Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Analysis of the Mammalian MHC class I Immunopeptidome at the Organism-Wide Scale

Understanding the molecular principles that govern the composition of the mammalian MHC-I immunopeptidome (MHC-Ii) across different primary tissues is fundamentally important to predict how T cell respond in different contexts in vivo. Here, we performed a global analysis of the mammalian MHC-Ii from 29 and 19 primary human and mouse tissues, respectively. First, we observed that different HLA-A, -B and -C allotypes do not contribute evenly to the global composition of the MHC-Ii across multiple human tissues. Second, we found that peptides that are presented in a tissue-dependent and -independent manner share very distinct properties. Third, we discovered that proteins that are evolutionarily hyperconserved represent the primary source of the MHC-Ii at the organism-wide scale. Finally, we uncovered new components of the antigen processing and presentation network that may drive the high level of heterogeneity of the MHC-Ii across different tissues in mammals. This study opens up new avenues toward a system-wide understanding of antigen presentation in vivo and may serve as ground work to understand tissue-dependent T cell responses in autoimmunity, infectious diseases and cancer.
Technical aspects of biodiesel production from vegetable oils

Biodiesel, a promising substitute as an alternative fuel has gained significant attention due to the finite nature of fossil energy sources and does not produce sulfur oxides and minimize the soot particulate in comparison with the existing one from petroleum diesel. The utilization of liquid fuels such as biodiesel produced from vegetable oil by transesterification process represents one of the most promising options for the use of conventional fossil fuels. In the first step of this experimental research, edible rice bran oil used as test material and converted into methyl ester and non-edible jatropha vegetable oil is converted into jatropha oil methyl ester, which are known as biodiesel and they are prepared in the presence of homogeneous acid catalyst and optimized their operating parameters like reaction temperature, quantity of alcohol and the catalyst requirement, stirring rate and time of esterification. In the second step, the physical properties such as density, flash point, kinematic viscosity, cloud point, and pour point were found out for the above vegetable oils and their methyl esters. The same characteristics study was also carried out for the diesel fuel for obtaining the baseline data for analysis. The values obtained from the rice bran oil methyl ester and jatropha oil methyl ester are closely matched with the values of conventional diesel and it can be used in the existing diesel engine without any hardware modification. In the third step the storage characteristics of biodiesel are also studied.
Biodegradation of hydrocarbons in untreated produce water using pure fungal cultures

Biodegradation studies of hydrocarbons in untreated produce water from an oil production facility in Nigeria were undertaken over a period of time using pure fungal cultures (Penicillium sp. and Aspergillus niger). The rate of reduction in some petroleum hydrocarbon fractions, such as n-alkanes, aromatics, nitrogen, sulfur and oxygen (NSO)-containing compounds and polycyclic aromatic hydrocarbons (PAHs), were monitored by means of gas chromatography and mass spectrometery, using mechanically treated produce water as a reference. Gas chromatographic analysis showed that untreated produce water with an oil and grease content of 1407 mg/l contained various petroleum hydrocarbon fractions, including n-alkanes (608 mg/l), aromatics (13.88 mg/l), NSO compounds (12.68 mg/l) and PAHs (0.833 mg/l). Upon mechanical treatment, the oil and grease content of the produce water was reduced to 44 mg/l, while n-alkanes, aromatics, NSO compounds and PAHs were reduced to 38.4, 2.65, 1.78 and 0.0655 mg/l, respectively. A pure culture of Penicillium sp. reduced the oil and grease content to 72.3 mg/l, comprising of n-alkanes (65.50 mg/l), aromatics (0.98 mg/l), NSO compounds (1.64 mg/l) and PAHs (0.0021 mg/l) after 120 days of exposure. However, A. niger reduced the oil and grease content to 59.1 mg/l, comprising of n-alkanes (56.50 mg/l), aromatics (0.65 mg/l), NSO compounds (0.96 mg/l) and PAHs (0.008 mg/l) after 120 days of exposure. The results indicate that produce water is readily biodegradable and that fungal cultures have the capability to degrade the recalcitrant PAH component of the petroleum hydrocarbon mixture in produce water. Biodegradation rates were, however, slightly more enhanced by using A. niger than Penicillium sp. Key words: Biodegradation, fungal cultures, petroleum hydrocarbons, produce water.
Effect of Ethanol, Dimethylether, and Oxygen, When Mixed with Acetylene, on the Formation of Soot and Gas Products

The purpose of this research work is to analyze the effect of ethanol, dimethylether (DME), and oxygen on the formation of gas and soot products when mixed with acetylene and reacted under different conditions, the influence of the oxygenated compounds’ molecular structure, and the impact of the origin of the carbon and oxygen in the different reacting compounds. Experiments have been carried out in a quartz flow reactor in the temperature interval of 975−1475 K, and gas and soot products have been identified and quantified. Experimental data have been interpreted in terms of a literature detailed gas phase kinetic model. Results show that the presence of ethanol and DME in the reacting mixtures results in a diminution of the production of soot compared to the pyrolysis of pure acetylene; less soot is produced in the pyrolysis of acetylene−DME mixtures. The source of C and O is a key factor, being directly related to the soot-forming tendency of the molecules and the different bonds in the molecules. The .
Nitroxide mediated controlled synthesis of glycidyl methacrylate-rich copolymers enabled by SG1-based alkoxyamines bearing succinimidyl ester groups

The controlled nitroxide-mediated copolymerization of glycidyl methacrylate (GMA) and styrene (S) with varying GMA molar feed fractions (fGMA,0 = 0.12–0.94) was accomplished by using a SG1-based alkoxyamine initiator bearing a N-succinimidyl ester group (NHS-BlocBuilder) in 50 wt% 1,4-dioxane solution at 90 °C. Copolymerizations indicated linear evolution of number average molecular weight Mn with respect to conversion up to approximately 50% and narrow molecular weight distributions with Mw/Mn = 1.22–1.44 and GMA incorporation into copolymer (FGMA) as high as 0.92. No additional SG1 free nitroxide was required to control polymerizations, even at high fGMA,0. Chain extensions of poly(GMA-ran-S) macroinitiators with S at 110 °C yielded a high fraction of block copolymer in most cases (except at the highest FGMA), as clear, monomodal shifts in Mn using gel permeation chromatography (GPC) were observed, thereby suggesting the poly(GMA-ran-S) macroinitiators were substantially “living”.
Dielectric Constant and Conductance Changes in Wool Fibers Produced by Step Changes in the Relative Humidity

The dielectric constant of single wool fibers has been measured at a frequency of 1592 cycles, sec and a temperature of 35° C for a range of relative humidities. Also the changes in dielectric constant that occur when the relative humidity surrounding single fibers is suddenly increased from zero have been examined as a function of time. The conductance of similar fibers has been measured under the same experimental conditions. There was an initial rise in the dielectric constant and conductance, followed by a , decrease to an equilibrium value. Both the dielectric constant and the conductance continued to change after an equilibrium water content had been reached. The impli cations of the results upon theories of the polarization and conductance of keratin are discussed, and the relationships between the electrical results and the shape of the absorption/time curves for the keratin-water system are examined. A practical consideration arising from the study is that transient effects (due to changes in the relative humidity) may cause inaccuracies when electrical methods are used for determining water content.
Factors of Influence for Transfer Learning Across Diverse Appearance Domains and Task Types

Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e., pre-training a model for image classification on the ILSVRC dataset, and then fine-tune on any target task. However, previous systematic studies of transfer learning have been limited and the circumstances in which it is expected to work are not fully understood. In this paper we carry out an extensive experimental exploration of transfer learning across vastly different image domains (consumer photos, autonomous driving, aerial imagery, underwater, indoor scenes, synthetic, close-ups) and task types (semantic segmentation, object detection, depth estimation, keypoint detection). Importantly, these are all complex, structured output tasks types relevant to modern computer vision applications. In total we carry out over 2000 transfer learning experiments, including many where the source and target come from different image domains, task types, or both. We systematically analyze these experiments to understand the impact of image domain, task type, and dataset size on transfer learning performance. Our study leads to several insights and concrete recommendations: (1) for most tasks there exists a source which significantly outperforms ILSVRC’12 pre-training; (2) the image domain is the most important factor for achieving positive transfer; (3) the source dataset should include the image domain of the target dataset to achieve best results; (4) at the same time, we observe only small negative effects when the image domain of the source task is much broader than that of the target; (5) transfer across task types can be beneficial, but its success is heavily dependent on both the source and target task types.
Social Shaping of Competitive Equilibriums for Resilient Multi-Agent Systems

In this paper, we study entirely self-sustained multi-agent systems with decentralized resource allocation. Agents make local resource decisions, and sometimes, trading decisions to maximize their individual payoffs accruing from the utility of consumption and the income or expenditure from trading. A competitive equilibrium is achieved if all agents maximize their individual payoffs; a social welfare equilibrium is achieved if the total agent utilities are maximized. First, we consider multi-agent systems with static local allocation, and prove from duality theory that under general convexity assumptions, the competitive equilibrium and the social welfare equilibrium exist and agree. Next, we define a social shaping problem for a competitive equilibrium under which the optimal resource price is socially acceptable, and show that agent utility functions can be prescribed in a family of socially admissible quadratic functions, under which the pricing at the competitive equilibrium is always below a threshold. Finally, we extend the study to dynamical multi-agent systems where agents are associated with dynamical states from linear processes, and prove that the dynamic competitive equilibrium and social welfare equilibrium continue to exist and coincide with each other.
Greedy-Based Feature Selection for Efficient LiDAR SLAM

Modern LiDAR-SLAM (L-SLAM) systems have shown excellent results in large-scale, real-world scenarios. However, they commonly have a high latency due to the expensive data association and nonlinear optimization. This paper demonstrates that actively selecting a subset of features significantly improves both the accuracy and efficiency of an L-SLAM system. We formulate the feature selection as a combinatorial optimization problem under a cardinality constraint to preserve the information matrix's spectral attributes. The stochastic-greedy algorithm is applied to approximate the optimal results in real-time. To avoid ill-conditioned estimation, we also propose a general strategy to evaluate the environment's degeneracy and modify the feature number online. The proposed feature selector is integrated into a multi-LiDAR SLAM system. We validate this enhanced system with extensive experiments covering various scenarios on two sensor setups and computation platforms. We show that our approach exhibits low localization error and speedup compared to the state-of-the-art L-SLAM systems. To benefit the community, we have released the source code: https://ram-lab.com/file/site/m-loam.
Detecting Phishing Sites - An Overview

Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing attacks like spear phishing, whaling, vishing, smishing, pharming and so on. There are various phishing detection techniques based on white-list, black-list, content-based, URL-based, visual-similarity and machine-learning. In this paper, we discuss various kinds of phishing attacks, attack vectors and detection techniques for detecting the phishing sites. Performance comparison of 18 different models along with nine different sources of datasets are given. Challenges in phishing detection techniques are also given.
Computational Physics

We introduce a simple, rigorous, and uniﬁed framework for solving nonlinear partial differential equations (PDEs), and for solving inverse problems (IPs) involving the identiﬁcation of parameters in PDEs, using the framework of Gaussian processes. The proposed approach: (1) provides a natural generalization of collocation kernel methods to nonlinear PDEs and IPs; (2) has guaranteed convergence for a very general class of PDEs, and comes equipped with a path to compute error bounds for speciﬁc PDE approximations; (3) inherits the state-of-the-art computational complexity of linear solvers for dense kernel matrices. The main idea of our method is to approximate the solution of a given PDE as the maximum a posteriori (MAP) estimator of a Gaussian process conditioned on solving the PDE at a ﬁnite number of collocation points. Although this optimization problem is inﬁnite-dimensional, it can be reduced to a ﬁnite-dimensional one by introducing additional variables corresponding to the values of the derivatives of the solution at collocation points; this generalizes the representer theorem arising in Gaussian process regression. The reduced optimization problem has the form of a quadratic objective function subject to nonlinear constraints; it is solved with a variant of the Gauss–Newton method. The resulting algorithm (a) can be interpreted as solving successive linearizations of the nonlinear PDE, and (b) in practice is found to converge in a small number of iterations (2 to 10), for a wide range of PDEs. Most traditional approaches to IPs interleave parameter updates with numerical solution of the PDE; our algorithm solves for both parameter and PDE solution simultaneously. Experiments on nonlinear elliptic PDEs, Burgers’ equation, a regularized Eikonal equation, and an IP for permeability identiﬁcation in Darcy ﬂow illustrate the eﬃcacy and scope of our framework.
Low-Resource Machine Translation for Low-Resource Languages: Leveraging Comparable Data, Code-Switching and Compute Resources

We conduct an empirical study of unsupervised neural machine translation (NMT) for truly low resource languages, exploring the case when both parallel training data and compute resource are lacking, reﬂecting the reality of most of the world’s languages and the researchers working on these languages. We propose a simple and scalable method to improve unsupervised NMT, showing how adding comparable data mined using a bilingual dictionary along with modest additional compute resource to train the model can sig-niﬁcantly improve its performance. We also demonstrate how the use of the dictionary to code-switch monolingual data to create more comparable data can further improve performance. With this weak supervision, our best method achieves BLEU scores that improve over supervised results for English → Gujarati (+18.88), English → Kazakh (+5.84), and English → Somali (+1.16), showing the promise of weakly-supervised NMT for many low resource languages with modest compute resource in the world. To the best of our knowledge, our work is the ﬁrst to quantitatively showcase the impact of different modest compute resource in low resource NMT.
Convergence Rate Analysis for Deep Ritz Method

Using deep neural networks to solve PDEs has attracted a lot of attentions recently. However, why the deep learning method works is falling far behind its empirical success. In this paper, we provide a rigorous numerical analysis on deep Ritz method (DRM) \cite{wan11} for second order elliptic equations with Neumann boundary conditions. We establish the first nonasymptotic convergence rate in $H^1$ norm for DRM using deep networks with $\mathrm{ReLU}^2$ activation functions. In addition to providing a theoretical justification of DRM, our study also shed light on how to set the hyper-parameter of depth and width to achieve the desired convergence rate in terms of number of training samples. Technically, we derive bounds on the approximation error of deep $\mathrm{ReLU}^2$ network in $H^1$ norm and on the Rademacher complexity of the non-Lipschitz composition of gradient norm and $\mathrm{ReLU}^2$ network, both of which are of independent interest.
TEMPERATURE EFFECT ON TDR MEASUREMENT OF WATER CONTENT OF UNBOUND MATERIALS

A seasonal monitoring program of flexible pavement performance has been performed in Tennessee since 1996, in which the water content of the subgrade is measured by Time Domain Reflectometry (TDR) probes. Five-segment probes were chosen to increase the number of measurements and volume of soil monitored. The variations of water content measured in the subgrade and the base course closely follow the temperature trends. This finding suggests the significance of temperature effects on TDR measurement. A laboratory calibration program was thus carried out to study the temperature effects on TDR measurement of volumetric water content. Temperature effects on TDR measurement are related to the energy of the signal. After correcting for temperature effects, the field TDR measurements of volumetric water content appear to track a seasonal variation.
Relation-aware Instance Refinement for Weakly Supervised Visual Grounding

Visual grounding, which aims to build a correspondence between visual objects and their language entities, plays a key role in cross-modal scene understanding. One promising and scalable strategy for learning visual grounding is to utilize weak supervision from only image-caption pairs. Previous methods typically rely on matching query phrases directly to a precomputed, fixed object candidate pool, which leads to inaccurate localization and ambiguous matching due to lack of semantic relation constraints. In our paper, we propose a novel context-aware weakly-supervised learning method that incorporates coarse-to-fine object refinement and entity relation modeling into a two-stage deep network, capable of producing more accurate object representation and matching. To effectively train our network, we introduce a self-taught regression loss for the proposal locations and a classification loss based on parsed entity relations. Extensive experiments on two public benchmarks Flickr30K Entities and ReferItGame demonstrate the efficacy of our weakly grounding framework. The results show that we outperform the previous methods by a considerable margin, achieving 59.27% top-1 accuracy in Flickr30K Entities and 37.68% in the ReferItGame dataset respectively1.
Direct statistical simulation of low-order dynamosystems

In this paper, we investigate the effectiveness of direct statistical simulation (DSS) for two low-order models of dynamo action. The first model, which is a simple model of solar and stellar dynamo action, is third order and has cubic nonlinearities while the second has only quadratic nonlinearities and describes the interaction of convection and an aperiodically reversing magnetic field. We show how DSS can be used to solve for the statistics of these systems of equations both in the presence and the absence of stochastic terms, by truncating the cumulant hierarchy at either second or third order. We compare two different techniques for solving for the statistics: timestepping, which is able to locate only stable solutions of the equations for the statistics, and direct detection of the fixed points. We develop a complete methodology and symbolic package in Python for deriving the statistical equations governing the low-order dynamic systems in cumulant expansions. We demonstrate that although direct detection of the fixed points is efficient and accurate for DSS truncated at second order, the addition of higher order terms leads to the inclusion of many unstable fixed points that may be found by direct detection of the fixed point by iterative methods. In those cases, timestepping is a more robust protocol for finding meaningful solutions to DSS.
Dust-enshrouded AGNs Can Dominate Host-galaxy-scale Cold Dust Emission

It is widely assumed that long-wavelength infrared (IR) emission from cold dust (T ∼ 20–40 K) is a reliable tracer of star formation even in the presence of a bright active galactic nucleus (AGN). Based on radiative transfer (RT) models of clumpy AGN tori, hot dust emission from the torus contributes negligibly to the galaxy spectral energy distribution (SED) at λ ≳ 100 μm. However, these models do not include AGN heating of host-galaxy-scale diffuse dust, which may have far-IR (FIR) colors comparable to cold diffuse dust heated by stars. To quantify the contribution of AGN heating to host-galaxy-scale cold dust emission at λ ≳ 100 μm, we perform dust RT calculations on a simulated galaxy merger both including and excluding the bright AGN that it hosts. By differencing the SEDs yielded by RT calculations with and without AGNs that are otherwise identical, we quantify the FIR cold dust emission arising solely from reprocessed AGN photons. In extreme cases, AGN-heated host-galaxy-scale dust can increase galaxy-integrated FIR flux densities by factors of 2–4; star formation rates calculated from the FIR luminosity assuming no AGN contribution can overestimate the true value by comparable factors. Because the FIR colors of such systems are similar to those of purely star-forming galaxies and redder than torus models, broadband SED decomposition may be insufficient for disentangling the contributions of stars and heavily dust-enshrouded AGNs in the most IR-luminous galaxies. We demonstrate how kiloparsec-scale resolved observations can be used to identify deeply dust-enshrouded AGNs with cool FIR colors when spectroscopic and/or X-ray detection methods are unavailable.
Czert – Czech BERT-like Model for Language Representation

This paper describes the training process of the first Czech monolingual language representation models based on BERT and ALBERT architectures. We pre-train our models on more than 340K of sentences, which is 50 times more than multilingual models that include Czech data. We outperform the multilingual models on 9 out of 11 datasets. In addition, we establish the new state-of-the-art results on nine datasets. At the end, we discuss properties of monolingual and multilingual models based upon our results. We publish all the pre-trained and fine-tuned models freely for the research community.