Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemometric profiling of methanolic leaf extract of Cnidoscolus aconitifolius (Euphorbiaceae) using UV-VIS, FTIR and GC-MS techniques

The leaf of Cnidoscolus aconitifolius (Euphorbiaceae) was reported to be an important food crop and medicinal plant. The aim of the current study was to characterize the bioactive constituents present in methanolic leaf extracts of C. aconitifolius which will be useful for its proper identification. Chemometric profile of methanolic leaf extract of C. aconitifolius was performed by using ultraviolet-visible spectrophotometry (UV-VIS) (UV-2500PC spec.), Fourier transform infrared spectroscopy (FTIR) (Model-8400S spec.) and GC-MS (Model-QP2010 plus spec.). The compound detection employed the NIST Ver. 2.0-Year 2005 library. The biological activities are based on Dr. Duke’s phytochemical and ethnobotanical databases. The UV-VIS profile showed peaks corresponding to phenolic compounds and flavonoids or their derivatives. The FTIR spectrum confirmed the presence of alcohols, alkanes, alkenes, and alkynes. The chemometric profile of the methanolic leaf extract of C. aconitifolius revealed 10 phyto-chemotypes with different therapeutic activities; 9-Octadecenoic acid (Z) (53.97%) was the prevailing compound. The presence of some of these phyto-constituents in the plant will be useful in differentiating the specie from adulterants, act as a biochemical marker and also provides the scientific basis for the therapeutic properties of the plant and thus recommended as a plant of phyto-pharmaceutical importance.
Experiments on causal exclusion

Intuitions play an important role in the debate on the causal status of high-level properties. For instance, Kim has claimed that his “ exclusion argument ” relies on “ a perfectly intuitive … understanding of the causal relation. ” We report the results of three experiments examining whether laypeople really have the relevant intuitions. We find little support for Kim's view and the principles on which it relies. Instead, we find that laypeople are willing to count both a multiply realized property and its realizers as causes, and regard the systematic overdetermination implied by this view as unproblematic.
The relationship between relative aerobic load, energy cost, and speed of walking in individuals post-stroke

Background: Individuals post-stroke walk slower than their able-bodied peers, which limits participation. This might be attributed to neurological impairments, but could also be caused by a mismatch between aerobic capacity and aerobic load of walking. Research question: What is the potential impact of aerobic capacity and aerobic load of walking on walking ability post-stroke? Methods: In a cross-sectional study, forty individuals post-stroke (more impaired N=21; preferred walking speed (PWS)<0.8m/s, less impaired N=19), and 15 able-bodied individuals performed five, 5-minute treadmill walking trials at 70%, 85%, 100%, 115% and 130% of PWS. Energy expenditure (mlO2/kg/min) and energy cost (mlO2/kg/m) were derived from oxygen uptake (VO2). Relative load was defined as energy expenditure divided by peak aerobic capacity (%VO2peak) and by VO2 at ventilatory threshold (%VO2-VT). Relative load and energy cost at PWS were compared between groups with one-way ANOVAs. The effect of speed on these parameters was modeled with GEE. Results: Both more and less impaired individuals post-stroke showed lower PWS than able-bodied controls (0.44[0.19-0.76] and 1.04[0.81-1.43] vs 1.36[0.89-1.53] m/s) and higher relative load at PWS (50.2{+/-}14.4 and 51.7{+/-}16.8 vs 36.2{+/-}7.6 %VO2 peak and 101.9{+/-}20.5 and 97.0{+/-}27.3 vs 64.9{+/-}13.8 %VO2-VT). No differences in relative load were found between stroke groups. Energy cost at PWS of more impaired (0.30[.19-1.03] mlO2/kg/m) was higher than less-impaired (0.19[0.10-0.24] mlO2/kg/m) and able-bodied (0.15[0.13-0.18] mlO2/kg/m). For post-stroke individuals, increasing walking speed above PWS decreased energy cost, but resulted in a relative load above endurance threshold. Significance: Individuals post-stroke seem to reduce walking speed to prevent unsustainably high relative aerobic loads at the expense of reduced economy. When aiming to improve walking ability in individuals post-stroke, it is important to consider training aerobic capacity.
Fairness in Ranking: A Survey

In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. In this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking. Then, we present methods that incorporate fairness in supervised learning, and also give representative examples of recent work on fairness in recommendation and matchmaking systems. We also discuss evaluation frameworks for fair score-based ranking and fair learning-to-rank, and draw a set of recommendations for the evaluation of fair ranking methods.
Theory of Dirac Spin-Orbital Liquids: monopoles, anomalies, and applications to $SU(4)$ honeycomb models

Dirac spin liquids represent a class of highly-entangled quantum phases in two dimensional Mott insulators, featuring exotic properties such as critical correlation functions and absence of well-defined low energy quasi-particles. Existing numerical works suggest that the spin-orbital $SU(4)$ symmetric Kugel-Khomskii model of Mott insulators on the honeycomb lattice realizes a Dirac spin-orbital liquid, described at low energy by $(2+1)d$ quantum electrodynamics (QED$_3$) with $N_f=8$ Dirac fermions. We generalize methods previously developed for $SU(2)$ spin systems to analyze the symmetry properties and stability of the Dirac spin-orbital liquid. We conclude that the standard Dirac state in the $SU(4)$ honeycomb system, based on a simple parton mean-field ansatz, is intrinsically unstable at low energy due to the existence of a monopole perturbation that is allowed by physical symmetries and relevant under renormalization group flow. We propose two plausible alternative scenarios compatible with existing numerics. In the first scenario, we propose an alternative $U(1)$ Dirac spin-orbital liquid, which is similar to the standard one except for its monopole symmetry quantum numbers. This alternative $U(1)$ state represents a stable gapless phase. In the second scenario, we start from the standard $U(1)$ Dirac liquid and Higgs the $U(1)$ gauge symmetry down to $\mathbb{Z}_4$. The resulting $\mathbb{Z}_4$ Dirac spin-orbital liquid is stable. We also discuss the continuous quantum phase transitions from the $\mathbb{Z}_4$ Dirac liquids to conventional symmetry-breaking orders, described by the QED$_3$ theory with $N_f=8$ supplemented with a critical charge-$4$ Higgs field. We discuss possible ways to distinguish these scenarios in numerics. We also extend previous calculations of the quantum anomalies of QED$_3$ and match with generalized lattice Lieb-Schultz-Mattis constraints.
A Broad Study on the Transferability of Visual Representations with Contrastive Learning

Tremendous progress has been made in visual representation learning, notably with the recent success of self-supervised contrastive learning methods. Supervised contrastive learning has also been shown to outperform its cross-entropy counterparts by leveraging labels for choosing where to contrast. However, there has been little work to explore the transfer capability of contrastive learning to a different domain. In this paper, we conduct a comprehensive study on the transferability of learned representations of different contrastive approaches for linear evaluation, full-network transfer, and few-shot recognition on 12 downstream datasets from different domains, and object detection tasks on MSCOCO and VOC0712. The results show that the contrastive approaches learn representations that are easily transferable to a different downstream task. We further observe that the joint objective of self-supervised contrastive loss with cross-entropy/supervised-contrastive loss leads to better transferability of these models over their supervised counterparts. Our analysis reveals that the representations learned from the contrastive approaches contain more low/mid-level semantics than cross-entropy models, which enables them to quickly adapt to a new task. Our codes and models will be publicly available to facilitate future research on transferability of visual representations.1
Recovering Quantitative Models of Human Information Processing with Differentiable Architecture Search

The integration of behavioral phenomena into mechanistic models of cognitive function is a fundamental staple of cognitive science. Yet, researchers are beginning to accumulate increasing amounts of data without having the temporal or monetary resources to integrate these data into scientiﬁc the- ories. We seek to overcome these limitations by incorporat-ing existing machine learning techniques into an open-source pipeline for the automated construction of quantitative models. This pipeline leverages the use of neural architecture search to automate the discovery of interpretable model architectures, and automatic differentiation to automate the ﬁtting of model parameters to data. We evaluate the utility of these methods based on their ability to recover quantitative models of human information processing from synthetic data. We ﬁnd that these methods are capable of recovering basic quantitative motifs from models of psychophysics, learning and decision making. We also highlight weaknesses of this framework and discuss future directions for their mitigation.
Examining mobility data justice during 2017 Hurricane Harvey

Natural disasters can significantly disrupt human mobility in urban areas. Studies have attempted to understand and quantify such disruptions using crowdsourced mobility data sets. However, limited research has studied the justice issues of mobility data in the context of natural disasters. The lack of research leaves us without an empirical foundation to quantify and control the possible biases in the data. This study, using 2017 Hurricane Harvey as a case study, explores three aspects of mobility data that could potentially cause injustice: representativeness, quality, and precision. We find representativeness being a major factor contributing to mobility data injustice. There is a persistent disparity of representativeness across neighborhoods of different socioeconomic characteristics before, during, and after the hurricane's landfall. Additionally, we observed significant drops of data precision during the hurricane, adding uncertainty to locate people and understand their movements during extreme weather events. The findings highlight the necessity in understanding and controlling the possible bias of mobility data as well as developing practical tools through data justice lenses in collecting and analyzing data during disasters.
Understanding X-ray and optical selection of galaxy clusters: a comparison of the XXL and CAMIRA cluster catalogues obtained in the common XXL-HSC SSP area

Large samples of galaxy clusters provide knowledge of both astrophysics in the most massive virialised environments and the properties of the cosmological model that defines our Universe. However, an important issue that affects the interpretation of galaxy cluster samples is the role played by the selection waveband and the potential for this to introduce a bias in the physical properties of clusters thus selected. We aim to investigate waveband-dependent selection effects in the identification of galaxy clusters by comparing the X-ray Multi-Mirror (XMM) Ultimate Extra-galactic Survey (XXL) and Subaru Hyper Suprime-Cam (HSC) CAMIRA cluster samples identified from a common 22.6 deg2 sky area. We compare 150 XXL and 270 CAMIRA clusters in a common parameter space defined by X-ray aperture brightness and optical richness. We find that 71/150 XXL clusters are matched to the location of a CAMIRA cluster, the majority of which (67/71) display richness values N > 15 that exceed the CAMIRA catalogue richness threshold. We find that 67/270 CAMIRA clusters are matched to the location of an XXL cluster (defined within XXL as an extended X-ray source). Of the unmatched CAMIRA clusters, the majority display low X-ray fluxes consistent with the lack of an XXL counterpart. However, a significant fraction (64/107) CAMIRA clusters that display high X-ray fluxes are not asociated with an extended source in the XXL catalogue. We demonstrate that this disparity arises from a variety of effects including the morphological criteria employed to identify X-ray clusters and the properties of the XMM PSF.
The effect of oxygen on the photobleaching of hematoporphrin monomethyl ether (HMME) in different solutions

To analyse the effect of oxygen on the photobleaching of HMME, photobleaching rates of HMME when bubbled with oxygen or argon, added with singlet oxygen quenchers or hydroxyl radical quencher were compared. According to the solvents in which HMME was dissolved, four groups were set, HMME-DMSO, HMME-PBS, HMME-Albumin buffer, HMME-Cells suspension. Each group was divided into four subgroups, bubbled with oxygen or argon, added with singlet oxygen quenchers or hydroxyl radical quencher respectively. Spectra measurement was performed at 10min intervals for a period of 40 min before and after laser irradiation, photobleaching rates under different condition were compared. Photobleaching was the fastest when bubbled with oxygen, apparentely inhibited when bubbled with argon or added with singlet oxygen quenchers. Hydroxyl radical quencher can inhibit photopleaching of HMME slightly, but its inhibition was gentler than that of singlet oxygen quenchers and argon. Oxygen plays an inportant role in thre photobleaching of HMME, and the process is singlet oxygen mediated self-sensitized photooxidation.
Harvested Power Region of Two-user MISO WPT Systems With Non-linear EH Nodes

In this paper, we determine the harvested power region of a two-user multiple-input single-output (MISO) wireless power transfer (WPT) system for a non-linear model of the rectennas at the energy harvester (EH) nodes. To this end, we characterize the distributions of the transmit symbol vector that achieve individual points on the boundary of this region. Each distribution is obtained as solution of an optimization problem where we maximize a weighted sum of the average harvested powers at the EH nodes under a constraint on the power budget of the transmitter. We prove that the optimal transmit strategy employs two beamforming vectors and scalar unit norm transmit symbols with arbitrary phase. To determine the beamforming vectors, we propose an iterative algorithm based on a two-dimensional grid search, semi-definite relaxation, and successive convex approximation. Our numerical results reveal that the proposed design outperforms two baseline schemes based on a linear EH model and a single beamforming vector, respectively. Finally, we observe that the harvested power region is convex and the power harvested at one EH node can be traded for a higher harvested power at the other node.
Subspectral Normalization for Neural Audio Data Processing

Convolutional Neural Networks are widely used in various machine learning domains. In image processing, the features can be obtained by applying 2D convolution to all spatial dimensions of the input. However, in the audio case, frequency domain input like Mel-Spectrogram has different and unique characteristics in the frequency dimension. Thus, there is a need for a method that allows the 2D convolution layer to handle the frequency dimension differently. In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group. SSN also includes an affine transformation that can be applied to each group. Our method removes the inter-frequency deflection while the network learns a frequency-aware characteristic. In the experiments with audio data, we observed that SSN can efficiently improve the network’s performance.
Higher codimension behavior in equivariant Iwasawa theory for CM-fields

In classical Iwasawa theory, we mainly study codimension one behavior of arithmetic modules. Relatively recently, F. M. Bleher, T. Chinburg, R. Greenberg, M. Kakde, G. Pappas, R. Sharifi, and M. J. Taylor started studying higher codimension behavior of unramified Iwasawa modules which are conjectured to be pseudo-null. In this paper, by developing a general algebraic theory on perfect complexes, we obtain a new perspective of their work. That allows us to extend the results to equivariant settings and, even in non-equivariant settings, to obtain more refined results concerning the higher codimension behavior.
Progressive-X+: Clustering in the Consensus Space

We propose Progressive-X + , a new algorithm for ﬁnding an unknown number of geometric models, e.g . , homo-graphies. The problem is formalized as ﬁnding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems. Also, we propose a sampler reﬂecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively growing neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include using multiple homogra-phies to estimate relative poses for global SfM; pose estimation from generalized homographies; and trajectory estimation of fast-moving objects.
Deepfake Forensics via an Adversarial Game

With the progress in AI-based facial forgery (i.e., deepfake), people are concerned about its abuse. Albeit effort has been made for training models to recognize such forgeries, existing models suffer from poor generalization to unseen forgery technologies and high sensitivity to changes in image/video quality. In this paper, we advocate robust training for improving the generalization ability. We believe training with samples that are adversarially crafted to attack the classification models improves the generalization ability considerably. Considering that AI-based face manipulation often leads to high-frequency artifacts that can be easily spotted (by models) yet difficult to generalize, we further propose a new adversarial training method that attempts to blur out these artifacts, by introducing pixel-wise Gaussian blurring. Plenty of empirical evidence show that, with adversarial training, models are forced to learn more discriminative and generalizable features. Our code: https://github.com/ah651/deepfake_adv.
Narrowband Observations of Comet 46P/Wirtanen during Its Exceptional Apparition of 2018/19. II. Photometry, Jet Morphology, and Modeling Results

We report on our extensive photometry and imaging of comet 46P/Wirtanen during its 2018/19 apparition and use these data to constrain the modeling of Wirtanen’s activity. Narrowband photometry was obtained in 9 epochs from 2018 October through 2019 March as well as 10 epochs during the 1991, 1997, and 2008 apparitions. The ensemble photometry reveals a typical composition and a secular decrease in activity since 1991. Production rates were roughly symmetric around perihelion for the carbon-bearing species (CN, C3, and C2), but steeper for OH and NH outbound. Our imaging program emphasized CN, whose coma morphology and lightcurve yielded rotation periods reported in a companion paper (Farnham et al. 2021). Here, we compare the gas and dust morphology on the 18 nights for which observations of additional species were obtained. The carbon-bearing species exhibited similar morphology that varied with rotation. OH and NH had broad, hemispheric brightness enhancements in the tailward direction that did not change significantly with rotation, which we attribute to their originating from a substantial icy grain component. We constructed a Monte Carlo model that replicates the shape, motion, and brightness distribution of the CN coma throughout the apparition with a single, self-consistent solution in principal axis rotation. Our model yields a pole having (R.A., decl.) = 319°, −5° (pole obliquity of 70°) and two large sources (radii of 50° and 40°) centered at near-equatorial latitudes and separated in longitude by ∼160°. Applications of the model to explain observed behaviors are discussed.
Nonlinear inviscid damping and shear‐buoyancy instability in the two‐dimensional Boussinesq equations

We investigate the long‐time properties of the two‐dimensional inviscid Boussinesq equations near a stably stratified Couette flow, for an initial Gevrey perturbation of size ε. Under the classical Miles‐Howard stability condition on the Richardson number, we prove that the system experiences a shear‐buoyancy instability: the density variation and velocity undergo an O(t−1/2)$O(t^{-1/2})$ inviscid damping while the vorticity and density gradient grow as O(t1/2)$O(t^{1/2})$ . The result holds at least until the natural, nonlinear timescale t≈ε−2$t \approx \varepsilon ^{-2}$ . Notice that the density behaves very differently from a passive scalar, as can be seen from the inviscid damping and slower gradient growth. The proof relies on several ingredients: (A) a suitable symmetrization that makes the linear terms amenable to energy methods and takes into account the classical Miles‐Howard spectral stability condition; (B) a variation of the Fourier time‐dependent energy method introduced for the inviscid, homogeneous Couette flow problem developed on a toy model adapted to the Boussinesq equations, that is, tracking the potential nonlinear echo chains in the symmetrized variables despite the vorticity growth.
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents

Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.