Miracle-dz/UniFAR-FTUs huggingface dataset:
Concurrence of negative in-plane piezoelectricity and photocatalytic properties in 2D ScAgP2S6 monolayers

Apart from poly(vinylidene fluoride) and its copolymers, 2D multilayered CuInP2S6 (CIPS) is experimentally known to show negative out-of-plane piezoelectric coefficient (d 33) [2019 Sci. Adv. 5 1–10], which is attributable to weak interlayer van der Waals binding. However, CIPS monolayers exhibit positive in-plane piezoelectricity (d 11) [Phys. Status Solidi RRL, 14 2000321], as per DFT calculations. We ascribe it to strong in-plane interatomic bonding. Weakening of the in-plane interatomic bonding may even induce a negative in-plane piezoelectric coefficient, which is exceptionally rare, as observed in this work based on DFT calculations. Much smaller in-plane elastic constants found in ScAgP2S6 monolayers relative to that in CIPS monolayers indicate much weaker in-plane interatomic bonding, which induces negative in-plane piezoelectric coefficient, d 22 = −2.81 pm V−1, in ScAgP2S6 monolayers. In addition to its all-around stability, ScAgP2S6 monolayer is a semiconducting showing a bandgap of 2.04 eV, thereby making it suitable for electronics and solar energy harvesting. Upon the application of 4% biaxial strain on this flexible and stretchable 2D material, d 22 has been found to be drastically increased to −591 pm V−1. The factors causing such an enhancement in d 22 have been ascertained through the maximum rise in the Born effective charges and maximum drop in the elastic constants. With the further drop in elastic constants relative to ScAgP2S6 monolayer, d 22 reaches −3.45 pm V−1 and −13.48 pm V−1 in ScAgP2Se6 and ScAgP2Te6 monolayers respectively. 2D ScAgP2S6 monolayer is found to be a promising photocatalyst as well, since its band edges straddle the water redox potential over the entire range in pH (0–14), in addition to having a high absorption coefficient (≈105 cm−1) in the visible region. It is understood that 2D ScAgP2S6 will substantially expand the scope for its applications in piezoelectricity and visible light harvesting at the nanoscale.
The investigation of IL-1β and oxytocin levels among teenager with primary dysmenorrhea

Primary dysmenorrhea is one of gynecological problems and it commonly occurred among adolescents and reproductive age women during menstruation with some complaints such as pain, cramps and back pain. In primary dysmenorrhea there is an increase of PGF2α levels, vasopressin (VAP), Oxytocin (OT), IL-6 and IL-1β level. The aim of this study was assessed the differences of IL-1β levels and oxytocin among adolescents with primary dysmenorrhea and without primary dysmenorrhea. The research design in this study used Case Control. The population of this study were all students of Health Science Faculty, Unipdu Jombang. The sample of this study was 16 respondents, and they were divided into 2 groups: the case group (n = 8) and the control group (n = 8) that met the inclusion and exclusion criteria. Sampling method used Purposive Sampling technique. The measurement of IL-1β and Oxytocin levels used ELISA method. The data were analyzed by using Independent Sample T-Test with α ≤ 0.05. The results of this study showed that an average level of IL-1β in case group was 23.830 pg/ml, in control group was 14.715 pg/ml; the average OT levels in case group was 384.662 pg/ml, the control group was 353.262 pg/ml. Independent Sample T-Test showed that there was a significant differences of IL-1β and oxytocin level between case group and control group (p<0.05). IL-1β and Oxytocin (OT) Levels among adolescents with primary dysmenorrhea were higher than adolescents who menstruate without primary dysmenorrhea.
A Retrospective Cross-Sectional Study of the Utility of Cartridge-Based Nucleic Acid Amplification Test in Diagnosis of Pulmonary and Extrapulmonary Tuberculosis in People Living with HIV in Second Highest HIV Prevalent State in India

Abstract Introduction Tuberculosis (TB) in people living with human immunodeficiency virus (PLHIV) is difficult to diagnose due to fewer organisms in sputum and extrapulmonary samples. Sputum culture takes 4 to 8 weeks for growth of the mycobacteria. Delayed treatment for TB in PLHIV leads to increased mortality. This study evaluated cartridge-based nucleic acid amplification test (CBNAAT) as a diagnostic tool for diagnosis of pulmonary TB (PTB) and extrapulmonary TB (EPTB) in PLHIV in the second most HIV prevalent state in India and for comparing its efficacy between Ziehl–Neelsen (ZN) staining sputum smear–positive and sputum smear–negative TB. Methods This cross-sectional study was conducted in RIMS, Imphal, with 167 PLHIV patients, age 15 years or older, having signs and symptoms of TB. Appropriate samples for sputum microscopy and CBNAAT were sent. Conclusion The overall sensitivity of sputum smear for acid-fast bacillus (AFB) was found to be 30.71% and that of CBNAAT was 38.57%. Sensitivity of CBNAAT for sputum smear–positive and sputum smear–negative TB was 100 and 11.3%, respectively. Sensitivity of ZN smear for AFB of EPTB sample was 48.1% and that of CBNAAT was 59.25%. In both PTB and EPTB, CBNAAT showed an increase in diagnosis of microbiologically confirmed PTB cases by 7.8 and 11.1%, respectively, over and above the cases diagnosed by ZN smear microscopy. Rifampicin resistance was detected in five patients. We conclude that CBNAAT is a rapid test with better sensitivity in diagnosis of PTB and EPTB in PLHIV, compared with ZN smear microscopy. It detects rifampicin resistance for multidrug-resistant TB and helps in early treatment intervention.
Sensitivity Analysis with respect to a Stock Price Model with Rough Volatility via a Bismut-Elworthy-Li Formula for Singular SDEs

In this paper, we show the existence of unique Malliavin differentiable solutions to SDE‘s driven by a fractional Brownian motion with Hurst parameter H < 12 and singular, unbounded drift vector fields, for which we also prove a stability result. Further, using the latter results, we propose a stock price model with rough and correlated volatility, which also allows for capturing regime switching effects. Finally, we also derive a Bismut-Elworthy-Li formula with respect to our stock price model for certain classes of vector fields.
How to Manage Output Uncertainty: Targeting the Actual End User Problem in Interactions with AI

Given the opaqueness and complexity of modern AI algorithms, there is currently a strong focus on developing transparent and explainable AI, especially in high-stakes domains. We claim that opaqueness and complexity are not the core issues for end users when interacting with AI. Instead, we propose that the output uncertainty inherent to AI systems is the actual problem, with opaqueness and complexity as contributing factors. Transparency and explainability should therefore not be the end goals, as such a focus tends to place the human into a passive supervisory role in what is in reality an algorithm-centered system design. To enable effective management of output uncertainty, we believe it is necessary to focus on truly human-centered AI designs that keep the human in an active role of control. We discuss the conceptual implications of such a shift in focus and give examples from literature to illustrate the more holistic, interactive designs that we envision.
Carcass characteristics, meat quality and composition of lambs finished in cultivated pastures

The use of cultivated pastures is a strategy for feeding sheep, as it contributes to a reduction in seasonality and production costs (Emerenciano et al., 2017). In these cultivated systems, grasses of the genera Brachiaria brizantha (Syn. Urochloa brizantha) and Panicum maximum (Syn. Megathyrsus maximus) are predominantly used due to their adaptation to tropical and subtropical climates and high productivity (Gomes et al., 2011; Veras et al., 2020). This can be an alternative for the production of early carcasses with an adequate amount of muscle and good fat deposition. However, the wide variety of cultivars available can show considerable variations in productivity and nutritional value (Emerenciano et al., 2020; Fernandes et al., 2020), all of which can interfere with animal performance and, consequently, the quality of the carcass and meat.
Evidence for Centrifugal Breakout around the Young M Dwarf TIC 234284556

Magnetospheric clouds have been proposed as explanations for depth-varying dips in the phased light curves of young, magnetically active stars such as σ Ori E and RIK-210. However, the stellar theory that first predicted magnetospheric clouds also anticipated an associated mass-balancing mechanism known as centrifugal breakout for which there has been limited empirical evidence. In this paper, we present data from the Transiting Exoplanet Survey Satellite, Las Cumbres Observatory, All-Sky Automated Survey for Supernovae, and Veloce on the 45 Myr M3.5 star TIC 234284556, and propose that it is a candidate for the direct detection of centrifugal breakout. In assessing this hypothesis, we examine the sudden (∼1 day timescale) disappearance of a previously stable (∼1 month timescale) transit-like event. We also interpret the presence of an anomalous brightening event that precedes the disappearance of the signal, analyze rotational amplitudes and optical flaring as a proxy for magnetic activity, and estimate the mass of gas and dust present immediately prior to the potential breakout event. After demonstrating that our spectral and photometric data support a magnetospheric cloud and centrifugal breakout model and disfavor alternate scenarios, we discuss the possibility of a coronal mass ejection or stellar wind origin of the corotating material and we introduce a reionization mechanism as a potential explanation for more gradual variations in eclipse parameters. Finally, after comparing TIC 234284556 with previously identified “flux-dip” stars, we argue that TIC 234284556 may be an archetypal representative of a whole class of young, magnetically active stars.
Automated Learning Rate Scheduler for Large-batch Training

Large-batch training has been essential in leveraging large-scale datasets and models in deep learning. While it is computationally beneficial to use large batch sizes, it often requires a specially designed learning rate (LR) schedule to achieve a comparable level of performance as in smaller batch training. Especially, when the number of training epochs is constrained, the use of a large LR and a warmup strategy is critical in the final performance of large-batch training due to the reduced number of updating steps. In this work, we propose an automated LR scheduling algorithm which is effective for neural network training with a large batch size under the given epoch budget. In specific, the whole schedule consists of two phases: adaptive warmup and predefined decay, where the LR is increased until the training loss no longer decreases and decreased to zero until the end of training. Here, whether the training loss has reached the minimum value is robustly checked with Gaussian process smoothing in an online manner with a low computational burden. Coupled with adaptive stochastic optimizers such as AdamP and LAMB, the proposed scheduler successfully adjusts the LRs without cumbersome hyperparameter tuning and achieves comparable or better performances than tuned baselines on various image classification benchmarks and architectures with a wide range of batch sizes.
Learnability of Learning Performance and Its Application to Data Valuation

For most machine learning (ML) tasks, evaluating learning performance on a given dataset requires intensive computation. On the other hand, the ability to efﬁciently estimate learning performance may beneﬁt a wide spectrum of applications, such as active learning, data quality management, and data valuation. Recent empirical studies show that for many common ML models, one can accurately learn a parametric model that predicts learning performance for any given input datasets using small amount of samples. However, the theoretical underpinning of the learnability of such performance prediction models is still missing. In this work, we develop the ﬁrst theoretical analysis of the ML performance learning problem. We propose a relaxed notion for submodularity that can well describe the behavior of learning performance as a function of input datasets. We give a learning algorithm that achieves a constant-factor approximation under certain assumptions. Further, we give a learning algorithm that achieves arbitrarily small error based on a newly derived structural result. We then discuss a natural, important use case of learning performance learning — data valuation, which is known to suffer computational challenges due to the requirement of estimating learning performance for many data combinations. We show that performance learning can signiﬁcantly improve the accuracy of data valuation.
Codified audio language modeling learns useful representations for music information retrieval

We demonstrate that language models pre-trained on codified (discretely-encoded) music audio learn representations that are useful for downstream MIR tasks. Specifically, we explore representations from Jukebox (Dhariwal et al. 2020): a music generation system containing a language model trained on codified audio from 1M songs. To determine if Jukebox's representations contain useful information for MIR, we use them as input features to train shallow models on several MIR tasks. Relative to representations from conventional MIR models which are pre-trained on tagging, we find that using representations from Jukebox as input features yields 30% stronger performance on average across four MIR tasks: tagging, genre classification, emotion recognition, and key detection. For key detection, we observe that representations from Jukebox are considerably stronger than those from models pre-trained on tagging, suggesting that pre-training via codified audio language modeling may address blind spots in conventional approaches. We interpret the strength of Jukebox's representations as evidence that modeling audio instead of tags provides richer representations for MIR.
ETH Tight Algorithms for Geometric Intersection Graphs: Now in Polynomial Space

De Berg et al. in [SICOMP 2020] gave an algorithmic framework for subexponential algorithms on geometric graphs with tight (up to ETH) running times. This framework is based on dynamic programming on graphs of weighted treewidth resulting in algorithms that use super-polynomial space. We introduce the notion of weighted treedepth and use it to refine the framework of de Berg et al. for obtaining polynomial space (with tight running times) on geometric graphs. As a result, we prove that for any fixed dimension $d \ge 2$ on intersection graphs of similarly-sized fat objects many well-known graph problems including Independent Set, $r$-Dominating Set for constant $r$, Cycle Cover, Hamiltonian Cycle, Hamiltonian Path, Steiner Tree, Connected Vertex Cover, Feedback Vertex Set, and (Connected) Odd Cycle Transversal are solvable in time $2^{O(n^{1-1/d})}$ and within polynomial space.
High-resolution spectroscopic follow-up of the most metal-poor candidates from SkyMapper DR1.1

We present chemical abundances for 21 elements (from Li to Eu) in 150 metal-poor Galactic stars spanning −4.1 < [Fe/H] < −2.1. The targets were selected from the SkyMapper survey and include 90 objects with [Fe/H] ≤ −3 of which some 15 have [Fe/H] ≤ −3.5. When combining the sample with our previous studies, we find that the metallicity distribution function has a power-law slope of Δ(log N)/Δ[Fe/H] = 1.51 ± 0.01 dex per dex over the range −4 ≤ [Fe/H] ≤ −3. With only seven carbon-enhanced metal-poor stars in the sample, we again find that the selection of metal-poor stars based on SkyMapper filters is biased against highly carbon-rich stars for [Fe/H] > −3.5. Of the 20 objects for which we could measure nitrogen, 11 are nitrogen-enhanced metal-poor (NEMP) stars. Within our sample, the high NEMP fraction (55 per cent ± 21 per cent) is compatible with the upper range of predicted values (between 12 per cent and 35 per cent). The chemical abundance ratios [X/Fe] versus [Fe/H] exhibit similar trends to previous studies of metal-poor stars and Galactic chemical evolution models. We report the discovery of nine new r-I stars, four new r-II stars, one of which is the most metal-poor known, nine low-α stars with [α/Fe] ≤ 0.15 as well as one unusual star with [Zn/Fe] = +1.4 and [Sr/Fe] = +1.2 but with normal [Ba/Fe]. Finally, we combine our sample with literature data to provide the most extensive view of the early chemical enrichment of the Milky Way Galaxy.
Safety and progress proofs for a reactive planner and controller for autonomous driving

In this paper, we perform safety and performance analysis of an autonomous vehicle that implements reactive planner and controller for navigating a race lap. Unlike traditional planning algorithms that have access to a map of the environment, reactive planner generates the plan purely based on the current input from sensors. Our reactive planner selects a waypoint on the local Voronoi diagram and we use a pure-pursuit controller to navigate towards the waypoint. Our safety and performance analysis has two parts. The first part demonstrates that the reactive planner computes a plan that is locally consistent with the Voronoi plan computed with full map. The second part involves modeling of the evolution of vehicle navigating along the Voronoi diagram as a hybrid automata. For proving the safety and performance specification, we compute the reachable set of this hybrid automata and employ some enhancements that make this computation easier. We demonstrate that an autonomous vehicle implementing our reactive planner and controller is safe and successfully completes a lap for five different circuits. In addition, we have implemented our planner and controller in a simulation environment as well as a scaled down autonomous vehicle and demonstrate that our planner works well for a wide variety of circuits.
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions

Generative Adversarial Networks (GANs) are commonly used for modeling complex distributions of data. Both the generators and discriminators of GANs are often modeled by neural networks, posing a non-transparent optimization problem which is non-convex and non-concave over the generator and discriminator, respectively. Such networks are often heuristically optimized with gradient descent-ascent (GDA), but it is unclear whether the optimization problem contains any saddle points, or whether heuristic methods can find them in practice. In this work, we analyze the training of Wasserstein GANs with two-layer neural network discriminators through the lens of convex duality, and for a variety of generators expose the conditions under which Wasserstein GANs can be solved exactly with convex optimization approaches, or can be represented as convex-concave games. Using this convex duality interpretation, we further demonstrate the impact of different activation functions of the discriminator. Our observations are verified with numerical results demonstrating the power of the convex interpretation, with applications in progressive training of convex architectures corresponding to linear generators and quadratic-activation discriminators for CelebA image generation. The code for our experiments is available at https://github.com/ardasahiner/ProCoGAN.
ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer

We propose ST-DETR, a Spatio-Temporal Transformer-based architecture for object detection from a sequence of temporal frames. We treat the temporal frames as sequences in both space and time and employ the full attention mechanisms to take advantage of the features correlations over both dimensions. This treatment enables us to deal with frames sequence as temporal object features traces over every location in the space. We explore two possible approaches; the early spatial features aggregation over the temporal dimension, and the late temporal aggregation of object query spatial features. Moreover, we propose a novel Temporal Positional Embedding technique to encode the time sequence information. To evaluate our approach, we choose the Moving Object Detection (MOD)task, since it is a perfect candidate to showcase the importance of the temporal dimension. Results show a significant 5% mAP improvement on the KITTI MOD dataset over the 1-step spatial baseline.
Computational Fluid Dynamics Analysis of Flow Patterns, Pressure Drop, and Heat Transfer Coefficient in Staggered and Inline Shell-Tube Heat Exchangers

In this numerical study, the heat transfer performance of shell-and-tube heat exchangers (STHXs) has been compared for two different tube arrangements. STHX having 21 and 24 tubes arranged in the inline and staggered grid has been considered for heat transfer analysis. Shell-and-tube heat exchanger with staggered grid arrangement has been observed to provide lesser thermal stratification as compared to the inline arrangement. Further, the study of variation in the mass flow rate of shell-side fluid having constant tube-side flow rate has been conducted for staggered grid structure STHX. The mass flow rate for the shell side has been varied from 0.1 kg/s to 0.5 kg/s, respectively, keeping the tube-side mass flow rate as constant at 0.25 kg/s. The influence of bulk mass-influx transfer rate on heat transfer efficiency, effectiveness, and pressure drop of shell-tube heat exchangers has been analyzed. CFD results were compared with analytical solutions, and it shows a good agreement between them. It has been observed that pressure drop is minimum for the flow rate of 0.1 kg/s, and outlet temperatures at the shell side and tube side have been predicted to be 40.94°C and 63.63°C, respectively.
First-order Logic with Connectivity Operators

First-order logic (FO) can express many algorithmic problems on graphs, such as the independent set and dominating set problem parameterized by solution size. However, FO cannot express the very simple algorithmic question whether two vertices are connected. We enrich FO with connectivity predicates that are tailored to express algorithmic graph problems that are commonly studied in parameterized algorithmics. By adding the atomic predicates connk(x,y,z_1,., zk) that hold true in a graph if there exists a path between (the valuations of) x and y after (the valuations of) z1,. , zk have been deleted, we obtain separator logic FO + conn. We show that separator logic can express many interesting problems, such as the feedback vertex set problem and elimination distance problems to first-order definable classes. Denote by FO + connk the fragment of separator logic that is restricted to connectivity predicates with at most k + 2 variables (that is, at most k deletions), we show that FO + connk + 1 is strictly more expressive than FO + connk for all k ≥ 0. We then study the limitations of separator logic and prove that it cannot express planarity, and, in particular, not the disjoint paths problem. We obtain the stronger disjoint-paths logic FO + DP by adding the atomic predicates disjoint-pathsk[(x1, y1),. , (xk, yk) that evaluate to true if there are internally vertex-disjoint paths between (the valuations of) xi and yi for all 1 ≤ i ≤ k. Disjoint-paths logic can express the disjoint paths problem, the problem of (topological) minor containment, the problem of hitting (topological) minors, and many more. Again, we show that the fragments FO + DPk that use predicates for at most k disjoint paths form a strict hierarchy of expressiveness. Finally, we compare the expressive power of the new logics with that of transitive-closure logics and monadic second-order logic.