Miracle-dz/UniFAR-FTUs huggingface dataset:
A Naive Approach for Automatic Line-level Code Completion

Coding is an integral aspect of programming. A programmer can automatically complete a code fragment after writing a few tokens and the process of automatically completing a code fragment is known as code completion. A number of research on code completion have previously been conducted for method body completion and method parameter completion. However, this fundamental study explores the idea of automatically completing any program statement that might not even be part of a method. The goal of this study is to provide a number of suggestions to the programmer for completing code throughout the codebase by identifying and analyzing code similarities. The proposed methodology of this study can be regarded as a fundamental framework for code completion in the field of automated code completion. From the investigation of hundreds of revisions of four subject systems written in C and Java, it is observed that the proposed method can automatically complete around 22% of code statements with an average accuracy of 87% that a programmer writes during development which will accelerate software development time. This paper's empirical analysis further demonstrated that the proposed approach can be used with programming language neutrality. The study concludes by illustrating that taking 10 characters as prefixes before invoking completion provides maximum precision.
Difficult control is related to instability in biologically inspired Boolean networks

Previous work in Boolean dynamical networks has suggested that the number of components that must be controlled to select an existing attractor is typically set by the number of attractors admitted by the dynamics, with no dependence on the size of the network. Here we study the rare cases of networks that defy this expectation, with attractors that require controlling most nodes. We find empirically that unstable fixed points are the primary recurring characteristic of networks that prove more difficult to control. We describe an efficient way to identify unstable fixed points and show that, in both existing biological models and ensembles of random dynamics, we can better explain the variance of control kernel sizes by incorporating the prevalence of unstable fixed points. In the end, the fact that these exceptions are associated with dynamics that are unstable to small perturbations hints that they are likely an artifact of using deterministic models. These exceptions are likely to be biologically irrelevant, supporting the generality of easy controllability in biological networks.
The numeraire e-variable

We consider testing a composite null hypothesis $\mathcal{P}$ against a point alternative $\mathsf{Q}$. This paper establishes a powerful and general result: under no conditions whatsoever on $\mathcal{P}$ or $\mathsf{Q}$, there exists a special e-variable $X^*$ that we call the numeraire. It is strictly positive and for every $\mathsf{P} \in \mathcal{P}$, $\mathbb{E}_\mathsf{P}[X^*] \le 1$ (the e-variable property), while for every other e-variable $X$, we have $\mathbb{E}_\mathsf{Q}[X/X^*] \le 1$ (the numeraire property). In particular, this implies $\mathbb{E}_\mathsf{Q}[\log(X/X^*)] \le 0$ (log-optimality). $X^*$ also identifies a particular sub-probability measure $\mathsf{P}^*$ via the density $d \mathsf{P}^*/d \mathsf{Q} = 1/X^*$. As a result, $X^*$ can be seen as a generalized likelihood ratio of $\mathsf{Q}$ against $\mathcal{P}$. We show that $\mathsf{P}^*$ coincides with the reverse information projection (RIPr) when additional assumptions are made that are required for the latter to exist. Thus $\mathsf{P}^*$ is a natural definition of the RIPr in the absence of any assumptions on $\mathcal{P}$ or $\mathsf{Q}$. In addition to the abstract theory, we provide several tools for finding the numeraire in concrete cases. We discuss several nonparametric examples where we can indeed identify the numeraire, despite not having a reference measure. We end with a more general optimality theory that goes beyond the ubiquitous logarithmic utility. We focus on certain power utilities, leading to reverse R\'enyi projections in place of the RIPr, which also always exist.
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations

Rationales in the form of manually annotated input spans usually serve as ground truth when evaluating explainability methods in NLP. They are, however, time-consuming and often biased by the annotation process. In this paper, we debate whether human gaze, in the form of webcam-based eye-tracking recordings, poses a valid alternative when evaluating importance scores. We evaluate the additional information provided by gaze data, such as total reading times, gaze entropy, and decoding accuracy with respect to human rationale annotations. We compare WebQAmGaze, a multilingual dataset for information-seeking QA, with attention and explainability-based importance scores for 4 different multilingual Transformer-based language models (mBERT, distil-mBERT, XLMR, and XLMR-L) and 3 languages (English, Spanish, and German). Our pipeline can easily be applied to other tasks and languages. Our findings suggest that gaze data offers valuable linguistic insights that could be leveraged to infer task difficulty and further show a comparable ranking of explainability methods to that of human rationales.
Surface Luttinger surfaces and Luttinger-Lifshitz transitions in topological band structures

The standard paradigm of topological phases posits that the surface two phases with the same symmetries are necessarily separated by a bulk phase transition, while breaking the symmetry unlocks a path in parameter space that allows the phases to be connected adiabatically. Moreover, if the symmetry is broken only on the boundary, single-particle properties are expected to be blind to distinction between the two phases. In this work, we prove that this expectation is incorrect. We first reveal that the single-particle \emph{surface} Green's function contains zeros or ``LSs'' that respect the same bulk-boundary correspondence that the well-known topological surface states do. At non-zero temperatures, these modes contribute negatively to an effective surface specific heat. Remarkably, the LSs survive symmetry-breaking perturbations that destroy the surface states. Thus, a bulk topological phase transition causes a reconstruction of Luttinger surfaces on the surface and manifests as a discontinuity in the surface specific heat.
How Much Annotation is Needed to Compare Summarization Models?

Modern instruction-tuned models have become highly capable in text generation tasks such as summarization, and are expected to be released at a steady pace. In practice one may now wish to choose confidently, but with minimal effort, the best performing summarization model when applied to a new domain or purpose. In this work, we empirically investigate the test sample size necessary to select a preferred model in the context of news summarization. Empirical results reveal that comparative evaluation converges quickly for both automatic and human evaluation, with clear preferences for a system emerging from under 100 examples. The human preference data allows us to quantify how well automatic scores can reproduce preference rankings across a variety of downstream summarization tasks. We find that, while automatic metrics are stable at smaller sample sizes, only some automatic metrics are able to moderately predict model win rates according to human preference.
Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games

Similarity estimation is essential for many game AI applications, from the procedural generation of distinct assets to automated exploration with game-playing agents. While similarity metrics often substitute human evaluation, their alignment with our judgement is unclear. Consequently, the result of their application can fail human expectations, leading to e.g. unappreciated content or unbelievable agent behaviour. We alleviate this gap through a multi-factorial study of two tile-based games in two representations, where participants (N=456) judged the similarity of level triplets. Based on this data, we construct domain-specific perceptual spaces, encoding similarity-relevant attributes. We compare 12 metrics to these spaces and evaluate their approximation quality through several quantitative lenses. Moreover, we conduct a qualitative labelling study to identify the features underlying the human similarity judgement in this popular genre. Our findings inform the selection of existing metrics and highlight requirements for the design of new similarity metrics benefiting game development and research.
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models

This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model's online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from new samples, in conjunction with the learned hyperparameters. In the second phase, we propose a safety filter based on high-order control barrier functions (HOCBFs), synergized with the previously trained learning model. By leveraging the compound kernel from the first phase, we effectively address the inherent limitations of GPs in handling high-dimensional problems for real-time applications. The derived controller ensures a rigorous lower bound on the probability of satisfying the safety specification. Finally, the efficacy of our proposed algorithm is demonstrated through real-time obstacle avoidance experiments executed using both a simulation platform and a real-world 7-DOF robot.
On non-negative solutions of stochastic Volterra equations with jumps and non-Lipschitz coefficients

We consider one-dimensional stochastic Volterra equations with jumps for which we establish conditions upon the convolution kernel and coefficients for the strong existence and pathwise uniqueness of a non-negative c\`adl\`ag solution. By using the approach recently developed in arXiv:2302.07758, we show the strong existence by using a nonnegative approximation of the equation whose convergence is proved via a variant of the Yamada--Watanabe approximation technique. We apply our results to L\'evy-driven stochastic Volterra equations. In particular, we are able to define a Volterra extension of the so-called alpha-stable Cox--Ingersoll--Ross process, which is especially used for applications in Mathematical Finance.
Spatial Variation-Aware Read Disturbance Defenses: Experimental Analysis of Real DRAM Chips and Implications on Future Solutions

Read disturbance in modern DRAM chips is a widespread phenomenon and is reliably used for breaking memory isolation, a fundamental building block for building robust systems. RowHammer and RowPress are two examples of read disturbance in DRAM where repeatedly accessing (hammering) or keeping active (pressing) a memory location induces bitflips in other memory locations. Unfortunately, shrinking technology node size exacerbates read disturbance in DRAM chips over generations. As a result, existing defense mechanisms suffer from significant performance and energy overheads, limited effectiveness, or prohibitively high hardware complexity. In this paper, we tackle these shortcomings by leveraging the spatial variation in read disturbance across different memory locations in real DRAM chips. To do so, we 1) present the first rigorous real DRAM chip characterization study of spatial variation of read disturbance and 2) propose Sv\"ard, a new mechanism that dynamically adapts the aggressiveness of existing solutions based on the row-level read disturbance profile. Our experimental characterization on 144 real DDR4 DRAM chips representing 10 chip designs demonstrates a large variation in read disturbance vulnerability across different memory locations: in the part of memory with the worst read disturbance vulnerability, 1) up to 2x the number of bitflips can occur and 2) bitflips can occur at an order of magnitude fewer accesses, compared to the memory locations with the least vulnerability to read disturbance. Sv\"ard leverages this variation to reduce the overheads of five state-of-the-art read disturbance solutions, and thus significantly increases system performance.
Deterministic Molecular Assembly with a Finite Set of Building Blocks: Universal Assembly Kits for Backbone-Assisted and Sequence-Directed Abstract Tile Assembly Models

Backbone-assisted assembly processes -- such as protein folding -- allow the assembly of a large number of structures with high accuracy from only a small handful of fundamental building blocks. We aim to explore general principles underlying this phenomenon by studying variants of the temperature-1 abstract tile assembly model (aTAM). We consider the existence of finite sets of tile types that can deterministically assemble any shape producible by a given assembly model; we call such tile type sets universal assembly kits. Our first model, which we call the ``backboned aTAM", generates backbone-assisted assembly by forcing tiles to be be added to lattice positions neighbouring the immediately preceding tile, using a predetermined sequence of tile types. We demonstrate the existence of universal assembly kit for the backboned aTAM, and show that the existence of this set is maintained even under stringent restrictions to the rules of assembly. We hypothesise that the advantage of the backboned aTAM relative to the conventional aTAM -- which does not possess such a set -- is in part due to the specification of a sequence, and in part due to the geometric restrictions imposed by the backbone. To explore this intuition, we develop a second model call the ``sequenced aTAM". The sequenced aTAM uses a predetermined sequence of tiles, but does not constrain a tile to neighbour the immediately preceding tiles. We prove that this model has no universal assembly kit. The lack of such a kit is surprising, given that the number of tile sequences of length $N$ scales faster than the number of producible shapes of size $N$ for a sufficiently large -- but finite -- set of tiles.
Sensing atomic superfluid rotation beyond the standard quantum limit

Atomic superfluids formed using Bose-Einstein condensates (BECs) in a ring trap are currently being investigated in the context of superfluid hydrodynamics, quantum sensing and matter-wave interferometry. The characterization of the rotational properties of such superfluids is important, but can presently only be performed by using optical absorption imaging, which completely destroys the condensate. Recent studies have proposed coupling the ring BEC to optical cavity modes carrying orbital angular momentum to make minimally destructive measurements of the condensate rotation. The sensitivity of these proposals, however, is bounded below by the standard quantum limit set by the combination of laser shot noise and radiation pressure noise. In this work, we provide a theoretical framework that exploits the fact that the interaction between the scattered modes of the condensate and the light reduces to effective optomechanical equations of motion. We present a detailed theoretical analysis to demonstrate that the use of squeezed light and backaction evasion techniques allows the angular momentum of the condensate to be sensed with noise well below the standard quantum limit. Our proposal is relevant to atomtronics, quantum sensing and quantum information.
Noisy intermediate-scale quantum simulation of the one-dimensional wave equation

We design and implement quantum circuits for the simulation of the one-dimensional wave equation on the Quantinuum H1-1 quantum computer. The circuit depth of our approach scales as $O(n^{2})$ for $n$ qubits representing the solution on $2^n$ grid points, and leads to infidelities of $O(2^{-4n} t^{2})$ for simulation time $t$ assuming smooth initial conditions. By varying the qubit count we study the interplay between the algorithmic and physical gate errors to identify the optimal working point of minimum total error. Our approach to simulating the wave equation can readily be adapted to other quantum processors and serve as an application-oriented benchmark.
MambaStock: Selective state space model for stock prediction

The stock market plays a pivotal role in economic development, yet its intricate volatility poses challenges for investors. Consequently, research and accurate predictions of stock price movements are crucial for mitigating risks. Traditional time series models fall short in capturing nonlinearity, leading to unsatisfactory stock predictions. This limitation has spurred the widespread adoption of neural networks for stock prediction, owing to their robust nonlinear generalization capabilities. Recently, Mamba, a structured state space sequence model with a selection mechanism and scan module (S6), has emerged as a powerful tool in sequence modeling tasks. Leveraging this framework, this paper proposes a novel Mamba-based model for stock price prediction, named MambaStock. The proposed MambaStock model effectively mines historical stock market data to predict future stock prices without handcrafted features or extensive preprocessing procedures. Empirical studies on several stocks indicate that the MambaStock model outperforms previous methods, delivering highly accurate predictions. This enhanced accuracy can assist investors and institutions in making informed decisions, aiming to maximize returns while minimizing risks. This work underscores the value of Mamba in time-series forecasting. Source code is available at https://github.com/zshicode/MambaStock.
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain

This study investigates gender fairness in personalized pain care recommendations using machine learning algorithms. Leveraging a contextual bandits framework, personalized recommendations are formulated and evaluated using LinUCB algorithm on a dataset comprising interactions with $164$ patients across $10$ sessions each. Results indicate that while adjustments to algorithm parameters influence the quality of pain care recommendations, this impact remains consistent across genders. However, when certain patient information, such as self-reported pain measurements, is absent, the quality of pain care recommendations for women is notably inferior to that for men.
On the Improvement of Predictive Modeling Using Bayesian Stacking and Posterior Predictive Checking

Model uncertainty is pervasive in real world analysis situations and is an often-neglected issue in applied statistics. However, standard approaches to the research process do not address the inherent uncertainty in model building and, thus, can lead to overconfident and misleading analysis interpretations. One strategy to incorporate more flexible models is to base inferences on predictive modeling. This approach provides an alternative to existing explanatory models, as inference is focused on the posterior predictive distribution of the response variable. Predictive modeling can advance explanatory ambitions in the social sciences and in addition enrich the understanding of social phenomena under investigation. Bayesian stacking is a methodological approach rooted in Bayesian predictive modeling. In this paper, we outline the method of Bayesian stacking but add to it the approach of posterior predictive checking (PPC) as a means of assessing the predictive quality of those elements of the stacking ensemble that are important to the research question. Thus, we introduce a viable workflow for incorporating PPC into predictive modeling using Bayesian stacking without presuming the existence of a true model. We apply these tools to the PISA 2018 data to investigate potential inequalities in reading competency with respect to gender and socio-economic background. Our empirical example serves as rough guideline for practitioners who want to implement the concepts of predictive modeling and model uncertainty in their work to similar research questions.
Simple Calibration of Block Copolymer Melt Models

According to the universality hypothesis, the phase behavior of different block copolymer melt models having fixed composition depends solely on two parameters: the invariant chain length $\bar{N}$ and the effective interaction parameter $\chi N$. If models behave universally, they can be compared to each other and can predict experiment quantitatively. Here, we present a simple way to achieve this universality for coarse-grained models. Our method relies on the properties of the monomer interaction potential energy $z$ distribution. In particular, models having near-symmetric $z$-distributions exhibit universal phase behavior using the standard linear definition of the Flory-Huggins parameter $\chi\propto\alpha$, where $\alpha = \epsilon_{AB}-(\epsilon_{AA}+\epsilon_{BB})/2$, and $\epsilon_{xy}$ is the interaction energy between monomers of type $x$ and $y$. Previously, universality had been achieved using a nonlinear $\chi(\alpha)$ function which is difficult to obtain and interpret physically. The main parameter controlling the symmetry of the $z$-distribution is the monomer density $\rho$. Above certain $\rho$, models have symmetric $z$-distributions, and their order-disorder transition points follow the universal curve predicted by Fredrickson-Helfand theory in the experimentally relevant $\bar{N}>10^2$ range. On the other hand, low-$\rho$ models exhibit skewed $z$-distributions, and the simple $\chi\propto\alpha$ formula is no longer universally applicable to them. Our results can be used for correct block copolymer model building leading to a simple and direct comparison of simulations to experiments, which will facilitate the screening of new block copolymer morphologies and support materials design.
Needed computations and computational capabilities for stellarators

Stellarator plasmas are externally controlled to a degree unparalleled by any other fusion concept, magnetic or inertial. This control is largely through the magnetic fields produced by external coils. The development of fusion energy could be expedited by carrying out remarkably straight-forward computations to define strategies for exploiting this external control. In addition to these computations, which have a reliability limited only by competence, certain physics areas that affect the develop of stellarator power plants should have more intense study. The low cost and speed with which computations can be carried out relative to experiments has implications for the development of fusion. Computations should be used to develop a strategy that to the extent possible allows major issues to be circumvented. Required computations for this strategy are the subject of this paper.