Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Text Generation

Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between users and their editor. This is understandable as training AI models with real users is not only slow and costly, but what these models learn may be specific to user interface design choices. Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help. We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users, by using user simulators that provide edits that guide the model towards a given target text. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts, even when all models are given the same budget of user inputs or edits.
An Improved Christofides Mechanism for Local Differential Privacy Framework

The development of Internet technology enables an analysis on the whole population rather than a certain number of samples, and leads to increasing requirement for privacy protection. Local differential privacy (LDP) is an effective standard of privacy measurement; however, its large variance of mean estimation causes challenges in application. To address this problem, this paper presents a new LDP approach, an improved Christofides mechanism. It compared four statistical survey methods for conducting surveys on sensitive topics -- modified Warner, Simmons, Christofides, and the improved Christofides mechanism. Specifically, Warner, Simmons and Christofides mechanisms have been modified to draw a sample from the population without replacement, to decrease variance. Furthermore, by drawing cards without replacement based on modified Christofides mechanism, we introduce a new mechanism called the improved Christofides mechanism, which is found to have the smallest variance under certain assumption when using LDP as a measurement of privacy leakage. The assumption is do satisfied usually in the real world. Actually, we decrease the variance to 28.7% of modified Christofides mechanism's variance in our experiment based on the HCOVANY dataset -- a real world dataset of IPUMS USA. This means our method gets a more accurate estimate by using LDP as a measurement of privacy leakage. This is the first time the improved Christofides mechanism is proposed for LDP framework based on comparative analysis of four mechanisms using LDP as the same measurement of privacy leakage.
Early stages of oxygen segregation and precipitation in silicon

The early stages of oxygen segregation at dislocation and precipitation in the bulk have been investigated by high‐resolution electron microscopy in Czochralski grown silicon. Two kinds of precipitates are observed: a crystalline silica phase, coesite, and an amorphous phase. Both forms coexist after 650 °C heat treatment: the so‐called rodlike defects are in fact long 〈011〉 ribbons of coesite associated with interstitial dipoles. This crystalline form is favored by a high oxygen supersaturation and a low carbon content. Above 870 °C amorphous platelets of silica are formed on the {100} planes, whereas coesite is no longer observed but interstitial dislocation loops are always present. The strain produced by such precipitates is partially relaxed by Si interstitial emission, which explains the internal formation of dislocations. It is suggested that both forms are nucleated on two different species of nuclei. At 〈011〉 dislocation cores it is shown that the coesite phase is stabilized over a wide range of .
Scaling Behavior of Dynamic Ferroelectric Hysteresis in Soft PZT Ceramic: Stress Dependence

Effects of electric field-frequency, electric field-amplitude, and mechanical stress on the hysteresis area were investigated on soft PZT ceramic. At stress-free condition, the investigation found the area scales with frequency and field-amplitude in power-law form, ⟨A⟩ ∝ ƒ−0.25 E 0; however, with different set of exponents in comparing to those in the investigation on thin films structure. On the other hand, with compressive stresses turning on, the same set of the exponents with the stress-free condition is found to confirm universality. In addition, the scaling form of the area to triple parameters; i.e., frequency, field-amplitude, and stress in a power law form, ⟨A⟩ − ⟨A σ = 0⟩ = ⟨ A − A σ = 0⟩ ∝ ƒ− 0.25 E 0 σ 0.44 , was obtained.
On the Consistency of Circuit Lower Bounds for Non-deterministic Time

We prove the first unconditional consistency result for superpolynomial circuit lower bounds with a relatively strong theory of bounded arithmetic. Namely, we show that the theory V20 is consistent with the conjecture that NEXP ⊈ P/poly, i.e., some problem that is solvable in non-deterministic exponential time does not have polynomial size circuits. We suggest this is the best currently available evidence for the truth of the conjecture. Additionally, we establish a magnification result on the hardness of proving circuit lower bounds.
Building Dynamic Ontological Models for Place using Social Media Data from Twitter and Sina Weibo

Place holds human thoughts and experiences. Space is defined with geometric measurement and coordinate systems. Social media served as the connection between place and space. In this study, we use social media data (Twitter, Weibo) to build a dynamic ontological model in two separate areas: Beijing, China and San Diego, the U.S.A. Three spatial analytics methods are utilized to generate the place name ontology: 1) Kernel Density Estimation (KDE); 2) Dynamic Method Density-based spatial clustering of applications with noise (DBSCAN); 3) hierarchal clustering. We identified feature types of place name ontologies from geotagged social media data and classified them by comparing their default search radius of KDE of geo-tagged points. By tracing the seasonal changes of highly dynamic non-administrative places, seasonal variation patterns were observed, which illustrates the dynamic changes in place ontology caused by the change in human activities and conversation over time and space. We also investigate the semantic meaning of each place name by examining Pointwise Mutual Information (PMI) scores and word clouds. The major contribution of this research is to link and analyze the associations between place, space, and their attributes in the field of geography. Researchers can use crowd-sourced data to study the ontology of places rather than relying on traditional gazetteers. The dynamic ontology in this research can provide bright insight into urban planning and re-zoning and other related industries.
Interactive Exploration of the Temporal α-Shape

Shape is a powerful tool to understand point sets. A formal notion of shape is given by $\alpha$-shapes, which generalize the convex hull and provide adjustable level of detail. Many real-world point sets have an inherent temporal property as natural processes often happen over time, like lightning strikes during thunderstorms or moving animal swarms. To explore such point sets, where each point is associated with one timestamp, interactive applications may utilize $\alpha$-shapes and allow the user to specify different time windows and $\alpha$-values. We show how to compute the temporal $\alpha$-shape $\alpha_T$, a minimal description of all $\alpha$-shapes over all time windows, in output-sensitive linear time. We also give complexity bounds on $|\alpha_T|$. We use $\alpha_T$ to interactively visualize $\alpha$-shapes of user-specified time windows without having to constantly compute requested $\alpha$-shapes. Experimental results suggest that our approach outperforms an existing approach by a factor of at least $\sim$52 and that the description we compute has reasonable size in practice. The basis for our algorithm is an existing algorithm which computes all Delaunay triangles over all time windows using $\mathcal{O}(1)$ time per triangle. Our approach generalizes to higher dimensions with the same runtime for fixed $d$.
Effects and Effect Handlers for Programmable Inference

Inference algorithms for probabilistic programming are complex imperative programs with many moving parts. Efficient inference often requires customising an algorithm to a particular probabilistic model or problem, sometimes called inference programming . Most inference frameworks are implemented in languages that lack a disciplined approach to side effects, which can result in monolithic implementations where the structure of the algorithms is obscured and inference programming is hard. Functional programming with typed effects offers a more structured and modular foundation for programmable inference, with monad transformers being the primary structuring mechanism explored to date. This paper presents an alternative approach to programmable inference, based on algebraic effects, building on recent work that used algebraic effects to represent probabilistic models. Using effect signatures to specify the key operations of the algorithms, and effect handlers to modularly interpret those operations for specific variants, we develop three abstract algorithms, or inference patterns , representing three important classes of inference: Metropolis-Hastings, particle filtering, and guided optimisation. We show how our approach reveals the algorithms’ high-level structure, and makes it easy to tailor and recombine their parts into new variants. We implement the three inference patterns as a Haskell library, and discuss the pros and cons of algebraic effects vis-à-vis monad transformers as a structuring mechanism for modular imperative algorithm design. It should be possible to reimplement our library in any typed functional language able to emulate effects and effect handlers.
The"Sign problem"of the 3rd order anomalous Hall effect in topological magnetic materials

The anomalous Hall effect (AHE), which provides a bridgeway between the geometry of quantum wavefunctions and transport measurements, has been a key focus of intensive studies. In addition to the well-studied linear AHE, governed by the electronic Berry curvature, nonlinear AHE originated from higher-order Berry-curvature multipoles has also been observed in recent studies. Inspired by the 3rd order AHE and its room temperature sign switching in kagome antiferromagnet FeSn [S. Sankar, R. Liu, X. Gao, et al, to be published (2023)], we investigate the generic sign structure of Berry-curvature induced 3rd order AHE in topological magnetic material. We find that in contrast to the linear Hall coefficient, whose sign is determined by the broken time-reversal symmetry, the sign of the 3rd order Hall coefficient is dictated by the interplay between time-reversal symmetry breaking, magnetic order, and spin-orbit couplings. Our calculations give a possible solution for the"sign problem"of the 3rd order AHE response in the phase space spanned by the in- and out-of-plane magnetization, the spin-orbital coupling strength and chemical potential. We further propose realistic experiment setups to systematically reveal the sign structure in the 3rd order AHE response via continuously rotating the magnetic field directions.
Distinguishing Engagement Facets: An Essential Component for AI-based Interactive Healthcare 37-47

Engagement in Human-Machine Interaction is the process by which entities participating in the interaction establish, maintain, and end their perceived connection. It is essential to monitor the engagement state of patients in various AI-based interactive healthcare paradigms. This includes medical conditions that alter social behavior such as Autism Spectrum Disorder (ASD) or Attention-Deficit/Hyperactivity Disorder (ADHD). Engagement is a multi-faceted construct which is composed of behavioral, emotional, and mental components. Previous research has neglected this multi-faceted nature of engagement and focused on the detection of engagement level or binary engagement label. In this paper, a system is presented to distinguish these facets using contextual and relational features. This can facilitate further fine-grained analysis. Several machine learning classifiers including traditional and deep learning models are compared for this task. An F-Score of 0.74 was obtained on a balanced dataset of 22242 instances with neural network-based classification. The proposed framework shall serve as a baseline for further research on engagement facets recognition, and its integration is socially assistive robotic applications.
Learning From Yourself: A Self-Distillation Method For Fake Speech Detection

In this paper, we propose a novel self-distillation method for fake speech detection (FSD), which can significantly improve the performance of FSD without increasing the model complexity. For FSD, some fine-grained information is very important, such as spectrogram defects, mute segments, and so on, which are often perceived by shallow networks. However, shallow networks have much noise, which can not capture this very well. To address this problem, we propose using the deepest network instruct shallow network for enhancing shallow networks. Specifically, the networks of FSD are divided into several segments, the deepest network being used as the teacher model, and all shallow networks become multiple student models by adding classifiers. Meanwhile, the distillation path between the deepest network feature and shallow network features is used to reduce the feature difference. A series of experimental results on the ASVspoof 2019 LA and PA datasets show the effectiveness of the proposed method, with significant improvements compared to the baseline.
Compensating for Sensing Failures via Delegation in Human–AI Hybrid Systems

Given the increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g., perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human–AI teaming case where a managing agent is tasked with identifying when to perform a delegated assignment and whether the human or autonomous system should gain control. In this context, the manager will estimate its best action based on the likelihood of either (human, autonomous) agent’s failure as a result of their sensing capabilities and possible deficiencies. We model how the environmental context can contribute to, or exacerbate, these sensing deficiencies. These contexts provide cases where the manager must learn to identify agents with capabilities that are suitable for decision-making. As such, we demonstrate how a reinforcement learning manager can correct the context–delegation association and assist the hybrid team of agents in outperforming the behavior of any agent working in isolation.
Littlewood-Richardson coefficients and the eigenvalues of integral line graphs

We first describe a system of inequalities (Horn's inequalities) that characterize eigenvalues of sums of Hermitian matrices. When we apply this system for integral Hermitian matrices, one can directly test it by using Littlewood-Richardson coefficients. In this paper, we apply Horn's inequalities to analysis the eigenvalues of an integral line graph $G$ of a connected bipartite graph. Then we show that the diameter of $G$ is at most $2\omega(G)$, where $\omega(G)$ is the clique number of $G$. Also using Horn's inequalities, we show that for every odd integer $k\geq 19$, a non-complete $k$-regular Ramanujan graph has an eigenvalue less than $-2$.
The kinetic temperature and density of the Sagittarius B2 molecular cloud from observations of methyl cyanide.

From observations with the Bell Laboratories 7 m telescope of the K components of the CH/sub 3/CH J = 4. -->. 3 rotational transition at 73.6 GHz, 6. -->. 5 transition at 110.4 GHz, and 7. -->. 6 transition at 128.7 GHz, a mean kinetic temperature of 85 +- 10 K and a mean H/sub 2/ density of (1.1 +- 0.5) x 10/sup 5/ cm/sup -3/ are deduced for the central 2' of the Sgr B2 molecular cloud. Within the K = 0--4 ladders of CH/sub 3/CN in Sgr B2 the populations of the radiatively coupled J levels are relaxed, with a rotational temperature of approximately 16 K: similar to that of several linear molecules.
Causal Inference Despite Limited Global Confounding via Mixture Models

A Bayesian Network is a directed acyclic graph (DAG) on a set of $n$ random variables (the vertices); a Bayesian Network Distribution (BND) is a probability distribution on the random variables that is Markovian on the graph. A finite $k$-mixture of such models is graphically represented by a larger graph which has an additional ``hidden'' (or ``latent'') random variable $U$, ranging in $\{1,\ldots,k\}$, and a directed edge from $U$ to every other vertex. Models of this type are fundamental to causal inference, where $U$ models an unobserved confounding effect of multiple populations, obscuring the causal relationships in the observable DAG. By solving the mixture problem and recovering the joint probability distribution with $U$, traditionally unidentifiable causal relationships become identifiable. Using a reduction to the more well-studied ``product'' case on empty graphs, we give the first algorithm to learn mixtures of non-empty DAGs.
On the spectral radius of the $(L,\kappa)$-lazy Markov chain

We consider an $(L,\kappa)$-lazy operation on an irreducible Markov transition probability $P$ with state space $S$ where $L \subset S$ and $\kappa\in[0,1)$. For each $x \in L$ and $y\in S$, this $(L,\kappa)$-operation replaces $P(x,y)$, the transition probability from $x$ to $y$, by $\kappa 1_{\{x=y\}} + (1-\kappa)P(x,y)$. We are interested in how $L$ and $\kappa$ influence the spectral radius $\rho^L_\kappa$ of this new transition probability. We first show that $\rho^L_\kappa$ is non-decreasing and continuous in $\kappa$. We then show that: (1) If $L$ is nonempty and finite, then $P$ being rho-transient is equivalent to that the growth of $\displaystyle (\rho^L_\kappa)_{\kappa\in[0,1)}$ exhibits a phase transition: There exists a critical value $\kappa_c(L) \in (0,1)$ such that $\kappa \mapsto \rho^L_\kappa$ is a constant on $[0,\kappa_c(L)]$ and increases strictly on $[\kappa_c(L),1)$; (2) For every $\kappa\in(0,1)$, if $S\setminus L$ is nonempty and finite, then $\rho^L_\kappa=\rho^S_\kappa$ if and only if $P$ is not strictly rho-recurrent.
A Monte Carlo Method for Computing the HPD Interval for Ratio of Two Multivariate Normal Generalized Variances

ABSTRACT In this article, we develop a method for computing a Bayesian HPD (highest probability density) interval for a ratio of two multivariate normal generalized variances. The method gives a way of comparing two multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approaches for the interval are intractable and thus a Bayesian HPD interval is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach introduced by Chen and Shao (1999). Necessary theory involved in the method and computation is provided.
Lattice Boltzmann study of droplet motion inside a grooved channel

A droplet moving inside a grooved channel is studied by using a new lattice Boltzmann model for multiphase flows with large density ratio. A constant body force is applied to drive the droplet. Flows under different surface tensions, driving forces, density ratios, wall wettabilities, and groove geometries are investigated. It is found that the drag on the droplet and the flow pattern are strongly affected by the wall wettability and topography when the system scale is small. The effects of the driving force on the droplet are investigated through comparison of two different ways of applying it. Besides, the density ratio is varied over a wide range to assess its effects in the present setup. Special attention is paid to grooved hydrophilic walls which tend to enhance the droplet-wall contact. For such walls, two distinctive types of shape of the interface inside the groove are found and series of numerical investigations are carried out to find the critical wall contact angle, groove width and depth that.
Period-multiplying cascades for diffeomorphisms of the disc

It is a well-known result in one-dimensional dynamics that if a continuous map of the interval has positive topological entropy, then it has a periodic orbit of period 2i for each integer i ≥ 0 [15] (see also [12]). In fact, one can say rather more: such a map has a sequence of periodic orbits (P)i ≥ 0 with per (Pi) = 2i which form a period-doubling cascade (that is, whose points are ordered and permuted in the way which would occur had the orbits been created in a sequence of period-doubling bifurcations starting from a single fixed point). This result reflects the central role played by period-doubling in transitions to positive entropy in a one-dimensional setting. In this paper we prove an analogous result for positive-entropy orientation-preserving diffeomorphisms of the disc. Using the notion [9] of a two-dimensional cascade, we shall show that such diffeomorphisms always have infinitely many ‘zero-entropy’ cascades of periodic orbits (including a period-doubling cascade, though this need not begin from a fixed point).
A novel ocular phenotype associated with pathogenic variants in MFSD8 leading to macular dystrophy

ABSTRACT Background The major facilitator superfamily domain-containing protein 8 (MFSD8) pathogenic variants are classically associated with autosomal recessive neuronal ceroid lipofuscinosis-7. Case reports have recently demonstrated an association of MFSD8 variants causing autosomal recessive macular dystrophy with central cone involvement without neurologic sequelae. We report a patient with a novel ocular phenotype associated with MFSD8 pathogenic variants causing macular dystrophy without systemic findings. Case presentation A 37-year-old female presented with a 20-year history of progressive bilateral vision loss. Fundus examination was notable for a slight pigmentary ring around the fovea in both eyes. Optical coherence tomography (OCT) of the macula showed bilateral subfoveal ellipsoid zone loss without outer retinal changes. Fundus autofluorescence (FAF) demonstrated foveal hypo-autofluorescence (AF) in both eyes as well as hyper-AF nasally to the optic nerve in the perifoveal area. Full-field and multifocal electroretinography demonstrated cone dysfunction with diffuse macular changes in both eyes. Subsequent genetic testing identified two pathogenic MFSD8 variants. The patient had no neurologic symptoms consistent with variant-late infantile neuronal ceroid lipofuscinosis. Conclusion MFSD8 pathogenic variants are known to cause macular dystrophies. We report a novel MFSD8-associated macular dystrophy phenotype demonstrating foveal-limited disease with cavitary changes on OCT without inner retinal atrophy and foveal-specific changes on FAF. A threshold model can explain how a hypomorphic missense variant heterozygous with a loss-of-function nonsense variant can lead to a predominantly ocular phenotype with preserved neurologic function. We recommend careful monitoring of these patients for future signs of both retinal and systemic disease progression.