Miracle-dz/UniFAR-FTUs huggingface dataset:
LLM-Assisted Code Cleaning For Training Accurate Code Generators

Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on increasing the quantity and functional correctness of training sets while disregarding other stylistic elements of programs. More recently, data quality has garnered a lot of interest and multiple works have showcased its importance for improving performance. In this work, we investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system. We build a novel data-cleaning pipeline that uses these principles to transform existing programs by 1. ) renaming variables, 2. ) modularizing and decomposing complex code into smaller helper sub-functions, and 3. ) inserting natural-language based plans via LLM based transformations. We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B on our transformed modularized programs improves the performance by up to 30% compared to fine-tuning on the original dataset. Additionally, we demonstrate improved performance from using a smaller amount of higher-quality data, finding that a model fine-tuned on the entire original dataset is outperformed by a model trained on 15% of our cleaned dataset. Even in comparison to closed-source models, our models outperform the much larger AlphaCoder models.
AnySyn: A Cost-Generic Logic Synthesis Framework with Customizable Cost Functions

Modern technology-independent logic synthesis has been developed to optimize for the size and depth of AND-Inverter Graphs (AIGs) as a proxy of CMOS circuit area and delay. However, for non-CMOS-based emerging technologies, AIG size and depth may not be good cost estimations. Dedicated algorithms optimizing for more complex cost functions have been proven effective for their specific target applications yet require time and experts in both logic synthesis and the targeted technology to develop. In this work, we propose AnySyn, a cost-generic optimization framework for agile experimentation and prototyping of various customized cost functions before investing in developing specialized algorithms. Experimental results show that AnySyn outperforms non-specialized size and depth optimization algorithms by 14% and 19% on average and achieves comparable results to specialized algorithms within acceptable CPU time.
Algorithm Evolution Using Large Language Model

Optimization can be found in many real-life applications. Designing an effective algorithm for a specific optimization problem typically requires a tedious amount of effort from human experts with domain knowledge and algorithm design skills. In this paper, we propose a novel approach called Algorithm Evolution using Large Language Model (AEL). It utilizes a large language model (LLM) to automatically generate optimization algorithms via an evolutionary framework. AEL does algorithm-level evolution without model training. Human effort and requirements for domain knowledge can be significantly reduced. We take constructive methods for the salesman traveling problem as a test example, we show that the constructive algorithm obtained by AEL outperforms simple hand-crafted and LLM-generated heuristics. Compared with other domain deep learning model-based algorithms, these methods exhibit excellent scalability across different problem sizes. AEL is also very different from previous attempts that utilize LLMs as search operators in algorithms.
High-Order Reduced-Gradient Methods for Composite Variational Inequalities

This paper can be seen as an attempt of rethinking the {\em Extra-Gradient Philosophy} for solving Variational Inequality Problems. We show that the properly defined {\em Reduced Gradients} can be used instead for finding approximate solutions to Composite Variational Inequalities by the higher-order schemes. Our methods are optimal since their performance is proportional to the lower worst-case complexity bounds for corresponding problem classes. They enjoy the provable hot-start capabilities even being applied to minimization problems. The primal version of our schemes demonstrates a linear rate of convergence under an appropriate uniform monotonicity assumption.
Berndt-Type Integrals of Order Three and Series Associated with Jacobi Elliptic Functions

In this paper, we first establish explicit evaluations of six classes of hyperbolic sums by special values of the Gamma function by using the tools of the Fourier series expansions and the Maclaurin series expansions of a few Jacobi elliptic functions developed in our previous paper. Then, using the method of contour integrations involving hyperbolic and trigonometric functions, we establish explicit evaluations of two families of Berndt-type integrals of order three by special values of the Gamma function. Furthermore, we present some interesting consequences and illustrative examples.
RTSMS: Randomized Tucker with single-mode sketching

We propose RTSMS (Randomized Tucker via Single-Mode-Sketching), a randomized algorithm for approximately computing a low-rank Tucker decomposition of a given tensor. It uses sketching and least-squares to compute the Tucker decomposition in a sequentially truncated manner. The algorithm only sketches one mode at a time, so the sketch matrices are significantly smaller than alternative approaches. The algorithm is demonstrated to be competitive with existing methods, sometimes outperforming them by a large margin.
Problems in the astrophysics of accretion onto compact celestial bodies

Although during the last decade new observations and new theoretical results have brought better understanding of the physics of accretion onto compact objects, many old and several new questions and problems await answers and solutions. I show how the disc thermal-viscous instability model applied to both cataclysmic variable stars and X-ray binary transients compels us to conclude that assuming the existence in these systems of a flat accretion disc extending down to the accretor's surface or to the last stable orbit and fed with matter at its outer edge is too simple and inadequate a description of these objects. It is also clear that, in most cases, these discs cannot driven by (anomalous) viscosity only. The origin of the superhumps observed in cataclysmic variables and X-ray binaries is, contrary to the common opinion, still unknown. In accreting magnetic white dwarf systems outbursts not of the dwarf-nova type can be due to the magnetic gating instability and/or thermonuclear micronova explosions. Although the"typical"lightcvurves of X-ray transients can be described by analytical formulae (but their decay phase is not exponential), observations show that in many cases the light variations in these systems are much more complex. An elementary argument shows the impossibility of magnetars in pulsing ultraluminous X-ray systems, but we still do not have a complete, self-consistent description of supercritical accretion onto magnetized neutron stars and the resulting (necessarily beamed) emission. Although it is (almost) universally believed that active galactic nuclei contain accretion discs of the same type as those observed in binary systems, the evidence supporting this alleged truth is slim and the structure of accretion flows onto supermassive black holes is still to be determined.
Reinforcement Learning from Diffusion Feedback: Q* for Image Search

Large vision-language models are steadily gaining personalization capabilities at the cost of fine-tuning or data augmentation. We present two models for image generation using model-agnostic learning that align semantic priors with generative capabilities. RLDF, or Reinforcement Learning from Diffusion Feedback, is a singular approach for visual imitation through prior-preserving reward function guidance. This employs Q-learning (with standard Q*) for generation and follows a semantic-rewarded trajectory for image search through finite encoding-tailored actions. The second proposed method, noisy diffusion gradient, is optimization driven. At the root of both methods is a special CFG encoding that we propose for continual semantic guidance. Using only a single input image and no text input, RLDF generates high-quality images over varied domains including retail, sports and agriculture showcasing class-consistency and strong visual diversity. Project website is available at https://infernolia.github. io/RLDF.
Roadmap on machine learning glassy liquids

Unraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge in the field of the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key features related to structural relaxation and transport properties. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. This roadmap article explores the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present successful ML applications, as well as many open problems for the future, such as transferability and interpretability of ML approaches. We highlight new ideas and directions in which ML could provide breakthroughs to better understand glassy liquids. To foster a collaborative community effort, the article introduces the"GlassBench"dataset, providing simulation data and benchmarks for both two-dimensional and three-dimensional glass-formers. Emphasizing the importance of benchmarks, we identify critical metrics for comparing the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. The goal of this roadmap is to provide guidelines for the development of ML techniques in systems displaying slow dynamics, while inspiring new directions to improve our understanding of glassy liquids.
Folded symplectic forms in contact topology

We establish the relationship between folded symplectic forms and convex hypersurface theory in contact topology. As an application, we use convex hypersurface theory to reprove and strengthen the existence result for folded symplectic forms due to Cannas da Silva, and we generalize to all even dimensions Baykur's $4$-dimensional existence result of folded Weinstein structures and folded Lefschetz fibrations.
Visual cognition in multimodal large language models

A chief goal of artificial intelligence is to build machines that think like people. Yet it has been argued that deep neural network architectures fail to accomplish this. Researchers have asserted these models' limitations in the domains of causal reasoning, intuitive physics, and intuitive psychology. Yet recent advancements, namely the rise of large language models, particularly those designed for visual processing, have rekindled interest in the potential to emulate human-like cognitive abilities. This paper evaluates the current state of vision-based large language models in the domains of intuitive physics, causal reasoning, and intuitive psychology. Through a series of controlled experiments, we investigate the extent to which these modern models grasp complex physical interactions, causal relationships, and intuitive understanding of others' preferences. Our findings reveal that, while these models demonstrate a notable proficiency in processing and interpreting visual data, they still fall short of human capabilities in these areas. The models exhibit a rudimentary understanding of physical laws and causal relationships, but their performance is hindered by a lack of deeper insights - a key aspect of human cognition. Furthermore, in tasks requiring an intuitive theory of mind, the models fail altogether. Our results emphasize the need for integrating more robust mechanisms for understanding causality, physical dynamics, and social cognition into modern-day, vision-based language models, and point out the importance of cognitively-inspired benchmarks.
Fully Authentic Visual Question Answering Dataset from Online Communities

Visual Question Answering (VQA) entails answering questions about images. We introduce the first VQA dataset in which all contents originate from an authentic use case. Sourced from online question answering community forums, we call it VQAonline. We characterize this dataset and how it relates to eight mainstream VQA datasets. Observing that answers in our dataset tend to be much longer (i.e., a mean of 173 words) and so incompatible with standard VQA evaluation metrics, we instead utilize popular metrics for longer text evaluation for evaluating six state-of-the-art VQA models on VQAonline and report where they struggle most. Finally, we analyze which evaluation metrics align best with human judgments. To facilitate future extensions, we publicly-share the dataset at: https://vqaonline.github. io/.
A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation

Monocular Re-Localization (MRL) is a critical component in autonomous applications, estimating 6 degree-of-freedom ego poses w.r. t. the scene map based on monocular images. In recent decades, significant progress has been made in the development of MRL techniques. Numerous algorithms have accomplished extraordinary success in terms of localization accuracy and robustness. In MRL, scene maps are represented in various forms, and they determine how MRL methods work and how MRL methods perform. However, to the best of our knowledge, existing surveys do not provide systematic reviews about the relationship between MRL solutions and their used scene map representation. This survey fills the gap by comprehensively reviewing MRL methods from such a perspective, promoting further research. 1) We commence by delving into the problem definition of MRL, exploring current challenges, and comparing ours with existing surveys. 2) Many well-known MRL methods are categorized and reviewed into five classes according to the representation forms of utilized map, i.e., geo-tagged frames, visual landmarks, point clouds, vectorized semantic map, and neural network-based map. 3) To quantitatively and fairly compare MRL methods with various map, we introduce some public datasets and provide the performances of some state-of-the-art MRL methods. The strengths and weakness of MRL methods with different map are analyzed. 4) We finally introduce some topics of interest in this field and give personal opinions. This survey can serve as a valuable referenced materials for MRL, and a continuously updated summary of this survey is publicly available to the community at: https://github.com/jinyummiao/map-in-mono-reloc.
A limit theorem of nonlinear filtering for multiscale McKean-Vlasov stochastic systems

The work concerns about multiscale McKean-Vlasov stochastic systems. First of all, we prove an average principle for these systems in the $L^2$ sense. Moreover, a convergence rate is presented. Then we define the nonlinear filtering of these systems and establish a limit theorem about nonlinear filtering of them in the $L^2$ sense.
Concentration inequalities for the number of real zeros of Kac polynomials

We study concentration inequalities for the number of real roots of the classical Kac polynomials $$f_{n} (x) = \sum_{i=0}^n \xi_i x^i$$ where $\xi_i$ are independent random variables with mean 0, variance 1, and uniformly bounded $(2+\ep_0)$-moments. We establish polynomial tail bounds, which are optimal, for the bulk of roots. For the whole real line, we establish sub-optimal tail bounds.
MadRadar: A Black-Box Physical Layer Attack Framework on mmWave Automotive FMCW Radars

Frequency modulated continuous wave (FMCW) millimeter-wave (mmWave) radars play a critical role in many of the advanced driver assistance systems (ADAS) featured on today's vehicles. While previous works have demonstrated (only) successful false-positive spoofing attacks against these sensors, all but one assumed that an attacker had the runtime knowledge of the victim radar's configuration. In this work, we introduce MadRadar, a general black-box radar attack framework for automotive mmWave FMCW radars capable of estimating the victim radar's configuration in real-time, and then executing an attack based on the estimates. We evaluate the impact of such attacks maliciously manipulating a victim radar's point cloud, and show the novel ability to effectively `add' (i.e., false positive attacks), `remove' (i.e., false negative attacks), or `move' (i.e., translation attacks) object detections from a victim vehicle's scene. Finally, we experimentally demonstrate the feasibility of our attacks on real-world case studies performed using a real-time physical prototype on a software-defined radio platform.
On Oda's problem and special loci

Oda's problem, which deals with the fixed field of the universal monodromy representation of moduli spaces of curves and its independence with respect to the topological data, is a central question of anabelian arithmetic geometry. This paper emphasizes the stack nature of this problem by establishing the independence of monodromy fields with respect to finer special loci data of curves with symmetries, which we show provides a new proof of Oda's prediction.
What monads can and cannot do with a bit of extra time

The delay monad provides a way to introduce general recursion in type theory. To write programs that use a wide range of computational effects directly in type theory, we need to combine the delay monad with the monads of these effects. Here we present a first systematic study of such combinations. We study both the coinductive delay monad and its guarded recursive cousin, giving concrete examples of combining these with well-known computational effects. We also provide general theorems stating which algebraic effects distribute over the delay monad, and which do not. Lastly, we salvage some of the impossible cases by considering distributive laws up to weak bisimilarity.
The generalized Vaidya spacetime with polytropic equation of state

The process of the gravitational collapse might lead not only to a black hole but also to naked singularity formation. In this paper, we consider the generalized Vaidya spacetime with polytropic and generalized polytropic equations of state. We solve the Einstein and Einstein-Maxwell equations to obtain the explicit form of a mass function. We consider the limiting cases of solutions and find out, that generalized Vaidya spacetime might behave like Vaidya-de Sitter and Bonnor-Vaidya-de sitter solutions. Moreover, we explicitly show, that the part of solution, which depends on the polytropic index, is similar to cosmological fields surrounding both Vaidya and Bonnor-Vaidya black holes. The process of the gravitational collapse has been then considered. We have found out that the conditions of the naked singularity formation don't depend on the polytropic index.
PLANNING FOR URBAN SPRAWL

This paper reviews recent trends in the pattern of human settlement in the United States as those trends bear on national policies concerning local transportation technology, system design, and infrastructure investment. It offers explicit projections of future settlement patterns by region for urban and rural environs, and discursive but less explicit forecasts of the dispersion of residences and work places within urbanized areas and of the general configuration of these areas. These projections and forecasts lead to estimates of local travel modes and volumes in the year 2020.
A Frequency-Domain Version of Willems' Fundamental Lemma

Willems' fundamental lemma has recently received an impressive amount of attention in the (data-driven) control community. In this paper, we formulate a frequency-domain equivalent of this lemma. In doing so, we bridge the gap between recent developments in data-driven analysis and control and the extensive knowledge on non-parametric frequency-domain identification that has accumulated, particularly in industry, through decades of working with classical (frequency-domain) control and identification techniques. Our formulation also allows for the combination of multiple data sets in the sense that, in the data, multiple input directions may be excited at the same frequency. We also illustrate the usefulness of our results by demonstrating how they can be applied to perform frequency-domain-data-driven simulation.
Procedural Fairness Through Decoupling Objectionable Data Generating Components

We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals. Inspired by John Rawls's advocacy for pure procedural justice, we view automated decision-making as a microcosm of social institutions, and consider how the data generating process itself can satisfy the requirements of procedural fairness. We propose a framework that decouples the objectionable data generating components from the neutral ones by utilizing reference points and the associated value instantiation rule. Our findings highlight the necessity of preventing disguised procedural unfairness, drawing attention not only to the objectionable data generating components that we aim to mitigate, but also more importantly, to the neutral components that we intend to keep unaffected.
Optically Induced Monopoles, Knots, and Skyrmions in Quantum Gases

We propose, and theoretically analyze, a practical protocol for the creation of topological monopole configurations, quantum knots, and skyrmions in Bose--Einstein condensates by employing fictitious magnetic fields induced by the interaction of the atomic cloud with coherent light fields. It is observed that a single coherent field is not enough for this purpose, but instead we find incoherent superpositions of several coherent fields that introduce topological point charges. We numerically estimate the experimentally achievable strengths and gradients of the induced fictitious magnetic fields and find them to be adjustable at will to several orders of magnitude greater than those of the physical magnetic fields employed in previous experimental studies. This property together with ultrafast control of the optical fields paves the way for advanced engineering of topological defects in quantum gases.
An empirical method of estimating the retention of phosphorus in lakes

The relationship between phosphorus retention and several other lake and watershed parameters was examined for 15 lakes. A model relating the areal water load (qs) to phosphorus retention was proposed:Rp = 0.426 exp (−0.271qs) + 0.574 exp (−0.00949qs). This model was found to be theoretically sound, and the predicted and measured values were in close agreement (r = 0.94).
Population fluctuation mechanism of the super-thermal photon statistic of LEDs with collective effects

We found that fluctuations in the number of emitters lead to a super-thermal photon statistics of small LEDs in a linear regime, with a strong emitter-field coupling and a bad cavity favorable for collective effects. A simple analytical expression for the second-order correlation function g_2 is found. g_2 increase up to g_2=6 in the two-level LED model is predicted. The super-thermal photon statistics is related to the population fluctuation increase of the spontaneous emission to the cavity mode.