Miracle-dz/UniFAR-FTUs huggingface dataset:
Eight-Partitioning Points in 3D, and Efficiently Too

An \emph{eight-partition} of a finite set of points (respectively, of a continuous mass distribution) in $\mathbb{R}^3$ consists of three planes that divide the space into $8$ octants, such that each open octant contains at most $1/8$ of the points (respectively, of the mass). In 1966, Hadwiger showed that any mass distribution in $\mathbb{R}^3$ admits an eight-partition; moreover, one can prescribe the normal direction of one of the three planes. The analogous result for finite point sets follows by a standard limit argument. We prove the following variant of this result: Any mass distribution (or point set) in $\mathbb{R}^3$ admits an eight-partition for which the intersection of two of the planes is a line with a prescribed direction. Moreover, we present an efficient algorithm for calculating an eight-partition of a set of $n$ points in~$\mathbb{R}^3$ (with prescribed normal direction of one of the planes) in time $O^{*}(n^{5/2})$.
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering

Knowledge Graph Question Answering (KGQA) methods seek to answer Natural Language questions using the relational information stored in Knowledge Graphs (KGs). With the recent advancements of Large Language Models (LLMs) and their remarkable reasoning abilities, there is a growing trend to leverage them for KGQA. However, existing methodologies have only focused on answering factual questions, e.g.,"In which city was Silvio Berlusconi's first wife born? ", leaving questions involving commonsense reasoning that real-world users may pose more often, e.g.,"Do I need separate visas to see the Venus of Willendorf and attend the Olympics this summer? "unaddressed. In this work, we first observe that existing LLM-based methods for KGQA struggle with hallucination on such questions, especially on queries targeting long-tail entities (e.g., non-mainstream and recent entities), thus hindering their applicability in real-world applications especially since their reasoning processes are not easily verifiable. In response, we propose Right for Right Reasons (R3), a commonsense KGQA methodology that allows for a verifiable reasoning procedure by axiomatically surfacing intrinsic commonsense knowledge of LLMs and grounding every factual reasoning step on KG triples. Through experimental evaluations across three different tasks--question answering, claim verification, and preference matching--our findings showcase R3 as a superior approach, outperforming existing methodologies and notably reducing instances of hallucination and reasoning errors.
Few-shot Learner Parameterization by Diffusion Time-steps

Even when using large multi-modal foundation models, few-shot learning is still challenging -- if there is no proper inductive bias, it is nearly impossible to keep the nuanced class attributes while removing the visually prominent attributes that spuriously correlate with class labels. To this end, we find an inductive bias that the time-steps of a Diffusion Model (DM) can isolate the nuanced class attributes, i.e., as the forward diffusion adds noise to an image at each time-step, nuanced attributes are usually lost at an earlier time-step than the spurious attributes that are visually prominent. Building on this, we propose Time-step Few-shot (TiF) learner. We train class-specific low-rank adapters for a text-conditioned DM to make up for the lost attributes, such that images can be accurately reconstructed from their noisy ones given a prompt. Hence, at a small time-step, the adapter and prompt are essentially a parameterization of only the nuanced class attributes. For a test image, we can use the parameterization to only extract the nuanced class attributes for classification. TiF learner significantly outperforms OpenCLIP and its adapters on a variety of fine-grained and customized few-shot learning tasks. Codes are in https://github.com/yue-zhongqi/tif.
Tappy: Predicting Tap Accuracy of User-Interface Elements by Reverse-Engineering Webpage Structures

Selecting a UI element is a fundamental operation on webpages, and the ease of tapping a target object has a significant impact on usability. It is thus important to analyze existing UIs in order to design better ones. However, tools proposed in previous studies cannot identify whether an element is tappable on modern webpages. In this study, we developed Tappy that can identify tappable UI elements on webpages and estimate the tap-success rate based on the element size. Our interviews of professional designers and engineers showed that Tappy helped discussions of UI design on the basis of its quantitative metric. Furthermore, we have launched this tool to be freely available to external users, so readers can access Tappy by visiting the website (https://tappy.yahoo.co.jp).
A molecular dynamics model of the orthogonal cutting process

Material removal, as found in the cutting and grinding processes, is important in many areas of fabrication technology. Examples include high-speed machining by single point turning, surface milling and drilling. An idealized model that contains the essential physics of these processes is the two-dimensional orthogonal cutting geometry. Following the work of Shaw, an attempt is made to obtain a fundamental understanding of the commonly observed phenomena in the chip forming process: the atomistic mechanisms of plastic deformation within the primary shear zone and the observed shear angle ({phi}); the resultant energy flow from the tool doing work; the stress, strain and material flow fields; the morphology of the chip (smooth, segmented, crystalline or amorphous); the role of different depths of cut (t), rake angles ({alpha}), and clearance angles ({theta}); and the role of the tool tip radius, roughness (friction), and wear. The molecular dynamics computer simulation method is an ideal tool for the study of the atomistic mechanisms of these deformation processes. Simulations are performed for a model of copper at room temperature using 10{sup 3}--10{sup 6} atoms, depths of cut in the range l--50nm. and cutting speeds of 1--2500 meters per second (m/s). These simulations are strictly two dimensionalmore » (plane strain). Here, focus is on the chip forming process, the atomistic mechanisms of plastic deformation, the flow of energy into the chip and workpiece, and the material flow and stress fields for the orthogonal geometry ({alpha} = 0), sharp tools, and shallow depths of cut (t {approx} 5 nm). 5 refs., 3 figs.« less
Large-scale variational Gaussian state-space models

We introduce an amortized variational inference algorithm and structured variational approximation for state-space models with nonlinear dynamics driven by Gaussian noise. Importantly, the proposed framework allows for efficient evaluation of the ELBO and low-variance stochastic gradient estimates without resorting to diagonal Gaussian approximations by exploiting (i) the low-rank structure of Monte-Carlo approximations to marginalize the latent state through the dynamics (ii) an inference network that approximates the update step with low-rank precision matrix updates (iii) encoding current and future observations into pseudo observations -- transforming the approximate smoothing problem into an (easier) approximate filtering problem. Overall, the necessary statistics and ELBO can be computed in $O(TL(Sr + S^2 + r^2))$ time where $T$ is the series length, $L$ is the state-space dimensionality, $S$ are the number of samples used to approximate the predict step statistics, and $r$ is the rank of the approximate precision matrix update in the update step (which can be made of much lower dimension than $L$).
RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules

Weakly supervised text classification (WSTC), also called zero-shot or dataless text classification, has attracted increasing attention due to its applicability in classifying a mass of texts within the dynamic and open Web environment, since it requires only a limited set of seed words (label names) for each category instead of labeled data. With the help of recently popular prompting Pre-trained Language Models (PLMs), many studies leveraged manually crafted and/or automatically identified verbalizers to estimate the likelihood of categories, but they failed to differentiate the effects of these category-indicative words, let alone capture their correlations and realize adaptive adjustments according to the unlabeled corpus. In this paper, in order to let the PLM effectively understand each category, we at first propose a novel form of rule-based knowledge using logical expressions to characterize the meanings of categories. Then, we develop a prompting PLM-based approach named RulePrompt for the WSTC task, consisting of a rule mining module and a rule-enhanced pseudo label generation module, plus a self-supervised fine-tuning module to make the PLM align with this task. Within this framework, the inaccurate pseudo labels assigned to texts and the imprecise logical rules associated with categories mutually enhance each other in an alternative manner. That establishes a self-iterative closed loop of knowledge (rule) acquisition and utilization, with seed words serving as the starting point. Extensive experiments validate the effectiveness and robustness of our approach, which markedly outperforms state-of-the-art weakly supervised methods. What is more, our approach yields interpretable category rules, proving its advantage in disambiguating easily-confused categories.
A Generalised Theory of Interactions -- II. The Theoretical Construction

After discussing the significance of interactions to understand complex multiscale stochastic systems (CMSS), we turn our attention to the construction of a Generalised Theory of Interactions (GToI). We define interactions as discrete, localised events where the thermodynamically irreversible exchange of degrees of freedom involves signal propagation and relaxation phenomena. We proceed to define the mechanics of such space, as well as how these approximately map onto dynamical manifolds. By characterising various systems through the effect of transformations across interaction space, we develop a view of systems where features such as emergence and information representation become apparent. Our theory appears to capture structural and dynamical features of CMSS efficiently by making explicit the extent of the dynamical range of action in them; we identify the boundaries of such range with statistical limits corresponding to physical laws, and derive a generalised form of the Correspondence Principle.
ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates

Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates -- the process by which the agent obtains its true state information from the sensors -- are limited, presents a significant challenge. Traditional methods often struggle to balance resource conservation, accurate state estimation and precise tracking, resulting in suboptimal performance. This problem is particularly pronounced in environments with large action spaces, where the need for frequent, accurate state data is paramount, yet the capacity for active localization updates is restricted by external limitations. This paper introduces ComTraQ-MPC, a novel framework that combines Deep Q-Networks (DQN) and Model Predictive Control (MPC) to optimize trajectory tracking with constrained active localization updates. The meta-trained DQN ensures adaptive active localization scheduling, while the MPC leverages available state information to improve tracking. The central contribution of this work is their reciprocal interaction: DQN's update decisions inform MPC's control strategy, and MPC's outcomes refine DQN's learning, creating a cohesive, adaptive system. Empirical evaluations in simulated and real-world settings demonstrate that ComTraQ-MPC significantly enhances operational efficiency and accuracy, providing a generalizable and approximately optimal solution for trajectory tracking in complex partially observable environments.
IoT Device Labeling Using Large Language Models

The IoT market is diverse and characterized by a multitude of vendors that support different device functions (e.g., speaker, camera, vacuum cleaner, etc.). Within this market, IoT security and observability systems use real-time identification techniques to manage these devices effectively. Most existing IoT identification solutions employ machine learning techniques that assume the IoT device, labeled by both its vendor and function, was observed during their training phase. We tackle a key challenge in IoT labeling: how can an AI solution label an IoT device that has never been seen before and whose label is unknown? Our solution extracts textual features such as domain names and hostnames from network traffic, and then enriches these features using Google search data alongside catalog of vendors and device functions. The solution also integrates an auto-update mechanism that uses Large Language Models (LLMs) to update these catalogs with emerging device types. Based on the information gathered, the device's vendor is identified through string matching with the enriched features. The function is then deduced by LLMs and zero-shot classification from a predefined catalog of IoT functions. In an evaluation of our solution on 97 unique IoT devices, our function labeling approach achieved HIT1 and HIT2 scores of 0.7 and 0.77, respectively. As far as we know, this is the first research to tackle AI-automated IoT labeling.
Encodings for Prediction-based Neural Architecture Search

Predictor-based methods have substantially enhanced Neural Architecture Search (NAS) optimization. The efficacy of these predictors is largely influenced by the method of encoding neural network architectures. While traditional encodings used an adjacency matrix describing the graph structure of a neural network, novel encodings embrace a variety of approaches from unsupervised pretraining of latent representations to vectors of zero-cost proxies. In this paper, we categorize and investigate neural encodings from three main types: structural, learned, and score-based. Furthermore, we extend these encodings and introduce \textit{unified encodings}, that extend NAS predictors to multiple search spaces. Our analysis draws from experiments conducted on over 1.5 million neural network architectures on NAS spaces such as NASBench-101 (NB101), NB201, NB301, Network Design Spaces (NDS), and TransNASBench-101. Building on our study, we present our predictor \textbf{FLAN}: \textbf{Fl}ow \textbf{A}ttention for \textbf{N}AS. FLAN integrates critical insights on predictor design, transfer learning, and \textit{unified encodings} to enable more than an order of magnitude cost reduction for training NAS accuracy predictors. Our implementation and encodings for all neural networks are open-sourced at \href{https://github.com/abdelfattah-lab/flan_nas}{https://github.com/abdelfattah-lab/flan\_nas}.
Aharonov-Borm oscillation and Microwave-induced edge-magnetoplasmon modes enhanced by quantum point contact

AB oscillation in weak magnetic field (B<1.5kG) is observed in QPC due to interference between electrons propagating along different QPC channels. We also investigate photo-induced magnetoresistance oscillation in open-regime split-gate QPC under MW irradiation. It is attributed to EMPs interfering in the QPC region. The influence of MW power, frequency and split gate voltage is discussed thoroughly. We unify the result of photoconductance at B=0 with EMP theories.
AI-Powered Reminders for Collaborative Tasks: Experiences and Futures

Email continues to serve as a central medium for managing collaborations. While unstructured email messaging is lightweight and conducive to coordination, it is easy to overlook commitments and requests for collaborations that are embedded in the text of free-flowing communications. Twenty-one years ago, Bellotti et al. proposed TaskMaster with the goal of redesigning the email interface to have explicit task management capabilities. Recently, AI-based task recognition and reminder services have been introduced in major email systems as one approach to managing asynchronous collaborations. While these services have been provided to millions of people around the world, there is little understanding of how people interact with and benefit from them. We explore knowledge workers' experiences with Microsoft's Viva Daily Briefing Email to better understand how AI-powered reminders can support asynchronous collaborations. Through semi-structured interviews and surveys, we shed light on how AI-powered reminders are incorporated into workflows to support asynchronous collaborations. We identify what knowledge workers prefer AI-powered reminders to remind them about and how they would like to interact with these reminders. Using mixed methods and a self-assessment methodology, we investigate the relationship between information workers' work styles and the perceived value of the Viva Daily Briefing Email to identify users who are more likely to benefit from AI-powered reminders for asynchronous collaborations. We conclude by discussing the experiences and futures of AI-powered reminders for collaborative tasks and asynchronous collaborations.
Dr Wenowdis: Specializing dynamic language C extensions using type information

C-based interpreters such as CPython make extensive use of C"extension"code, which is opaque to static analysis tools and faster runtimes with JIT compilers, such as PyPy. Not only are the extensions opaque, but the interface between the dynamic language types and the C types can introduce impedance. We hypothesise that frequent calls to C extension code introduce significant overhead that is often unnecessary. We validate this hypothesis by introducing a simple technique,"typed methods", which allow selected C extension functions to have additional metadata attached to them in a backward-compatible way. This additional metadata makes it much easier for a JIT compiler (and as we show, even an interpreter! ) to significantly reduce the call and return overhead. Although we have prototyped typed methods in PyPy, we suspect that the same technique is applicable to a wider variety of language runtimes and that the information can also be consumed by static analysis tooling.
RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features

In order to successfully perform manipulation tasks in new environments, such as grasping, robots must be proficient in segmenting unseen objects from the background and/or other objects. Previous works perform unseen object instance segmentation (UOIS) by training deep neural networks on large-scale data to learn RGB/RGB-D feature embeddings, where cluttered environments often result in inaccurate segmentations. We build upon these methods and introduce a novel approach to correct inaccurate segmentation, such as under-segmentation, of static image-based UOIS masks by using robot interaction and a designed body frame-invariant feature. We demonstrate that the relative linear and rotational velocities of frames randomly attached to rigid bodies due to robot interactions can be used to identify objects and accumulate corrected object-level segmentation masks. By introducing motion to regions of segmentation uncertainty, we are able to drastically improve segmentation accuracy in an uncertainty-driven manner with minimal, non-disruptive interactions (ca. 2-3 per scene). We demonstrate the effectiveness of our proposed interactive perception pipeline in accurately segmenting cluttered scenes by achieving an average object segmentation accuracy rate of 80.7%, an increase of 28.2% when compared with other state-of-the-art UOIS methods.
Noncentrosymmetric Nowotny Chimney Ladder Ferromagnet Cr4Ge7 with a High Curie Temperature of ~ 207 K

Noncentrosymmetric magnets usually host intriguing magnetic interactions inherent the crystal structure with broken inversion symmetry, which can give rise to rich magnetic behaviors. We report herein the high-pressure synthesis, crystal structure, magnetizations and magnetic structure of a so-called Nowotny chimney ladder compound Cr4Ge7. Our analysis on the powder neutron diffraction data revises the crystal structure as a noncentrosymmetric space group (P-4c2, No.116). It exhibits two magnetic orders within the temperature range of 2 - 400 K. The first order at ~ 207 K associated with a small magnetic moment of ~ 0.75 miuB is assigned to a commensurate ferromagnetic structure with a propagation vector k = (0, 0, 0). The weak itinerant ferromagnet nature should be caused by the complex Cr spin orders from different Wyckoff positions. The second order at ~ 18 K is assumed to arise from a competition between the Dzyaloshinskii-Moria and Heisenberg interactions. The results provide an excellent platform for study on intricate interactions between various magnetic exchanges as well as for the exploration of high temperature exotic magnetic properties which host potential applications in next-generation spintronics.
Addressing the Influence of Unmeasured Confounding in Observational Studies with Time-to-Event Outcomes: A Semiparametric Sensitivity Analysis Approach

In this paper, we develop a semiparametric sensitivity analysis approach designed to address unmeasured confounding in observational studies with time-to-event outcomes. We target estimation of the marginal distributions of potential outcomes under competing exposures using influence function-based techniques. We derived the non-parametric influence function for uncensored data and mapped the uncensored data influence function to the observed data influence function. Our methodology is motivated by and applied to an observational study evaluating the effectiveness of radical prostatectomy (RP) versus external beam radiotherapy with androgen deprivation (EBRT+AD) for the treatment of prostate cancer. We also present a simulation study to evaluate the statistical properties of our methodology.
Soft Reasoning on Uncertain Knowledge Graphs

The study of machine learning-based logical query-answering enables reasoning with large-scale and incomplete knowledge graphs. This paper further advances this line of research by considering the uncertainty in the knowledge. The uncertain nature of knowledge is widely observed in the real world, but \textit{does not} align seamlessly with the first-order logic underpinning existing studies. To bridge this gap, we study the setting of soft queries on uncertain knowledge, which is motivated by the establishment of soft constraint programming. We further propose an ML-based approach with both forward inference and backward calibration to answer soft queries on large-scale, incomplete, and uncertain knowledge graphs. Theoretical discussions present that our methods share the same complexity as state-of-the-art inference algorithms for first-order queries. Empirical results justify the superior performance of our approach against previous ML-based methods with number embedding extensions.