Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformer Models for Type Inference in the Simply Typed Lambda Calculus: A Case Study in Deep Learning for Code

Despite a growing body of work at the intersection of deep learning and formal languages, there has been relatively little systematic exploration of transformer models for reasoning about typed lambda calculi. This is an interesting area of inquiry for two reasons. First, typed lambda calculi are the lingua franc of programming languages. A set of heuristics that relate various typed lambda calculi to effective neural architectures would provide a systematic method for mapping language features (e.g., polymorphism, subtyping, inheritance, etc.) to architecture choices. Second, transformer models are widely used in deep learning architectures applied to code, but the design and hyperparameter space for them is large and relatively unexplored in programming language applications. Therefore, we suggest a benchmark that allows us to explore exactly this through perhaps the simplest and most fundamental property of a programming language: the relationship between terms and types. Consequently, we begin this inquiry of transformer architectures for typed lambda calculi by exploring the effect of transformer warm-up and optimizer selection in the task of type inference: i.e., predicting the types of lambda calculus terms using only transformers. We find that the optimization landscape is difficult even in this simple setting. One particular experimental finding is that optimization by Adafactor converges much faster compared to the optimization by Adam and RAdam. We conjecture that such different performance of optimizers might be related to the difficulties of generalization over formally generated dataset.
Bounds on Maximum Weight Directed Cut

We obtain lower and upper bounds for the maximum weight of a directed cut in the classes of weighted digraphs and weighted acyclic digraphs as well as in some of their subclasses. We compare our results with those obtained for the maximum size of a directed cut in unweighted digraphs. In particular, we show that a lower bound obtained by Alon, Bollobas, Gyafas, Lehel and Scott (J Graph Th 55(1) (2007)) for unweighted acyclic digraphs can be extended to weighted digraphs with the maximum length of a cycle being bounded by a constant and the weight of every arc being at least one. We state a number of open problems.
Is ChatGPT a Good Recommender? A Preliminary Study

Recommendation systems have witnessed significant advancements and have been widely used over the past decades. However, most traditional recommendation methods are task-specific and therefore lack efficient generalization ability. Recently, the emergence of ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. Nonetheless, the application of ChatGPT in the recommendation domain has not been thoroughly investigated. In this paper, we employ ChatGPT as a general-purpose recommendation model to explore its potential for transferring extensive linguistic and world knowledge acquired from large-scale corpora to recommendation scenarios. Specifically, we design a set of prompts and evaluate ChatGPT's performance on five recommendation scenarios. Unlike traditional recommendation methods, we do not fine-tune ChatGPT during the entire evaluation process, relying only on the prompts themselves to convert recommendation tasks into natural language tasks. Further, we explore the use of few-shot prompting to inject interaction information that contains user potential interest to help ChatGPT better understand user needs and interests. Comprehensive experimental results on Amazon Beauty dataset show that ChatGPT has achieved promising results in certain tasks and is capable of reaching the baseline level in others. We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models. And the human evaluations show ChatGPT can truly understand the provided information and generate clearer and more reasonable results. We hope that our study can inspire researchers to further explore the potential of language models like ChatGPT to improve recommendation performance and contribute to the advancement of the recommendation systems field.
On the Linear Quadratic Regulator for Descriptor Systems

This paper deals with the linear quadratic optimal regulator problem for descriptor systems without performing a preliminary transformation for a descriptor system. We derive a generalized Riccati differential equation (GRDE) based on the two-point boundary value problem for a Hamiltonian equation. We then obtain an optimal feedback control and the optimal cost in terms of the solution of GRE. A simple example is included.
Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra

Neural Radiance Fields (NeRFs) are a very recent and very popular approach for the problems of novel view synthesis and 3D reconstruction. A popular scene representation used by NeRFs is to combine a uniform, voxel-based subdivision of the scene with an MLP. Based on the observation that a (sparse) point cloud of the scene is often available, this paper proposes to use an adaptive representation based on tetrahedra obtained by Delaunay triangulation instead of uniform subdivision or point-based representations. We show that such a representation enables efficient training and leads to state-of-the-art results. Our approach elegantly combines concepts from 3D geometry processing, triangle-based rendering, and modern neural radiance fields. Compared to voxel-based representations, ours provides more detail around parts of the scene likely to be close to the surface. Compared to point-based representations, our approach achieves better performance. The source code is publicly available at: https://jkulhanek.com/tetra-nerf.
Moduli Spaces of Filtered G-local Systems on Curves

In this paper, we construct the moduli spaces of filtered $G$-local systems on curves for an arbitrary reductive group $G$ over an algebraically closed field of characteristic zero. This provides an algebraic construction for the Betti moduli spaces in the tame nonabelian Hodge correspondence for vector bundles/principal bundles on noncompact curves. As a direct application, the tame nonabelian Hodge correspondence on noncompact curves holds not only for the relevant categories, but also for the moduli spaces.
Learning Temporal Distribution and Spatial Correlation Toward Universal Moving Object Segmentation

The goal of moving object segmentation is separating moving objects from stationary backgrounds in videos. One major challenge in this problem is how to develop a universal model for videos from various natural scenes since previous methods are often effective only in specific scenes. In this paper, we propose a method called Learning Temporal Distribution and Spatial Correlation (LTS) that has the potential to be a general solution for universal moving object segmentation. In the proposed approach, the distribution from temporal pixels is first learned by our Defect Iterative Distribution Learning (DIDL) network for a scene-independent segmentation. Notably, the DIDL network incorporates the use of an improved product distribution layer that we have newly derived. Then, the Stochastic Bayesian Refinement (SBR) Network, which learns the spatial correlation, is proposed to improve the binary mask generated by the DIDL network. Benefiting from the scene independence of the temporal distribution and the accuracy improvement resulting from the spatial correlation, the proposed approach performs well for almost all videos from diverse and complex natural scenes with fixed parameters. Comprehensive experiments on standard datasets including LASIESTA, CDNet2014, BMC, SBMI2015 and 128 real world videos demonstrate the superiority of proposed approach compared to state-of-the-art methods with or without the use of deep learning networks. To the best of our knowledge, this work has high potential to be a general solution for moving object segmentation in real world environments. The code and real-world videos can be found on GitHub https://github.com/guanfangdong/LTS-UniverisalMOS.
Triangle groups in the complex hyperbolic plane and special fibers of the momentum map in PU(2,1)

In this work, we consider relative character varieties for representations of the 3-punctured sphere group in PU(2,1). We provide necessary and sufficient conditions on the peripheral conjugacy classes, for such a representation to admit a decomposition as products of special elliptic elements. We prove that the representations satisfying these conditions form fibers of the so-called momentum map for PU(2,1). We apply these results to representations of the even subgroup of triangle groups, and describe components of the associated character variety.
Near-infrared and Optical Observations of Type Ic SN 2021krf: Luminous Late-time Emission and Dust Formation

We present near-infrared (NIR) and optical observations of the Type Ic supernova (SN Ic) SN 2021krf obtained between days 13 and 259 at several ground-based telescopes. The NIR spectrum at day 68 exhibits a rising K-band continuum flux density longward of ∼2.0 μm, and a late-time optical spectrum at day 259 shows strong [O i] 6300 and 6364 Å emission-line asymmetry, both indicating the presence of dust, likely formed in the SN ejecta. We estimate a carbon-grain dust mass of ∼2 × 10−5 M ⊙ and a dust temperature of ∼900–1200 K associated with this rising continuum and suggest the dust has formed in SN ejecta. Utilizing the one-dimensional multigroup radiation-hydrodynamics code STELLA, we present two degenerate progenitor solutions for SN 2021krf, characterized by C–O star masses of 3.93 and 5.74 M ⊙, but with the same best-fit 56Ni mass of 0.11 M ⊙ for early times (0–70 days). At late times (70–300 days), optical light curves of SN 2021krf decline substantially more slowly than those expected from 56Co radioactive decay. Lack of H and He lines in the late-time SN spectrum suggests the absence of significant interaction of the ejecta with the circumstellar medium. We reproduce the entire bolometric light curve with a combination of radioactive decay and an additional powering source in the form of a central engine of a millisecond pulsar with a magnetic field smaller than that of a typical magnetar.
AI Autonomy: Self-initiated Open-world Continual Learning and Adaptation

As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances. As the real-world is an open environment that is full of unknowns or novelties, the capabilities of detecting novelties, characterizing them, accommodating/adapting to them, gathering ground-truth training data and incrementally learning the unknowns/novelties become critical in making the AI agent more and more knowledgeable, powerful and self-sustainable over time. The key challenge here is how to automate the process so that it is carried out continually on the agent's own initiative and through its own interactions with humans, other agents and the environment just like human on-the-job learning. This paper proposes a framework (called SOLA) for this learning paradigm to promote the research of building autonomous and continual learning enabled AI agents. To show feasibility, an implemented agent is also described.
Combatting Front-Running in Smart Contracts: Attack Mining, Benchmark Construction and Vulnerability Detector Evaluation

Front-running attacks have been a major concern on the blockchain. Attackers launch front-running attacks by inserting additional transactions before upcoming victim transactions to manipulate victim transaction executions and make profits. Recent studies have shown that front-running attacks are prevalent on the Ethereum blockchain and have caused millions of US dollars loss. It is the vulnerabilities in smart contracts, which are blockchain programs invoked by transactions, that enable the front-running attack opportunities. Although techniques to detect front-running vulnerabilities have been proposed, their performance on real-world vulnerable contracts is unclear. There is no large-scale benchmark based on real attacks to evaluate their capabilities. We make four contributions in this paper. First, we design an effective algorithm to mine real-world attacks in the blockchain history. The evaluation shows that our mining algorithm is more effective and comprehensive, achieving higher recall in finding real attacks than the previous study. Second, we propose an automated and scalable vulnerability localization approach to localize code snippets in smart contracts that enable front-running attacks. The evaluation also shows that our localization approaches are effective in achieving higher precision in pinpointing vulnerabilities compared to the baseline technique. Third, we build a benchmark consisting of 513 real-world attacks with vulnerable code labeled in 235 distinct smart contracts, which is useful to help understand the nature of front-running attacks, vulnerabilities in smart contracts, and evaluate vulnerability detection techniques. Last but not least, we conduct an empirical evaluation of seven state-of-the-art vulnerability detection techniques on our benchmark. The evaluation experiment reveals the inadequacy of existing techniques in detecting front-running vulnerabilities, with a low recall of $\leq$≤ 6.04%. Our further analysis identifies four common limitations in existing techniques: lack of support for inter-contract analysis, inefficient constraint solving for cryptographic operations, improper vulnerability patterns, and lack of token support.
Multiscale analysis of Galactic dust emission using complex wavelet transforms – I. Separation of Gaussian and non-Gaussian fluctuations in Herschel observations

We use anisotropic complex wavelet transforms to make a multiscale analysis of the distribution of ﬂuctuations of dust emission at 250 μ m from the Herschel infrared Galactic Plane Survey of the Herschel Space Observatory . By reproducing the Fourier power spectrum with complex wavelet transforms we show that different power distributions at each scale can have a signiﬁcant effect on the measured power law. Moreover, with an iterative algorithm we separate the Gaussian and non-Gaussian part of wavelet coefﬁcient distributions in each scale and azimuthal direction. The reconstructed map of non-Gaussian ﬂuctuations is well correlated to small-scale structures of the 13 CO emission map as well as its power spectrum which has a power-law index of − 2.3. The Gaussian part of the map shows more diffuse structures with a steeper power law of − 3.1. Non-Gaussian coefﬁcients are almost undetectable at scales greater than ∼ 0.15 arcmin − 1 ( ∼ 4–26 pc). This characteristic scale could be associated with the size of the larger molecular cloud in the region. We also demonstrate that exponentiated Gaussian random ﬁeld cannot reproduce the ﬂatter power law of the non-Gaussian component observed in the thermal dust emission, which seems to be a property of interstellar clouds.
Socioeconomic, Disease Burden, Physical Functioning, Psychosocial, and Environmental Factors Associated With Mortality Among Older Adults: The Brazilian Longitudinal Study of Ageing (ELSI-Brazil)

Objectives: There is little nationally representative information about factors associated with longevity among older Brazilians. Methods: Baseline survey data from the Brazilian longitudinal Study of Aging (ELSI-Brazil) were linked to vital statistics systems. Mortality rates and life expectancy estimates were calculated and compared to official sources. Cox Proportional Hazards models and Population Attributable Fractions (PAFs) identified significant predictors of mortality. Results: Calculated mortality rates and life expectancy estimates were similar to official statistics for most ages with higher risk of death among older ages, as expected. High School completion, being partnered, and female sex were negatively associated with mortality, while being underweight, previous diagnosis of a chronic condition, having any functional limitations, poor self-rated health, low grip strength, and smoking were all associated with higher mortality risk. Discussion: The ELSI-Brazil study has potential to identify factors associated with longevity and to inform programs and policies designed to enhance healthy aging among older adults in Brazil.
Willingness to use and intention to adhere to pre-exposure prophylaxis (PrEP) among men who have sex with men in Jiangsu Province, China

ABSTRACT Pre-exposure prophylaxis (PrEP) is a biomedical prevention approach that significantly reduces HIV acquisition. Our study aimed to explore factors associated with PrEP willingness and intention to adhere to PrEP among MSM through a cross-sectional survey in Nanjing, Jiangsu province, China. Location sampling (TLS) and online recruitment were used to recruit participants to gauge their PrEP willingness and intention to adhere. Of 309 HIV-negative/unknown serostatus MSM, 75.7% were willing to use PrEP and 55.3% had high intention to take PrEP daily. Willingness to use PrEP was positively associated with having a college degree or higher (AOR = 1.90, 95%CI: 1.11–3.26) and higher anticipated HIV stigma (AOR = 2.74, 95%CI: 1.13–6.61). Facilitators of intention to adhere included higher education levels (AOR = 2.12, 95%CI: 1.33–3.39) and higher anticipated HIV stigma (AOR = 3.65, 95%CI: 1.36–9.80), whereas a primary barrier was community homophobia (AOR = 0.43, 95%CI: 0.20–0.92). This study documented high willingness to use PrEP, yet lower intention to adhere to PrEP in a sample of MSM in China. Public interventions and programs to promote adherence of PrEP for MSM is urgently needed in China. Psychosocial factors should be addressed and taken into consideration for PrEP implementation and adherence programs.
Venous Thromboembolism Prophylaxis and Treatment in Patients With Cancer: ASCO Guideline Update

PURPOSE To conduct an update of the ASCO venous thromboembolism (VTE) guideline. METHODS After publication of potentially practice-changing clinical trials, identified through ASCO's signals approach to updating, an updated systematic review was performed for two guideline questions: perioperative thromboprophylaxis and treatment of VTE. PubMed and the Cochrane Library were searched for randomized controlled trials (RCTs) published between November 1, 2018, and June 6, 2022. RESULTS Five RCTs provided information that contributed to changes to the 2019 recommendations. Two RCTs addressed direct factor Xa inhibitors (either rivaroxaban or apixaban) for extended thromboprophylaxis after surgery. Each of these postoperative trials had important limitations but suggested that these two oral anticoagulants are safe and effective in the settings studied. An additional three RCTs addressed apixaban in the setting of VTE treatment. Apixaban was effective in reducing the risk of recurrent VTE, with a low risk of major bleeding. RECOMMENDATIONS Apixaban and rivaroxaban were added as options for extended pharmacologic thromboprophylaxis after cancer surgery, with a weak strength of recommendation. Apixaban was also added as an option for the treatment of VTE, with high quality of evidence and a strong recommendation. Additional information is available at www.asco. org/supportive-care-guidelines.
Few-Shot network intrusion detection based on prototypical capsule network with attention mechanism

Network intrusion detection plays a crucial role in ensuring network security by distinguishing malicious attacks from normal network traffic. However, imbalanced data affects the performance of intrusion detection system. This paper utilizes few-shot learning to solve the data imbalance problem caused by insufficient samples in network intrusion detection, and proposes a few-shot intrusion detection method based on prototypical capsule network with the attention mechanism. Our method is mainly divided into two parts, a temporal-spatial feature fusion method using capsules for feature extraction and a prototypical network classification method with attention and vote mechanisms. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods on imbalanced datasets.
Clinical and Radiographic Outcomes of Total Elbow Arthroplasty Using a Semi‐constrained Prosthesis with a Triceps‐preserving Approach over a Minimum Follow‐up Period of 4 Years

Complications related to triceps after total elbow arthroplasty (TEA) have become a major surgical concern. The triceps‐preserving approach has the advantage of not disturbing the insertion of triceps but is disadvantaged by the reduced exposure of the elbow joint. The aim of this study was to investigate the clinical and radiological outcomes of TEA with a triceps‐preserving approach and to compare the outcomes of TEA to treat arthropathy with that of TEA to treat acute distal humerus fracture.
The ripple effects of offshoring in the United States: Boosting local productivity and capital investment

We investigate the offshoring effect on local productivity, physical and intellectual capital investment at the U.S. county level from 1999 to 2006. By using regression with fixed effects and instrumental variable to account for possible endogeneity, we find that offshoring can increase overall local productivity and capital investment. Through industry linkages, an increase in productivity and capital investment from offshoring enhances those increases in non-offshoring industries. Industries in both MSA (urban) and non-MSA (rural) counties receive benefits of productivity expansion and capital investment from offshoring. The increased capital investment from offshoring could be a channel of local productivity and capital investment expansion.
Advances in therapies for neurological lysosomal storage disorders

Lysosomal Storage Disorders (LSDs) are a diverse group of inherited, monogenic diseases caused by functional defects in specific lysosomal proteins. The lysosome is a cellular organelle that plays a critical role in catabolism of waste products and recycling of macromolecules in the body. Disruption to the normal function of the lysosome can result in the toxic accumulation of storage products, often leading to irreparable cellular damage and organ dysfunction followed by premature death. The majority of LSDs have no curative treatment, with many clinical subtypes presenting in early infancy and childhood. Over two‐thirds of LSDs present with progressive neurodegeneration, often in combination with other debilitating peripheral symptoms. Consequently, there is a pressing unmet clinical need to develop new therapeutic interventions to treat these conditions. The blood–brain barrier is a crucial hurdle that needs to be overcome in order to effectively treat the central nervous system (CNS), adding considerable complexity to therapeutic design and delivery. Enzyme replacement therapy (ERT) treatments aimed at either direct injection into the brain, or using blood–brain barrier constructs are discussed, alongside more conventional substrate reduction and other drug‐related therapies. Other promising strategies developed in recent years, include gene therapy technologies specifically tailored for more effectively targeting treatment to the CNS. Here, we discuss the most recent advances in CNS‐targeted treatments for neurological LSDs with a particular emphasis on gene therapy‐based modalities, such as Adeno‐Associated Virus and haematopoietic stem cell gene therapy approaches that encouragingly, at the time of writing are being evaluated in LSD clinical trials in increasing numbers. If safety, efficacy and improved quality of life can be demonstrated, these therapies have the potential to be the new standard of care treatments for LSD patients.