Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of T169V and P172H Mutations in the Bile Salt Hydrolase (BSH) from Lactobacillus plantarum B14 and Biochemical Analysis of the Mutant BSHs

Bile Salt Hydrolase (BSH), synthesized by some intestinal bacteria, has an important role in the host metabolism relating with lipid absorption, energy homeostasis and glucose metabolism. Glycine and taurine in bile salts can be liberated by BSH and this process is called deconjugation. Deconjugation property of BSH exerts health benefits to the host such as reduction of blood cholesterol level. Moreover, using BSH active stains as an alternative to Antibiotic Growth Promoter (AGP) is under research. Because of the link between BSH and human health, the functional and biochemical features of BSHs necessitate an intensive study. Although BSHs exhibit variations in their catalytic activities, in addition to the five conserved amino acids, threonine-169 (T169) and proline-172 (P172) amino acids are also strictly conserved in all members of the N-terminal nucleophile (Ntn) hydrolase superfamily. However, their functions remain to be elucidated. In order to analyze the correlation between these two conserved amino acids and catalytic activity or stability of BSH, the small T169 and P172 amino acids of BSH enzyme were substituted for the aliphatic valine-169 (V169) and aromatic histidine-172 (H172) amino acids respectively by PCR-based site directed mutagenesis. Then the mutant recombinant BSHs were expressed in Escherichia coli BLR (DE3). While the effects of the mutations on catalytic activity were detected by the ninhydrin assay, the effects of the mutations on formation of the BSHs were visualized by SDS-PAGE analysis. The results showed that T169V mutation decreased the activity of BSH. Distinctly; inactive enzyme was obtained from P172H mutant. Meanwhile, assembly and folding of the BSH proteins were not affected by these mutations. Our finding demonstrated that these two amino acids might be responsible for catalytic activity or substrate binding of BSH enzyme but not for stability of it.
Understanding equivalence trials (and why we should care)

In the April 2000 issue of CJEM, Wood and colleagues compared titrated IV meperidine to single-dose IV ketorolac in patients with acute renal colic. They achieved successful pain relief in 72% of patients who were administered ketorolac vs. 64% of patients who were administered meperidine, yet concluded “equivalence. ” Why? To compound the confusion, they indicate that they tested the nullhypothesis that meperidine is 20% better than ketorolac. But isn’t that backwards? Isn’t the “null hypothesis” that there is no difference between groups? What we have here is a different animal — an equivalence trial — and you’ll be seeing many more of these in the future.
Konstruksi Komponen Intellectual Capital untuk Perguruan Tinggi di Indonesia

The purpose of this study is to construct a concept of intellectual capital (IC) components that relevant to Indonesian universities. This concept will very useful for universities to disclose their information on IC, both in their website and annual report. This concept will useful for researchers to assess the extent of information on IC that disclosed by universities. This study based on Leitner ’s framework (2002) that has been widely used in research on university’s IC. The methods used in this study are documentation, interview, and peer examination. The interview was conducted with UMM internal stakeholders which has a direct relationship with the university management of IC. Besides, interview also conducted with BAN-Dikti’s assessors to accelerate between components of IC with accreditation standards BAN-PT . The result show that this study success to construct a set of component of IC that relevant for Indonesian universities. The resulting number of IC components is 46 items, 7 items more than IC component composed by Leitner (2002). Normal 0 false false false EN-US X-NONE X-NONE
Title How supervisors respond to employee voice: an experimental study in China and Japan

To examine how supervisors respond to employee voice in the Asian context, we conducted a scenario-based experiment in China and Japan and investigated the effects of the target of voice (immediate supervisors or skip-level leaders), voicing employees’ expertise, and supervisors’ sense of power on supervisors’ responses. Data from both samples showed that voicing employees’ target of voice and expertise have significant effects on the overall evaluations rated by supervisors, partially mediated by liking or perceived threat. Moreover, data from the Chinese sample showed that supervisors’ sense of power moderates the effect of voicing employees’ expertise on supervisors’ responses.
Zero-Shot Self-Supervised Learning for MRI Reconstruction

Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but often necessitates a database of fully-sampled measurements for training. Recent self-supervised and unsupervised learning approaches enable training without fully-sampled data. However, a database of undersampled measurements may not be available in many scenarios, especially for scans involving contrast or translational acquisitions in development. Moreover, recent studies show that database-trained models may not generalize well when the unseen measurements differ in terms of sampling pattern, acceleration rate, SNR, image contrast, and anatomy. Such challenges necessitate a new methodology to enable subject-specific DL MRI reconstruction without external training datasets, since it is clinically imperative to provide high-quality reconstructions that can be used to identify lesions/disease for \emph{every individual}. In this work, we propose a zero-shot self-supervised learning approach to perform subject-specific accelerated DL MRI reconstruction to tackle these issues. The proposed approach partitions the available measurements from a single scan into three disjoint sets. Two of these sets are used to enforce data consistency and define loss during training for self-supervision, while the last set serves to self-validate, establishing an early stopping criterion. In the presence of models pre-trained on a database with different image characteristics, we show that the proposed approach can be combined with transfer learning for faster convergence time and reduced computational complexity. The code is available at \url{https://github.com/byaman14/ZS-SSL}.
ALMA confirmation of an obscured hyperluminous radio-loud AGN at z = 6.853 associated with a dusty starburst in the 1.5 deg2 COSMOS field

We present band 6 ALMA observations of a heavily obscured radio-loud (L1.4 GHz = 1025.4 W Hz−1) active galactic nucleus (AGN) candidate at zphot = 6.83 ± 0.06 found in the 1.5 deg2 COSMOS field. The ALMA data reveal detections of exceptionally strong [C ii]158 $\mu$m (z[C ii] = 6.8532) and underlying dust continuum emission from this object (COS-87259), where the [C ii] line luminosity, line width, and 158 $\mu$m continuum luminosity are comparable to those seen from z ∼ 7 sub-mm galaxies and quasar hosts. The 158 $\mu$m continuum detection suggests a total infrared luminosity of $9\times 10^{12}\, \mathrm{ L}_\odot$ with corresponding very large obscured star formation rate (1300 M⊙ yr−1) and dust mass ($2\times 10^9\, \mathrm{ M}_\odot$). The strong break seen between the VIRCam and IRAC photometry perhaps suggests that COS-87259 is an extremely massive reionization-era galaxy with $M_\ast \approx 1.7\times 10^{11}\, \mathrm{ M}_\odot$. Moreover, the MIPS, PACS, and SPIRE detections imply that this object harbours an AGN that is heavily obscured ($\tau _{_{\mathrm{9.7\,\mu m}}}=2.3$) with a bolometric luminosity of approximately $5\times 10^{13}\, \mathrm{ L}_\odot$. Such a very high AGN luminosity suggests that this object is powered by an ≈1.6 × 10$^9\, \mathrm{ M}_\odot$ black hole if accreting near the Eddington limit, and is effectively a highly obscured version of an extremely ultraviolet (UV)-luminous (M1450 ≈ −27.3) z ∼ 7 quasar. Notably, these z ∼ 7 quasars are an exceedingly rare population (∼0.001 deg−2), while COS-87259 was identified over a relatively small field. Future very wide area surveys with e.g. Roman and Euclid have the potential to identify many more extremely red yet UV-bright z ≳ 7 objects similar to COS-87259, providing richer insight into the occurrence of intense obscured star formation and supermassive black hole growth among this population.
Relative Unification in Intuitionistic Logic: Towards provability logic of HA

This paper studies relative unification and admissibility in the intuitionistic logic. We generalize results of [Ghilardi, 1999; Iemhoff, 2001a] and prove them relative in NNIL(par) propositions, the class of propositions with No Nested Implications in the Left made up from parameters. The main application of such generalization is to characterize provability logic of Heyting Arithmetic HA and prove its decidability [Mojtahedi, 2022].
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training

Personalized federated learning is proposed to handle the data heterogeneity problem amongst clients by learning dedicated tailored local models for each user. However, existing works are often built in a centralized way, leading to high communication pressure and high vulnerability when a failure or an attack on the central server occurs. In this work, we propose a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL, which employs personalized sparse masks to customize sparse local models on the edge. To further save the communication and computation cost, we propose a decentralized sparse training technique, which means that each local model in Dis-PFL only maintains a fixed number of active parameters throughout the whole local training and peer-to-peer communication process. Comprehensive experiments demonstrate that Dis-PFL significantly saves the communication bottleneck for the busiest node among all clients and, at the same time, achieves higher model accuracy with less computation cost and communication rounds. Furthermore, we demonstrate that our method can easily adapt to heterogeneous local clients with varying computation complexities and achieves better personalized performances.
Public Release of A-SLOTH: Ancient Stars and Local Observables by Tracing Halos

The semianalytical model a-sloth (Ancient Stars and Local Observables by Tracing Halos) is the first public code that connects the formation of the first stars and galaxies to observables. After several successful projects with this model, we publish the source code (https://gitlab.com/thartwig/asloth) and describe the public version in this paper. The model is based on dark matter merger trees that can either be generated based on Extended Press–Schechter theory or be imported from dark matter simulations. On top of these merger trees, a-sloth applies analytical recipes for baryonic physics to model the formation of both metal-free and metal-poor stars and the transition between them with unprecedented precision and fidelity. a-sloth samples individual stars and includes radiative, chemical, and mechanical feedback. It is calibrated based on six observables, such as the optical depth to Thomson scattering, the stellar mass of the Milky Way and its satellite galaxies, the number of extremely metal-poor stars, and the cosmic star formation rate density at high redshift. a-sloth has versatile applications with moderate computational requirements. It can be used to constrain the properties of the first stars and high-z galaxies based on local observables, predicts properties of the oldest and most metal-poor stars in the Milky Way, can serve as a subgrid model for larger cosmological simulations, and predicts next-generation observables of the early universe, such as supernova rates or gravitational wave events.
EFEKTIFITAS KEGIATAN PRAKTIKUM TERINTEGRASI DALAM PEMBELAJARAN PADA MATERI KESETIMBANGAN KIMIA KELAS XI SMA/MA

ABSTRACT This study aims to determine the effectiveness of an integrated lab activities in learning the material chemical equilibrium. Implementation of learning activities integrated in the learning lab is expected to improve student learning outcomes in the cognitive domain. This type of research is a quasi experimental research. Design study "Randomized Control Group Posttest Only Design" with two classes of experiments on the pair (maching posttest comparison group design). Samples contested-united with cluster sampling method, taking classes with consideration of participants in early-diknya have similar chemical knowledge. The population is the entire class XI student of SMAN 4 Payakumbuh, and the sample is a class XI IPA 2 as the experimental class 1 given instruction with an integrated lab activities in learning and class XI IPA 3 2 given experimental class teaching with practical activities are not integrated. Hypothesis testing method used is independent sample t-test (t-test). From the data analysis using t-test at the 0.05 level of significance was found significant difference in learning outcomes among students with learning lab activities are integrated with learning lab activities are not integrated. Where the learning outcomes of students with learning integrated chemistry lab activities significantly higher than the results of student learning with learning chemistry lab activities are not integrated. From these results it can be concluded that learning with integrated lab activities are effective in improving student learning outcomes in the cognitive domain Keywords: Effectiveness, Integrated Laboratory, Chemical Equilibrium
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining

Text images contain both visual and linguistic information. However, existing pre-training techniques for text recognition mainly focus on either visual representation learning or linguistic knowledge learning. In this paper, we propose a novel approach MaskOCR to unify vision and language pre-training in the classical encoder-decoder recognition framework. We adopt the masked image modeling approach to pre-train the feature encoder using a large set of unlabeled real text images, which allows us to learn strong visual representations. In contrast to introducing linguistic knowledge with an additional language model, we directly pre-train the sequence decoder. Specifically, we transform text data into synthesized text images to unify the data modalities of vision and language, and enhance the language modeling capability of the sequence decoder using a proposed masked image-language modeling scheme. Significantly, the encoder is frozen during the pre-training phase of the sequence decoder. Experimental results demonstrate that our proposed method achieves superior performance on benchmark datasets, including Chinese and English text images.
The Distribution of Satellite Galaxies in the IllustrisTNG100 Simulation

We investigate the spatial distribution of the satellites of isolated host galaxies in the IllustrisTNG100 simulation. In agreement with a previous, similar analysis of the Illustris-1 simulation, the satellites are typically poor tracers of the mean host mass density. Unlike the Illustris-1 satellites, here the spatial distribution of the complete satellite sample is well fitted by an NFW profile; however, the concentration is a factor of ∼2 lower than that of the mean host mass density. The spatial distributions of the brightest 50% and faintest 50% of the satellites are also well fitted by NFW profiles, but the concentrations differ by a factor of ∼2. When the sample is subdivided by host color and luminosity, the number density profiles for blue satellites generally fall below the mean host mass density profiles, while the number density profiles for red satellites generally rise above the mean host mass density profiles. These opposite, systematic offsets combine to yield a moderately good agreement between the mean mass density profile of the brightest blue hosts and the corresponding number density profile of their satellites. Lastly, we subdivide the satellites according to the redshifts at which they joined their hosts. From this, we find that neither the oldest one-third of the satellites nor the youngest one-third of the satellites faithfully trace the mean host mass density.
Augmenting Message Passing by Retrieving Similar Graphs

Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing. In this paper, motivated by the success of retrieval-based models, we propose a non-parametric scheme called GraphRetrieval, in which similar training graphs associated with their ground-truth labels are retrieved to be jointly utilized with the input graph representation to complete various graph-based predictive tasks. In particular, we take a well-trained model with its parameters fixed and then we add an adapter based on self-attention with only a few trainable parameters per task to explicitly learn the interaction between an input graph and its retrieved similar graphs. Our experiments on 12 different datasets involving different tasks (classification and regression) show that GraphRetrieval is able to achieve substantial improvements on all twelve datasets compared to three strong GNN baseline models. Our work demonstrates that GraphRetrieval is a promising augmentation for message passing.
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction

In industry, feature selection is a standard but necessary step to search for an optimal set of informative feature fields for efficient and effective training of deep Click-Through Rate (CTR) models. Most previous works measure the importance of feature fields by using their corresponding continuous weights from the model, then remove the feature fields with small weight values. However, removing many features that correspond to small but not exact zero weights will inevitably hurt model performance and not be friendly to hot-start model training. There is also no theoretical guarantee that the magnitude of weights can represent the importance, thus possibly leading to sub-optimal results if using these methods. To tackle this problem, we propose a novel Learnable Polarizing Feature Selection (LPFS) method using a smoothed-$\ell^0$ function in literature. Furthermore, we extend LPFS to LPFS++ by our newly designed smoothed-$\ell^0$-liked function to select a more informative subset of features. LPFS and LPFS++ can be used as gates inserted at the input of the deep network to control the active and inactive state of each feature. When training is finished, some gates are exact zero, while others are around one, which is particularly favored by the practical hot-start training in the industry, due to no damage to the model performance before and after removing the features corresponding to exact-zero gates. Experiments show that our methods outperform others by a clear margin, and have achieved great A/B test results in KuaiShou Technology.
Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling

Atomistic simulations are an important tool in materials modeling. Interatomic potentials (IPs) are at the heart of such molecular models, and the accuracy of a model's predictions depends strongly on the choice of IP. Uncertainty quantification (UQ) is an emerging tool for assessing the reliability of atomistic simulations. The Open Knowledgebase of Interatomic Models (OpenKIM) is a cyberinfrastructure project whose goal is to collect and standardize the study of IPs to enable transparent, reproducible research. Part of the OpenKIM framework is the Python package, KIM-based Learning-Integrated Fitting Framework (KLIFF), that provides tools for fitting parameters in an IP to data. This paper introduces a UQ toolbox extension to KLIFF. We focus on two sources of uncertainty: variations in parameters and inadequacy of the functional form of the IP. Our implementation uses parallel-tempered Markov chain Monte Carlo (PTMCMC), adjusting the sampling temperature to estimate the uncertainty due to the functional form of the IP. We demonstrate on a Stillinger–Weber potential that makes predictions for the atomic energies and forces for silicon in a diamond configuration. Finally, we highlight some potential subtleties in applying and using these tools with recommendations for practitioners and IP developers.
Optimization with access to auxiliary information

We investigate the fundamental optimization question of minimizing a target function $f$, whose gradients are expensive to compute or have limited availability, given access to some auxiliary side function $h$ whose gradients are cheap or more available. This formulation captures many settings of practical relevance, such as i) re-using batches in SGD, ii) transfer learning, iii) federated learning, iv) training with compressed models/dropout, Et cetera. We propose two generic new algorithms that apply in all these settings; we also prove that we can benefit from this framework under the Hessian similarity assumption between the target and side information. A benefit is obtained when this similarity measure is small; we also show a potential benefit from stochasticity when the auxiliary noise is correlated with that of the target function.
To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier

Algorithmic fairness has emerged as an important consideration when using machine learning to make high-stakes societal decisions. Yet, improved fairness often comes at the expense of model accuracy. While aspects of the fairness-accuracy tradeoff have been studied, most work reports the fairness and accuracy of various models separately; this makes model comparisons nearly impossible without a model-agnostic metric that reflects the balance of the two desiderata. We seek to identify, quantify, and optimize the empirical Pareto frontier of the fairness-accuracy tradeoff. Specifically, we identify and outline the empirical Pareto frontier through Tradeoff-between-Fairness-and-Accuracy (TAF) Curves; we then develop a metric to quantify this Pareto frontier through the weighted area under the TAF Curve which we term the Fairness-Area-Under-the-Curve (FAUC). TAF Curves provide the first empirical, model-agnostic characterization of the Pareto frontier, while FAUC provides the first metric to impartially compare model families on both fairness and accuracy. Both TAF Curves and FAUC can be employed with all group fairness definitions and accuracy measures. Next, we ask: Is it possible to expand the empirical Pareto frontier and thus improve the FAUC for a given collection of fitted models? We answer affirmately by developing a novel fair model stacking framework, FairStacks, that solves a convex program to maximize the accuracy of model ensemble subject to a score-bias constraint. We show that optimizing with FairStacks always expands the empirical Pareto frontier and improves the FAUC; we additionally study other theoretical properties of our proposed approach. Finally, we empirically validate TAF, FAUC, and FairStacks through studies on several real benchmark data sets, showing that FairStacks leads to major improvements in FAUC that outperform existing algorithmic fairness approaches.