Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Modern Compiler Fuzzing

Most software that runs on computers undergoes processing by compilers. Since compilers constitute the fundamental infrastructure of software development, their correctness is paramount. Over the years, researchers have invested in analyzing, understanding, and characterizing the bug features over mainstream compilers. These studies have demonstrated that compilers correctness requires greater research attention, and they also pave the way for compiler fuzzing. To improve compilers correctness, researchers have proposed numerous compiler fuzzing techniques. These techniques were initially developed for testing traditional compilers such as GCC/LLVM and have since been generalized to test various newly developed, domain-specific compilers, such as graphics shader compilers and deep learning (DL) compilers. In this survey, we provide a comprehensive summary of the research efforts for understanding and addressing compilers defects. Specifically, this survey mainly covers two aspects. First, it covers researchers investigation and expertise on compilers bugs, such as their symptoms and root causes. The compiler bug studies cover GCC/LLVM, JVM compilers, and DL compilers. In addition, it covers researchers efforts in designing fuzzing techniques, including constructing test programs and designing test oracles. Besides discussing the existing work, this survey outlines several open challenges and highlights research opportunities.
Proof of the Clustered Hadwiger Conjecture

Hadwiger’s Conjecture asserts that every $K_{h}$-minor-free graph is properly $(h-1)$-colourable. We prove the following improper analogue of Hadwiger’s Conjecture: for fixed h, every $K_{h}$-minor-free graph is $(h-1)$-colourable with monochromatic components of bounded size. The number of colours is best possible regardless of the size of monochromatic components. It solves an open problem of Edwards, Kang, Kim, Oum and Seymour [SIAM J. Disc. Math. 2015], and concludes a line of research initiated in 2007. Similarly, for fixed $t \geqslant s$, we show that every $K_{s, t}$-minor-free graph is $(s+1)$-colourable with monochromatic components of bounded size. The number of colours is best possible, solving an open problem of van den Heuvel and Wood [J. London Math. Soc. 2018]. We actually prove a single theorem from which both of the above results are immediate corollaries. For an excluded apex minor, we strengthen the result as follows: for fixed $t \geqslant s \geqslant 3$, and for any fixed apex graph X, every $K_{s, t}$-subgraph-free X-minor-free graph is $(s+1)$-colourable with monochromatic components of bounded size. The number of colours is again best possible.
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction

In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks. Cross Entropy (CE) loss has been shown to be not robust to noisy labels due to its unboundedness. To alleviate this issue, existing works typically design specialized robust losses with the symmetric condition, which usually lead to the underfitting issue. In this paper, our key idea is to induce a loss bound at the logit level, thus universally enhancing the noise robustness of existing losses. Specifically, we propose logit clipping (LogitClip), which clamps the norm of the logit vector to ensure that it is upper bounded by a constant. In this manner, CE loss equipped with our LogitClip method is effectively bounded, mitigating the overfitting to examples with noisy labels. Moreover, we present theoretical analyses to certify the noise-tolerant ability of LogitClip. Extensive experiments show that LogitClip not only significantly improves the noise robustness of CE loss, but also broadly enhances the generalization performance of popular robust losses.
Testing the Broken Windows Theory in the context of Technical Debt

Context : The term technical debt (TD) describes the aggregation of sub-optimal solutions that serve to impede the evolution and maintenance of a system. Some claim that the broken windows theory (BWT), a concept borrowed from crimi-nology, also applies to software development projects. The theory states that the presence of indications of previous crime (such as a broken window) will increase the likelihood of further criminal activity; TD could be considered the broken windows of software systems. Objective : To empirically investigate the causal relationship between the TD density of a system and the propensity of developers to introduce new TD during the extension of that system. Method: The study used a mixed-methods research strategy consisting of a controlled experiment with an accompanying survey and follow-up interviews. The experiment had a total of 29 developers of varying experience levels completing system extension tasks in already existing systems with high or low TD density. Results : The analysis revealed significant effects of TD level on the subjects’ tendency to re-implement (rather than reuse) functionality, choose non-descriptive variable names, and introduce other code smells identified by the software tool SonarQube, all with at least 95% credible intervals. Conclusions : Three separate significant results along with a validating qualitative result combine to form substantial evidence of the BWT’s existence in software engineering contexts. This study finds that existing TD can have a mayor impact on developers propensity to introduce new TD of various types during development.
Response Characteristics of Thermoluminescence and Alanine-based Dosemeters to 16 and 25 MeV Proton Beams

The proton response of LiF:Mg,Ti (TLD-100) and alanine dosemeters (prepared at the Istituto Superiore di Sanita, Rome, Italy) was studied. The irradiations were performed with proton beams with initial energies of 26.6 MeV and 18 MeV at various depths in a PMMA phantom. Results were compared with reference dosimetry provided by a Markus parallel-plate ionisation chamber and with TRIM range-effect calculations. A significant energy dependence of response was observed. The effectiveness relative to 60 Co of both dosemeters was evaluated.
Dynamics of Polymer Ejection from a Nano-sphere

Polymer ejection from nano-confinement has been of interest due to its relation to various fundamental sciences and applications. However, the ejection dynamics of a polymer with different persistence lengths from confinement through a nanopore is still poorly understood. In this manuscript, a theory is developed for the ejection dynamics of a polymer with the total length $L_0$ and persistence length $l$ from a sphere of diameter $D$. These length-scales specify different regimes, which determine the polymer dynamics and its ejection rate. It is seen that the polymer undergoes between two to three confinement regimes, in some cases. The total ejection time $\tau$ depends on the polymer dynamics in various relevant regimes that the polymer experiences. Dependence of the ejection time on the system parameters is discussed according to the theory. The theory predicts that $\alpha$ in $\tau \sim L_0^{\alpha}$ changes between 1 and 1.7, $\beta$ in $\tau \sim D^{\beta}$ changes between 3 and 5, and $\gamma$ in $\tau \sim l^{\gamma}$ is often smaller than 1, in the studied range of the parameters.
Interstellar Polarization Survey. III. Relation between Optical Polarization and Reddening in the General Interstellar Medium

Optical starlight can be partially polarized while propagating through the dusty, magnetized interstellar medium (ISM). The polarization efficiency describes the polarization intensity fraction per reddening unit, P V /E(B − V), related to the interstellar dust grains and magnetic field properties. The maximum value observed, [P V /E(B − V)]max, is thus achieved under optimal polarizing conditions of the ISM. Therefore, the analysis of polarization efficiency observations across the Galaxy contributes to the study of magnetic field topology, small-scale magnetic fluctuations, grain-alignment efficiency, and composition. Infrared observations from Planck satellite have set [P V /E(B − V)]max to 13% mag−1. However, recent optical polarization observations in Planck's highly polarized regions showed polarization efficiency values between 13.6% mag−1 and 18.2% mag−1 (depending on the extinction map used), indicating that [P V /E(B − V)]max is not well constrained yet. We used V-band polarimetry of the Interstellar Polarization Survey (consisting of ∼10,500 high-quality observations distributed in 34 fields of 0.°3 × 0.°3) to accurately estimate the polarization efficiency in the ISM. We estimated the upper limit of P V /E(B − V) with the weighted 99th percentile of the field. In five regions, the polarization efficiency upper limit is above 13% mag−1. Furthermore, we found [P V /E(B − V)] max=15.8−0.9+1.3 % mag−1 using diffuse intermediate-latitude (∣b∣ > 7.°5) regions with apparently strong regular Galactic magnetic field in the plane-of-sky. We studied the variations of P V /E(B − V) across the sky and tested toy models of polarization efficiency with Galactic longitude that showed some correspondence with a uniform spiral magnetic field.
UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization

Social media platforms are essential outlets for expressing opinions, providing a valuable resource for capturing public viewpoints via text analytics. However, for many users, passive browsing is their preferred mode of interaction, leading to their perspectives being overlooked by text analytics methods. Meanwhile, social media polls have emerged as a practical feature for gathering public opinions, allowing post authors to pose questions with pre-defined answer options for readers to vote on. To broaden the benefits of polls for posts without them, this article explores the automatic generation of a poll from a social media post by leveraging cutting-edge natural language generation (NLG) techniques. However, existing NLG techniques, primarily developed for general-domain texts, may be ineffective when applied to noisy social media data, which often feature implicit context-question-answer relations. To tackle these challenges, we enrich a post context with its comments and propose a novel unified poll generation framework called UniPoll. It employs prompt tuning with multi-objective optimization to bolster the connection exploration between contexts (posts and comments) and polls (questions and answers). Experimental comparisons on a large-scale Chinese Weibo dataset show that UniPoll significantly outperforms T5, the state-of-the-art NLG model, which generates question and answer separately. Comprehensive qualitative and quantitative analyses further underscore the superiority of UniPoll through various evaluation lenses.
Blocked Cross-Validation: A Precise and Efficient Method for Hyperparameter Tuning

Hyperparameter tuning plays a crucial role in optimizing the performance of predictive learners. Cross--validation (CV) is a widely adopted technique for estimating the error of different hyperparameter settings. Repeated cross-validation (RCV) has been commonly employed to reduce the variability of CV errors. In this paper, we introduce a novel approach called blocked cross-validation (BCV), where the repetitions are blocked with respect to both CV partition and the random behavior of the learner. Theoretical analysis and empirical experiments demonstrate that BCV provides more precise error estimates compared to RCV, even with a significantly reduced number of runs. We present extensive examples using real--world data sets to showcase the effectiveness and efficiency of BCV in hyperparameter tuning. Our results indicate that BCV outperforms RCV in hyperparameter tuning, achieving greater precision with fewer computations.
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting

Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning tasks, which aims at designing a simple prompt like ``Let's think step by step'' or multiple in-context exemplars with well-designed rationales to elicit Large Language Models (LLMs) to generate intermediate reasoning steps. However, the generated rationales often come with mistakes, making unfactual and unfaithful reasoning chains. To mitigate this brittleness, we propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting LLMs to generate explicit pieces of knowledge evidence in the form of structure triple. This is inspired by our human behaviors, i.e., we can draw a mind map or knowledge map as the reasoning evidence in the brain before answering a complex question. Benefiting from CoK, we additionally introduce a F^2-Verification method to estimate the reliability of the reasoning chains in terms of factuality and faithfulness. For the unreliable response, the wrong evidence can be indicated to prompt the LLM to rethink. Extensive experiments demonstrate that our method can further improve the performance of commonsense, factual, symbolic, and arithmetic reasoning tasks.
BLIND: Bias Removal With No Demographics

Models trained on real-world data tend to imitate and amplify social biases. Common methods to mitigate biases require prior information on the types of biases that should be mitigated (e.g., gender or racial bias) and the social groups associated with each data sample. In this work, we introduce BLIND, a method for bias removal with no prior knowledge of the demographics in the dataset. While training a model on a downstream task, BLIND detects biased samples using an auxiliary model that predicts the main model’s success, and down-weights those samples during the training process. Experiments with racial and gender biases in sentiment classification and occupation classification tasks demonstrate that BLIND mitigates social biases without relying on a costly demographic annotation process. Our method is competitive with other methods that require demographic information and sometimes even surpasses them.
Exploring the Influence of Language on Time-Reward Perceptions in Large Language Models: A Study Using GPT-3.5 ∗

Language has a strong influence on our perceptions of time and rewards. This raises the question of whether large language models, when asked the same question in different languages, show different preferences for rewards over time and if their choices are similar to those of humans. In this study, we analyze the responses of GPT-3.5 (hereafter referred to as GPT) to prompts in multiple languages, exploring preferences between smaller, sooner rewards and larger, later rewards. Our results show that GPT displays greater patience when prompted in languages with weak future tense references (FTR), such as German and Mandarin, compared to languages with strong FTR, like English and French. These findings are consistent with the existing literature and suggest a correlation between GPT’s choices and the preferences of speakers of these languages. However, further analysis reveals that the preference for earlier or later rewards does not systematically change with reward gaps, indicating a lexicographic preference for earlier payments. While GPT may capture intriguing variations across languages, our findings indicate that the choices made by these models do not correspond to those of human decision-makers.
Single-Model Attribution of Generative Models Through Final-Layer Inversion

Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution. Such methods predict whether a sample was generated by a specific generator or not, for instance, to prove intellectual property theft. However, previous works are either limited to the closed-world setting or require undesirable changes to the generative model. We address these shortcomings by, first, viewing single-model attribution through the lens of anomaly detection. Arising from this change of perspective, we propose FLIPAD, a new approach for single-model attribution in the open-world setting based on final-layer inversion and anomaly detection. We show that the utilized final-layer inversion can be reduced to a convex lasso optimization problem, making our approach theoretically sound and computationally efficient. The theoretical findings are accompanied by an experimental study demonstrating the effectiveness of our approach and its flexibility to various domains.
Coulomb Corrections for Bose–Einstein Correlations from One- and Three-Dimensional Lévy-Type Source Functions

In the study of femtoscopic correlations in high-energy physics, besides Bose–Einstein correlations, one has to take final-state interactions into account. Amongst them, Coulomb interactions play a prominent role in the case of charged particles. Recent measurements have shown that in heavy-ion collisions, Bose–Einstein correlations can be best described by Lévy-type sources instead of the more common Gaussian assumption. Furthermore, three-dimensional measurements have indicated that, depending on the choice of frame, a deviation from spherical symmetry observed under the assumption of Gaussian source functions persists in the case of Lévy-type sources. To clarify such three-dimensional Lévy-type correlation measurements, it is thus important to study the effect of Coulomb interactions in the case of non-spherical Lévy sources. We calculated the Coulomb correction factor numerically in the case of such a source function for assorted kinematic domains and parameter values using the Metropolis–Hastings algorithm and compared our results with previous methods to treat Coulomb interactions in the presence of Lévy sources.
EXIT Chart Analysis of Non-Regular Signal Constellation Sets for BICM-ID

A key design element of bit-interleaved coded modulation with iterative decoding (BICM-ID) is the mapping of the bit patterns to the elements of the signal constellation set (SCS). Recently, we proposed mappings and new non-regular SCSs with superior asymptotic performance, i.e., lower error o ors. These proposed mappings use signal constellation sets with less elements than the number of bit patterns to be transmitted and an appropriate assignment of the bit patterns to them. The puncturing introduced by the not one-to-one relation between assigned bit patterns and the elements of the SCS is compensated by the usage of extrinsic information obtained in the iterations. We further show the theoretical limits for the improvement of BICM-ID by this technique. Based on the mutual information concept of EXIT charts, a method is presented allowing easy determination of the achievable gains by dieren t mappings for dieren t kinds of channels. The results are conrmed by simulations.
Serial concatenation of simple codes and differential modulations

Error-correcting codes are used in communication systems to provide reliable transmission on practical communication channels and thus reduce the number of retransmissions. The ability of a particular code to be able to detect and correct errors at the receiver depends on the individual code structure as well as on the decoding algorithm. Codes that provide strong error correcting capabilities usually involve a number of operations on the information bits and hence are not easily decodable at the receiver. Rather than relying on the use of a single high complexity code, a concatenation of two or more simple codes can be used. Serially concatenated encoding structures are very popular means to achieving close to capacity performance. Good performance of concatenated codes with practically viable decoding times is attributed to a technique known as iterative decoding. Iterative decoding can only be used in a situation where the component decoders are capable of generating soft information for the transmitted data. Soft information for symbol-by-symbol estimation is usually obtained by using the Bahl Cocke Jelinek Raviv (BCJR) algorithm or some sub-optimal version of it. Differential encoding of the data at the transmitter is regarded as an effective approach to combat phase ambiguities at the receiver, when using phase shift keying (PSK) schemes. Since the information is transmitted in difference of phases rather than the absolute phase of the transmitted symbol, there is more protection against an unknown channel phase at the receiver. The serial concatenation of an error correcting code with
Wave effects on the hydroelastic response of a surface-piercing hydrofoil. Part 2. Cavitating and ventilating flows

Abstract The objective of this paper is to understand the effects of waves and vaporous cavitation upon the hydrodynamic and hydroelastic responses of a flexible surface-piercing hydrofoil, adding to the subcavitating results presented in Part 1. In general, the presence of a sufficiently large vaporous cavitation bubble facilitated the formation of a ventilated cavity, substantially reducing the angle of attack or speed required to induce fully ventilated flow, relative to subcavitating conditions. A new co-analysis procedure was used to isolate synchronous hydrodynamic modes and structural operating deflection shapes. Significant dynamic load amplification occurred when the resonant frequencies of the first twisting and second bending modes coalesced in both fully wetted and partially cavitating flows. The presence of waves did not diminish the effect of frequency coalescence, but did encourage intermittent lock-in with both leading edge cavity shedding and trailing edge vortex shedding at certain speeds. Partial cavity shedding typically had a negligible impact on the power spectral densities of structural motions because of incoherent cavity shedding. However, lock-in between the cavity shedding frequency and modal coalescence frequency led to shifting of the primary frequency peak, as well as amplified harmonics and interactions between the cavity shedding frequency and vortex shedding frequency. The transition from partially cavitating to fully ventilated flow caused sudden and large drops in the mean hydrodynamic loads and deformations, as well as substantial reductions in the intensity of the fluctuations.
A fibre-reinforced fluid model of anisotropic plant cell growth

Many growing plant cells undergo rapid axial elongation with negligible radial expansion. Growth is driven by high internal turgor pressure causing viscous stretching of the cell wall, with embedded cellulose microfibrils providing the wall with strongly anisotropic properties. We present a theoretical model of a growing cell, representing the primary cell wall as a thin axisymmetric fibre-reinforced viscous sheet supported between rigid end plates. Asymptotic reduction of the governing equations, under simple sets of assumptions about the fibre and wall properties, yields variants of the traditional Lockhart equation, which relates the axial cell growth rate to the internal pressure. The model provides insights into the geometric and biomechanical parameters underlying bulk quantities such as wall extensibility, and shows how either dynamical changes in wall material properties or passive fibre reorientation may suppress cell elongation.