Miracle-dz/UniFAR-FTUs huggingface dataset:
A new method for target selection in direct imaging programmes with COPAINS

We present COPAINS (Code for Orbital Parametrization of Astrometrically Inferred New Systems), an innovative tool developed to identify previously undiscovered companions detectable via direct imaging, based on changes in stellar proper motions across multiple astrometric catalogues. This powerful procedure allows for dynamical predictions of the possible masses and separations of unseen companions compatible with observed astrometric trends, marginalized over unknown orbital elements. Validating our approach using well-constrained systems, we found that our tool provides a good indication of the region of the parameter space where undetected secondaries may be located. Comparing the output of the code to the measured or expected sensitivity from various imaging instruments, this in turn enables us to robustly select the most promising targets for direct imaging campaigns searching for low-mass companions. Such an informed selection method promises to reduce the null detection rates from current programmes and will significantly increase the current census of wide brown dwarfs and planetary companions to stars, which remain extremely rare in the surveys conducted so far.
The modification, implementation, and evaluation of an HIV/AIDS education programme for students in year 10

This research addresses the issue of HIV/AIDS education for Year 10 students. The purpose of this study was twofold: to provide baseline data on students' knowledge, beliefs, and attitudes about HIV and AIDS (Phase 1 ), and to evaluate the effect of an education intervention on possible determinants of behaviour (Phase 2). Previous studies have indicated that improved knowledge may influence risk-reducing behaviour. In addition, adolescents have numerous misconceptions about the cause and transmission of HIV. However, there has been no known assessment of Al DS knowledge, attitudes, beliefs, and behaviours of high school students in Western Australia. The study was guided by the Health Belief Model (HBM), which attempts to explain preventative health behaviour in terms of attitudes, values, and beliefs. Using a descriptive, quasi-experimental design, the study drew a non­ random convenience sample of 269 students aged 14-15 years from two senior high schools in Perth, Western Australia. One school received the education intervention, and the other school was used as the control group. Phase 1: A questionnaire-was administered to gather baseline data on AIDS knowledge, attitudes, perceived risk, and intention to change risk behaviours. Students were also asked to identify sources of HIV/AIDS information. Results revealed that students knew most about the prevention of HIV/AIDS, and least about its transmission. Understanding of the severity of the disease was poor, students reported high levels of fear, and varying degrees of willingness to practice risk-reducing behaviours. There was a significant relationship between knowledge of HIV/AIDS and: perceived severity, perceived barriers, and perceived pressures. Schools, television, and magazines were major sources of AIDS information .
Teechain: a secure payment network with asynchronous blockchain access

Blockchains such as Bitcoin and Ethereum execute payment transactions securely, but their performance is limited by the need for global consensus. Payment networks overcome this limitation through off-chain transactions. Instead of writing to the blockchain for each transaction, they only settle the final payment balances with the underlying blockchain. When executing off-chain transactions in current payment networks, parties must access the blockchain within bounded time to detect misbehaving parties that deviate from the protocol. This opens a window for attacks in which a malicious party can steal funds by deliberately delaying other parties' blockchain access and prevents parties from using payment networks when disconnected from the blockchain. We present Teechain, the first layer-two payment network that executes off-chain transactions asynchronously with respect to the underlying blockchain. To prevent parties from misbehaving, Teechain uses treasuries, protected by hardware trusted execution environments (TEEs), to establish off-chain payment channels between parties. Treasuries maintain collateral funds and can exchange transactions efficiently and securely, without interacting with the underlying blockchain. To mitigate against treasury failures and to avoid having to trust all TEEs, Teechain replicates the state of treasuries using committee chains, a new variant of chain replication with threshold secret sharing. Teechain achieves at least a 33X higher transaction throughput than the state-of-the-art Lightning payment network. A 30-machine Teechain deployment can handle over 1 million Bitcoin transactions per second.
DNA sequence evolution in the Yule process.

We study a fundamental model of DNA evolution in a growing population of cells: cell divisions follow the Yule process, and each cell contains a sequence of nucleotides which can mutate at cell division. Following typical parameter values in bacteria and cancer cell populations, we take the mutation rate to zero and the final number of cells to infinity. We prove that almost every site (entry of the sequence) is mutated in only a finite number of cells, and these numbers are independent across sites. However independence breaks down for the rare sites which are mutated in a positive fraction of the population. The model is free from the popular but disputed infinite sites assumption. Violations of the infinite sites assumption are widespread while their impact on mutation frequencies is negligible at the scale of population fractions. Some results are generalised to allow for cell death, selection, and site-specific mutation rates. We also estimate mutation rates in a lung adenocarcinoma.
Exploring the Local Black Hole Mass Function below 106 Solar Masses

The local black hole mass function (BHMF) is of great interest to a variety of astrophysical problems, ranging from black hole binary merger rates to an indirect census of the dominant seeding mechanism of supermassive black holes. In this Letter, we combine the latest galaxy stellar mass function from the Galaxy And Mass Assembly survey with X-ray-based constraints to the local black hole occupation fraction to probe the BHMF below 106 M⊙. Notwithstanding the large uncertainties inherent to the choice of a reliable observational proxy for black hole mass, the resulting range of BHMFs yields a combined normalization uncertainty of ≲1 dex over the [105–106] M⊙ range, where upcoming, space-based gravitational wave detectors are designed to be most sensitive.
An Embarrassingly Simple Approach for Knowledge Distillation

Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model. Previous KD methods typically train a student by minimizing a task-related loss and the KD loss simultaneously, using a pre-defined loss weight to balance these two terms. In this work, we propose to first transfer the backbone knowledge from a teacher to the student, and then only learn the task-head of the student network. Such a decomposition of the training process circumvents the need of choosing an appropriate loss weight, which is often difficult in practice, and thus makes it easier to apply to different datasets and tasks. Importantly, the decomposition permits the core of our method, Stage-by-Stage Knowledge Distillation (SSKD), which facilitates progressive feature mimicking from teacher to student. Extensive experiments on CIFAR-100 and ImageNet suggest that SSKD significantly narrows down the performance gap between student and teacher, outperforming state-of-the-art approaches. We also demonstrate the generalization ability of SSKD on other challenging benchmarks, including face recognition on IJB-A dataset as well as object detection on COCO dataset.
Ground Truth Force Distribution for Learning-Based Tactile Sensing: A Finite Element Approach

Skin-like tactile sensors provide robots with rich feedback related to the force distribution applied to their soft surface. The complexity of interpreting raw tactile information has driven the use of machine learning algorithms to convert the sensory feedback to the quantities of interest. However, the lack of ground truth sources for the entire contact force distribution has mainly limited these techniques to the sole estimation of the total contact force and the contact center on the sensor’s surface. The method presented in this article uses a finite element model to obtain ground truth data for the three-dimensional force distribution. The model is obtained with state-of-the-art material characterization methods and is evaluated in an indentation setup, where it shows high agreement with the measurements retrieved from a commercial force-torque sensor. The proposed technique is applied to a vision-based tactile sensor, which aims to reconstruct the contact force distribution purely from images. Thousands of images are matched to ground truth data and are used to train a neural network architecture, which is suitable for real-time predictions.
A Gaia view of the two OB associations Cygnus OB2 and Carina OB1: the signature of their formation process

OB associations are the prime star-forming sites in galaxies. However, the detailed formation process of such stellar systems still remains a mystery. In this context, identifying the presence of substructures may help in tracing the footprints of their formation process. Here, we present a kinematic study of the two massive OB associations Cygnus OB2 and Carina OB1 using the precise astrometry from the Gaia Data Release 2 and radial velocities. From the parallaxes of stars, these OB associations are confirmed to be genuine stellar systems. Both Cygnus OB2 and Carina OB1 are composed of a few dense clusters and a halo which have different kinematic properties: the clusters occupy regions of 5–8 parsecs in diameter and display small dispersions in proper motion, while the haloes spread over tens of parsecs with two to three times larger dispersions in proper motion. This is reminiscent of the so-called line width–size relation of molecular clouds related to turbulence. Considering that the kinematics and structural features were inherited from those of their natal clouds would then imply that the formation of OB associations may result from structure formation driven by supersonic turbulence, rather than from the dynamical evolution of individual embedded clusters.
Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

Abstract We present a novel method for publishing differentially private synthetic attributed graphs. Our method allows, for the first time, to publish synthetic graphs simultaneously preserving structural properties, user attributes and the community structure of the original graph. Our proposal relies on CAGM, a new community-preserving generative model for attributed graphs. We equip CAGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release communitypreserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments, we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph, as well as degree sequences and clustering coefficients.
Supplementary: Monocular Neural Image Based Rendering with Continuous View Control

Figure. 2 shows the synthesized views produced by both the pixel and depth branch, as well as the blended results. The network tends to use information from the depth branch as much as possible, and uses the pixel branch to fill-in parts that are not in the source view via a learned image prior. The mask branch correctly predicts the visibility of source view pixels in the target view. This mask can be used not only to fuse results from the pixel and depth branches, but also to combine information from multiple source views when they are available.
Paraneoplastic syndromes in primary liver cell carcinoma in Nigeria.

: Seventy patients with primary liver cell carcinoma (PLC) were studied for the occurrence and relative incidence of paraneoplastic syndromes (PNS) between 1985 and 1990. Here we communicate results of the study involving hypercholesterolaemia, hypercalcemia, hypoglycaemia, gynaecomastia and polycythaemia. Hypercholesterolaemia occurred in 20% of our patients and is the most common PNS in our series; polycythaemia occurred in 15.6%, hypoglycaemia in 8.6% and hypercalcaemia in 7%. It is noteworthy that hypoglycaemia, which is one of the commonest and certainly the most dangerous PNS associated with PLC, occurred in few of our patients. The occurrence and incidence of PNS as obtained in the present study slightly differ from those in developed countries. The pathophysiological mechanisms underlying these systemic manifestations are discussed.
Training Compact Neural Networks via Auxiliary Overparameterization

It is observed that overparameterization (i.e., designing neural networks whose number of parameters is larger than statistically needed to fit the training data) can improve both optimization and generalization while compact networks are more difficult to be optimized. However, overparameterization leads to slower testtime inference speed and more power consumption. To tackle this problem, we propose a novel auxiliary module to simulate the effect of overparameterization. During training, we expand the compact network with the auxiliary module to formulate a wider network to assist optimization while during inference only the original compact network is kept. Moreover, we propose to automatically search the hierarchical auxiliary structure to avoid adding supervisions heuristically. In experiments, we explore several challenging resource constraint tasks including light-weight classification, semantic segmentation and multi-task learning with hard parameter sharing. We empirically find that the proposed auxiliary module can maintain the complexity of the compact network while significantly improving the performance.
ForkNet: Multi-Branch Volumetric Semantic Completion From a Single Depth Image

We propose a novel model for 3D semantic completion from a single depth image, based on a single encoder and three separate generators used to reconstruct different geometric and semantic representations of the original and completed scene, all sharing the same latent space. To transfer information between the geometric and semantic branches of the network, we introduce paths between them concatenating features at corresponding network layers. Motivated by the limited amount of training samples from real scenes, an interesting attribute of our architecture is the capacity to supplement the existing dataset by generating a new training dataset with high quality, realistic scenes that even includes occlusion and real noise. We build the new dataset by sampling the features directly from latent space which generates a pair of partial volumetric surface and completed volumetric semantic surface. Moreover, we utilize multiple discriminators to increase the accuracy and realism of the reconstructions. We demonstrate the benefits of our approach on standard benchmarks for the two most common completion tasks: semantic 3D scene completion and 3D object completion.
Nested Named Entity Recognition via Second-best Sequence Learning and Decoding

Abstract When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively.
Study of Violent Asphyxial Death

Background : An increasing death rate as a result of violence constitutes a large group in medico-legal autopsies especially deaths due to asphyxia are one of the most important cause in violent deaths. Method: It was a prospective study of all medico-legal autopsies performed between December 2008 and November 2010 at mortuary of Civil Hospital affiliated with B.J.Medical College, Ahmedabad, Gujarat, India. Out of the total autopsies conducted at the Mortuary of Civil Hospital, Ahmedabad, those where the victim died as a result of violent mechanical interference with respiration like hanging, strangulation, and drowning were included. Results and Conclusion: Incidence of violent asphyxia deaths is 5.63% of total autopsies. Hanging (82.48%) is the most commonly encountered violent asphyxia death. Males are most common victims with male:female ratio 1.69:1. Most commonly involved age group is 21-30 years (128 cases forming 32.99% of total) with 200 victims (51.54%) aged 21-40 years. 312 out of 320 cases (97.5%) of hanging were suicidal and rest 8 (2.5%) were accidental in nature. Homicidal hanging is not recorded in present study. All 12 strangulation cases were of homicide, 32 out of 56 (57.14%) cases of drowning were accidental and remaining 24 (42.86%) were suicidal.
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?

Neural Networks have been shown to be sensitive to common perturbations such as blur, Gaussian noise, rotations, etc. They are also vulnerable to some artificial malicious corruptions called adversarial examples. The adversarial examples study has recently become very popular and it sometimes even reduces the term "adversarial robustness" to the term "robustness". Yet, we do not know to what extent the adversarial robustness is related to the global robustness. Similarly, we do not know if a robustness to various common perturbations such as translations or contrast losses for instance, could help with adversarial corruptions. We intend to study the links between the robustnesses of neural networks to both perturbations. With our experiments, we provide one of the first benchmark designed to estimate the robustness of neural networks to common perturbations. We show that increasing the robustness to carefully selected common perturbations, can make neural networks more robust to unseen common perturbations. We also prove that adversarial robustness and robustness to common perturbations are independent. Our results make us believe that neural network robustness should be addressed in a broader sense.
Entropy-driven winds: Outflows and fountains lifted gently by buoyancy

We present a new theoretical framework for using entropy to understand how outflows driven by supernovae are launched from disc galaxies: via continuous, buoyant acceleration through the circumgalactic medium (CGM). When young star clusters detonate supernovae in the interstellar medium (ISM) of a galaxy, they generate hot, diffuse bubbles that push on the surrounding ISM and evaporate that ISM into their interiors. As these bubbles reach the scale height of the ISM, they break out of the disc, rising into the CGM. Once these bubbles break out, if they have sufficiently high entropy, they will feel an upward acceleration, owing to a local buoyant force. This upward force will accelerate these bubbles, driving them to high galactocentric radii, keeping them in the CGM for > Gyr, even if their initial velocity is much lower than the local escape velocity. We derive an equation of motion for these entropy-driven winds that connects the ISM properties, halo mass, and CGM profile of galaxies to the ultimate evolution of feedback-driven winds. We explore the parameter space of these equations, and show how this novel framework can explain both self-consistent simulations of star formation and galactic outflows as well as the new wealth of observations of CGM kinematics. We show that these entropy-driven winds can produce long wind recycling times, while still carrying a significant amount of mass. Comparisons to simulations and observations show entropy-driven winds convincingly explain the kinematics of galactic outflows.
An extended Pal 5 stream in Gaia DR2

We present the results of a detailed search for members of the Pal 5 tidal tail system in Gaia Data Release 2 (DR2). Tidal tails provide a sensitive method for measuring the current and past gravitational potential of their host galaxy as well as for testing predictions for the abundance of dark matter subhaloes. The Pal 5 globular cluster and its associated tails are an excellent candidate for such analysis; however, only ∼23° of arc are currently known, with in particular the leading tail much shorter than the trailing. Using Gaia DR2 and its precise astrometry, we extend the known extent of the Pal 5 tail to ∼30°, 7° of which are newly detected along the leading arm. The detected leading and trailing arms are symmetric in length and remain near constant width. This detection constrains proposed models in which the Galactic bar truncates Pal 5’s leading arm. Follow-up spectroscopic observations are necessary to verify the candidate stream stars are consistent with the known tidal tails. If confirmed, this Pal 5 stream extension opens up new possibilities to constrain the Galactic potential.
Therapeutic efficacy of seabuckthorn (Hippophae rhamnoides) in infected cutaneous wounds in bovines

Healing of infected cutaneous wounds was evaluated following application of seabuckthorn (Hippoplzae rhamnoides) ointment in 9 male calves. The wounds were treated with liquid paraffin in group I, with 5% povidone iodine ointment in group 2 and with seabuckthorn ointment in group 3. The efficacy ofdifferent treatments was monitored by clinical, haematological and microbiological parameters at 0, 3, 7, 14, 21 and 28 days. The inflammation subsided earlier in groups 2 and 3. The wounds ofgroup 3 also showed faster and most extensive granulation tissue formation in the early stages ofwound healing. These wounds were markedly drier and showed least amount of exudation. However, the contraction ofthe wounds was comparable in all the groups. No significant changes were recorded in various other clinical and haematological parameters, irrespective ofthe treatment adopted except that there was a significant neutrophilia before the treatment and significant lymphocytosis after the treatment.