Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet use in an orthopaedic outpatient population

BackgroundOver 60% of people nationally now have internet access. We aimed to assess the level of access to the internet within our practice population and gauge the level of internet use by these patients. MethodsThis was a questionnaire-based study. Patients attending a mixture of trauma and elective outpatient clinics were invited to complete a questionnaire collecting basic demographic details and history of information seeking. ResultsTwo-hundred and nintey-two patients completed the questionnaire (146 men, 146 women). Seventeen were incomplete and excluded from final analysis. Multiple logistic regression found younger age (O.R. 2.22 in 20-35 age group), possession of health insurance (O.R. 2.65) and higher levels of education (O.R. 8.22 for tertiary education) all were significantly associated with a higher level of access to the internet. Among patients with internet access, a second regression analysis showed that a positive history of surgery (O.R. 2.82) and possession of a trade qualification (O.R. 5.15) were the best predictors of internet use to research a patient’s orthopaedic condition. ConclusionsOur study was consistent with previous studies showing that younger and better educated patients had greater access to the internet. We believe there is a niche for increased information provision for those patients who require surgery, but we must be aware that access is not available to everyone in the community.
Studies on the Serum Therapy of Type E Botulism (Part II)

The use of antitoxic serum has been remarkably successful in the treatment of type E botulism in man [1]. The efficacy of serum therapy in food poisoning with type E toxin has more recently been confirmed clinically by investigators in Canada [2] and in the United States [3]. It is to be anticipated that the clinical effectiveness of antitoxic serum is affected by such factors as the amount of toxin ingested, interval of time between ingestion of toxin and administration of antitoxic serum, dose of antitoxin, and method of administration. In a previous study [4] the influence of these factors upon the effectiveness of antitoxic therapy in mice was explored, and it was found that the quantitative relationship between the subcutaneous dose of toxin and that of anti-
Comprehensive understanding of water-driven graphene wrinkle life-cycle towards applications in flexible electronics: A computational study.

The presence of wrinkles in Graphene Nanoribbons (GNR) and other two-dimensional (2D) materials significantly alter their mechanical, electronic, optical properties, which can be either beneficial or detrimental. Experimentally, it has been observed that during the commonly used growth process of GNR, water molecules, sourced from ambient humidity, can be diffused in between GNR and the substrate. The water diffusion causes wrinkle formation in GNR, which influences its properties. Furthermore, the diffused water eventually dries, creating the alteration not only in the geometry of Wrinkled Graphene Nanoribbons (WGNR) but also its features. Computational analysis of these phenomena can provide an atomistic-level understanding of the phenomena. Therefore, in this work, Molecular Dynamics (MD) simulations are performed to model the water diffusion and evaporation in between GNR and its substrate, and their effect on wrinkle formation and dynamics. Additionally, Density Functional Theory (DFT)-based analysis is used to characterize the difference in the electronic structure of WGNR caused by the change in wrinkle geometry. Our study reveals that the initially distributed wrinkles tend to coalesce to form a localized wrinkle whose configuration depends on the initial wrinkle geometry and the amount of diffused water. The wrinkle configuration changes upon drying, while it remains static until the complete drying. The movement of the localized wrinkle is the combination of three fundamental modes - bending, buckling, and sliding. The stress analysis reveals that the maximum stress is at the base of the wrinkle, and its magnitude is always below the plasticity limit. The DFT results provide insight into the potential of using the wrinkles to control the direction of electron flow for the applications in flexible electronics.
A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimization

Multi-tasking optimization can usually achieve better performance than traditional single-tasking optimization through knowledge transfer between tasks. However, current multi-tasking optimization algorithms have some deficiencies. For high similarity problems, the knowledge that can accelerate the convergence rate of tasks has not been fully taken advantages of. For low similarity problems, the probability of generating negative transfer is high, which may result in optimization performance degradation. In addition, some knowledge transfer methods proposed previously do not fully consider how to deal with the situation in which the population falls into local optimum. To solve these issues, a two-stage adaptive knowledge transfer evolutionary multi-tasking optimization algorithm based on population distribution, labeled as EMT-PD, is proposed. EMT-PD can accelerate and improve the convergence performance of tasks based on the knowledge extracted from the probability model that reflects the search trend of the whole population. At the first transfer stage, an adaptive weight is used to adjust the step size of individual's search, which can reduce the impact of negative transfer. At the second stage of knowledge transfer, the individual's search range is further adjusted dynamically, which can improve the diversity of population and be beneficial for jumping out of local optimum. Experimental results on multi-tasking multi-objective optimization test suites show that EMT-PD is superior to other six state-of-the-art evolutionary multi/single-tasking algorithms. To further investigate the effectiveness of EMT-PD on many-objective optimization problems, a multi-tasking many-objective test suite is also designed in this paper. The experimental results on the new test suite also demonstrate the competitiveness of EMT-PD.
Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model

The nonliteral interpretation of a text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language. In this study, inspired by human reading comprehension, we propose a novel, simple, and effective deep neural framework, called Skim and Intensive Reading Model (SIRM), for figuring out implied textual meaning. The proposed SIRM consists of two main components, namely the skim reading component and intensive reading component. N-gram features are quickly extracted from the skim reading component, which is a combination of several convolutional neural networks, as skim (entire) information. An intensive reading component enables a hierarchical investigation for both local (sentence) and global (paragraph) representation, which encapsulates the current embedding and the contextual information with a dense connection. More specifically, the contextual information includes the near-neighbor information and the skim information mentioned above. Finally, besides the normal training loss function, we employ an adversarial loss function as a penalty over the skim reading component to eliminate noisy information arisen from special figurative words in the training data. To verify the effectiveness, robustness, and efficiency of the proposed architecture, we conduct extensive comparative experiments on several sarcasm benchmarks and an industrial spam dataset with metaphors. Experimental results indicate that (1) the proposed model, which benefits from context modeling and consideration of figurative language, outperforms existing state-of-the-art solutions, with comparable parameter scale and training speed; (2) the SIRM yields superior robustness in terms of parameter size sensitivity; (3) compared with ablation and addition variants of the SIRM, the final framework is efficient enough.
Ergodic Sum Rate Analysis of UAV-Based Relay Networks With Mixed RF-FSO Channels

Unmanned aerial vehicle (UAV)-based communications is a promising new technology that can add a wide range of new capabilities to the current network infrastructure. Given the flexibility, cost-efficiency, and convenient use of UAVs, they can be deployed as temporary base stations (BSs) for on-demand situations like BS overloading or natural disasters. In this work, a UAV-based communication system with radio frequency (RF) access links to the mobile users (MUs) and a free-space optical (FSO) backhaul link to the ground station (GS) is considered. In particular, the RF and FSO channels in this network depend on the UAV’s positioning and (in)stability. The relative position of the UAV with respect to the MUs impacts the likelihood of a line-of-sight (LOS) connection in the RF link and the instability of the hovering UAV affects the quality of the FSO channel. Thus, taking these effects into account, we analyze the end-to-end system performance of networks employing UAVs as buffer-aided (BA) and non-buffer-aided (non-BA) relays in terms of the ergodic sum rate. Simulation results validate the accuracy of the proposed analytical derivations and reveal the benefits of buffering for compensation of the random fluctuations caused by the UAV’s instability. Our simulations also show that the ergodic sum rate of both BA and non-BA UAV-based relays can be enhanced considerably by optimizing the positioning of the UAV. We further study the impact of the MU density and the weather conditions on the end-to-end system performance.
Making Sense of Reinforcement Learning and Probabilistic Inference

Reinforcement learning (RL) combines a control problem with statistical estimation: The system dynamics are not known to the agent, but can be learned through experience. A recent line of research casts 'RL as inference' and suggests a particular framework to generalize the RL problem as probabilistic inference. Our paper surfaces a key shortcoming in that approach, and clarifies the sense in which RL can be coherently cast as an inference problem. In particular, an RL agent must consider the effects of its actions upon future rewards and observations: The exploration-exploitation tradeoff. In all but the most simple settings, the resulting inference is computationally intractable so that practical RL algorithms must resort to approximation. We demonstrate that the popular 'RL as inference' approximation can perform poorly in even very basic problems. However, we show that with a small modification the framework does yield algorithms that can provably perform well, and we show that the resulting algorithm is equivalent to the recently proposed K-learning, which we further connect with Thompson sampling.
Sharing Reserves through HVDC: Potential Cost Savings in the Nordic Countries

During summer 2018, the Nordic system's kinetic energy dropped below a critical level. As a consequence, Svenska kraftnat, the Swedish transmission system operator (TSO), requested the largest production unit to reduce its power output to guarantee system's security. This action resulted in a deviation from the generation dispatch determined by the market and in high costs for the Nordic TSOs. In this regard, this paper presents a tool for comparing mitigation strategies from an economic point of view and evaluates potential economic benefits of utilizing the Emergency Power Control (EPC) functionality of HVDC lines for the provision of fast reserves as a compliment to Frequency Containment Reserves (FCR). Moreover, the analysis is extended to the years 2020 and 2025 using inertia estimations from the Nordic TSOs. The findings of the paper suggest that the frequency of redispatching actions will increase in the future and that the cost of security for Nordic TSOs could be reduced by 70\% if HVDC links are used for frequency support.
Broadband Ultrahigh-Resolution chip-scale Scanning Soliton Dual-Comb Spectroscopy

We present a chip-scale scanning dual-comb spectroscopy (SDCS) approach for broadband ultrahigh-resolution spectral acquisition. SDCS uses Si3N4 microring resonators that generate two single soliton micro-combs spanning 37 THz (300 nm) on the same chip from a single 1550-nm laser, forming a high-mutual-coherence dual-comb. We realize continuous tuning of the dual-comb system over the entire optical span of 37.5 THz with high precision using integrated microheater-based wavelength trackers. This continuous wavelength tuning is enabled by simultaneous tuning of the laser frequency and the two single soliton micro-combs over a full free spectral range of the microrings. We measure the SDCS resolution to be 319+-4.6 kHz. Using this SDCS system, we perform the molecular absorption spectroscopy of H13C14N over its 2.3 THz (18 nm)-wide overtone band, and show that the massively parallel heterodyning offered by the dual-comb expands the effective spectroscopic tuning speed of the laser by one order of magnitude. Our chip-scale SDCS opens the door to broadband spectrometry and massively parallel sensing with ultrahigh spectral resolution.
Neutron diffraction and ab initio studies on the fully compensated ferrimagnetic characteristics of Mn2V1−x Co x Ga Heusler alloys

Neutron diffraction and ab initio studies were carried out on Mn2V1−x Co x Ga (x = 0, 0.25, 0.5, 0.75, 1) Heusler alloys which exhibits high T C fully compensated ferrimagnetic characteristics for x = 0.5. A combined analysis of neutron diffraction and ab initio calculations revealed the crystal structure and magnetic configuration which could not be determined from the x-ray diffraction and magnetic measurements. As reported earlier, Rietveld refinement of neutron diffraction data confirmed L21 structure for Mn2VGa and X a structure for Mn2CoGa. The alloys with x = 0.25 and 0.5 possess L21 structure with Mn(C)–Co disorder. As the Co concentration reaches 0.75, a structural transition has been observed from disordered L21 to disordered X a. Detailed ab initio studies also confirmed this structural transition. The reason for the magnetic moment compensation in Mn2(V1−x Co x )Ga was identified to be different from that of the earlier reported fully compensated ferrimagnet (MnCo)VGa. With the help of neutron diffraction and ab initio studies, it is identified that the disordered L21 structure with antiparallel coupling between the ferromagnetically aligned magnetic moments of (Mn(A)–Mn(C)) and (V–Co) atom pairs enables the compensation in Mn2V1−x Co x Ga.
Drug delivery in tumors is enhanced by bacterial proteolytic activity in a size and binding affinity dependent manner -A mechanistic understanding

The use of bacteria has been attractive to cancer researchers as drug delivery vehicle because motile bacteria are able to penetrate in tumors. In particular, the combination of therapeutic bacteria and conventional chemotherapy leads to dramatically high anti-tumor efficay. However, the mechanisms of the synergy, in part, remain unclear. To aim for understanding the mechanisms of the synergy of the combination therapy, simultaneous delivery of C. novyi-NT and chemotherapeutic agents in tumors is mathematically modeled from porous media approach. Simulated doxorubicin concentration in tumors after Doxil administration with or without bacteria agreed reasonably well with experimental literature. The simulated doxorubicin concentration in tumors by the combination of Doxil and C. novyi-NT is over twice higher than that of Doxil alone, as observed in previous experimental literature. This enhanced concentration is because of the degradation of extracellular matrix of collagen by bacterial proteolytic activity, which reduced interstitial fluid pressure in tumors by increasing hydraulic conductivity of interstitium, and thus increases convection through vessel walls. Additionally, solid stress alleviation caused by collagen degradation increases vessel density by decompressing blood vessels. On the other hand, the simulated doxorubicin concentration in tumors for non-liposomal doxorubicin is not enhanced by C. novyi-NT because vascular permeability of free-doxorubicin is larger than Doxil, and thus, increased but relatively small convection across vessel walls is outweighed by the efflux due to increased interstitial flow. A strategy to further enhance this combination therapy is discussed with sensitivity analysis.
Multivariate Temporal Point Process Regression

Abstract Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corresponding transferring coefficients in the form of a three-way tensor, then impose the low-rank, sparsity, and subgroup structures on this coefficient tensor. These structures help reduce the dimensionality, integrate information across different individual processes, and facilitate the interpretation. We develop a highly scalable optimization algorithm for parameter estimation. We derive the large sample error bound for the recovered coefficient tensor, and establish the subgroup identification consistency, while allowing the dimension of the multivariate point process to diverge. We demonstrate the efficacy of our method through both simulations and a cross-area neuronal spike trains analysis in a sensory cortex study.
Policy Synthesis for Switched Linear Systems With Markov Decision Process Switching

In this article, we study the synthesis of mode switching protocols for a class of discrete-time switched linear systems in which the mode jumps are governed by Markov decision processes (MDPs). We call such systems MDP-JLS for brevity. Each state of the MDP corresponds to a mode in the switched system. The probabilistic state transitions in the MDP represent the mode transitions. We focus on finding a policy that selects the switching actions at each mode such that the switched system is guaranteed to be stable. Given a policy in the MDP, the considered MDP-JLS reduces to a Markov jump linear system (MJLS). We consider both mean-square stability and stability with probability one. For mean-square stability, we leverage existing stability conditions for MJLSs and propose efficient semidefinite programming formulations to find a stabilizing policy in the MDP. For stability with probability one, we derive new sufficient conditions and compute a stabilizing policy using linear programming. We also extend the policy synthesis results to MDP-JLS with uncertain mode transition probabilities.
The Molecular Gas in the NGC 6240 Merging Galaxy System at the Highest Spatial Resolution

We present the highest-resolution—15 pc (0.″03)—ALMA 12CO(2–1) line emission and 1.3 mm continuum maps, tracers of the molecular gas and dust, respectively, in the nearby merging galaxy system NGC 6240, which hosts two supermassive black holes growing simultaneously. These observations provide an excellent spatial match to existing Hubble Space Telescope (HST) optical and near-infrared observations of this system. A significant molecular gas mass, ∼9 × 109 M⊙, is located between the two nuclei, forming a clumpy stream kinematically dominated by turbulence, rather than a smooth rotating disk, as previously assumed from lower-resolution data. Evidence for rotation is seen in the gas surrounding the southern nucleus but not in the northern one. Dynamical shells can be seen, likely associated with nuclear supernova remnants. We further detect the presence of significant high-velocity outflows, some of them reaching velocities >500 km s−1, affecting a significant fraction, ∼11%, of the molecular gas in the nuclear region. Inside the spheres of influence of the northern and southern supermassive black holes, we find molecular masses of 7.4 × 108 and 3.3 × 109 M⊙, respectively. We are thus directly imaging the reservoir of gas that can accrete onto each supermassive black hole. These new ALMA maps highlight the critical need for high-resolution observations of molecular gas in order to understand the feeding of supermassive black holes and its connection to galaxy evolution in the context of a major galaxy merger.
Emerging material systems for integrated optical Kerr frequency combs

The experimental realization of a Kerr frequency comb represented the convergence of research in materials, physics, and engineering, and this symbiotic relationship continues to underpin efforts in comb innovation today. While the initial focus developing cavity-based frequency combs relied on existing microresonator architectures and classic optical materials, in recent years, this trend has been disrupted. This paper reviews the latest achievements in frequency comb generation using resonant cavities, placing them within the broader historical context of the field. After presenting well-established material systems and device designs, the emerging materials and device architectures are examined. Specifically, the unconventional material systems as well as atypical device designs that have enabled tailored dispersion profiles and improved comb performance are compared to the current state of art. The remaining challenges and future outlook for the field of cavity-based frequency combs is evaluated.
Explainable outlier detection through decision tree conditioning

This work describes an outlier detection procedure (named "OutlierTree") loosely based on the GritBot software developed by RuleQuest research, which works by evaluating and following supervised decision tree splits on variables, in whose branches 1-d confidence intervals are constructed for the target variable and potential outliers flagged according to these confidence intervals. Under this logic, it's possible to produce human-readable explanations for why a given value of a variable in an observation can be considered as outlier, by considering the decision tree branch conditions along with general distribution statistics among the non-outlier observations that fell into the same branch, which can then be contrasted against the value which lies outside the CI. The supervised splits help to ensure that the generated conditions are not spurious, but rather related to the target variable and having logical breakpoints.
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes

There has recently been much work on the "wide limit" of neural networks, where Bayesian neural networks (BNNs) are shown to converge to a Gaussian process (GP) as all hidden layers are sent to infinite width. However, these results do not apply to architectures that require one or more of the hidden layers to remain narrow. In this paper, we consider the wide limit of BNNs where some hidden layers, called "bottlenecks", are held at finite width. The result is a composition of GPs that we term a "bottleneck neural network Gaussian process" (bottleneck NNGP). Although intuitive, the subtlety of the proof is in showing that the wide limit of a composition of networks is in fact the composition of the limiting GPs. We also analyze theoretically a single-bottleneck NNGP, finding that the bottleneck induces dependence between the outputs of a multi-output network that persists through extreme post-bottleneck depths, and prevents the kernel of the network from losing discriminative power at extreme post-bottleneck depths.