Miracle-dz/UniFAR-FTUs huggingface dataset:
Sonography-guided trigger point injections in abdominal myofascial pain syndrome

Abstract Even though chronic abdominal pain is 1 of the most common reasons for hospital visits, diagnostic testing is often time-consuming and treatment is inadequate. Abdominal myofascial pain syndrome (AMPS) is usually not included as a differential diagnosis, but it should be considered in cases of chronic abdominal pain. The purpose of this study was to investigate the clinical characteristics of AMPS and to assess the effect of sonography-guided trigger point injections (TPI). A total of 100 patients with AMPS from 2012 to 2018 were retrospectively evaluated for clinical characteristics and TPI effects. AMPS was diagnosed using Srinivasan and Greenbaum's criteria, and the TPIs were performed at intervals of 2 to 4 weeks. The Visual Analog Scale (VAS) ratio was calculated by subtracting the final VAS from the initial VAS score and dividing it by the initial VAS score after injections, and the patients were divided into 4 groups: non-responders, mild, moderate, and good responders. The median duration of pain was 12 months, and the median number of hospital visits before TPI was 2. Of the 100 patients, 66 (66%) were categorized as good responders, 11 (11%) as moderate responders, 7 (6.9%) as mild responders, and 16 (15.7%) as non-responders. When the initial and final VAS scores were compared, the sonography-guided injections were found to be effective in alleviating pain (P < .001). Moreover, patients who received the injections 2 or more times tended to have more significant pain reduction than those who received a single injection (P < .001). Patients with AMPS suffer from long-term pain and undergo many hospital visits and diagnostic tests. TPI with lidocaine can be an effective and safe treatment for patients with chronic AMPS.
The volatile flavour components of commercial port wines

Gas chromatographic-mass spectrometric examination of aroma extracts from both Ruby and Tawny port has led to the identification of 141 components, notably 2-methoxyphenol, dihydro-2-(3H)-furanone, β-methyl-γ-octalactone, cis-4-hydroxy-methyl-2-methyl-l,3-dioxolane, ethyl pyroglutamate and vitispirane. Odour evaluation of the components separated by gas chromatography indicates five regions as being of potential importance in port. 2-Methoxyphenol and dihydro-2-(3H)-furanone were found in two of these regions, three others being associated with esters of succinic acid. None of these components can, however, wholly explain the port-like odour.
Orientation Matters: 6-DoF Autonomous Camera Movement for Minimally Invasive Surgery

We propose a new method for six-degree-of-freedom (6-DoF) autonomous camera movement for minimally invasive surgery, which, unlike previous methods, takes into account both the position and orientation information from structures in the surgical scene. In addition to locating the camera for a good view of the manipulated object, our autonomous camera takes into account workspace constraints, including the horizon and safety constraints. We developed a simulation environment to test our method on the "wire chaser" surgical training task from validated training curricula in conventional laparoscopy and robot-assisted surgery. Furthermore, we propose, for the first time, the application of the proposed autonomous camera method in video-based surgical skill assessment, an area where videos are typically recorded using fixed cameras. In a study with N=30 human subjects, we show that video examination of the autonomous camera view as it tracks the ring motion over the wire leads to more accurate user error (ring touching the wire) detection than when using a fixed camera view, or camera movement with a fixed orientation. Our preliminary work suggests that there are potential benefits to autonomous camera positioning informed by scene orientation, and this can direct designers of automated endoscopes and surgical robotic systems, especially when using chip-on-tip cameras that can be wristed for 6-DoF motion.
EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture

In this work, we address the Text-to-Speech (TTS) task by proposing a non-autoregressive architecture called EfficientTTS. Unlike the dominant non-autoregressive TTS models, which are trained with the need of external aligners, EfficientTTS optimizes all its parameters with a stable, end-to-end training procedure, while allowing for synthesizing high quality speech in a fast and efficient manner. EfficientTTS is motivated by a new monotonic alignment modeling approach (also introduced in this work), which specifies monotonic constraints to the sequence alignment with almost no increase of computation. By combining EfficientTTS with different feed-forward network structures, we develop a family of TTS models, including both text-to-melspectrogram and text-to-waveform networks. We experimentally show that the proposed models significantly outperform counterpart models such as Tacotron 2 and Glow-TTS in terms of speech quality, training efficiency and synthesis speed, while still producing the speeches of strong robustness and great diversity. In addition, we demonstrate that proposed approach can be easily extended to autoregressive models such as Tacotron 2.
On query-to-communication lifting for adversary bounds

We investigate query-to-communication lifting theorems for models related to the quantum adversary bounds. Our results are as follows: 1. We show that the classical adversary bound lifts to a lower bound on randomized communication complexity with a constant-sized gadget. We also show that the classical adversary bound is a strictly stronger lower bound technique than the previously-lifted measure known as critical block sensitivity, making our lifting theorem one of the strongest lifting theorems for randomized communication complexity using a constant-sized gadget. 2. Turning to quantum models, we show a connection between lifting theorems for quantum adversary bounds and secure 2-party quantum computation in a certain "honest-but-curious" model. Under the assumption that such secure 2-party computation is impossible, we show that a simplified version of the positive-weight adversary bound lifts to a quantum communication lower bound using a constant-sized gadget. We also give an unconditional lifting theorem which lower bounds bounded-round quantum communication protocols. 3. Finally, we give some new results in query complexity. We show that the classical adversary and the positive-weight quantum adversary are quadratically related. We also show that the positive-weight quantum adversary is never larger than the square of the approximate degree. Both relations hold even for partial functions.
Mapping Network States using Connectivity Queries

Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is nontrivial, considering that often only a fraction of components may be accessible or observable after a disruptive event. Past work has looked into inferring failed components given point probes, i.e. with a direct sample of failed components. In contrast, we study the harder problem of inferring failed components given partial information of some ‘serviceable’ reachable nodes and a small sample of point probes, being the first often more practical to obtain. We formulate this novel problem using the Minimum Description Length (MDL) principle, and then present a greedy algorithm that minimizes MDL cost effectively. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake. Our algorithm successfully identifies failed components, especially the critical ones affecting the overall system performance.
A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation

There is a need for socially assistive robots (SARs) to provide transparency in their behavior by explaining their reasoning. Additionally, the reasoning and explanation should represent the user's preferences and goals. To work towards satisfying this need for interpretable reasoning and representations, we propose the robot uses Analogical Theory of Mind to infer what the user is trying to do and uses the Hint Engine to find an appropriate assistance based on what the user is trying to do. If the user is unsure or confused, the robot provides the user with an explanation, generated by the Explanation Synthesizer. The explanation helps the user understand what the robot inferred about the user's preferences and why the robot decided to provide the assistance it gave. A knowledge-driven approach provides transparency to reasoning about preferences, assistance, and explanations, thereby facilitating the incorporation of user feedback and allowing the robot to learn and adapt to the user.
Multi-Instrumentalist Net: Unsupervised Generation of Music from Body Movements

We propose a novel system that takes as an input body movements of a musician playing a musical instrument and generates music in an unsupervised setting. Learning to generate multi-instrumental music from videos without labeling the instruments is a challenging problem. To achieve the transformation, we built a pipeline named 'Multi-instrumentalistNet' (MI Net). At its base, the pipeline learns a discrete latent representation of various instruments music from log-spectrogram using a Vector Quantized Variational Autoencoder (VQ-VAE) with multi-band residual blocks. The pipeline is then trained along with an autoregressive prior conditioned on the musician's body keypoints movements encoded by a recurrent neural network. Joint training of the prior with the body movements encoder succeeds in the disentanglement of the music into latent features indicating the musical components and the instrumental features. The latent space results in distributions that are clustered into distinct instruments from which new music can be generated. Furthermore, the VQ-VAE architecture supports detailed music generation with additional conditioning. We show that a Midi can further condition the latent space such that the pipeline will generate the exact content of the music being played by the instrument in the video. We evaluate MI Net on two datasets containing videos of 13 instruments and obtain generated music of reasonable audio quality, easily associated with the corresponding instrument, and consistent with the music audio content.
Contrastive Divergence Learning is a Time Reversal Adversarial Game

Contrastive divergence (CD) learning is a classical method for fitting unnormalized statistical models to data samples. Despite its wide-spread use, the convergence properties of this algorithm are still not well understood. The main source of difficulty is an unjustified approximation which has been used to derive the gradient of the loss. In this paper, we present an alternative derivation of CD that does not require any approximation and sheds new light on the objective that is actually being optimized by the algorithm. Specifically, we show that CD is an adversarial learning procedure, where a discriminator attempts to classify whether a Markov chain generated from the model has been time-reversed. Thus, although predating generative adversarial networks (GANs) by more than a decade, CD is, in fact, closely related to these techniques. Our derivation settles well with previous observations, which have concluded that CD's update steps cannot be expressed as the gradients of any fixed objective function. In addition, as a byproduct, our derivation reveals a simple correction that can be used as an alternative to Metropolis-Hastings rejection, which is required when the underlying Markov chain is inexact (e.g. when using Langevin dynamics with a large step).
Porosity and specific surface for interpenetrable‐sphere models of two‐phase random media

We derive and numerically evaluate expressions for the porosity (matrix volume fraction) φ and the specific surface (interface area per unit volume) s for two specific models of a random two‐phase medium in which the medium is considered as a suspension of interpenetrable spheres of radius R, embedded in a uniform matrix. The models and quantities considered have applications to a wide range of problems concerning transport, mechanical, and chemical properties of composite media. Both models contain a continuously variable hardness parameter e, such that for e=1 they reduce to mutually impenetrable spheres, and for e=0 they reduce to fully penetrable spheres. The first of these models, the permeable‐sphere model, has been defined only in the context of the Percus–Yevick approximation, which yields for it a unique pair distribution function g2(r1, r2). To find the associated gn(r1,.,rn) for n≳2, which are needed to evaluate φ and s we use the generalized superposition approximation. The second model, the.
Influence of carrier agents on the physicochemical properties of blackberry powder produced by spray drying

Summary The aim of this work was to study the effect of different carrier agents (maltodextrin, gum arabic or a blend of both carrier agents) on the physicochemical properties of blackberry powder produced by spray drying. Moisture content, anthocyanin retention, antioxidant activity, colour parameters, bulk and absolute density, porosity, wettability, sorption isotherms, particle size and morphology of blackberry powders were evaluated. The use of maltodextrin resulted in less hygroscopic powders with lower moisture content and better reconstitution properties. Powders produced with maltodextrin or a blend of maltodextrin and gum arabic presented the best anthocyanin retention and the highest antioxidant activity. Experimental data of water adsorption were well fitted to GAB model. All the samples exhibited a large number of irregular particles with spherical shapes. However, particles produced with gum arabic were smaller and showed more dented surfaces, which probably contributed to the increase in wettability values and lower pigments retention.
YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data

Large scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout its growth state. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts yield of multiple crops and concurrently considers the interaction between multiple crop’s yield. We propose a new model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. Numerical results demonstrate that our proposed method accurately predicts yield from one to four months before the harvest, and is competitive to other state-of-the-art approaches.
Review of Potentially Toxic Rare Earth Elements, Thallium and Tellurium in Plant‐based Foods

Abstract In the last decades, there is an increasing inclusion of various trace metals and metalloids such as thallium, tellurium and rare earth elements (REEs; lanthanides, scandium, and yttrium) in the composition and production of alloys, in agricultural and medicinal applications, as well as in the manufacturing of hi‐tech products. All these activities have led to an accumulation of the aforementioned elements both in soil and water bodies and consequently in the food chain, through discharges from mining and mineral processing, liquid industrial waste or disposal of urban and industrial products. It has been demonstrated that chronic exposure to some of these elements, even at low doses, might lead to a wide range of adverse health effects, even from the early stages of life, such as neurotoxicity, neurodevelopmental toxicity and hepatic alterations. Particularly in children, there have been studies suggesting that some of these elements might negatively affect the children's spatial learning and memory ability indirectly. Such effects are triggered by processes like the production of reactive oxygen species (ROS), lipid peroxidation and modulation of antioxidant activities. Nevertheless, the limited data from toxicological studies and their so‐far naturally low occurrence levels in the environment acted as a deterrent in measuring their concentrations during routine analyses of metals in foodstuff. Thus, it is important to collect information on their occurrence data both in adults and in children's daily diet. This review sumrises the current knowledge on the concentration of these elements, in plant‐based food products to identify whether a potential health risk occurs. As side projects, this Fellowship provided hands‐on training on the evaluation of new biocides application and participation in the given advice to the Danish Food and Veterinary Administration, Danish Environmental Protection Agency, the Danish Medical Agency and the European Chemicals Agency.
DICE: A Bayesian model for early dose finding in phase I trials with multiple treatment courses

Dose‐finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short‐term dose‐limiting toxicities (DLTs). Model‐based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modeled taking into account the cumulative effect of the administered drug and the DLT cycle of occurrence. We propose a design, called DICE (Dose‐fInding CumulativE), for dose escalation and de‐escalation according to previously observed toxicities, which aims at finding the MTD sequence (MTS). We performed an extensive simulation study comparing this approach to the time‐to‐event continual reassessment method (TITE‐CRM) and a benchmark. In general, our approach achieved a better or comparable percentage of correct MTS selection. Moreover, we investigated the DICE prediction ability.
Cardiogenic Shock Secondary to Dynamic Left Ventricular Outflow Tract Obstruction and Apical Ballooning after Nonmitral Cardiovascular Surgery

Background The dynamic obstruction of the left ventricular outflow tract (LVOT) is a well-known complication in mitral annuloplasty but rarely seen in nonmitral cardiovascular surgery. The dynamic LVOT obstruction can lead to hemodynamic instability, even shock and the treatment is significantly different from the standard approach. Case Presentation. We reported a case of low cardiac output syndrome (LCOS) with severe mitral regurgitation (MR), dramatically reduced left ventricular ejection fraction (LVEF) after coronary artery bypass grafting in a 72-year-old female requiring an escalation of inotropic support, volume restriction, and mechanical support. The detailed echocardiography combined with lung ultrasound revealed a dynamic systolic anterior movement of the anterior mitral leaflet (SAM), apical ballooning, and no significant lung congestion. Intravenous fluids were given, diuretics withdrawn, inotrope discontinued, and vasopressors uptitrated. The dynamic SAM was rapidly relieved, the hemodynamics was stabilized, and the LVEF was improving. The patient was discharged in good condition without residual LVOT obstruction and trace MR. Conclusion We strongly suggest that a detailed echocardiography should be performed in any patient who presents in shock to rule out a dynamic LVOT obstruction. Lung ultrasound should be a routine examination in addition to echocardiography. Once SAM is detected, treatment should be based on volume expansion, inotrope discontinuation, and a careful afterload increasing.
Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item in that space and can be efficiently solved by least square methods with optimal estimation. However, such optimal estimation methods are prone to overfitting due to the extreme sparsity of user-item interactions. In this paper, we propose a Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on observed user-item interactions, we build a probabilistic factor model in which the regularization is introduced via placing prior constraint on latent factors, and the likelihood function is established over observations and parameters. Then we draw samples of latent factors from the posterior distribution with Variational Inference (VI) to predict expected value. We further make an extension to BLFM, called BLFMBias, incorporating user-dependent and item-dependent biases into the model for enhancing performance. Extensive experiments on the movie rating dataset show the effectiveness of our proposed models by compared with several strong baselines.
TediGAN: Text-Guided Diverse Image Generation and Manipulation

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module is to train an image encoder to map real images to the latent space of a well-trained StyleGAN. The visual-linguistic similarity is to learn the text-image matching by mapping the image and text into a common embedding space. The instance-level optimization is for identity preservation in manipulation. Our model can provide the lowest effect guarantee, and produce diverse and high-quality images with an unprecedented resolution at 1024. Using a control mechanism based on style-mixing, our TediGAN inherently supports image synthesis with multi-modal inputs, such as sketches or semantic labels with or without instance (text or real image) guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method. Code and data are available at https://github.com/weihaox/TediGAN.