Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an \emph{unobserved} sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing the representations of more than \num{12600} trained state-of-the-art disentangled models, we observe that several disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed.
Learning Nearest Neighbor Graphs from Noisy Distance Samples

We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people's preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and the triangle inequality. Furthermore, we demonstrate efficiency of our method empirically and theoretically, needing only O(n log(n)Delta^-2) queries in favorable settings, where Delta^-2 accounts for the effect of noise. Using crowd-sourced data collected for a subset of the UT Zappos50K dataset, we apply our algorithm to learn which shoes people believe are most similar and show that it beats both an active baseline and ordinal embedding.
Factors predisposing to perinatal death related to uterine rupture during attempted vaginal birth after caesarean section: retrospective cohort study

Abstract Objective To determine the factors associated with an increased risk of perinatal death related to uterine rupture during attempted vaginal birth after caesarean section. Design Population based retrospective cohort study. Setting Data from the linked Scottish Morbidity Record and Stillbirth and Infant Death Survey of births in Scotland, 1985-98. Participants All women with one previous caesarean delivery who gave birth to a singleton infant at term by a means other than planned repeat caesarean section (n = 35 854). Main outcome measures All intrapartum uterine rupture and uterine rupture resulting in perinatal death (that is, death of the fetus or neonate). Results The overall proportion of vaginal births was 74.2% and of uterine rupture was 0.35%. The risk of intrapartum uterine rupture was higher among women who had not previously given birth vaginally (adjusted odds ratio 2.5, 95% confidence interval 1.6 to 3.9, P < 0.001) and those whose labour was induced with prostaglandin (2.9, 2.0 to 4.3, P < 0.001). Both factors were also associated with an increased risk of perinatal death due to uterine rupture. Delivery in a hospital with < 3000 births a year did not increase the overall risk of uterine rupture (1.1, 0.8 to 1.5, P = 0.67). However, the risk of perinatal death due to uterine rupture was significantly higher in hospitals with < 3000 births a year (one per 1300 births) than in hospitals with ≥ 3000 births a year (one per 4700; 3.4, 1.0 to 14.3, P = 0.04). Conclusion Women who have not previously given birth vaginally and those whose labour is induced with prostaglandin are at increased risk of uterine rupture when attempting vaginal birth after caesarean section. The risk of consequent death of the infant is higher in units with lower annual numbers of births.
Probing gaseous halos of galaxies with radio jets

Context. Gaseous halos play a key role in understanding inflow, feedback, and the overall baryon budget in galaxies. Literature models predict transitions of the state of the gaseous halo between cold and hot accretion, winds, fountains, and hydrostatic halos at certain galaxy masses. Since luminosities of radio AGN are sensitive to halo densities, any significant transition would be expected to show up in the radio luminosities of large samples of galaxies. The LOw Frequency ARray (LOFAR) Two-Metre Sky Survey (LoTSS) has identified a galaxy stellar mass scale, 1011 M⊙, above which the radio luminosities increase disproportionately. Aims. We investigate if radio luminosities of galaxies, especially the marked rise at galaxy masses around 1011 M⊙, can be explained with standard assumptions regarding jet powers, scaling between black hole mass and galaxy mass, and gaseous halos. Methods. Based on observational data and theoretical constraints, we developed models for the radio luminosity of radio AGN in halos under infall, galactic wind, and hydrostatic conditions. We compared these models to LoTSS data for a large sample of galaxies in the mass range between 108.5 M⊙ and 1012 M⊙. Results. Under the assumption that the same characteristic upper limit to jet powers known from high galaxy masses holds at all masses, we find the maximum radio luminosities for the hydrostatic gas halos to lie close to the upper envelope of the distribution of the LOFAR data. The marked rise in radio luminosity at 1011 M⊙ is matched in our model and is related to a significant change in halo gas density around this galaxy mass, which is a consequence of lower cooling rates at a higher virial temperature. Wind and infall models overpredict the radio luminosities for small galaxy masses and have no particular steepening of the run of the radio luminosities predicted at any galaxy mass. Conclusions. Radio AGN could have the same characteristic Eddington-scaled upper limit to jet powers in galaxies of all masses in the sample if the galaxies have hydrostatic gas halos in phases when radio AGN are active. We find no evidence of a change of the type of galaxy halo with the galaxy mass. Galactic winds and quasi-spherical cosmological inflow phases cannot frequently occur at the same time as powerful jet episodes unless the jet properties in these phases are significantly different from what we assumed in our model.
End to end learning and optimization on graphs

Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. Standard approaches treat learning and optimization entirely separately, while recent machine learning work aims to predict the optimal solution directly from the inputs. Here, we propose an alternative decision-focused learning approach that integrates a differentiable proxy for common graph optimization problems as a layer in learned systems. The main idea is to learn a representation that maps the original optimization problem onto a simpler proxy problem that can be efficiently differentiated through. Experimental results show that our ClusterNet system outperforms both pure end-to-end approaches (that directly predict the optimal solution) and standard approaches that entirely separate learning and optimization. Code for our system is available at this https URL.
The Degree of Aminoacidemia after Dairy Protein Ingestion Does Not Modulate the Postexercise Anabolic Response in Young Men: A Randomized Controlled Trial.

BACKGROUND Resistance exercise and dietary protein stimulate muscle protein synthesis (MPS). The rate at which proteins are digested and absorbed into circulation alters peak plasma amino acid concentrations and may modulate postexercise MPS. A novel mineral modified milk protein concentrate (mMPC), with identical amino acid composition to standard milk protein concentrate (MPC), was formulated to induce rapid aminoacidemia. OBJECTIVES The aim of this study was to determine whether rapid aminoacidemia and greater peak essential amino acid (EAA) concentrations induced by mMPC would stimulate greater postresistance exercise MPS, anabolic signaling, and ribosome biogenesis compared to standard dairy proteins, which induce a small but sustained plasma essential aminoacidemia. METHODS Thirty healthy young men (22.5 ± 3.0 y; BMI 23.8 ± 2.7 kg/m2) received primed constant infusions of l-[ring-13C6]-phenylalanine and completed 3 sets of leg presses and leg extensions at 80% of 1 repetition. Afterwards, participants were randomly assigned in a double-blind fashion to consume 25 g mMPC, MPC, or calcium caseinate (CAS). Vastus lateralis biopsies were collected at rest, and 2 and 4 h post exercise. RESULTS Plasma EAA concentrations, including leucine, were 19.2-26.6% greater in the mMPC group 45-90 min post ingestion than in MPC and CAS groups (P < 0.001). Myofibrillar fractional synthetic rate from baseline to 4 h was increased by 82.6 ± 64.8%, 137.8 ± 72.1%, and 140.6 ± 52.4% in the MPC, mMPC, and CAS groups, respectively, with no difference between groups (P = 0.548). Phosphorylation of anabolic signaling targets (P70S6KThr389, P70S6KThr421/Ser424, RPS6Ser235/236, RPS6Ser240/244, P90RSKSer380, 4EBP1) were elevated by <3-fold at both 2 and 4 h post exercise in all groups (P < 0.05). CONCLUSIONS The amplitude of plasma leucine and EAA concentrations does not modulate the anabolic response to resistance exercise after ingestion of 25 g dairy protein in young men. This trial was registered at http://www.anzctr. org.au/ as ACTRN12617000393358.
Baryogenesis from axion inflation

The coupling of an axion-like particle driving inflation to the Standard Model particle content through a Chern-Simons term generically sources a dual production of massless helical gauge fields and chiral fermions. We demonstrate that the interplay of these two components results in a highly predictive baryogenesis model, which requires no further ingredients beyond the Standard Model. If the helicity stored in the hyper magnetic field and the effective chemical potential induced by the chiral fermion production are large enough to avoid magnetic diffusion from the thermal plasma but small enough to sufficiently delay the chiral plasma instability, then the non-vanishing helicity survives until the electroweak phase transition and sources a net baryon asymmetry which is in excellent agreement with the observed value. If any of these two conditions is violated, the final baryon asymmetry vanishes. The observed baryon asymmetry can be reproduced if the energy scale of inflation is around Hinf ∼ 1010–1012 GeV with a moderate dependence on inflation model parameters.
Distribution of red clump stars does not support the X-shaped Galactic bulge

Context. Claims of an X-shaped Galactic bulge were based on the assumption of red clump stars as standard candles in some lines of sight crossing the off-plane bulge. However, some doubts have been cast on whether the two peaks in star counts along the line of sight really represent a double peak in the density distribution, or whether there is something wrong with the assumption of a unique constant absolute magnitude for all of these stars. Aims. With the advent of Gaia-DR2 parallaxes in combination with near-infrared VISTA-VVV data, we are able to check which of the hypotheses is correct. Methods. We calculated the median absolute magnitude MK corresponding to both peaks of putative red clumps in seven lines of sight with the lowest extinction in the interesting coordinates’ range. Results. The difference between the absolute magnitude of the bright and the faint peak is ΔMK ≈ 0.4. The selected stars in both peaks cannot be represented by the same red clump giants with constant MK ≈ −1.6. Conclusions. The hypothesis that the bulge contains an X-shape is based on the assumption that the faint and bright peaks of the density distribution towards the bulge are dominated by standard red clump stars. However, we show that both the faint and bright peaks cannot be dominated by standard red clump stars simultaneously.
A Lightweight Recurrent Network for Sequence Modeling

Recurrent networks have achieved great success on various sequential tasks with the assistance of complex recurrent units, but suffer from severe computational inefficiency due to weak parallelization. One direction to alleviate this issue is to shift heavy computations outside the recurrence. In this paper, we propose a lightweight recurrent network, or LRN. LRN uses input and forget gates to handle long-range dependencies as well as gradient vanishing and explosion, with all parameter related calculations factored outside the recurrence. The recurrence in LRN only manipulates the weight assigned to each token, tightly connecting LRN with self-attention networks. We apply LRN as a drop-in replacement of existing recurrent units in several neural sequential models. Extensive experiments on six NLP tasks show that LRN yields the best running efficiency with little or no loss in model performance.
Hodge-Riemann bilinear relations for Schur classes of ample vector bundles

Let $X$ be a $d$ dimensional projective manifold, $E$ be an ample vector bundle on $X$ and $0\le \lambda_N\le \lambda_{N-1} \le \cdots \le \lambda_1 \le \operatorname{rank}(E)$ be a partition of $d-2$. We prove that the Schur class $s_{\lambda}(E)\in H^{d-2,d-2}(X)$ has the Hard Lefschetz property and satisfies the Hodge-Riemann bilinear relations. As a consequence we obtain various new inequalities between characteristic classes of ample vector bundles, including a higher-rank version of the Khovanskii-Teissier inequalities.
Job satisfaction and perceived future roles of Malaysian dental therapists: findings from a national survey.

OBJECTIVE To assess Malaysian dental therapists' perceptions of their job satisfaction and future roles. METHODS A nationwide postal survey involving all Malaysian dental therapists who met the inclusion criteria (n = 1726). RESULTS The response rate was 76.8%. All respondents were females; mean age 35.4 years (SD = 8.4). Majority were married (85.5%) and more than one-half had a working experience of <10 years (56.1%). Majority worked in community dental service (94.3%) and in urban areas (61.7%). Overall, they were highly satisfied with most aspects of their career. However, they were least satisfied with administrative workload (58.1%), career advancement opportunities (51.9%) and remuneration package; specifically income (45.2%), allowances (45.2%) and non-commensurate between pay and performance (44.0%). Majority perceived their role as very important in routine clinical tasks such as examination and diagnosis, preventive treatment, extraction of deciduous teeth and oral health promotion. However, fewer than one-half consider complex treatment such as placement of preformed crowns on deciduous teeth (37.1%) and extraction of permanent teeth (37.2%) as very important tasks. CONCLUSION Majority expressed high career satisfaction with most aspects of their employment but expressed low satisfaction in remuneration, lack of career advancement opportunities and administrative tasks. We conclude that most Malaysian dental therapists have positive perceptions of their current roles but do not favour wider expansion of their roles. These findings imply that there was a need to develop a more attractive career pathway for therapists to ensure sustainability of effective primary oral healthcare delivery system for Malaysia's children.
Comparative Analysis of State-of-the-Art EDoS Mitigation Techniques in Cloud Computing Environment

A new variant of the DDoS attack, called Economic Denial of Sustainability attack has emerged. Since the cloud service is based on the pay-per-use model, the EDoS attack endeavors to scale up the resource usage over time to the point the purveyor of the server is financially incapable of sustaining the service due to the incurred unaffordable usage charges. The implication of the EDoS attack is a major security implication as more elastic cloud services are being deployed. Existing techniques to detect and mitigate such attacks are either have low accuracy or ineffective and, in some cases, aggravate the attack even further. Therefore, an Enhanced Mitigation Mechanism is proposed to address these shortcomings using OpenFlow and statistical techniques, i.e. Hellinger Distance and Entropy. The experiments clearly depicted that EMM is able to detect and mitigate EDoS attacks with high accuracy and it is effective in terms of resource utilization compared to existing mitigation techniques. Thus, can be deployed in the cloud environment without the need for additional resource requirements.
Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm

Deep 3-dimensional (3D) Convolutional Network (ConvNet) has shown promising performance on video recognition tasks because of its powerful spatio-temporal information fusion ability. However, the extremely intensive requirements on memory access and computing power prohibit it from being used in resource-constrained scenarios, such as portable and edge devices. So in this paper, we first propose a two-stage Fully Separable Block (FSB) to significantly compress the model sizes of 3D ConvNets. Then a feature enhancement approach named Temporal Residual Gradient (TRG) is developed to improve the performance of compressed model on video tasks, which provides higher accuracy, faster convergency and better robustness. Moreover, in order to further decrease the computing workload, we propose a hybrid Fast Algorithm (hFA) to drastically reduce the computation complexity of convolutions. These methods are effectively combined to design a light-weight and efficient ConvNet for video recognition tasks. Experiments on the popular dataset report 2.3x compression rate, 3.6x workload reduction, and 6.3% top-1 accuracy gain, over the state-of-the-art SlowFast model, which is already a highly compact model. The proposed methods also show good adaptability on traditional 3D ConvNet, demonstrating 7.4x more compact model, 11.0x less workload, and 3.0% higher accuracy
The whole picture of the large-scale structure of the CL1604 supercluster at z ∼ 0.9

We present the large-scale structure over a more than 50 comoving Mpc scale at $z \sim 0.9$ where the CL1604 supercluster, which is one of the largest structures ever known at high redshifts, is embedded. The wide-field deep imaging survey by the Subaru Strategic Program with the Hyper Suprime-Cam reveals that the already-known CL1604 supercluster is a mere part of larger-scale structure extending to both the north and the south. We confirm that there are galaxy clusters at three slightly different redshifts in the northern and southern sides of the supercluster by determining the redshifts of 55 red-sequence galaxies and 82 star-forming galaxies in total via follow-up spectroscopy with Subaru/FOCAS and Gemini-N/GMOS. This suggests that the structure known as the CL1604 supercluster is the tip of the iceberg. We investigate the stellar population of the red-sequence galaxies using 4000 Å break and Balmer H$\delta$ absorption lines. Almost all of the red-sequence galaxies brighter than $21.5\:$mag in the z band show an old stellar population of $\gtrsim\! 2\:$Gyr. The comparison of composite spectra of the red-sequence galaxies in the individual clusters show that the galaxies at a similar redshift have a similar stellar population age, even if they are located $\sim\! 50\:$Mpc apart from each other. However, there could be a large variation in the star formation history. Therefore, it is likely that galaxies associated with the large-scale structure on a 50 Mpc scale formed at almost the same time, have assembled into the denser regions, and then have evolved with different star formation history along the hierarchical growth of the cosmic web.
Learning Robust Global Representations by Penalizing Local Predictive Power

Despite their renowned predictive power on i.i. d. data, convolutional neural networks are known to rely more on high-frequency patterns that humans deem superficial than on low-frequency patterns that agree better with intuitions about what constitutes category membership. This paper proposes a method for training robust convolutional networks by penalizing the predictive power of the local representations learned by earlier layers. Intuitively, our networks are forced to discard predictive signals such as color and texture that can be gleaned from local receptive fields and to rely instead on the global structures of the image. Across a battery of synthetic and benchmark domain adaptation tasks, our method confers improved generalization out of the domain. Also, to evaluate cross-domain transfer, we introduce ImageNet-Sketch, a new dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale.
PAC-Bayesian Transportation Bound

Empirically, the PAC-Bayesian analysis is known to produce tight risk bounds for practical machine learning algorithms. However, in its naive form, it can only deal with stochastic predictors while such predictors are rarely used and deterministic predictors often performs well in practice. To fill this gap, we develop a new generalization error bound, the PAC-Bayesian transportation bound, unifying the PAC-Bayesian analysis and the chaining method in view of the optimal transportation. It is the first PAC-Bayesian bound that relates the risks of any two predictors according to their distance, and capable of evaluating the cost of de-randomization of stochastic predictors faced with continuous loss functions. As an example, we give an upper bound on the de-randomization cost of spectrally normalized neural networks (NNs) to evaluate how much randomness contributes to the generalization of NNs.
3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework Using Fully 3D Convolutional Neural Networks

The capability to perform facial analysis from video sequences has significant potential to positively impact in many areas of life. One such area relates to the medical domain to specifically aid in the diagnosis and rehabilitation of patients with facial palsy. With this application in mind, this paper presents an end-to-end framework, named 3DPalsyNet, for the tasks of mouth motion recognition and facial palsy grading. 3DPalsyNet utilizes a 3D CNN architecture with a ResNet backbone for the prediction of these dynamic tasks. Leveraging transfer learning from a 3D CNNs pre-trained on the Kinetics data set for general action recognition, the model is modified to apply joint supervised learning using center and softmax loss concepts. 3DPalsyNet is evaluated on a test set consisting of individuals with varying ranges of facial palsy and mouth motions and the results have shown an attractive level of classification accuracy in these tasks of 82% and 86% respectively. The frame duration and the loss function affect was studied in terms of the predictive qualities of the proposed 3DPalsyNet, where it was found shorter frame duration’s of 8 performed best for this specific task. Centre loss and softmax have shown improvements in spatio-temporal feature learning than softmax loss alone, this is in agreement with earlier work involving the spatial domain.