Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-modal Garden Dataset and Hybrid 3D Dense Reconstruction Framework Based on Panoramic Stereo Images for a Trimming Robot

Recovering an outdoor environment's surface mesh is vital for an agricultural robot during task planning and remote visualization. Our proposed solution is based on a newly-designed panoramic stereo camera along with a hybrid novel software framework that consists of three fusion modules. The panoramic stereo camera with a pentagon shape consists of 5 stereo vision camera pairs to stream synchronized panoramic stereo images for the following three fusion modules. In the disparity fusion module, rectified stereo images produce the initial disparity maps using multiple stereo vision algorithms. Then, these initial disparity maps, along with the intensity images, are input into a disparity fusion network to produce refined disparity maps. Next, the refined disparity maps are converted into full-view point clouds or single-view point clouds for the pose fusion module. The pose fusion module adopts a two-stage global-coarse-to-local-fine strategy. In the first stage, each pair of full-view point clouds is registered by a global point cloud matching algorithm to estimate the transformation for a global pose graph's edge, which effectively implements loop closure. In the second stage, a local point cloud matching algorithm is used to match single-view point clouds in different nodes. Next, we locally refine the poses of all corresponding edges in the global pose graph using three proposed rules, thus constructing a refined pose graph. The refined pose graph is optimized to produce a global pose trajectory for volumetric fusion. In the volumetric fusion module, the global poses of all the nodes are used to integrate the single-view point clouds into the volume to produce the mesh of the whole garden. The proposed framework and its three fusion modules are tested on a real outdoor garden dataset to show the superiority of the performance.
On the impact of noise on quennching for a nonlocal diffusion model driven by a mixture of Brownian and fractional Brownian motions

In this paper, we study a stochastic parabolic problem involving a nonlocal diffusion operator associated with nonlocal Robin-type boundary conditions. The stochastic dynamics under consideration are driven by a mixture of a classical Brownian and a fractional Brownian motions with Hurst index $H\in(\frac{1}{2}, 1). $ We first establish local in existence result of the considered model and then explore conditions under which the resulting SPDE exhibits finite-time quenching. Using the probability distribution of perpetual integral functional of Brownian motion as well as tail estimates of fractional Brownian motion we provide analytic estimates for certain statistics of interest, such as quenching times and the corresponding quenching probabilities. The existence of global in time solutions is also investigated and as a consequence a lower estimate of the quenching time is also derived. Our analytical results demonstrate the non-trivial impact of the considered noise on the dynamics of the system. Next, a connection of a special case of the examined model is drawn in the context of MEMS technology. Finally, a numerical investigation of the considered model for a fractional Laplacian diffusion and Dirichlet-type boundary conditions is delivered.
DNN Verification, Reachability, and the Exponential Function Problem

Deep neural networks (DNNs) are increasingly being deployed to perform safety-critical tasks. The opacity of DNNs, which prevents humans from reasoning about them, presents new safety and security challenges. To address these challenges, the verification community has begun developing techniques for rigorously analyzing DNNs, with numerous verification algorithms proposed in recent years. While a significant amount of work has gone into developing these verification algorithms, little work has been devoted to rigorously studying the computability and complexity of the underlying theoretical problems. Here, we seek to contribute to the bridging of this gap. We focus on two kinds of DNNs: those that employ piecewise-linear activation functions (e.g., ReLU), and those that employ piecewise-smooth activation functions (e.g., Sigmoids). We prove the two following theorems: 1) The decidability of verifying DNNs with a particular set of piecewise-smooth activation functions is equivalent to a well-known, open problem formulated by Tarski; and 2) The DNN verification problem for any quantifier-free linear arithmetic specification can be reduced to the DNN reachability problem, whose approximation is NP-complete. These results answer two fundamental questions about the computability and complexity of DNN verification, and the ways it is affected by the network's activation functions and error tolerance; and could help guide future efforts in developing DNN verification tools.
Auditing Cross-Cultural Consistency of Human-Annotated Labels for Recommendation Systems

Recommendation systems increasingly depend on massive human-labeled datasets; however, the human annotators hired to generate these labels increasingly come from homogeneous backgrounds. This poses an issue when downstream predictive models—based on these labels—are applied globally to a heterogeneous set of users. We study this disconnect with respect to the labels themselves, asking whether they are “consistently conceptualized” across annotators of different demographics. In a case study of video game labels, we conduct a survey on 5,174 gamers, identify a subset of inconsistently conceptualized game labels, perform causal analyses, and suggest both cultural and linguistic reasons for cross-country differences in label annotation. We further demonstrate that predictive models of game annotations perform better on global train sets as opposed to homogeneous (single-country) train sets. Finally, we provide a generalizable framework for practitioners to audit their own data annotation processes for consistent label conceptualization, and encourage practitioners to consider global inclusivity in recommendation systems starting from the early stages of annotator recruitment and data-labeling.
Korean Named Entity Recognition Based on Language-Specific Features

In this paper, we propose a novel way of improving named entity recognition (NER) in the Korean language using its language-specific features. While the field of NER has been studied extensively in recent years, the mechanism of efficiently recognizing named entities (NEs) in Korean has hardly been explored. This is because the Korean language has distinct linguistic properties that present challenges for modeling. Therefore, an annotation scheme for Korean corpora by adopting the CoNLL-U format, which decomposes Korean words into morphemes and reduces the ambiguity of NEs in the original segmentation that may contain functional morphemes such as postpositions and particles, is proposed herein. We investigate how the NE tags are best represented in this morpheme-based scheme and implement an algorithm to convert word-based and syllable-based Korean corpora with NEs into the proposed morpheme-based format. Analyses of the results of traditional and neural models reveal that the proposed morpheme-based format is feasible, and the varied performances of the models under the influence of various additional language-specific features are demonstrated. Extrinsic conditions were also considered to observe the variance of the performances of the proposed models, given different types of data, including the original segmentation and different types of tagging formats.
A Theory of Inaccessible Information

What would be the consequences if there were fundamental limits to our ability to experimentally explore the world? In this work we seriously consider this question. We assume the existence of statements whose truth value is not experimentally accessible. That is, there is no way, not even in theory, to directly test if these statements are true or false. We further develop a theory in which experimentally accessible statements are a union of a fixed minimum number of inaccessible statements. For example, the value of truth of the statements"a"and"b"is not accessible, but the value of truth of the statement"a or b"is accessible. We do not directly assume probability theory, we exclusively define experimentally accessible and inaccessible statements and build on these notions using the rules of classical logic. We find that an interesting structure emerges. Developing this theory, we relax the logical structure to a probabilistic one, obtaining a theory rich in structure that we call"theory of inaccessible information". Surprisingly, the simplest model of theory of inaccessible information is the qubit in quantum mechanics. Along the path for the construction of this theory, we characterise and study a family of multiplicative information measures that we call"inaccessibility measures".
Wide scale EPR retrospective dosimetry. Results and problems

The results of the first wide-scale application of electron paramagnetic resonance (EPR) with tooth enamel for retrospective dosimetry for permanent residents of Borovskii and Zhukovskii districts (Kaluga region, Russia) are presented. Since these districts have never been radioactively contaminated, their population could be used for comparison studies of the suitability of molars, premolars, canines and incisors for EPR dosimetry purposes. Altogether 136 samples have been investigated. For molars and premolars (103 samples) a linear correlation between the dose absorbed in tooth enamel and the tooth age has been identified. For incisors and canines no such correlation has been found; also, in this case, the doses absorbed in tooth enamel exceeded the dose values for molars and premolars. The data obtained may be connected with the exposure of front teeth to sunlight, which is supported by dedicated EPR experiments with exposure to sunlight and laboratory ultraviolet (UV) radiation. The proposed interpretation and problem areas are discussed.
Symmetry and Nonlinearity of Spin Wave Resonance Excited by Focused Surface Acoustic Waves

The use of a complex ferromagnetic system to manipulate GHz surface acoustic waves (SAWs) is a rich current topic under investigation, but the high‐power nonlinear regime is under‐explored. Focused SAWs are introduced, which provide a way to access this regime with modest equipment. The symmetry of the magneto‐acoustic interaction can be tuned by interdigitated transducer design which can introduce additional strain components. Here, the impact of focused acoustic waves versus standard unidirectional acoustic waves in significantly enhancing the magnon‐phonon coupling behavior is compared. Analytical simulation results based on the modified Landau–Lifshitz–Gilbert theory show good agreement with experimental findings. Nonlinear input power dependence of the transmission through the device is also reported. This experimental observation is supported by the micromagnetic simulation using mumax3 to model the nonlinear dependence. These results pave the way for extending the understanding and design of acoustic wave devices for the exploration of acoustically driven spin wave resonance physics.
Dynamical He Flashes in Double White Dwarf Binaries

The detonation of an overlying helium layer on a 0.8–1.1 M ⊙ carbon–oxygen (CO) white dwarf (WD) can detonate the CO WD and create a thermonuclear supernova (SN). Many authors have recently shown that when the mass of the He layer is low (≲0.03 M ⊙), the ashes from its detonation minimally impact the spectra and light curve from the CO detonation, allowing the explosion to appear remarkably similar to Type Ia SNe. These new insights motivate our investigation of dynamical He shell burning and our search for a binary scenario that stably accumulates thermally unstable He shells in the 0.01–0.08 M ⊙ range, thick enough to detonate, but also often thin enough for minimal impact on the observables. We first show that our improved nonadiabatic evolution of convective He shell burning in this range of shell mass leads to conditions ripe for a He detonation. We also find that a stable mass transfer scenario with a high-entropy He WD donor of mass 0.15–0.25 M ⊙ yields the He shell masses needed to achieve the double detonations. This scenario also predicts that the surviving He donor leaves with a spatial velocity consistent with the unusual runaway object, D6-2. We find that hot He WD donors originate in common-envelope events when a 1.3–2.0 M ⊙ star fills its Roche lobe at the base of the red giant branch at orbital periods of 1–10 days with the CO WD.
The Hazy and Metal-rich Atmosphere of GJ 1214 b Constrained by Near- and Mid-infrared Transmission Spectroscopy

The near-infrared transmission spectrum of the warm sub-Neptune exoplanet GJ 1214 b has been observed to be flat and featureless, implying a high metallicity atmosphere with abundant aerosols. Recent JWST MIRI Low Resolution Spectrometer observations of a phase curve of GJ 1214 b showed that its transmission spectrum is flat out into the mid-infrared. In this paper, we use the combined near- and mid-infrared transmission spectrum of GJ 1214 b to constrain its atmospheric composition and aerosol properties. We generate a grid of photochemical haze models using an aerosol microphysics code for a number of background atmospheres spanning metallicities from 100 to 1000× solar, as well as a steam atmosphere scenario. The flatness of the combined data set largely rules out atmospheric metallicities ≤300× solar due to their large corresponding molecular feature amplitudes, preferring values ≥1000× solar and column haze production rates ≥10−10 g cm−2 s−1. The steam atmosphere scenario with similarly high haze production rates also exhibits sufficiently small molecular features to be consistent with the transmission spectrum. These compositions imply that atmospheric mean molecular weights ≥15 g mol−1 are needed to fit the data. Our results suggest that haze production is highly efficient on GJ 1214 b and could involve non-hydrocarbon, non-nitrogen haze precursors. Further characterization of GJ 1214 b’s atmosphere would likely require multiple transits and eclipses using JWST across the near- and mid-infrared, potentially complemented by ground-based high-resolution transmission spectroscopy.
Visual Tuning

Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained parameters by updating far fewer parameters, enabling edge devices and downstream applications to reuse the increasingly large foundation models deployed on the cloud. With the aim of helping researchers get the full picture and future directions of visual tuning, this survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models. Specifically, it provides a detailed background of visual tuning and categorizes recent visual tuning techniques into five groups: prompt tuning, adapter tuning, parameter tuning, and remapping tuning. Meanwhile, it offers some exciting research directions for prospective pre-training and various interactions in visual tuning.
Privacy-Preserving Prompt Tuning for Large Language Model Services

Prompt tuning provides an efficient way for users to customize Large Language Models (LLMs) with their private data in the emerging LLM service scenario. However, the sensitive nature of private data brings the need for privacy preservation in LLM service customization. Based on prompt tuning, we propose Privacy-Preserving Prompt Tuning (RAPT), a framework that provides privacy guarantees for LLM services. \textsc{rapt} adopts a local privacy setting, allowing users to privatize their data locally with local differential privacy. As prompt tuning performs poorly when directly trained on privatized data, we introduce a novel privatized token reconstruction task that is trained jointly with the downstream task, allowing LLMs to learn better task-dependent representations. Despite the simplicity of our framework, experiments show that RAPT achieves competitive performance across tasks while providing privacy guarantees against adversaries.
Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation

FinTech lending (e.g., micro-lending) has played a significant role in facilitating financial inclusion. It has reduced processing times and costs, enhanced the user experience, and made it possible for people to obtain loans who may not have qualified for credit from traditional lenders. However, there are concerns about the potentially biased algorithmic decision-making during loan screening. Machine learning algorithms used to evaluate credit quality can be influenced by representation bias in the training data, as we only have access to the default outcome labels of approved loan applications, for which the borrowers' socioeconomic characteristics are better than those of rejected ones. In this case, the model trained on the labeled data performs well on the historically approved population, but does not generalize well to borrowers of low socioeconomic background. In this paper, we investigate the problem of representation bias in loan screening for a real-world FinTech lending platform. We propose a new Transformer-based sequential loan screening model with self-supervised contrastive learning and domain adaptation to tackle this challenging issue. We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor. We demonstrate the effectiveness of our model through extensive experimentation in the real-world micro-lending setting. Our results show that our model significantly promotes the inclusiveness of funding decisions, while also improving loan screening accuracy and profit by 7.10% and 8.95%, respectively. We also show that incorporating the test data into contrastive learning and domain adaptation and labeling a small ratio of test data can further boost model performance.
ComputeGPT: A computational chat model for numerical problems

Language models are not accurate in numerical problems. Their architecture does not allow for anything less than a probabilistic next word. This paper introduces ComputeGPT: an approach of creating a chat model able to answer computational problems through running on-demand code. ComputeGPT converts each question to relevant code, runs the code, and returns the computed answer as part of the chat. We combine this approach with a local browser-based Python interpretation and fine-tuned prompts in order to achieve state-of-the-art efficiency on numerical problems and provide a suitable front-end and safe environment for the code to be executed in.
Fast Distributed Inference Serving for Large Language Models

Large language models (LLMs) power a new generation of interactive AI applications exemplified by ChatGPT. The interactive nature of these applications demand low job completion time (JCT) for model inference. Existing LLM serving systems use run-to-completion processing for inference jobs, which suffers from head-of-line blocking and long JCT. We present FastServe, a distributed inference serving system for LLMs. FastServe exploits the autoregressive pattern of LLM inference to enable preemption at the granularity of each output token. FastServe uses preemptive scheduling to minimize JCT with a novel skip-join Multi-Level Feedback Queue scheduler. Based on the new semi information-agnostic setting of LLM inference, the scheduler leverages the input length information to assign an appropriate initial queue for each arrival job to join. The higher priority queues than the joined queue are skipped to reduce demotions. We design an efficient GPU memory management mechanism that proactively offloads and uploads intermediate states between GPU memory and host memory for LLM inference. We build a system prototype of FastServe based on NVIDIA FasterTransformer. Experimental results show that compared to the state-of-the-art solution Orca, FastServe improves the average and tail JCT by up to 5.1$\times$ and 6.4$\times$, respectively.
The Challenge of Defining "Family"

T he family is a critically important social institution with huge implications for laypeople, policy makers, and scholars alike. As Socha significantly noted in 1999, family comprises the first group to which a person belongs, and usually provides the most longlasting group membership for individuals. In addition, as we and others have commented (e.g. Floyd, Mikkelson, & Judd, 2006; Galvin, 2006; Turner & West, 2013), in the United States, family is seen as a group that is qualitatively different from any other group to which a person belongs. The enduring bonds of obligation, the unique communication behaviors, the evolution through time, as well as the expectations for affection and support that characterize family set it apart from other groups. Further, the family’s contributions to an individual’s sense of identity and self-worth have long been noted, and as Olson, BaiocchiWagner, Kratzer, and Symonds (2012) comment, “. our families provide the glue that connects all the parts of our lives—for better and for worse” (p. 1). In addition, Braithwaite and her colleagues (2010) report that the family is “the most pervasive and central of human institutions” (p. 389). Clearly, family is a term signifying something of importance to people, and a term that is fraught with meaning. The question is: what does it mean? When a term is as important to people as family is, then the process of defining it also becomes important. Yet, defining family often is a problem that bedevils policy makers, laypeople, and scholars alike. In the realm of public policy, Tankersley (2008) comments that the definition of family is at the core of many political debates as well as policymaking discussions. The task of defining family
Representation Projection Invariance Mitigates Representation Collapse

Fine-tuning contextualized representations learned by pre-trained language models remains a prevalent practice in NLP. However, fine-tuning can lead to representation degradation (also known as representation collapse), which may result in instability, sub-optimal performance, and weak generalization. In this paper, we propose Representation Projection Invariance (REPINA), a novel regularization method to maintain the information content of representation and reduce representation collapse during fine-tuning by discouraging undesirable changes in the representations. We study the empirical behavior of the proposed regularization in comparison to 5 comparable baselines across 13 language understanding tasks (GLUE benchmark and six additional datasets). When evaluating in-domain performance, REPINA consistently outperforms other baselines on most tasks (10 out of 13). We also demonstrate its effectiveness in few-shot settings and robustness to label perturbation. As a by-product, we extend previous studies of representation collapse and propose several metrics to quantify it. Our empirical findings show that our approach is significantly more effective at mitigating representation collapse.