Miracle-dz/UniFAR-FTUs huggingface dataset:
Isotropic Maximization Loss and Entropic Score: Fast, Accurate, Scalable, Unexposed, Turnkey, and Native Neural Networks Out-of-Distribution Detection.

Current out-of-distribution detection (ODD) approaches require cumbersome procedures that add undesired side-effects to the solution. In this paper, we argue that the uncertainty in neural networks is mainly due to SoftMax loss anisotropy. Consequently, we propose an isotropic loss (IsoMax) and a decision score (Entropic Score) to significantly improve the ODD performance while keeping the overall solution fast, accurate, scalable, unexposed, turnkey, and native. Our experiments indeed showed that uncertainty is extremely reduced simply by replacing the SoftMax loss without relying on techniques such as adversarial training/validation, special-purpose data augmentation, outlier exposure, ensembles methods, Bayesian mechanisms, generative approaches, metric learning, or additional classifiers/regressions. The results also showed that our straightforward proposal overcomes ODIN, ACET, and is competitive against the Mahalanobis approach besides avoiding their undesired requirements and weaknesses. Since IsoMax loss works as a direct and transparent SoftMax loss drop-in replacement, these techniques may be used combined with our loss to increase the overall performance even more if their associated drawbacks are not a concern in a particular use case.
Modelling the solar transition region using an adaptive conduction method

Modelling the solar Transition Region with the use of an Adaptive Conduction (TRAC) method permits fast and accurate numerical solutions of the field-aligned hydrodynamic equations, capturing the enthalpy exchange between the corona and transition region, when the corona undergoes impulsive heating. The TRAC method eliminates the need for highly resolved numerical grids in the transition region and the commensurate very short time steps that are required for numerical stability. When employed with coarse spatial resolutions, typically achieved in multi-dimensional magnetohydrodynamic codes, the errors at peak density are less than 5% and the computation time is three orders of magnitude faster than fully resolved field-aligned models. This paper presents further examples that demonstrate the versatility and robustness of the method over a range of heating events, including impulsive and quasi-steady footpoint heating. A detailed analytical assessment of the TRAC method is also presented, showing that the approach works through all phases of an impulsive heating event because (i) the total radiative losses and (ii) the total heating when integrated over the transition region are both preserved at all temperatures under the broadening modifications of the method. The results from the numerical simulations complement this conclusion.
Scaling data-driven robotics with reward sketching and batch reinforcement learning

We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show how to apply this framework to accomplish three different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly. Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth.
ADMM-Based One-Bit Quantized Signal Detection for Massive MIMO Systems With Hardware Impairments

This paper considers signal detection in massive multiple-input multiple-output (MIMO) systems with general additive hardware impairments and one-bit quantization. First, we present the quantization-unaware and Bussgang decomposition-based linear receivers by generalizing them for the considered hardware impairment model. We propose an optimization problem to estimate the uplink data signals by choosing a suitable cost function that treats the unquantized received signal at the base station as the variable. We exploit the additional structure of the one-bit quantization and signal modulation by including proper constraints. To solve the non-convex quadratically-constrained quadratic programming (QCQP) problem, we propose an ADMM-based algorithm with closed-form update equations. Then, we replace the harsh projectors in the updates with their soft versions to improve the detection performance. We show that the proposed ADMM-based algorithm outperforms the state-of-the-art linear receivers significantly in terms of bit error rate (BER) and the performance gain increases with the number of antennas and users.
A Deep Conditioning Treatment of Neural Networks

We study the role of depth in training randomly initialized overparameterized neural networks. We give a general result showing that depth improves trainability of neural networks by improving the conditioning of certain kernel matrices of the input data. This result holds for arbitrary non-linear activation functions under a certain normalization. We provide versions of the result that hold for training just the top layer of the neural network, as well as for training all layers, via the neural tangent kernel. As applications of these general results, we provide a generalization of the results of Das et al. (2019) showing that learnability of deep random neural networks with a large class of non-linear activations degrades exponentially with depth. We also show how benign overfitting can occur in deep neural networks via the results of Bartlett et al. (2019b). We also give experimental evidence that normalized versions of ReLU are a viable alternative to more complex operations like Batch Normalization in training deep neural networks.
Deep Differentiable Grasp Planner for High-DOF Grippers

We present an end-to-end algorithm for training deep neural networks to grasp novel objects. Our algorithm builds all the essential components of a grasping system using a forward-backward automatic differentiation approach, including the forward kinematics of the gripper, the collision between the gripper and the target object, and the metric for grasp poses. In particular, we show that a generalized Q1 grasp metric is defined and differentiable for inexact grasps generated by a neural network, and the derivatives of our generalized Q1 metric can be computed from a sensitivity analysis of the induced optimization problem. We show that the derivatives of the (self-)collision terms can be efficiently computed from a watertight triangle mesh of low-quality. Altogether, our algorithm allows for the computation of grasp poses for high-DOF grippers in an unsupervised mode with no ground truth data, or it improves the results in a supervised mode using a small dataset. Our new learning algorithm significantly simplifies the data preparation for learning-based grasping systems and leads to higher qualities of learned grasps on common 3D shape datasets [7, 49, 26, 25], achieving a 22% higher success rate on physical hardware and a 0.12 higher value on the Q1 grasp quality metric.
The persistent institutional effect of liberal colonialism: evidence from China’s financial policies

ABSTRACT The effect of liberal colonialism on the allocation of capital persists to this day. We exploit the colonial history of China during 1896-1911 with qualitative evidence to measure liberal colonialism. We document that liberal colonialism promotes the subsequent efficiency of financial policies on the capital allocation in 2004 through the quality of economic institutions.
Time to Death and its Predictors among Neonates Admitted in the Intensive Care Unit of the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia

Temesgen Worku Gudayu 1 Ejigu Gebeye Zeleke Ayenew Molla Lakew 2 1Department of Clinical Midwifery, School of Midwifery, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia; 2Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia Introduction: Neonatal mortality is among the leading concerns globally and its burden is highest in sub-Saharan Africa. Despite all the efforts made, neonatal mortality remains high in Ethiopia in general and in the Amhara region in particular. In this study, we assessed the time to death and its predictors among neonates admitted to the intensive care unit of the University of Gondar Comprehensive Specialized Hospital. Patients and Methods: A retrospective follow-up study was conducted among 504 randomly selected neonates admitted to the neonatal intensive care unit between January 1 and December 31, 2017. The extracted data were entered into epi-info version 7.2, exported to and analyzed using STATA version 14. Failure probability table and Kaplan–Meier failure function were computed. Gompertz proportional hazard regression model was used to identify predictors of time to neonatal death. The hazard ratio with a 95% confidence interval was reported and statistical significance was declared at a p-value of ≤0.05. Results: The proportion of neonatal mortality in this study was 17.3 (95% CI: 14.2, 20.9). Out of 504 studied neonates, 87 (173 per 1000 live births) died. Neonates who did not cry at birth and those who were born before 34 weeks had a higher risk of death in both births-todeath and admission-to-death analysis. Whereas, in admission-to-death analysis, neonates who were born at home had a higher risk of death. Conclusion: In this study, neonatal mortality is far higher than the national and sustainable development goal targets. Strengthening access to health facilities, improving the community’s awareness of giving birth at health facilities and strengthening facilities and providers’ capacity for caring for high-risk neonates prior to the referral is recommended.
Evidence of variable performance responses to the Nike 4% shoe: Definitely not a game-changer for all recreational runners

Purpose: We compared running economy (RE) and 3-km time-trial (TT) performances of male recreational runners wearing the Nike Vaporfly 4% (NIKE), lightweight racing flats (FLAT), and their habitual footwear (OWN). Methods: Eighteen male recreational runners [age: 33.5 (11.9) y, V̇O2peak: 55.8 (4.4) mL·kg-1·min-1] attended 4 sessions ~7 days apart. The first session consisted of a V̇O2peak test to inform subsequent RE speeds set at 60, 70, and 80% of the speed eliciting V̇O2peak. In subsequent sessions, treadmill RE and 3-km TT were assessed in the three footwear in a randomised, counterbalanced crossover design. Results: RE was improved in NIKE (3.6 to 4.5%, p ≤ 0.002) and FLAT (2.4 to 4.0%, p ≤ 0.042) versus OWN across intensities, with a trivial difference between NIKE and FLAT (1.0 to 1.6%, p ≥ 0.325). NIKE 3-km TT (11:07.6 ± 0:56.6 mm:ss) was superior to OWN by 16.6 s (2.4%, p = 0.005) and FLAT by 13.0 s (1.8%, , p = 0.032), with similar times between OWN and FLAT (0.5%, , p = 0.747). Only 29% of runners were more economical across intensities and faster in NIKE. Conclusions: Overall, our findings indicate that NIKE could benefit RE in male recreational runners at relative speeds when compared to OWN, but not when compared to FLAT. More runners exhibited better TT performances in NIKE (61%) versus FLAT (22%) and OWN (17%). The high variability in individual RE (-3.1 to 12.1%) and TT (-3.8 to 8.2%) shoe-responses suggests that individualisation of running footwear prescription is warranted.
Income Targeting and Farm Labor Supply

There is considerable anecdotal evidence that farm workers who are paid by piece rate tend to “income target,” or work only until they achieve a certain amount of daily income, and then stop work. We estimate reduced‐form and structural models derived from the reference‐dependent preference model of Koszegi and Rabin (2006) to test the income‐targeting hypothesis using data from the National Agricultural Workers Survey (NAWS). We find evidence that supports the income‐targeting hypothesis, in both the reduced‐form and structural econometric models. Our findings suggest that even higher piece rates may not help the widely reported shortage of agricultural labor on the intensive margin as labor‐supply curves can be backward bending.
The Effect of Banks' IT Investments on the Digitalization of their Customers

Banks all over the world are investing in new banking technologies at a time when bank customers are progressively going digital in several dimensions of their economic and social interactions. Together with their existing perceptions of digital services, new banking technologies may path the way to accelerate the digitalization of bank customers, thereby achieving private and social efficiency gains. This paper exploits the fact that banks’ IT investments are mostly allocated to digital technologies to examine if such investments affect the digitalization of bank customers. The results show that banks’ IT investments have a significant positive impact on the adoption of financial digitalization by customers. Banks’ IT investments also increase the likelihood that bank customers undertake their financial transactions through digital channels rather than in the physical branch. This represents a change in the relationship banking channel. These findings shed light on the impact of banks’ IT investments on end‐users and not just on bank productivity and efficiency.
Contribution of Phase Resetting to Adaptive Rhythm Control in Human Walking Based on the Phase Response Curves of a Neuromusculoskeletal Model

Humans walk adaptively in varying environments by manipulating their complicated and redundant musculoskeletal system. Although the central pattern generators in the spinal cord are largely responsible for adaptive walking through sensory-motor coordination, it remains unclear what neural mechanisms determine walking adaptability. It has been reported that locomotor rhythm and phase are regulated by the production of phase shift and rhythm resetting (phase resetting) for periodic motor commands in response to sensory feedback and perturbation. While the phase resetting has been suggested to make a large contribution to adaptive walking, it has only been investigated based on fictive locomotion in decerebrate cats, and thus it remains unclear if human motor control has such a rhythm regulation mechanism during walking. In our previous work, we incorporated a phase resetting mechanism into a motor control model and demonstrated that it improves the stability and robustness of walking through forward dynamic simulations of a human musculoskeletal model. However, this did not necessarily verify that phase resetting plays a role in human motor control. In our other previous work, we used kinematic measurements of human walking to identify the phase response curve (PRC), which explains phase-dependent responses of a limit cycle oscillator to a perturbation. This revealed how human walking rhythm is regulated by perturbations. In this study, we integrated these two approaches using a physical model and identification of the PRC to examine the hypothesis that phase resetting plays a role in the control of walking rhythm in humans. More specifically, we calculated the PRC using our neuromusculoskeletal model in the same way as our previous human experiment. In particular, we compared the PRCs calculated from two different models with and without phase resetting while referring to the PRC for humans. As a result, although the PRC for the model without phase resetting did not show any characteristic shape, the PRC for the model with phase resetting showed a characteristic phase-dependent shape with trends similar to those of the PRC for humans. These results support our hypothesis and will improve our understanding of adaptive rhythm control in human walking.
Immunoregulatory Sensory Circuits in Group 3 Innate Lymphoid Cell (ILC3) Function and Tissue Homeostasis

Recent years have seen a revolution in our understanding of how cells of the immune system are modulated and regulated not only via complex interactions with other immune cells, but also through a range of potent inputs derived from diverse and varied biological systems. Within complex tissue environments, such as the gastrointestinal tract and lung, these systems act to orchestrate and temporally align immune responses, regulate cellular function, and ensure tissue homeostasis and protective immunity. Group 3 Innate Lymphoid Cells (ILC3s) are key sentinels of barrier tissue homeostasis and critical regulators of host-commensal mutualism—and respond rapidly to damage, inflammation and infection to restore tissue health. Recent findings place ILC3s as strategic integrators of environmental signals. As a consequence, ILC3s are ideally positioned to detect perturbations in cues derived from the environment—such as the diet and microbiota—as well as signals produced by the host nervous, endocrine and circadian systems. Together these cues act in concert to induce ILC3 effector function, and form critical sensory circuits that continually function to reinforce tissue homeostasis. In this review we will take a holistic, organismal view of ILC3 biology and explore the tissue sensory circuits that regulate ILC3 function and align ILC3 responses with changes within the intestinal environment.
Inferring lncRNA Functional Similarity Based on Integrating Heterogeneous Network Data

Although lncRNAs lack the potential to be translated into proteins directly, their complicated and diversiform functions make them as a window into decoding the mechanisms of human physiological activities. Accumulating experiment studies have identified associations between lncRNA dysfunction and many important complex diseases. However, known experimentally confirmed lncRNA functions are still very limited. It is urgent to build effective computational models for rapid predicting of unknown lncRNA functions on a large scale. To this end, valid similarity measure between known and unknown lncRNAs plays a vital role. In this paper, an original model was developed to calculate functional similarities between lncRNAs by integrating heterogeneous network data. In this model, a novel integrated network was constructed based on the data of four single lncRNA functional similarity networks (miRNA-based similarity network, disease-based similarity network, GTEx expression-based network and NONCODE expression-based network). Using the lncRNA pairs that share the target mRNAs as the benchmark, the results show that this integrated network is more effective than any single networks with an AUC of 0.736 in the cross validation, while the AUC of four single networks were 0.703, 0.733, 0.611, and 0.602. To implement our model, a web server named IHNLncSim was constructed for inferring lncRNA functional similarity based on integrating heterogeneous network data. Moreover, the modules of network visualization and disease-based lncRNA function enrichment analysis were added into IHNLncSim. It is anticipated that IHNLncSim could be an effective bioinformatics tool for the researches of lncRNA regulation function studies. IHNLncSim is freely available at http://www.lirmed. com/ihnlncsim.
Geomorphic Response to Differential Uplift: River Long Profiles and Knickpoints From Guadalcanal and Makira (Solomon Islands)

Knickpoints have long been recognized as key geomorphic features that can be used to reveal the landscape evolution of a region. In particular, mobile knickpoints resulting from relative base-level fall record a landscape in the process of change and can encode information about the timing and rate of landscape response. Here, digital elevation model analysis is undertaken to; (a) identify topographic lineaments related to active faulting, and (b) extract geomorphic metrics and document associated knickpoints for rivers on Guadalcanal and Makira (San Cristobal) part of the Solomon Island chain. These islands have been experiencing uplift of up to 2 mm/year since at least the mid Holocene on the upper (Pacific) plate of the San Cristobal Trench of the Solomon Island Forearc. For Guadalcanal, 23 out of 53 studied rivers exhibit slope-break knickpoints, characteristic of base-level fall, and 27 new topographic lineaments with ∼E-W orientation are identified. By contrast, on Makira 14 of 41 studied rivers have slope-break knickpoints, where the rivers are steeper below the knickpoint than above. In addition, 76 new lineaments are inferred, trending NE-SW and likely to be extensional faults. For both Guadalcanal and Makira there is a good correlation between knickpoint elevation/catchment area and distance upstream from the sea, and a weak correlation between relief and knickpoint elevation. There are no clear relationships between the knickpoints and the new topographic lineaments. These data indicate that both islands are undergoing active river incision caused by regional tectonic uplift along an active subduction zone. On Makira, river steepness (ksn) scales with uplift, and K, coefficient of erosion, is in the range 1 × 10–5 – 7 × 10–6 m0.1yr–1, while K can be estimated as 1 × 10–5 – 5 × 10–8 m0.1yr–1 for Guadalcanal. Calculation of K for steady-state rivers demonstrates a rock strength control on the fluvial response and highlights the importance of lithology on river evolution. Furthermore, the distinct landscape response of the two islands supports the hypothesis that there are different arc segments present along the Solomon Arc and suggests that the Holocene uplift rates for Guadalcanal may not be representative of long-term uplift.
Characterization of Acinetobacter baumannii Copper Resistance Reveals a Role in Virulence

Acinetobacter baumannii is often highly drug-resistant and causes severe infections in compromised patients. These infections can be life threatening due to limited treatment options. Copper is inherently antimicrobial and increasing evidence indicates that copper containing formulations may serve as non-traditional therapeutics against multidrug-resistant bacteria. We previously reported that A. baumannii is sensitive to high concentrations of copper. To understand A. baumannii copper resistance at the molecular level, herein we identified putative copper resistance components and characterized 21 strains bearing mutations in these genes. Eight of the strains displayed a copper sensitive phenotype (pcoA, pcoB, copB, copA/cueO, copR/cusR, copS/cusS, copC, copD); the putative functions of these proteins include copper transport, oxidation, sequestration, and regulation. Importantly, many of these mutant strains still showed increased sensitivity to copper while in a biofilm. Inductively coupled plasma mass spectrometry revealed that many of these strains had defects in copper mobilization, as the mutant strains accumulated more intracellular copper than the wild-type strain. Given the crucial antimicrobial role of copper-mediated killing employed by the immune system, virulence of these mutant strains was investigated in Galleria mellonella; many of the mutant strains were attenuated. Finally, the cusR and copD strains were also investigated in the murine pneumonia model; both were found to be important for full virulence. Thus, copper possesses antimicrobial activity against multidrug-resistant A. baumannii, and copper sensitivity is further increased when copper homeostasis mechanisms are interrupted. Importantly, these proteins are crucial for full virulence of A. baumannii and may represent novel drug targets.