Miracle-dz/UniFAR-FTUs huggingface dataset:
Oral immunotherapy using boiled peanuts for treating peanut allergy: An open‐label, single‐arm trial

Peanut allergy affects 1%–3% of children in Western countries. Boiling peanuts has been demonstrated to result in a hypoallergenic product that may provide a safer way of inducing desensitization in peanut‐allergic patients by first inducing tolerance to boiled peanut. We aimed to assess the efficacy and safety of oral immunotherapy (OIT) using sequential doses of boiled peanuts followed by roasted peanuts for treating peanut allergy in children.
Spectroscopy of $A0620-00$: the mass of the black hole and an image of its accretion disc

We present spectra of the black hole binary A0620-00 taken at Hα and Hβ to study the distribution of emission-line flux from its accretion disc. Using the spectra of Hα, we have measured the K-type companion star's radial velocity semi-amplitude and rotational broadening to be K 2 =433±3 km s −1 and v sin i=83±5 km s −1 . Accounting for the Roche-lobe geometry of the companion, the rotational broadening gives the mass ratio q=M 2 /M 1 =0.06 7±0.01. The disc contributes 6±3 per cent of the light at Hα and 17±3 per cent at Hβ, with the K star providing the rest. The masses of the compact object and K star derived from our values of K 2 and q are M 1 =(3.09±0.09)sin −3 i M ○. and M 2 =(0.21±0.04)sin −3 i M ○.
Letter to the Editor Response: Exploring COVID-19 Vaccine Hesitancy Among Stakeholders in African American and Latinx Communities in the Deep South Through the Lens of the Health Belief Model

The purpose of this submission to respond to a Letter to the Editor recently submitted regarding our manuscript, “Exploring COVID-19 Vaccine Hesitancy among Stakeholders in African American and Latinx Communities in the Deep South through the Lens of the Health Belief Model” published in the American Journal of Health Promotion in February, 2022. The manuscript reported on a study that had as its purpose to qualitatively explore perceptions related to COVID-19 vaccination intention among African American and Latinx participants and suggest potential intervention strategies.
Fiber continuum seeded ultrafast parametric amplification

We report on an optical parametric amplification system which is pumped and seeded by fiber generated laser radiation. Due to its low broadening threshold, high spatial beam quality and high stability, the fiber based broad bandwidth signal generation is a promising alternative to white light generation in bulky glass or sapphire plates. We demonstrate a novel and successful signal engineering implemented in a setup for parametric amplification and subsequent recompression of resonant linear waves resulting from soliton fission in a highly nonlinear photonic crystal fiber. The applied pump source is a high repetition rate ytterbium-doped fiber chirped pulse amplification system. The presented approach results in the generation of ~50 fs pulses at MHz repetition rate. The potential of generating even shorter pulse duration and higher pulse energies will be discussed.
Rethinking complex-valued deep neural networks for monaural speech enhancement

Despite multiple efforts made towards adopting complex-valued deep neural networks (DNNs), it remains an open question whether complex-valued DNNs are generally more effective than real-valued DNNs for monaural speech enhancement. This work is devoted to presenting a critical assessment by systematically examining complex-valued DNNs against their real-valued counterparts. Specifically, we investigate complex-valued DNN atomic units, including linear layers, convolutional layers, long short-term memory (LSTM), and gated linear units. By comparing complex- and real-valued versions of fundamental building blocks in the recently developed gated convolutional recurrent network (GCRN), we show how different mechanisms for basic blocks affect the performance. We also find that the use of complex-valued operations hinders the model capacity when the model size is small. In addition, we examine two recent complex-valued DNNs, i.e. deep complex convolutional recurrent network (DCCRN) and deep complex U-Net (DCUNET). Evaluation results show that both DNNs produce identical performance to their real-valued counterparts while requiring much more computation. Based on these comprehensive comparisons, we conclude that complex-valued DNNs do not provide a performance gain over their real-valued counterparts for monaural speech enhancement, and thus are less desirable due to their higher computational costs.
DDoS Detection on Network Protocol Using Cosine Similarity and N-Gram+ Method

Data Packages containing malware can perform attacks on network protocols such as SMTP, TCP, UDP, POP, HTTP protocols. One way to detect attacks is to install the Intrusion Detection System. IDS can be installed either on the Host's side of the Network. Attacks that occur can damage the system and also can steal important files that exist in the system. This research proposes the N-Gram and Cosine Similarity methods to detect malicious code in the data packets. This technique is part of the anomaly-based malware-based detection process. The goal is to look for irregularities in the system and created first a normal profile of a system to facilitate the detection of intrusion that occurs in computer networks. The results of research using n-gram and similarity search with cosine similarity method proved to be used in intrusion detection system to detect Distributed Daniel of Service (DDoS) attacks and IDS that implemented can run in real-time especially HTTP and TCP protocol. With the Similarity and N-Gram + Cosine method, IDS is able to detect types of attacks that have not been detected before such as Adware whose detection accuracy reaches 75%. Because adware aims at promoting banners, deflecting visitors' traffic even sending data packets continuously.
Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images

In synchrotron-based Computed Tomography (CT) there is a trade-off between spatial resolution, field of view and speed of positioning and alignment of samples. The problem is even more prominent for high-throughput tomography--an automated setup, capable of scanning large batches of samples without human interaction. As a result, in many applications, only 20-30% of the reconstructed volume contains the actual sample. Such data redundancy clutters the storage and increases processing time. Hence, an automated sample localization becomes an important practical problem. In this work, we describe two self-supervised losses designed for biological CT. We further demonstrate how to employ the uncertainty estimation for sample localization. This approach shows the ability to localize a sample with less than 1.5\% relative error and reduce the used storage by a factor of four. We also show that one of the proposed losses works reasonably well as a pre-training task for the semantic segmentation.
An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information

Multi-agent path finding (MAPF) is the problem of moving agents to the goal vertex without collision. In the online MAPF problem, new agents may be added to the environment at any time, and the current agents have no information about future agents. The inability of existing online methods to reuse previous planning contexts results in redundant computation and reduces algorithm efficiency. Hence, we propose a three-level approach to solve online MAPF utilizing sustainable information, which can decrease its redundant calculations. The high-level solver, the Sustainable Replan algorithm (SR), manages the planning context and simulates the environment. The middle-level solver, the Sustainable Conflict-Based Search algorithm (SCBS), builds a conflict tree and maintains the planning context. The low-level solver, the Sustainable Reverse Safe Interval Path Planning algorithm (SRSIPP), is an efficient single-agent solver that uses previous planning context to reduce duplicate calculations. Experiments show that our proposed method has significant improvement in terms of computational efficiency. In one of the test scenarios, our algorithm can be 1.48 times faster than SOTA on average under different agent number settings.
Market Power Mitigation in Two-Stage Electricity Markets With Supply Function and Quantity Bidding

Two-stage settlement electricity markets, which include day-ahead and real-time markets, often observe undesirable price manipulation due to the price difference across stages, inadequate competition, and unforeseen circumstances. To mitigate this, some Independent System Operators (ISOs) have proposed system-level market power mitigation (MPM) policies in addition to existing local policies. These system-level policies aim to substitute noncompetitive bids with a default bid based on estimated generator costs. However, without accounting for the conflicting interest of participants, they may lead to unintended consequences when implemented. In this article, we model the competition between generators (bidding supply functions) and loads (bidding quantity) in a two-stage market with a stage-wise MPM policy. An equilibrium analysis shows that a real-time MPM policy leads to equilibrium loss, meaning no stable market outcome (Nash equilibrium) exists. A day-ahead MPM policy leads to Stackelberg-Nash game, with loads acting as leaders and generators as followers. Despite estimation errors, the competitive equilibrium is efficient, while the Nash equilibrium is comparatively robust to price manipulations. Moreover, analysis of inelastic loads shows their tendency to shift allocation and manipulate prices in the market. Numerical studies illustrate the impact of cost estimation errors, heterogeneity in generation cost, and load size on market equilibrium.
Multilingual Entity and Relation Extraction from Unified to Language-specific Training

Entity and relation extraction is a key task in information extraction, where the output can be used for downstream NLP tasks. Existing approaches for entity and relation extraction tasks mainly focus on the English corpora and ignore other languages. Thus, it is critical to improving performance in a multilingual setting. Meanwhile, multilingual training is usually used to boost cross-lingual performance by transferring knowledge from languages (e.g., high-resource) to other (e.g., low-resource) languages. However, language interference usually exists in multilingual tasks as the model parameters are shared among all languages. In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages. Specifically, we randomly concatenate sentences in different languages to train a Language-universal Aggregator (LA), which narrows the distance of embedding representations by obtaining the unified language representation. Then, we separate parameters to mitigate interference via tuning a Language-specific Switcher (LS), which includes several independent sub-modules to refine the language-specific feature representation. After that, to enhance the relational triple extraction, the sentence representations concatenated with the relation feature are used to recognize the entities. Extensive experimental results show that our method outperforms both the monolingual and multilingual baseline methods. Besides, we also perform detailed analysis to show that mERE is lightweight but effective on relational triple extraction and mERE{} is easy to transfer to other backbone models of multi-field tasks, which further demonstrates the effectiveness of our method.
Impact of a rapid diluted energy density on the halo mass function

We study dark energy cosmological models, extensions of the standard model of particles, characterized by having an extra relativistic energy density at very early times, and that rapidly dilute after a phase transition occurs. These models generate well localized features (or bumps) in the matter power spectrum for modes crossing the horizon around and before the phase transition epoch. This is because the presence of the additional energy component enhances the growth of matter fluctuations during the radiation dominated epoch. Instead of considering a particular model, we focus on a parametric family of Gaussian bumps in the matter power spectrum, which otherwise would be a $\Lambda$CDM one. We study the evolution of such bump cosmologies and their effects in the halo mass function and halo power spectrum using N-body simulations, the halo-model based HMcode method, and the peak background split framework. The bumps are subject to different nonlinear effects that become physically well understood, and from them we are able to predict that the most distinctive features will show up for intermediate halo masses $10^{12.3} \,h^{-1}M_{\odot}<M<10^{13.6} \,h^{-1}M_{\odot}$. Out of this range, we expect halos are not significantly affected regardless of the location of the primordial bump in the matter power spectrum. Our analytical results are accurate and in very satisfactory agreement with the simulated data.
Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk sampling

—Word embedding has become ubiquitous and is widely used in various text mining and natural language processing (NLP) tasks, such as information retrieval, semantic analysis, and machine translation, among many others. Unfortunately, it is prohibitively expensive to train the word embedding in a relatively large corpus. We propose a graph-based word embedding algorithm, called Word-Graph2vec, which converts the large corpus into a word co-occurrence graph, then takes the word sequence samples from this graph by randomly traveling and trains the word embedding on this sampling corpus in the end. We posit that because of the stable vocabulary, relative idioms, and ﬁxed expressions in English, the size and density of the word co-occurrence graph change slightly with the increase in the training corpus. So that Word-Graph2vec has stable runtime on the large-scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real-world datasets show that the proposed algorithm outperforms traditional Skip-Gram by four-ﬁve times in terms of efﬁciency, while the error generated by the random walk sampling is small.
Teleoperation of Humanoid Robots: A Survey

Teleoperation of humanoid robots enables the integration of the cognitive skills and domain expertise of humans with the physical capabilities of humanoid robots. The operational versatility of humanoid robots makes them the ideal platform for a wide range of applications when teleoperating in a remote environment. However, the complexity of humanoid robots imposes challenges for teleoperation, particularly in unstructured dynamic environments with limited communication. Many advancements have been achieved in the last decades in this area, but a comprehensive overview is still missing. This survey article gives an extensive overview of humanoid robot teleoperation, presenting the general architecture of a teleoperation system and analyzing the different components. We also discuss different aspects of the topic, including technological and methodological advances, as well as potential applications.
A new sampling methodology for defining heterogeneous subsets of samples for training image segmentation algorithms

Creating a dataset for training supervised machine learning algorithms can be a demanding task. This is especially true for medical image segmentation since one or more specialists are usually required for image annotation, and creating ground truth labels for just a single image can take up to several hours. In addition, it is paramount that the annotated samples represent well the different conditions that might affect the imaged tissues as well as possible changes in the image acquisition process. This can only be achieved by considering samples that are typical in the dataset as well as atypical, or even outlier, samples. We introduce a new sampling methodology for selecting relevant images from a large dataset in a way that evenly considers both prototypical as well as atypical samples. The methodology involves the generation of a uniform grid from a feature space representing the samples, which is then used for randomly drawing relevant images. The selected images provide a uniform covering of the original dataset, and thus define a heterogeneous set of images that can be annotated and used for training supervised segmentation algorithms. We provide a case example by creating a dataset containing a representative set of blood vessel microscopy images selected from a larger dataset containing thousands of images. The dataset, which we call VessMAP, is being made available online to aid the development of new blood vessel segmentation algorithms.
ON THE CLOSURE OF MASS BALANCE MODELS FOR TUMOR GROWTH

Mass balance equations typically adopted to describe tumor growth are to be closed by introducing a suitable velocity field. The first part of this paper is devoted to a critical review of some approaches devised to this aim in the relevant literature. In the second part we start from the observation that the phenomenological description of a tumor spheroid suggests to model it as a growing and deformable porous material. The concept of volume fraction and the essentials of the mechanics of multicomponent continua are then introduced and applied to the problem at hand. The system of equations regulating such a system is stated and its validity is then discussed at the light of numerical simulations.
Collision-free Source Seeking and Flocking Control of Multi-agents with Connectivity Preservation

We present a distributed source-seeking and flocking control method for networked multi-agent systems with non-holonomic constraints. Based solely on identical on-board sensor systems, which measure the source local field, the group objective is attained by appointing a leader agent to seek the source while the remaining follower agents form a cohesive flocking with their neighbors using a distributed flocking control law in a connectivity-preserved undirected network. To guarantee the safe separation and group motion for all agents and to solve the conflicts with the"cohesion"flocking rule of Reynolds, the distributed control algorithm is solved individually through quadratic-programming optimization problem with constraints, which guarantees the inter-agent collision avoidance and connectivity preservation. Stability analysis of the closed-loop system is presented and the efficacy of the methods is shown in simulation results.
Multiple-level Point Embedding for Solving Human Trajectory Imputation with Prediction

Sparsity is a common issue in many trajectory datasets, including human mobility data. This issue frequently brings more difficulty to relevant learning tasks, such as trajectory imputation and prediction. Nowadays, little existing work simultaneously deals with imputation and prediction on human trajectories. This work plans to explore whether the learning process of imputation and prediction could benefit from each other to achieve better outcomes. And the question will be answered by studying the coexistence patterns between missing points and observed ones in incomplete trajectories. More specifically, the proposed model develops an imputation component based on the self-attention mechanism to capture the coexistence patterns between observations and missing points among encoder-decoder layers. Meanwhile, a recurrent unit is integrated to extract the sequential embeddings from newly imputed sequences for predicting the following location. Furthermore, a new implementation called Imputation Cycle is introduced to enable gradual imputation with prediction enhancement at multiple levels, which helps to accelerate the speed of convergence. The experimental results on three different real-world mobility datasets show that the proposed approach has significant advantages over the competitive baselines across both imputation and prediction tasks in terms of accuracy and stability.
A novel approach to thermochromic liquid crystal calibration using neural networks

Liquid crystal thermography (LCT) is a common surface temperature measurement technique. Typically, the colour response is calibrated against temperature by building an analytical relation between the temperature and the hue of the colour. A suitable polynomial fit is then used to describe this relation after removing the discontinuity in the hue. The variability of hue at each calibration point determines the temperature resolution. However, this technique does not take into consideration the variability in R, G and B intensities used to determine the hue, leading to uncertainty in the measured temperature. This paper describes a novel technique using neural networks to calibrate thermochromic liquid crystal (TLC) material and compensate for high variability in RGB intensities along with other sources of noise in the data. A TLC-based temperature measurement system and calibration results are presented. In our measurements, the lighting intensity (8-bit mean intensity of black surface ± standard deviation) is changed from a minimum of 16.65 ± 2.30 to a maximum of 31.41 ± 3.85. The neural networks were trained on the steady-state TLC calibration system. The results indicate that the neural networks can cope with the variation in lighting by merging the shifted hue curves into a single curve determined by the regression analysis of the test data. Performance characteristics studied on various network configurations relevant to the analysis are described. This approach may be useful in developing liquid crystal thermography for various biomedical applications.
Learning What and Where: Disentangling Location and Identity Tracking Without Supervision

Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial abilities for conceptual planning and reasoning. Recent object reasoning datasets, such as CATER, have revealed fundamental shortcomings of current vision-based AI systems, particularly when targeting explicit object representations, object permanence, and object reasoning. Here we introduce a self-supervised LOCation and Identity tracking system (Loci), which excels on the CATER tracking challenge. Inspired by the dorsal and ventral pathways in the brain, Loci tackles the binding problem by processing separate, slot-wise encodings of `what' and `where'. Loci's predictive coding-like processing encourages active error minimization, such that individual slots tend to encode individual objects. Interactions between objects and object dynamics are processed in the disentangled latent space. Truncated backpropagation through time combined with forward eligibility accumulation significantly speeds up learning and improves memory efficiency. Besides exhibiting superior performance in current benchmarks, Loci effectively extracts objects from video streams and separates them into location and Gestalt components. We believe that this separation offers a representation that will facilitate effective planning and reasoning on conceptual levels.