Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Punctuation Restoration for Speech Transcripts via External Data

Automatic Speech Recognition (ASR) systems generally do not produce punctuated transcripts. To make transcripts more readable and follow the expected input format for downstream language models, it is necessary to add punctuation marks. In this paper, we tackle the punctuation restoration problem specifically for the noisy text (e.g., phone conversation scenarios). To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data. Moreover, we propose a two-stage fine-tuning approach that utilizes the sampled external data as well as our in-domain dataset for models based on BERT. Extensive experiments show that the proposed approach outperforms the baseline with an improvement of 1.12% F1 score.
Non-radial oscillation modes in hybrid stars: consequences of a mixed phase

We study the possibility of the existence of a deconfined quark matter in the core of neutron star (NS)s and its relation to non-radial oscillation modes in NSs and hybrid star (HS)s. We use relativistic mean field (RMF) models to describe the nuclear matter at low densities and zero temperature. The Nambu-Jona-Lasinio (NJL) model is used to describe the quark matter at high densities and zero temperature. A Gibbs construct is used to describe the hadron-quark phase transition (HQPT) at large densities. Within the model, as the density increases, a mixed phase (MP) appears at density about 2.5 times the nuclear matter saturation density (ρ 0) and ends at density about 5ρ 0 beyond which the pure quark matter phase appears. It turns out that a stable HS of maximum mass, M = 2.27 M ⊙ with radius R = 14 km (for NL3 parameterisation of nuclear RMF model), can exist with the quark matter in the core in a MP only. HQPT in the core of maximum mass HS occurs at radial distance, rc = 0.27R where the equilibrium speed of sound shows a discontinuity. Existence of quark matter in the core enhances the non-radial oscillation frequencies in HSs compared to NSs of the same mass. This enhancement is significantly large for the g modes. Such an enhancement of the g modes is also seen for a density dependent Bayesian (DDB) parmeterisation of the nucleonic EOS. The non-radial oscillation frequencies depend on the vector coupling in the NJL model. The values of g and f mode frequencies decrease with increase the vector coupling in quark matter.
Scientiﬁc evidence extraction

Recently, interest has grown in applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, progress in this area has been challenging both to make and to measure, due to several issues that arise in training and evaluating models from labeled data. This includes challenges as fundamental as the lack of a single deﬁnitive ground truth output for each input sample and the lack of an ideal metric for measuring partial correctness for this task. To address these we propose a new dataset, PubMed Tables One Million (PubTables-1M), and a new class of metric, grid table similarity (GriTS). PubTables-1M is nearly twice as large as the previous largest comparable dataset, can be used for models across multiple architectures and modalities, and addresses issues such as ambiguity and lack of consistency in the annotations. We apply DETR [1] to table extraction for the ﬁrst time and show that object detection models trained on PubTables-1M produce excellent results out-of-the-box for all three tasks of detection, structure recognition, and functional analysis. We describe the dataset in detail to enable others to build on our work and combine this data with other datasets for these and related tasks. It is our hope that PubTables-1M and the proposed metrics can further progress in this area by creating a benchmark suitable for training and evaluating a wide variety of models for table extraction. Data and code will be released at https: //github.com/microsoft/table-transformer .
Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices

With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the "smartness" of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: "What kinds of behaviors do humans expect from their IoT devices? " The dataset we collected contains the basic information of the IFTTT rules, trigger and action events, and how many people are using each rule.
Submodular Functions and Perfect Graphs

We give a combinatorial polynomial-time algorithm to find a maximum weight independent set in perfect graphs of bounded degree that do not contain a prism or a hole of length four as an induced subgraph. An even pair in a graph is a pair of vertices all induced paths between which are even. An even set is a set of vertices every two of which are an even pair. We show that every perfect graph that does not contain a prism or a hole of length four as an induced subgraph has a balanced separator which is the union of a bounded number of even sets, where the bound depends only on the maximum degree of the graph. This allows us to solve the maximum weight independent set problem using the well-known submodular function minimization algorithm. Funding: This work was supported by the Engineering and Physical Sciences Research Council [Grant EP/V002813/1]; the National Science Foundation [Grants DMS-1763817, DMS-2120644, and DMS-2303251]; and Alexander von Humboldt-Stiftung.
Navigating Higher Dimensional Spaces using Hyperbolic Geometry

Higher-dimensional spaces are ubiquitous in applications of mathematics. Yet, as we live in a three-dimensional space, visualizing, say, a four-dimensional space is challenging. We introduce a novel method of interactive visualization of higher-dimensional grids, based on hyperbolic geometry. In our approach, visualized objects are adjacent on the screen if and only if they are in adjacent cells of the grid. Previous attempts do not show the whole higher-dimensional space at once, put close objects in distant parts of the screen, or map multiple locations to the same point on the screen; our solution lacks these disadvantages, making it applicable in data visualization, user interfaces, and game design.
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System

Recently, textual information has been proved to play a positive role in recommendation systems. However, most of the existing methods only focus on representation learning of textual information in ratings, while potential selection bias induced by the textual information is ignored. In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation. This module can be embedded into recommendation systems that contain learning components of contextual information. Experimental results on three real-world datasets demonstrate the effectiveness of our proposal, and the state-of-the-art models with SAM significantly outperform the original ones.
RFID Exploitation and Countermeasures

Radio Frequency Identification (RFID) systems are among the most widespread computing technologies with technical potential and profitable opportunities in numerous applications worldwide. Further, RFID is the core technology behind the Internet of Things (IoT), which can accomplish the real-time transmission of information between objects without manual operation. However, RFID security has been taken for granted for several years, causing multiple vulnerabilities that can even damage human functionalities. The latest ISO/IEC 18000-63:2015 standard concerning RFID dates to 2015, and much freedom has been given to manufacturers responsible for making their devices secure. The lack of a substantial standard for devices that implement RFID technology creates many vulnerabilities that expose end-users to elevated risk. Hence, this paper gives the reader a clear overview of the technology, and it analyzes 23 well-known RFID attacks such as Reverse Engineering, Buffer Overflow, Eavesdropping, and Malware. Moreover, given the exceptional capabilities and utilities of RFID devices, this paper has focused on security measures and defenses for protecting them, such as Active Jamming, Shielding tag, and Authentication.
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device

Streaming end-to-end speech recognition models have been widely applied to mobile devices and show significant improvement in efficiency. These models are typically trained on the server using transcribed speech data. However, the server data distribution can be very different from the data distribution on user devices, which could affect the model performance. There are two main challenges for on device training, limited reliable labels and limited training memory. While self-supervised learning algorithms can mitigate the mismatch between domains using unlabeled data, they are not applicable on mobile devices directly because of the memory constraint. In this paper, we propose an incremental layer-wise self-supervised learning algorithm for efficient speech domain adaptation on mobile devices, in which only one layer is updated at a time. Extensive experimental results demonstrate that the proposed algorithm obtains a Word Error Rate (WER) on the target domain $24.2\%$ better than supervised baseline and costs $89.7\%$ less training memory than the end-to-end self-supervised learning algorithm.
Regularity and confluence of geodesics for the supercritical Liouville quantum gravity metric

We show that for each ${\mathbf c}_{\mathrm M} \in [1,25)$, there is a unique metric associated with Liouville quantum gravity (LQG) with matter central charge ${\mathbf c}_{\mathrm M}$. An earlier series of works by Ding-Dub\'edat-Dunlap-Falconet, Gwynne-Miller, and others showed that such a metric exists and is unique in the subcritical case ${\mathbf c}_{\mathrm M} \in (-\infty,1)$, which corresponds to coupling constant $\gamma \in (0,2)$. The critical case ${\mathbf c}_{\mathrm M} = 1$ corresponds to $\gamma=2$ and the supercritical case ${\mathbf c}_{\mathrm M} \in (1,25)$ corresponds to $\gamma \in \mathbb C$ with $|\gamma| = 2$. Our metric is constructed as the limit of an approximation procedure called Liouville first passage percolation, which was previously shown to be tight for $\mathbf c_{\mathrm M} \in [1,25)$ by Ding and Gwynne (2020). In this paper, we show that the subsequential limit is uniquely characterized by a natural list of axioms. This extends the characterization of the LQG metric proven by Gwynne and Miller (2019) for $\mathbf c_{\mathrm M} \in (-\infty,1)$ to the full parameter range $\mathbf c_{\mathrm M} \in (-\infty,25)$. Our argument is substantially different from the proof of the characterization of the LQG metric for $\mathbf c_{\mathrm M} \in (-\infty,1)$. In particular, the core part of the argument is simpler and does not use confluence of geodesics.
Properties of the neutron star crust: Quantifying and correlating uncertainties with improved nuclear physics

A compressible liquid-drop model (CLDM) is used to correlate uncertainties associated with the properties of the neutron star (NS) crust with theoretical estimates of the uncertainties associated with the equation of state (EOS) of homogeneous neutron and nuclear matter. For the latter, we employ recent calculations based on Hamiltonians constructed using Chiral Effective Field theory (χEFT). Fits to experimental nuclear masses are employed to constrain the CLDM further, and we find that they disfavor some of the χEFT Hamiltonians. The CLDM allows us to study the complex interplay between bulk, surface, curvature, and Coulomb contributions, and their impact on the NS crust. It also reveals how the curvature energy alters the correlation between the surface energy and the bulk symmetry energy. Our analysis quantifies how the uncertainties associated with the EOS of homogeneous matter implies significant uncertainties for the composition of the crust, its proton fraction, and the volume fraction occupied by nuclei. We find that the finite-size effects impact the crust composition, but have a negligible effect on the net isospin asymmetry of matter. The isospin asymmetry is largely determined by the bulk properties and the isospin dependence of the surface energy. The most significant uncertainties associated with matter properties in the densest regions of the crust, the precise location of the crust-core transition, are found to be strongly correlated with uncertainties associated with the Hamiltonians. By adopting a unified model to describe the crust and the core of NSs, we tighten the correlation between their global properties such as their mass-radius relationship, moment of inertia, crust thickness, and tidal deformability with uncertainties associated with the nuclear Hamiltonians.
TEACh: Task-driven Embodied Agents that Chat

Robots operating in human spaces must be able to engage in natural language interaction, both understanding and executing instructions, and using conversation to resolve ambiguity and correct mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human-human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates in natural language with a Follower. The Follower navigates through and interacts with the environment to complete tasks varying in complexity from "Make Coffee" to "Prepare Breakfast", asking questions and getting additional information from the Commander. We propose three benchmarks using TEACh to study embodied intelligence challenges, and we evaluate initial models' abilities in dialogue understanding, language grounding, and task execution.
Fully Spiking Variational Autoencoder

Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed and ultra-low energy consumption because of their binary and event-driven nature. Therefore, SNNs are expected to have various applications, including as generative models being running on edge devices to create high-quality images. In this study, we build a variational autoencoder (VAE) with SNN to enable image generation. VAE is known for its stability among generative models; recently, its quality advanced. In vanilla VAE, the latent space is represented as a normal distribution, and floating-point calculations are required in sampling. However, this is not possible in SNNs because all features must be binary time series data. Therefore, we constructed the latent space with an autoregressive SNN model, and randomly selected samples from its output to sample the latent variables. This allows the latent variables to follow the Bernoulli process and allows variational learning. Thus, we build the Fully Spiking Variational Autoencoder where all modules are constructed with SNN. To the best of our knowledge, we are the first to build a VAE only with SNN layers. We experimented with several datasets, and confirmed that it can generate images with the same or better quality compared to conventional ANNs. The code is available at https://github.com/kamata1729/FullySpikingVAE.
LEMON: Explainable Entity Matching

State-of-the-art entity matching (EM) methods are hard to interpret, and there is significant value in bringing explainable AI to EM. Unfortunately, most popular explainability methods do not work well out of the box for EM and need adaptation. In this paper, we identify three challenges of applying local post hoc feature attribution methods to entity matching: cross-record interaction effects, non-match explanations, and variation in sensitivity. We propose our novel model-agnostic and schema-flexible method LEMON that addresses all three challenges by (i) producing dual explanations to avoid cross-record interaction effects, (ii) introducing the novel concept of attribution potential to explain how two records could have matched, and (iii) automatically choosing explanation granularity to match the sensitivity of the matcher and record pair in question. Experiments on public datasets demonstrate that the proposed method is more faithful to the matcher and does a better job of helping users understand the decision boundary of the matcher than previous work. Furthermore, user studies show that the rate at which human subjects can construct counterfactual examples after seeing an explanation from our proposed method increases from 54% to 64% for matches and from 15% to 49% for non-matches compared to explanations from a standard adaptation of LIME.
Optimal Feedback Control for Modeling Human–Computer Interaction

Optimal feedback control (OFC) is a theory from the motor control literature that explains how humans move their body to achieve a certain goal, e.g., pointing with the finger. OFC is based on the assumption that humans aim at controlling their body optimally, within the constraints imposed by body, environment, and task. In this article, we explain how this theory can be applied to understanding Human-Computer Interaction (HCI) in the case of pointing. We propose that the human body and computer dynamics can be interpreted as a single dynamical system. The system state is controlled by the user via muscle control signals, and estimated from observations. Between-trial variability arises from signal-dependent control noise and observation noise. We compare four different models from optimal control theory and evaluate to what degree these models can replicate movements in the case of mouse pointing. We introduce a procedure to identify parameters that best explain observed user behavior. To support HCI researchers in simulating, analyzing, and optimizing interaction movements, we provide the Python toolbox OFC4HCI. We conclude that OFC presents a powerful framework for HCI to understand and simulate motion of the human body and of the interface on a moment-by-moment basis.
Cogrowth series for free products of finite groups

Given a finitely generated group with generating set $S$, we study the cogrowth sequence, which is the number of words of length $n$ over the alphabet $S$ that are equal to one. This is related to the probability of return for walks the corresponding Cayley graph. Muller and Schupp proved the generating function of the sequence is algebraic when $G$ has a finite-index free subgroup (using a result of Dunwoody). In this work we make this result effective for free products of finite groups: we determine bounds for the degree and height of the minimal polynomial of the generating function, and determine the minimal polynomial explicitly for some families of free products. Using these results we are able to prove that a gap theorem holds: if $S$ is a finite symmetric generating set for a group $G$ and if $a_n$ denotes the number of words of length $n$ over the alphabet $S$ that are equal to $1$ then $\limsup_n a_n^{1/n}$ exists and is either $1$, $2$, or at least $2\sqrt{2}$.
Mycochemical Screening Reveals Exopolysaccharide Secretion, Antioxidant And Larvicidal Activities Of Three Oyster Mushrooms.

BACKGROUND Higher fungi like oyster mushrooms have been well reported with vast therapeutic and nutritive importance due to their bio-active constituents. Today, many interesting facts are also emerging on their importance in pest control. In this study, Pleurotus floridanus, P. ostreatus, and P. sajor-caju were studied for exopolysacharide (EPS) and antioxidant productions under different carbon sources, and assayed for larvicidal properties. RESULTS We observed a positive response of these mushrooms to carbon sources. The highest EPS was produced by P. sajor-caju under sucrose carbon-source while P. ostreatus and P. floridanus prefer glucose. Antioxidant activity of their EPS was confirmed using the radical scavenging of 1,1-diphenyl-2-picrylhydrazyl (DPPH) and potassium ferricyanide reduction methods. We also establish the inhibition of lipid peroxidation by the EPS produced by these mushrooms, although this was concentration-dependent while P. floridanus EPS had the highest antioxidant properties. The EPS concentration of 100 mg/L from all the mushrooms was enough for inhibition of lipid peroxidation; this also had significant difference from the scavenging effect of butylated hydroxyl toluene (BHT). All the mushroom extracts in this study had positive inhibitory effects on the late third and early fourth stage of Anopheles gambiae. However, they were significantly different from each other as P. floridanus recorded a Lethal concentration (LC50 ) of 0.0. 563, P. ostreatus had LC50 of 0.352 while P. sajor-caju had LC50 of 0.715. CONCLUSION This study affirms the influence of carbon source on EPS production by selected oyster mushrooms, reveals their antioxidant properties, and their potential applications in pest control. This article is protected by copyright. All rights reserved.
Graphene and graphene-like materials in biomass conversion: paving the way to the future

Materials based on graphene and its derivatives have a dual relationship with biomass: (i) they can be synthesized using biomass or its platform chemicals and (ii) they can be employed as efficient catalysts or supports for nanoparticulate catalysts for biomass conversion. In this review article we discuss methods of catalyst synthesis using top-down and bottom-up approaches as well as different synthetic pathways where all or some catalyst components are prefabricated or the whole catalyst is prepared in situ from small molecules. We identify key factors influencing the efficiency of the catalysts formed and suggest our perspective for the development of this field.
Reconstructing chemical plumes from stand-off detection data of airborne chemicals using atmospheric dispersion models and data fusion

Abstract Stand-off detection of airborne chemical compounds has proven to be a useful method that is gaining popularity following technical progress. There are obvious advantages compared to in situ measurements when it comes to the security aspect and the ability to measure at locations otherwise hard to reach. However, an inherent limitation in many of the stand-off detection techniques lies in the fact that the measured signal from a chemical depends nonlinearly on the distance to the detector. Furthermore, the measured signal describes the summation of the responses from all chemicals spatially distributed in the line of sight of the instrument. In other words, the three dimensional extension of the chemical plume is converted into a two-dimensional image. Not only is important geometric information per se lost in this process, but the measured signal strength itself depends on the unknown plume distribution which implies that the interpretation of the observation data suffers from significant uncertainty. In this paper we investigate different and novel approaches to reconstruct the original three-dimensional distribution and concentration of the plume by implementation of atmospheric dispersion models and numerical retrieval methods. In particular our method does not require a priori assumptions on the three-dimensional distribution of the plume. We also strongly advocate the use of proper constraints to avoid unphysical solutions being derived (or post-process ‘adjustments’ to correct unphysical solutions). By applying such a reconstruction method, both improved and additional information is obtained from the original observation data, providing important intelligence to the analysts and decision makers.