Miracle-dz/UniFAR-FTUs huggingface dataset:
Fairness with Continuous Optimal Transport

Whilst optimal transport (OT) is increasingly being recognized as a powerful and flexible approach for dealing with fairness issues, current OT fairness methods are confined to the use of discrete OT. In this paper, we leverage recent advances from the OT literature to introduce a stochastic-gradient fairness method based on a dual formulation of continuous OT. We show that this method gives superior performance to discrete OT methods when little data is available to solve the OT problem, and similar performance otherwise. We also show that both continuous and discrete OT methods are able to continually adjust the model parameters to adapt to different levels of unfairness that might occur in real-world applications of ML systems.
The Clusters Hiding in Plain Sight (CHiPS) Survey: CHIPS1911+4455, a Rapidly Cooling Core in a Merging Cluster

We present high-resolution optical images from the Hubble Space Telescope, X-ray images from the Chandra X-ray Observatory, and optical spectra from the Nordic Optical Telescope for a newly discovered galaxy cluster, CHIPS1911+4455, at z = 0.485 ± 0.005. CHIPS1911+4455 was discovered in the Clusters Hiding in Plain Sight survey, which sought to discover galaxy clusters with extreme central galaxies that were misidentified as isolated X-ray point sources in the ROSAT All-Sky Survey. With new Chandra X-ray observations, we find the core (r = 10 kpc) entropy to be , suggesting a strong cool core, which is typically found at the centers of relaxed clusters. However, the large-scale morphology of CHIPS1911+4455 is highly asymmetric, pointing to a more dynamically active and turbulent cluster. Furthermore, the Hubble images reveal a massive, filamentary starburst near the brightest cluster galaxy (BCG). We measure the star formation rate for the BCG to be 140–190 M ⊙ yr−1, which is one of the highest rates measured in a central cluster galaxy to date. One possible scenario for CHIPS1911+4455 is that the cool core was displaced during a major merger and rapidly cooled, with cool, star-forming gas raining back toward the core. This unique system is an excellent case study for high-redshift clusters, where such phenomena are proving to be more common. Further studies of such systems will drastically improve our understanding of the relation between cluster mergers and cooling, and how these fit in the bigger picture of active galactic nuclei feedback.
Parabolic Higgs bundles on projecitive line, quiver varieties and Deligne-Simpson problem

We establish an isomorphism between the moduli space of parabolic Higgs bundles on the projective line and quiver variety of star-shaped quiver. As applications, we can solve the nilpotent case of the Deligne-Simpson problem in a geometrical way; and we can show that there is an algebraically completely integrable system structure on the moduli space as well as quiver variety we concerned.
An Integrated Attribute Guided Dense Attention Model for Fine-Grained Generalized Zero-Shot Learning

Fine-grained generalized zero-shot learning (GZSL) tasks require exploration of relevance between local visual features and attributes to discover ﬁne distinctive information for satisfactory performance. Embedding learning and feature synthesizing are two of the popular categories of GZSL methods. However, these methods do not explore ﬁne discriminative information as they ignore either the lo-cal features or direct guidance from the attributes. Consequently, they do not perform well. We propose a novel embedding learning network with a two-step dense attention mechanism, which uses direct attribute supervision to explore ﬁne distinctive local visual features for ﬁne-grained GZSL tasks. We further incorporate a feature synthesizing network, which uses the attribute-weighted visual features from the embedding learning network. Both networks are mutually trained in an end-to-end fashion to exploit mutually beneﬁcial information. Consequently, the proposed method can test both scenarios: when only the images of unseen classes are available (using the feature synthesizing network) or when both images and semantic descriptors of the unseen classes are available (via the embedding learning network). Moreover, to reduce bias towards the source domain during testing, we compute source-target class similarity based on mutual information and transfer-learn the target classes. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets.
Local Measurements of Shubnikov-de Haas Oscillations in Graphene Systems

Shubnikov-de Haas (SdH) oscillations, the most well-known magneto-oscillations caused by the quantization of electron energy levels in the presence of magnetic fields in two-dimensional (2D) electron systems, can be used to determine Fermi-surface properties and directly measure the Berry phase of the 2D systems. It is usually thought that transport measurements are required to measure the SdH oscillations. Contradicting this belief, we demonstrate that the SdH oscillations can be measured in graphene systems by carrying out scanning tunneling spectroscopy (STS) measurements. The energy-momentum dispersions and Berry phases of monolayer, Bernal-stacked bilayer, and ABC-stacked trilayer graphene are obtained according to the measured SdH oscillations in the STS spectra. It is possible to obtain the SdH oscillations when the size of the 2D systems is larger than the magnetic length and, importantly, no gate electrode is required in the STS measurement, therefore, the reported method in this work is applicable to a wide range of materials.
Factor Modelling for Clustering High-dimensional Time Series

We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific factors, the latent clusters. Numerical illustration with both simulated data as well as a real data example is also reported. As a spin-off, the proposed new approach also advances significantly the statistical inference for the factor model of Lam and Yao (2012).
SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering

Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and it is often difficult to reproduce previous results due to complex configurations. In this paper, we introduce SF-QA: simple and fair evaluation framework for open-domain QA. SF-QA framework modularizes the pipeline open-domain QA system, which makes the task itself easily accessible and reproducible to research groups without enough computing resources. The proposed evaluation framework is publicly available and anyone can contribute to the code and evaluations.
Exploring the Regulatory Function of the N‐terminal Domain of SARS‐CoV‐2 Spike Protein through Molecular Dynamics Simulation

SARS‐CoV‐2 is what has caused the COVID‐19 pandemic. Early viral infection is mediated by the SARS‐CoV‐2 homo‐trimeric Spike (S) protein with its receptor binding domains (RBDs) in the receptor‐accessible state. Molecular dynamics simulation on the S protein with a focus on the function of its N‐terminal domains (NTDs) is performed. The study reveals that the NTD acts as a “wedge” and plays a crucial regulatory role in the conformational changes of the S protein. The complete RBD structural transition is allowed only when the neighboring NTD that typically prohibits the RBD's movements as a wedge detaches and swings away. Based on this NTD “wedge” model, it is proposed that the NTD–RBD interface should be a potential drug target.
A Threat Modelling Approach to Analyze and Mitigate Botnet Attacks in Smart Home Use Case

Despite the surging development and utilization of IoT devices, the security of IoT devices is still in infancy. The security pitfalls of IoT devices have made it easy for hackers to take over IoT devices and use them for malicious activities like botnet attacks. With the rampant emergence of IoT devices, botnet attacks are surging. The botnet attacks are not only catastrophic for IoT device users but also for the rest of the world. Therefore, there is a crucial need to identify and mitigate the possible threats in IoT devices during the design phase. Threat modelling is a technique that is used to identify the threats in the earlier stages of the system design activity. In this paper, we propose a threat modelling approach to analyze and mitigate the botnet attacks in an IoT smart home use case. The proposed methodology identifies the development-level and application-level threats in smart home use case using STRIDE and VAST threat modelling methods. Moreover, we reticulate the identified threats with botnet attacks. Finally, we propose the mitigation techniques for all identified threats including the botnet threats.
A unified view for unsupervised representation learning with density ratio estimation: Maximization of mutual information, nonlinear ICA and nonlinear subspace estimation

Unsupervised representation learning is one of the most important problems in machine learning. Recent promising methods are based on contrastive learning. However, contrastive learning often relies on heuristic ideas, and therefore it is not easy to understand what contrastive learning is doing. This paper emphasizes that density ratio estimation is a promising goal for unsupervised representation learning, and promotes understanding to contrastive learning. Our primal contribution is to theoretically show that density ratio estimation unifies three frameworks for unsupervised representation learning: Maximization of mutual information (MI), nonlinear independent component analysis (ICA) and a novel framework for estimation of a lower-dimensional nonlinear subspace proposed in this paper. This unified view clarifies under what conditions contrastive learning can be regarded as maximizing MI, performing nonlinear ICA or estimating the lower-dimensional nonlinear subspace in the proposed framework. Furthermore, we also make theoretical contributions in each of the three frameworks: We show that MI can be maximized through density ratio estimation under certain conditions, while our analysis for nonlinear ICA reveals a novel insight for recovery of the latent source components, which is clearly supported by numerical experiments. In addition, some theoretical conditions are also established to estimate a nonlinear subspace in the proposed framework. Based on the unified view, we propose two practical methods for unsupervised representation learning through density ratio estimation: The first method is an outlier-robust method for representation learning, while the second one is a sample-efficient nonlinear ICA method. Finally, we numerically demonstrate usefulness of the proposed methods in nonlinear ICA and through application to a downstream task for classification.
A theoretical prediction of hydrogen molecule dissociation-recombination rates including an accurate treatment of internal state nonequilibrium effects

This paper presents the results of a detailed study of dissociation and recombination of H2 over the temperature range 1000 to 5000 K. The chemical processes are modeled by solving the master equation for the concentrations of the full set of rovibration states. All of the state‐to‐state energy transfer rate coefficients required by the master equation are evaluated by the quasiclassical trajectory method using a potential energy surface which is a fit to ab initio electronic structure calculations. The analysis of the results of the master equation to obtain the phenomenological rate coefficients was carried out using several techniques. This is required because there is a mixture of third bodies and their concentrations change as the reaction proceeds. The methods based on one‐way fluxes are not reliable, while the methods based upon an eigenvalue solution of the linearized master equation are in reasonable agreement with our preferred method, which is based on fitting the concentrations from a two comp.
PENGARUH IKLIM ORGANISASI TERHADAP KINERJA PEGAWAI PADA BIRO REKTOR UNIVERSITAS MUHAMMADIYAH TAPANULI SELATAN

Tujuan penelitian ini adalah melihat Pengaruh Iklim Organisasi Terhadap Kinerja Pegawai Pada Biro Rektor Universitas Muhammadiyah Tapanuli Selatan. Kajian dilakukan pada Biro Rektor dikarenakan Biro Rektor ini bisa dikatakan sebagai urat nadi dalam sebuah lembaga sehingga dituntut berfikir global, serta mempunyai visi dan misi yang jauh berwawasan ke depan. Dengan kata lain Jenis penelitian yang digunakan dalam penelitian ini adalah metode kuantitatif berupa statistik deskriptif berupa persentase rata-rata jawaban responden yang menjelaskan tentang pengaruh iklim organisasi terhadap kinerja pegawai pada Biro Rektor Universitas Muhammadiyah Tapanuli Selatan. Dimana Populasi dalam penelitian ini adalah pegawai yang tercatat di Biro Rektor Universitas Muhammadiyah Tapanuli Selatan dengan jumlah sampel penelitian sebanyak 40 responden. Data yang diperoleh dari lapangan, baik data sekunder maupun primer disusun dan disajikan dan dianalisis dengan menggunakan analisis regresi linier sederhana. Hasil penelitian memperlihatkan bahwa iklim organisasi sangat mempengaruhi kinerja pegawai Biro Rektor Universitas Muhammadiyah Tapanuli Selatan. Hal ini dapat dilihat dari data dimana 35,6% dan sisanya sebesar 64,4% dipengaruhi oleh faktor lain seperti tingkat upah, pelatihan, gaya kepemimpinan, jaminan sosial, jaminan kelangsungan kerja, menciptakan sistem kerja yang optimal, dan lain sebagainya. Iklim organisasi yang baik telah terbangun di Biro Rektor Universitas Muhammadiyah Tapanuli Selatan dimana rata-rata responden penelitian memberikan jawaban yang positif dan secara tidak langsung dengan adanya kondisi iklim organisasi yang baik akhirnya membuat kinerja pegawai meningkat.
Fluorphosphohedyphane, Ca2Pb3(PO4)3F, the first apatite supergroup mineral with essential Pb and F

Abstract The new mineral fluorphosphohedyphane, Ca2Pb3(PO4)3F, the F-analog of phosphohedyphane, is hexagonal with space group P63/m and cell parameters a = 9.6402(12), c = 7.0121(8) Å, V = 564.4(1) Å3, and Z = 2. It occurs in the oxidation zone of a small Pb-Cu-Zn-Ag deposit, the Blue Bell claims, about 11 km west of Baker, San Bernardino County, California. It forms as sub-parallel intergrowths and irregular clusters of transparent, colorless, highly lustrous, hexagonal prisms with pyramidal terminations. It is found in cracks and narrow veins in a highly siliceous hornfels in association with cerussite, chrysocolla, fluorite, fluorapatite, goethite, gypsum, mimetite, opal, phosphohedyphane, plumbogummite, plumbophyllite, plumbotsumite, pyromorphite, quartz, and wulfenite. The streak of the new mineral is white, the luster is subadamantine, and the Mohs hardness is about 4. The mineral is brittle with subconcoidal fracture and no cleavage. The calculated density is 5.445 g/cm3 based upon the empirical formula. Optical properties (589 nm): uniaxial (-), ω = 1.836(5), ε = 1.824(5), nonpleochroic. SEM-EDS analyses yielded the averages and ranges in wt%: O 21.28 (20.31-22.14), F 1.59 (1.38-1.91), P 10.33 (9.81-10.83), Ca 9.66 (8.97-10.67), Pb 60.08 (57.67-61.21), total 102.95 (102.02-103.88), providing the empirical formula (based on P = 3): Ca2.00(Pb2.61Ca0.17)Σ2. 78P3O11.91F0. 75. Infrared spectroscopy showed no evidence of OH or carbonate. The strongest powder X-ray diffraction lines are [d(hkl)I]: 8.38(100)22, 3.974(111)28, 3.506(002)25, 2.877(121,211)100, 1.878(213,123)26. Fluorphosphohedyphane has the apatite structure (R1 = 1.75% for 444 reflections with Fo > 4σF) with ordering of Ca and Pb in two cation sites, as in hedyphane and phosphohedyphane. The Pb2+ cation exhibits a stereoactive 6s2 lone-electron-pair. The X anion site at (0, 0, ½) is fully occupied by F forming six bonds of 2.867 Å to Pb atoms, in contrast to the six Pb-Cl bonds of 3.068 Å in phosphohedyphane
Controlling single and few-layer graphene crystals growth in a solid carbon source based chemical vapor deposition

Here, we reveal the growth process of single and few-layer graphene crystals in the solid carbon source based chemical vapor deposition (CVD) technique. Nucleation and growth of graphene crystals on a polycrystalline Cu foil are significantly affected by the injection of carbon atoms with pyrolysis rate of the carbon source. We observe micron length ribbons like growth front as well as saturated growth edges of graphene crystals depending on growth conditions. Controlling the pyrolysis rate of carbon source, monolayer and few-layer crystals and corresponding continuous films are obtained. In a controlled process, we observed growth of large monolayer graphene crystals, which interconnect and merge together to form a continuous film. On the other hand, adlayer growth is observed with an increased pyrolysis rate, resulting few-layer graphene crystal structure and merged continuous film. The understanding of monolayer and few-layer crystals growth in the developed CVD process can be significant to grow graphene with controlled layer numbers.
Excited-state dynamics of polymer-bound J-aggregates

The excited-state dynamics of polymer-bound J-aggregates formed in aqueous mixtures of pseudoisocyanine (PIC) chloride and poly(vinyl sulfonic acid sodium salt) (PVS) (MW≃11 200) have been studied by picosecond time-correlated single-photon counting and picosecond polarized pump-probe spectroscopy. At a concentration of 40 μM PIC and 5×10 -4 g/dL PVS, the absorption spectrum of the J-aggregate is characterized by a J-band at 565 nm with a fwhm of ≃500 cm -1 . For this particular mixture, a dye molecule is bound to each of the SO 3 - groups on the polymer chains. The physical size of the aggregate is therefore determined by the number of polymer residues (≃87) per chain
Biobased thermosetting resins composed of terpene and bismaleimide

Prepolymers prepared by reactions of 1,1′-(methylenedi-4,1-phenylene)bismaleimide (BMI) with myrcene (Myr) and limonene (Lim) in 1,3-dimethyl-2-imidazolidinone (DMI) at 150°C were compressed at 250°C to produce crosslinked Myr/BMI [molar ratio = 2:2–2:5 (MB22–MB25)] and Lim/BMI [molar ratio = 1:1 (LB11)] resins. The 1H-NMR analysis of the model reaction products of Lim and Myr with N-phenyl maleimide (PMI) in DMI at 150°C revealed that a Diels–Alder reaction for Myr/PMI and a vinyl copolymerization for Lim/PMI preferentially proceeded in addition to the occurrence of the ene reaction to some extent. The Fourier transform infrared data of the cured resins were consistent with the results of the model reactions. All of the cured resins, except for MB22, showed tan δ peak values and 10% weight loss temperatures that were higher than 330 and 440°C, respectively. The flexural strength and modulus values of the MBs were higher than those of LB11. Field emission scanning electron microscopy analysis revealed that MB22–MB24 were homogeneous, whereas some combined particles appeared in LB11. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
On the use of the Webb–Pearman–Leuning theory for closed-path eddy correlation measurements

We consider an imperfection of real closed-path eddy correlation systems—the decoupling of the water vapour and CO2 concentrations—with respect to the application of the Webb–Pearman–Leuning (WPL) theory. It is described why and how the current application of the WPL theory needs to be adapted to the processes in closed-path sensors. We show the quantitative effects of applying the WPL theory in different ways using CO2 flux measurements taken above the Danish Beech forest CarboEurope site near Sorø, Zealand. Using the WPL theory in closed-path sensors without taking amplitude damping and decoupling into account, overcorrected the annual flux by 21%, or 31 g m-2 yr-1, to which the decoupling effect contributed with 7%. We suggest either converting the raw data point-by-point to mixing ratios or using the uncorrected covariances of water vapour mole fractions with the vertical wind velocity that were calculated with the same time lag as for the scalar concentration when correcting the dilution effect.We showed that the two approaches yielded equivalent flux results. Correct ways of applying spectral corrections to CO2 fluxes calculated in either way are also shown. The findings reported here do not apply to open-path sensors.
Adsorption and Diffusion of H2 in the MOF Type Systems MIL-47(V) and MIL-53(Cr): A Combination of Microcalorimetry and QENS Experiments with Molecular Simulations

Quasi-elastic neutron scattering measurements are combined with molecular dynamics simulations to determine the self-diffusivity (Ds) profile of hydrogen in the metal organic framework materials MIL-47(V) and MIL-53(Cr) (MIL, Materials Institut Lavoisier) as a function of loading. Experimentally, a sudden increase in Ds for H2 at low loading (≤1 H2/unit cell) was observed with values at least two orders of magnitude higher than in zeolites. This unusual behavior has been denoted as “super-mobility”. Here, two different force fields available in the literature to represent the H2/H2 and H2/MOF framework interactions have been considered to capture such experimental findings via molecular dynamics simulations. We have shown that (i) a similar magnitude of the energetic contribution for the H2/H2 and H2/MOF framework interactions and (ii) a smoothness of the potential energy surfaces are required in order to match the supermobility of H2 at low loading. The diffusion mechanism at the microscopic scale was su.
Volumetrics of Petroleum Generation: Implications for Expulsion and Overpressure

In this study, the author explores the volumetrics of petroleum generation using a well constrained set of source rock and Kimmeridge Clay Formation petroleum samples at maturities covering the complete generation window from the North Sea. Densities of pure kerogen isolates were determined using density gradient centrifugation. Best estimates were derived for the composition of petroleum as a function of maturity. The PVTX properties of the petroleum were derived using PVTSim v.10. Combining the kerogen density data with the petroleum PVTX data, and placing these data into an algebraic mass balance scheme, the volume and pressure changes attendant upon petroleum generation were determined as a function of maturity. For source rock temperatures up to ∼130°C, the volume expansion is about 21% of the immature kerogen volume with an attendant pressure increase of about 10 MPa within the uncertainty of the methods in a closed system. Significant increases in volume, up to 71% with a concomitant pressure increase of about 33 MPa only occur at temperatures >130°C, when generated fluids have high gas-oil ratio. The results suggest that a completely closed source rock system can become overpressured during the main phase of oil generation.
Texture and Microstructure of Cassava (Manihot esculenta Crantz) Flour Extrudate

Cassava flour (Manihot esculenta Crantz) was texturized by single-screw extrusion processing. Relationships between texture and micro-structure as a function of extrusion variables were examined. The effect of feed moisture or screw speed was significant (P < 0.01) on all textural parameters except springiness and energy first bite. Hardness, gumminess (P < 0.05) fracturability and cohesiveness (P < 0.01) decreased with increasing temperature. Hardness, fracturability and firmness were lowest at screw speed 520 rpm with feed rate 300g/min, 120–125°C and 11% feed moisture. Scanning electron micrographs showed structural change from a coarse cell mass at high feed moisture to a porous, thin-walled structure with decreased feed moisture.