half-lives of the N=126 waiting point nuclei significantly, while they have only a small effect on the half-lives of the N=50 and 82 r-process nuclei.
Interpreting Bias in the Neural Networks: A Peek Into Representational Similarity

Neural networks trained on standard image classification data sets are shown to be less resistant to data set bias. It is necessary to comprehend the behavior objective function that might correspond to superior performance for data with biases. However, there is little research on the selection of the objective function and its representational structure when trained on data set with biases. In this paper, we investigate the performance and internal representational structure of convolution-based neural networks (e.g., ResNets) trained on biased data using various objective functions. We specifically study similarities in representations, using Centered Kernel Alignment (CKA), for different objective functions (probabilistic and margin-based) and offer a comprehensive analysis of the chosen ones. According to our findings, ResNets representations obtained with Negative Log Likelihood $(\mathcal{L}_{NLL})$ and Softmax Cross-Entropy ($\mathcal{L}_{SCE}$) as loss functions are equally capable of producing better performance and fine representations on biased data. We note that without progressive representational similarities among the layers of a neural network, the performance is less likely to be robust.
Parameterized Inapproximability of the Minimum Distance Problem over All Fields and the Shortest Vector Problem in All ℓp Norms

We prove that the Minimum Distance Problem (MDP) on linear codes over any fixed finite field and parameterized by the input distance bound is W[1]-hard to approximate within any constant factor. We also prove analogous results for the parameterized Shortest Vector Problem (SVP) on integer lattices. Specifically, we prove that SVP in the ℓp norm is W[1]-hard to approximate within any constant factor for any fixed p >1 and W[1]-hard to approximate within a factor approaching 2 for p=1. (We show hardness under randomized reductions in each case.) These results answer the main questions left open (and explicitly posed) by Bhattacharyya, Bonnet, Egri, Ghoshal, Karthik C. S. , Lin, Manurangsi, and Marx (Journal of the ACM, 2021) on the complexity of parameterized MDP and SVP. For MDP, they established similar hardness for binary linear codes and left the case of general fields open. For SVP in ℓp norms with p > 1, they showed inapproximability within some constant factor (depending on p) and left open showing such hardness for arbitrary constant factors. They also left open showing W[1]-hardness even of exact SVP in the ℓ1 norm.
Improving AFL++ CmpLog: Tackling the bottlenecks

The performance of the AFL++ CmpLog feature varies considerably for specific programs under test (PUTs). In this paper it is demonstrated that the main cause of the poor performance is low seed entropy, and a lack of deduplication of magic bytes candidates. An improvement is proposed by mapping comparisons to input bytes, in order to track which comparisons are controlled by what input bytes. This mapping is then used to fuzz only the comparison values that are magic byte candidates for that input part. Second, a caching mechanism is introduced to reduce the number of redundant executions. The evaluation of the improved versions shows a significant coverage gain compared to the original AFL++ implementation of CmpLog for all PUTs, without breaking functionality. The proposed solution in this paper provides a solid basis for a redesign of CmpLog.
A stable and jump-aware projection onto a discrete multi-trace space

This work is concerned with boundary element methods on singular geometries, specifically, those falling in the framework of ``multi-screens"by Claeys and Hiptmair. We construct a stable quasi-interpolant which preserves piecewise linear jumps on the multi-trace space. This operator is the boundary element analog of the Scott-Zhang quasi-interpolant used in the analysis of finite-element methods. More precisely, let $\Gamma$ be a multi-screen resolved by a triangulation $(\mathcal{M}_{\Gamma,h})$, and let $\mathbb{V}_h(\Gamma)$ be the space of continuous piecewise-linear multi-traces on $\Gamma$. We construct a linear operator $\Pi_h: \mathbb{H}^{1/2}(\Gamma) \to \mathbb{V}_h(\Gamma)$ with the following properties: (i) $\|\Pi_h u\|_{\mathbb{H}^{1/2}} \leq C_h \|u\|_{\mathbb{H}^{1/2}(\Gamma)}$ for all $u \in \mathbb{H}^{1/2}(\Gamma)$, (ii) $\Pi_h u_h = u_h$ for $u_h \in \mathbb{V}_h(\Gamma)$ and, (iii) $[\Pi_h u] = 0$ for every single trace $u \in H^{1/2}([\Gamma])$. The stability constant $C_h$ only depends on the aspect ratio of the elements of $\mathcal{M}_{\Omega,h}$, where $\mathcal{M}_{\Omega,h}$ is a tetrahedralization of $\mathcal{M}_{\Gamma,h}$. We deduce uniform bounds for the stability of the discrete harmonic lifting, and the equivalence of the $\widetilde{H}^{1/2}$ norm with a discrete quotient norm.