Miracle-dz/UniFAR-FTUs huggingface dataset:
A new sensing paradigm for the vibroacoustic detection of pedicle screw loosening

There is an unmet clinical need for developing novel methods to complement and replace the current radiation-emitting imaging-based methods for the detection of loose pedicle screws as a complication after spinal fusion surgery which fail to identify a substantial amount of loose implants. In this work, we propose a new methodology and paradigm for the radiation-free, non-destructive, and easy-to-integrate detection of pedicle screw loosening based on vibroacoustic sensing. Furthermore, we propose a novel simulation technique for pedicle screw loosening, which is biomechanically validated. For the detection of a loose implant, we excite the vertebra of interest with a sine sweep vibration at the spinous process and attach a custom highly-sensitive piezo contact microphone to the screw head to capture the propagated vibration characteristics which are analyzed using a detection pipeline based on spectrogram features and a SE-ResNet-18. To validate the proposed approach, we conducted experiments using four human cadaveric lumbar spine specimens and evaluate our algorithm in a cross validation experiment. Our method reaches a sensitivity of 91 . 50 ± 6 . 58% and a speciﬁcity of 91 . 10 ± 2 . 27%. The proposed system shows great potentials for the development of alternative assessment methods for implant loosening based on vibroacoustic sensing.
Chirality-induced intrinsic charge rectification in a tellurium-based field-effect transistor

We report gate-induced enhancement of intrinsic charge rectiﬁcation without p-n junctions in chiral semiconductor Te under magnetic ﬁeld B . As gating shifts the chemical potential to the valence band maximum of Te, the charge rectiﬁcation eﬃciency is enhanced hundredfold. By integrating model calculations, we attribute the charge rectiﬁcation to the B -induced asymmetry of the chiral band structure. We also show that the carrier density subject to this asymmetry is augmented by a saddle-point structure near the valence band maximum, which further enhances the gate-tunable charge rectiﬁcation together with its improved switchability and thermal robustness.
DELFI: Deep Mixture Models for Long-term Air Quality Forecasting in the Delhi National Capital Region

The identiﬁcation and control of human factors in climate change is a rapidly growing concern and robust, real-time air-quality monitoring and forecasting plays a critical role in allowing effective policy formulation and implementation. This paper presents DELFI, a novel deep learning-based mixture model to make effective long-term predictions of Particulate Matter (PM) 2.5 concentrations. A key novelty in DELFI is its multi-scale approach to the forecasting problem. The observation that point predictions are more suitable in the short-term and probabilistic predictions in the long-term allows accurate predictions to be made as much as 24 hours in advance. DELFI incorporates meteorological data as well as pollutant-based features to ensure a robust model that is divided into two parts: (i) a stack of three Long Short-Term Memory (LSTM) networks that perform differential modelling of the same window of past data, and (ii) a fully-connected layer enabling attention to each of the components. Experimental evaluation based on deployment of 13 stations in the Delhi National Capital Region (Delhi-NCR) in India establishes that DELFI offers far superior predictions especially in the long-term as compared to even non-parametric baselines. The Delhi-NCR recorded the 3rd highest PM levels amongst 39 mega-cities across the world during 2011-2015 and DELFI’s performance establishes it as a potential tool for effective long-term forecasting of PM levels to enable public health man-agement and environment protection.
Feature Necessity & Relevancy in ML Classifier Explanations

Given a machine learning (ML) model and a prediction, explanations can be defined as sets of features which are sufficient for the prediction. In some applications, and besides asking for an explanation, it is also critical to understand whether sensitive features can occur in some explanation, or whether a non-interesting feature must occur in all explanations. This paper starts by relating such queries respectively with the problems of relevancy and necessity in logic-based abduction. The paper then proves membership and hardness results for several families of ML classifiers. Afterwards the paper proposes concrete algorithms for two classes of classifiers. The experimental results confirm the scalability of the proposed algorithms.
AGReE: A system for generating Automated Grammar Reading Exercises

We describe the AGReE system, which takes user-submitted passages as input and automatically generates grammar practice exercises that can be completed while reading. Multiple-choice practice items are generated for a variety of different grammar constructs: punctuation, articles, conjunctions, pronouns, prepositions, verbs, and nouns. We also conducted a large-scale human evaluation with around 4,500 multiple-choice practice items. We notice for 95% of items, a majority of raters out of five were able to identify the correct answer and for 85% of cases, raters agree that there is only one correct answer among the choices. Finally, the error analysis shows that raters made the most mistakes for punctuation and conjunctions.
A Compact End-to-End Model with Local and Global Context for Spoken Language Identification

We introduce TitaNet-LID, a compact end-to-end neural network for Spoken Language Identification (LID) that is based on the ContextNet architecture. TitaNet-LID employs 1D depth-wise separable convolutions and Squeeze-and-Excitation layers to effectively capture local and global context within an utterance. Despite its small size, TitaNet-LID achieves performance similar to state-of-the-art models on the VoxLingua107 dataset while being 10 times smaller. Furthermore, it can be easily adapted to new acoustic conditions and unseen languages through simple fine-tuning, achieving a state-of-the-art accuracy of 88.2% on the FLEURS benchmark. Our model is scalable and can achieve a better trade-off between accuracy and speed. TitaNet-LID performs well even on short utterances less than 5s in length, indicating its robustness to input length.
Network Based Approach to Gene Prioritization at Genome-Wide Association Study Loci

Motivation: Genome-wide association studies (GWAS) have successfully identiﬁed thousands of genetic risk loci for complex traits and diseases. Most of these GWAS loci lie in regulatory regions of the genome and the gene through which each GWAS risk locus exerts its eﬀects is not always clear. Many computational methods utilizing biological data sources have been proposed to identify putative casual genes at GWAS loci; however, these methods can be improved upon. Results: We present the Relations-Maximization Method, a dense module searching method to identify putative causal genes at GWAS loci through the generation of candidate sub-networks derived by integrating association signals from GWAS data into the gene co-regulation network. We employ our method in a chronic obstructive pulmonary disease GWAS. We perform an exten-sive, comparative study of Relations-Maximization Method’s performance against well-established baselines. Availability: The code and the datasets used in this manuscript are available at the following website:
Hyper-Connected Transformer Network for Co-Learning Multi-Modality PET-CT Features

—[18F]-Fluorodeoxyglucose (FDG) positron emission tomography – computed tomography (PET-CT) has become the imaging modality of choice for diagnosing many cancers. Co-learning complementary PET-CT imaging features is a fundamental requirement for automatic tumor segmentation and for developing computer aided cancer diagnosis systems. We propose a hyper-connected transformer (HCT) network that integrates a transformer network (TN) with a hyper connected fusion for multi-modality PET-CT images. The TN was leveraged for its ability to provide global dependencies in image feature learning, which was achieved by using image patch embeddings with a self-attention mechanism to capture image-wide contextual information. We extended the single-modality definition of TN with multiple TN based branches to separately extract image features. We introduced a hyper connected fusion to fuse the contextual and complementary image features across multiple transformers in an iterative manner. Our results with two non-small cell lung cancer and soft-tissue sarcoma datasets show that HCT achieved better performance in segmentation accuracy when compared to state-of-the-art methods. We also show that HCT produces consistent performance across various image fusion strategies and network backbones.
Online Learning Models for Vehicle Usage Prediction During COVID-19

Today, there is an ongoing transition to more sustainable transportation, for which an essential part is the switch from combustion engine vehicles to battery electric vehicles (BEVs). BEVs have many advantages from a sustainability perspective, but issues such as limited driving range and long recharge times slow down the transition from combustion engines. One way to mitigate these issues is by performing battery thermal preconditioning, which increases the energy efficiency of the battery. However, to optimally perform battery thermal preconditioning, the vehicle usage pattern needs to be known, i.e., how and when the vehicle will be used. This study attempts to predict the departure time and distance of the first drive each day using online machine learning models. The online machine learning models are trained and evaluated on historical driving data collected from a fleet of BEVs during the COVID-19 pandemic. Additionally, the prediction models are extended to quantify the uncertainty of their predictions, which can be used to decide whether the prediction should be used or dismissed. Based on our results, the best-performing prediction models yield an aggregated mean absolute error of 2.75 hours when predicting departure time and 13.37 km when predicting trip distance.
BRATsynthetic: Text De-identification using a Markov Chain Replacement Strategy for Surrogate Personal Identifying Information

Objective: Implement and assess personal health identifying information (PHI) substitution strategies and quantify their privacy preserving benefits. Materials and Methods: We implement and assess 3 different “Hiding in Plain Sight” (HIPS) strategies for PHI replacement including a standard Consistent replacement strategy, a Random replacement strategy and a novel Markov model-based strategy. We evaluate the privacy preserving benefits of these strategies on a synthetic PHI distribution and real clinical corpora from 2 different institutions using a range of false negative error rates (FNER). Results: Using FNER ranging from 0.1% to 5% PHI leakage at the document level could be reduced from 27.1% to 0.1% (0.1% FNER) and from 94.2% to 57.7% (5% FNER) utilizing the Markov chain strategy versus the Consistent strategy on a corpus containing a diverse set of notes from the University of Alabama at Birmingham (UAB). The Markov chain substitution strategy also consistently outperformed the Consistent and Random substitution strategies in a MIMIC corpus of discharge summaries and on a range of synthetic clinical PHI distributions. Discussion: We demonstrate that a Markov chain surrogate generation strategy substantially reduces the chance of inadvertent PHI release across a range of assumed PHI FNER and release our implementation “BRATsynthetic” on Github. Conclusion: The Markov chain replacement strategy allows for the release of larger de-identified corpora at the same risk level relative to corpora released using a consistent HIPS strategy.
LBF: Learnable Bilateral Filter For Point Cloud Denoising

Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising. However, it often involves continual yet manual parameter adjustment; this inconvenience discounts the efficiency and user experience to obtain satisfied denoising results. We propose LBF, an end-to-end learnable bilateral filtering network for point cloud denoising; to our knowledge, this is the first time. Unlike the conventional BF and its variants that receive the same parameters for a whole point cloud, LBF learns adaptive parameters for each point according its geometric characteristic (e.g., corner, edge, plane), avoiding remnant noise, wrongly-removed geometric details, and distorted shapes. Besides the learnable paradigm of BF, we have two cores to facilitate LBF. First, different from the local BF, LBF possesses a global-scale feature perception ability by exploiting multi-scale patches of each point. Second, LBF formulates a geometry-aware bi-directional projection loss, leading the denoising results to being faithful to their underlying surfaces. Users can apply our LBF without any laborious parameter tuning to achieve the optimal denoising results. Experiments show clear improvements of LBF over its competitors on both synthetic and real-scanned datasets.
Magnetorheological Axisymmetric Actuator with Permanent Magnet

This study examines the concept of axisymmetric actuator based on the magnetorheological membrane, electromagnet and permanent magnet. The construction of the actuator enables its application in wide range of practical devices like pumping, loudspeaker or varying-stiffness button. This work will highlight its working principle especially the influence of permanent magnet. Furthermore, the model of devices will be defined relaying on the Hammerstein model. To show the properties of the actuator and to perform the model identification, the set of experiments was run taking into account static and dynamic working conditions.
Ultrafast modulations in stellar, solar and galactic spectra: Dark Matter and numerical ghosts, stellar flares and SETI

From new results presented in the literature we discuss the hypothesis that the ultrafast periodic spectral modulations at $f_S \simeq 0.607$ THz found in the spectra of $236$ stars of the Sloan Digital Sky Survey (SDSS) [1] were due to oscillations induced by dark matter (DM) cores in their centers [2] behaving as oscillating boson stars [3,4]. Two additional frequencies in the redshift-corrected SDSS galactic spectra were found [5], $ f_{1,G} \simeq 9.5$ THz, the beating between $f_S$ and a spurious frequency, $f_{2,G} \simeq 8.9$ THz, introduced during the data analysis [6]. The indication that $f_S$ can be real is its detection in a real solar spectrum but not in the Kurucz's artificial solar spectrum [6,7,8]. Then, independent SETI observations of four of these stars could not confirm with high confidence, but not completely exclude, the presence of $f_S$ in their power spectra [9] while the radio SETI deep-learning analysis with artificial intelligence confirmed indirectly $f_S$ detecting a narrowband Doppler drifting of radio signals in two of these stars over a sample of $7$ with a high S/N [10]. Numerical simulations suggest that the drifting can be due to frequency and phase modulation in time of the observed frequencies at $1.3-1.7$ GHz with $f_S$. This would imply a DM upper mass limit $m_a \lesssim 2.4 \times 10^{3}~ \mathrm{\mu eV}$ [2] which also agrees with the results from the gamma ray burst GRB221009A [11,12,13], laser interferometry [14], suggesting new physics for the muon g-2 anomaly [15].
Towards Improving Workers' Safety and Progress Monitoring of Construction Sites Through Construction Site Understanding

An important component of computer vision research is object detection. In recent years, there has been tremendous progress in the study of construction site images. However, there are obvious prob-lemsinconstructionobjectdetection, includingcomplexbackgrounds, varyingsizedobjects, andpoor imaging quality. In the state-of-the-art approaches, elaborate attention mechanisms are developed to handle space-time features, but rarely address the importance of channel-wise feature adjustments. We propose a lightweight Optimized Positioning (OP) module to improve channel relation based on global feature aﬃnity association, which can be used to determine the Optimized weights adaptively for each channel. OP ﬁrst computes the intermediate optimized position by comparing each channel with the remaining channels for a given set of feature maps. A weighted aggregation of all the chan- nels will then be used to represent each channel. The OP-Net module is a general deep neural network module that can be plugged into any deep neural network. Algorithms that utilize deep learning have demonstrated their ability to identify a wide range of objects from images nearly in real time. Machine intelligence can potentially beneﬁt the construction industry by automatically analyzing productivity and monitoring safety using algorithms that are linked to construction images. The beneﬁts of on-site automatic monitoring are immense when it comes to hazard prevention. Construction monitoring tasks can also be automated once construction objects have been correctly recognized. Object detection task in construction site images is experimented with extensively to demonstrate its eﬃcacy and eﬀectiveness. A benchmark test using SODA demonstrated that our OP-Net was capable of achieving newstate-of-the-artperformanceinaccuracywhilemaintainingareasonablecomputationaloverhead.
Deep Network Series for Large-Scale High-Dynamic Range Imaging

We propose a new approach for large-scale high-dynamic range computational imaging. Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging problems almost instantaneously. While unfolded architectures provide robustness to measurement setting variations, embedding large-scale measurement operators in DNN architectures is impractical. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, have proven effective to address scalability and high-dynamic range challenges, but rely on highly iterative algorithms. We propose a residual DNN series approach, also interpretable as a learned version of matching pursuit, where the reconstructed image is a sum of residual images progressively increasing the dynamic range, and estimated iteratively by DNNs taking the back-projected data residual of the previous iteration as input. We demonstrate on radio-astronomical imaging simulations that a series of only few terms provides a reconstruction quality competitive with PnP, at a fraction of the cost.
Closed 3-forms in five dimensions and embedding problems

In this paper we begin the study of an embedding question for 3-forms on 5-dimensional manifolds. Let Z be a complex Calabi–Yau threefold, that is, a 3-dimensional complex manifold with a nowhere-vanishing holomorphic 3-form, which we write as Ψ + i ˆΨ where Ψ , ˆΨ are real 3-forms. From another point of view, Z is a manifold with a torsion-free SL(3 , C )-structure. Let M be a 5-manifold and ψ a given closed 3-form on M . The question we consider is the existence of an embedding F : M → Z such that F ∗ Ψ = ψ . The main case we have in mind is when Z is C 3 with its standard holomorphic 3-form. Our original motivation for considering this question comes from Hitchin’s approach to SL(3 , C )-structures [9]. A special feature of the algebra of 3-forms in dimension 6 is that there is an open set of forms Ψ which determine an almost-complex structure and the imaginary part ˆΨ algebraically. The structural equations are equivalent to the conditions that Ψ and ˆΨ are closed 3-forms: they can be viewed as a system of partial diﬀerential equations for Ψ. On a closed 6-manifold, Hitchin gave a variational formulation in terms of the volume functional on forms Ψ in a given de Rham cohomology class. In this setting there is a natural boundary value problem on a 6-manifold Z 0 with boundary ∂Z 0 = M and with a given 3-form ψ on M . One seeks a solution Ψ to the partial diﬀerential equations which restrict to ψ on the boundary. This is the analogue, in dimension 6, of the 7-dimensional theory for G 2 -structures studied in [4]. We plan to develop this boundary value theory further in another article but, in the perturbative
Adaptive Importance Sampling Based on Fault Tree Analysis for Piecewise Deterministic Markov Process

Piecewise deterministic Markov processes (PDMPs) can be used to model complex dynamical industrial systems. The counterpart of this modeling capability is their simulation cost, which makes reliability assessment untractable with standard Monte Carlo methods. A significant variance reduction can be obtained with an adaptive importance sampling (AIS) method based on a cross-entropy (CE) procedure. The success of this method relies on the selection of a good family of approximations of the committor function of the PDMP. In this paper original families are proposed. They are well adapted to high-dimensional industrial systems. Their forms are based on reliability concepts related to fault tree analysis: minimal path sets and minimal cut sets. The proposed method is discussed in detail and applied to academic systems and to a realistic system from the nuclear industry.
Constraining the merger history of primordial-black-hole binaries from GWTC-3

Primordial black holes (PBHs) can be not only cold dark matter candidates but also progenitors of binary black holes observed by LIGO-Virgo-KAGRA (LVK) Collaboration. The PBH mass can be shifted to the heavy distribution if multi-merger processes occur. In this work, we constrain the merger history of PBH binaries using the gravitational wave events from the third Gravitational-Wave Transient Catalog (GWTC-3). Considering four commonly used PBH mass functions, namely the log-normal, power-law, broken power-law, and critical collapse forms, we find that the multi-merger processes make a subdominant contribution to the total merger rate. Therefore, the effect of merger history can be safely ignored when estimating the merger rate of PBH binaries. We also find that GWTC-3 is best fitted by the log-normal form among the four PBH mass functions and confirm that the stellar-mass PBHs cannot dominate cold dark matter.
Knowledge-Guided Exploration in Deep Reinforcement Learning

This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP). Two types of permissibility are defined under SAP. The first type says that after an action $a_t$ is performed in a state $s_t$ and the agent has reached the new state $s_{t+1}$, the agent can decide whether $a_t$ is permissible or not permissible in $s_t$. The second type says that even without performing $a_t$ in $s_t$, the agent can already decide whether $a_t$ is permissible or not in $s_t$. An action is not permissible in a state if the action can never lead to an optimal solution and thus should not be tried (over and over again). We incorporate the proposed SAP property and encode action permissibility knowledge into two state-of-the-art deep RL algorithms to guide their state-action exploration together with a virtual stopping strategy. Results show that the SAP-based guidance can markedly speed up RL training.