Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Locking for Various Phosphorus Pollutants in Water Bodies: of 1 Organophosphate by Lanthanum/Aluminum-Hydroxide Composite

ABSTRCT: Lanthanum-bearing materials have been widely used to remove 11 phosphorus (P) especially orthophosphate in water treatment. However, its binding 12 ability and interaction mechanism to other P species in natural waters such as organic 13 phosphate and condensed phosphate are not well understood. Here, a 14 lanthanum/aluminum-hydroxide (LAH) composite was used to investigate its 15 adsorption efficiencies and mechanisms to myo-inositol hexakisphosphate (IHP) and 16 pyrophosphate (Pyro-P) with the contrast of orthophosphate (Ortho-P). The maximum 17 IHP and Pyro-P adsorption capacities by LAH were 36.4 mg P g -1 and 21.8 mg P g -1 , 18 respectively. Zeta potential, 31 P solid-state nuclear magnetic resonance (NMR) 19 spectroscopy and P K-edge X-ray absorption near edge structure (XANES) analyses 20 possibly indicated that the interaction between LAH and P species was surface 21 adsorption by the formation of inner-sphere complexes. Liner combination fitting 22 results of XANES showed that IHP and Pyro-P were preferentially bonded with 23 La-hydroxide in LAH under alkaline conditions (pH 8.5). This work elucidates the 24 adsorption properties and mechanisms of IHP and Pyro-P on lanthanum bearing 25 compounds at the molecular level, indicating that LAH was a promising material for 26 eutrophication control. Our recent study has demonstrated that lanthanum/aluminum hydroxide composite 73 (LAH) exhibited highly effective adsorption capacity of Ortho-P. 10 These observations 74 promote the hypothesis that LAH may also show an affinity to IHP and Pyro-P existed 75 in water bodies. The objective of this study is to investigate the removal properties of 76 LAH to IHP and Pyro-P and illustrate their adsorption mechanisms at the molecular 77 level. The properties of three P compounds associated with LAH, including potassium 78 dihydrogen phosphate, inositol hexakisphosphate acid sodium salt, and sodium 79 pyrophosphate, were studied by batch experiments and multiple characterization 80 techniques. The experiments of adsorption isotherm, desorption behavior, and 81 adsorption kinetics were conducted to determine the macroscopic binding properties 82 of three P species on LAH. 31 P SSNMR and P K-edge XANES techniques were used 83 to explore the microstructures of P bonded on LAH. Liner combination fitting (LCF) 84 analysis was used to investigate the P distribution proportions with La-hydroxide and 85 Al-hydroxide in LAH. The results of our study has demonstrated the interaction 86 mechanism of IHP and Pyro-P with LAH, which is important to design and implement 87 La-bearing materials for eutrophication control.
The Intersection of Tobacco Use, Health Disparities, and Inequalities in Lung Cancer Treatment and Survival

Tobacco use and socioeconomic status are related with each other and important determinants of disparities and inequalities to access to care. There is overall reduction in number of smokers but still most number of smokers is represented by people from low socioeconomic status, with less number of these people having an access to the treatment centers. Patients who are tobacco users have shown to be less likely to receive any form of treatment for lung cancer, whether that be chemotherapy, radiotherapy, or surgery. As there is less awareness about signs and symptoms of lung cancer, lower socioeconomic patients are likely to report to hospitals at advanced stages or many times as a medical emergency. We are well aware that patients with advanced lung cancer have lower chances of survival in view of incomplete response to treatment. As there is an intersection between tobacco use, health disparities, and inequalities and lung cancer treatment and survival, this issue needs better focus and attention to minimize disparities and inequalities in access to care and outcomes.
Using Wiktionary to Create Specialized Lexical Resources and Datasets

This paper describes an approach aiming at utilizing Wiktionary data for creating specialized lexical datasets which can be used for enriching other lexical (semantic) resources or for generating datasets that can be used for evaluating or improving NLP tasks, like Word Sense Disambiguation, Word-in-Context challenges, or Sense Linking across lexicons and dictionaries. We have focused on Wiktionary data about pronunciation information in English, and grammatical number and grammatical gender in German.
and Alberto (2016) Single-phase T-type inverter performance benchmark using Si IGBTs, SiC MOSFETs and GaN HEMTs. IEEE

—In this paper, benchmark of Si IGBT, SiC MOSFET , 5 and Gallium nitride (GaN) HEMT power switches at 600-V class 6 is conducted in single-phase T-type inverter. Gate driver require- 7 ments, switching performance, inverter efﬁciency performance, 8 heat sink volume, output ﬁlter volume, and dead-time effect 9 for each technology is evaluated. Gate driver study shows that 10 GaN has the lowest gate driver losses above 100 kHz and below 11 100 kHz, SiC has lowest gate losses. GaN has the best switching 12 performance among three technologies that allows high efﬁciency 13 at high-frequency applications. GaN-based inverter operated at 14 160-kHz switching frequency with 97.3% efﬁciency at 2.5-kW out- 15 put power. Performance of three device technologies at different 16 temperature, switching frequency, and load conditions shows that 17 heat sink volume of the converter can be reduced by 2.5 times by 18 switching from Si to GaN solution at 60 ◦ C case temperature, and 19 for SiC and GaN, heat sink volume can be reduced by 2.36 and 4.92 20 times, respectively, by increasing heat sink temperature to 100 ◦ C. 21 Output ﬁlter volume can be reduced by 43% with 24, 26, and 22 61 W increase in device power loss for GaN-, SiC-, and Si-based 23 converters, respectively. WBG devices allow reduction of harmonic 24 distortion at output current from 3.5% to 1.5% at 100 kHz. 25
Cross-lingual Linking of Automatically Constructed Frames and FrameNet

A semantic frame is a conceptual structure describing an event, relation, or object along with its participants. Several semantic frame resources have been manually elaborated, and there has been much interest in the possibility of applying semantic frames designed for a particular language to other languages, which has led to the development of cross-lingual frame knowledge. However, manually developing such cross-lingual lexical resources is labor-intensive. To support the development of such resources, this paper presents an attempt at automatic cross-lingual linking of automatically constructed frames and manually crafted frames. Specifically, we link automatically constructed example-based Japanese frames to English FrameNet by using cross-lingual word embeddings and a two-stage model that first extracts candidate FrameNet frames for each Japanese frame by taking only the frame-evoking words into account, then finds the best alignment of frames by also taking frame elements into account. Experiments using frame-annotated sentences in Japanese FrameNet indicate that our approach will facilitate the manual development of cross-lingual frame resources.
A midpoint projection algorithm for stochastic differential equations on manifolds

Stochastic differential equations projected onto manifolds occur in physics, chemistry, biology, engineering, nanotechnology and optimization, with interdisciplinary applications. Intrinsic coordinate stochastic equations on the manifold are often computationally impractical, and numerical projections are useful in many cases. We show that the Stratonovich interpretation of the stochastic calculus is obtained using adiabatic elimination with a constraint potential. We derive intrinsic stochastic equations for spheroidal and hyperboloidal surfaces for comparison purposes, and review some earlier projection algorithms. In this paper, a combined midpoint projection algorithm is proposed that uses a midpoint projection onto a tangent space, combined with a subsequent normal projection to satisfy the constraints. Numerical examples are given for a range of manifolds, including circular, spheroidal, hyperboloidal, and catenoidal cases, as well as higher-order polynomial constraints and a ten-dimensional hypersphere. We show that in all cases the combined midpoint method has greatly reduced errors compared to methods using a combined Euler projection approach or purely tangential projection. Our technique can handle multiple constraints. This allows manifolds that embody several conserved quantities. The algorithm is accurate, simple and efficient. An order of magnitude error reduction in diffusion distance is typically found compared to the other methods, with reductions of several orders of magnitude in constraint errors.
Fundamental exact sequence for the pro-étale
fundamental group

The pro-\'etale fundamental group of a scheme, introduced by Bhatt and Scholze, generalizes formerly known fundamental groups -- the usual \'etale fundamental group $\pi_1^{\mathrm{et}}$ defined in SGA1 and the more general group defined in SGA3. It controls local systems in the pro-\'etale topology and leads to an interesting class of"geometric covers"of schemes, generalizing finite \'etale covers. We prove the homotopy exact sequence over a field for the pro-\'etale fundamental group of a geometrically connected scheme $X$ of finite type over a field $k$, i.e. that the sequence $$1 \rightarrow \pi_1^{\mathrm{proet}}(X_{\bar{k}}) \rightarrow \pi_1^{\mathrm{proet}}(X) \rightarrow \mathrm{Gal}_k \rightarrow 1$$ is exact as abstract groups and the map $\pi_1^{\mathrm{proet}}(X_{\bar{k}}) \rightarrow \pi_1^{\mathrm{proet}}(X)$ is a topological embedding. On the way, we prove a general van Kampen theorem and the K\"unneth formula for the pro-\'etale fundamental group.
MutFormer: A context-dependent transformer-based model to predict deleterious missense mutations from protein sequences in the human genome

Various machine-learning models, including deep neural network models, have already been developed to predict deleteriousness of missense (non-synonymous) mutations. Still, potential improvements to the current state of the art may benefit from a fresh look at the biological problem using more sophisticated self-adaptive machine-learning approaches. Recent advances in the natural language processing field show transformer models–a type of deep neural network–to be particularly powerful at modeling sequence information with context dependence. In this study, we introduce MutFormer, a transformer-based model for the prediction of deleterious missense mutations. MutFormer uses reference and mutated protein sequences from the human genome as the primary features. It uses a combination of self-attention layers and convolutional layers to learn both long-range and short-range dependencies between amino acid mutations in a protein sequence. We pre-trained MutFormer on reference protein sequences and mutated protein sequences resulting from common genetic variants observed in human populations. Next, we examined different fine-tuning methods to successfully apply the model to deleteriousness prediction of missense mutations. Finally, we evaluated MutFormer’s performance on multiple testing data sets. We found that MutFormer showed similar or improved performance over a variety of existing tools, including those that used conventional machine-learning approaches (e.g., support vector machine, convolutional neural network, recurrent neural network). We conclude that MutFormer successfully considers sequence features that are not explored in previous studies and could potentially complement existing computational predictions or empirically generated functional scores to improve our understanding of disease variants.
A quantum stochastic equivalence leads to a retrocausal model for measurement consistent with macroscopic realism

While classical retrocausal dynamics can account for violation of Bell inequalities, such models are not generally thought to be equivalent to quantum mechanics. If retrocausal mechanisms exist, it remains unclear why retrocausality does not manifest at a macroscopic level. It is also unclear whether macroscopic realism, a requirement for macroscopic causality, holds. In this paper, we show how retrocausality arises naturally from within quantum mechanics, and explains quantum measurement consistently with macroscopic realism. We analyze a measurement ˆ x on a system prepared in a superposition of eigenstates | x j (cid:105) where the measurement is modeled by ampliﬁcation. By deriving a path-integral theorem, we prove an equivalence between a quantum probability distribution Q ( x, p, t ) and simultaneous back-in-time and forward-in-time stochastic equations for amplitudes x ( t ) and p ( t ) , respectively. The backward and forward trajectories are linked at the initial-time boundary. A Deustch-like ’causal consistency’ and Born’s rule emerge naturally. A feature is the vacuum noise associated with the eigenstate. Unlike the eigenvalue, this noise is not ampliﬁed and is not measurable, the precise ﬂuctuations originating from past and future boundary conditions. We ﬁnd consistency with macroscopic realism: For macroscopic superpositions, the macroscopic outcome of the measurement ˆ x is considered determined prior to the onset of the measurement. This leads to hybrid macro-causal and micro-retrocausal relations, and other models of realism. Our results support that the ’collapse’ of the wave function occurs with ampliﬁcation: The distribution Q j ( x, p, 0) for the initial ’state’ postselected on the outcome x j is not a quantum state but approache s the eigenstate | x j (cid:105) for a macroscopic superposition. The full irreversible collapse is simulated by coupling to a meter. We discuss Einstein-Podolsky-Rosen and Bell correlations.
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications

Egocentric videos offer fine-grained information for high-fidelity modeling of human behaviors. Hands and interacting objects are one crucial aspect of understanding a viewer’s behaviors and in-tentions. We provide a labeled dataset consisting of 11,243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. Our dataset is the first to label detailed hand-object contact boundaries. We introduce a context-aware compositional data augmentation technique to adapt to out-of-distribution YouTube egocentric video. We show that our robust hand-object segmentation model and dataset can serve as a foundational tool to boost or enable several downstream vision applications, including hand state classification, video activity recognition, 3D mesh reconstruction of hand-object interactions, and video inpainting of hand-object foregrounds in egocentric videos. Dataset and code are available at: https://github.com/owenzlz/EgoHOS In this supplementary materials, we first describe the following details of this work: 1). details of video frame collection; 2). training details of segmentation network; 3). how we chose the quantity of data augmentation; 4). the performance of using 100-DOH followed by PointRend. Please note that we also provide results of per-frame hand-object segmentations and ”see-through hand” application in egocentric videos. Please check our ”. mp4” file in the supplementary materials for details.
The Sufficiency of Off-Policyness and Soft Clipping: PPO Is Still Insufficient according to an Off-Policy Measure

The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space. Does there exist better policies outside of this space? By using a novel surrogate objective that employs the sigmoid function (which provides an interesting way of exploration), we found that the answer is "YES", and the better policies are in fact located very far from the clipped space. We show that PPO is insufficient in "off-policyness", according to an off-policy metric called DEON. Our algorithm explores in a much larger policy space than PPO, and it maximizes the Conservative Policy Iteration (CPI) objective better than PPO during training. To the best of our knowledge, all current PPO methods have the clipping operation and optimize in the clipped policy space. Our method is the first of this kind, which advances the understanding of CPI optimization and policy gradient methods. Code is available at https://github.com/raincchio/P3O.
Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data

Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is challenging due to sectioning distortions, presence of noise and artifacts and intensity/contrast variations. In this work, we propose a deep learning method with a self-supervised loss function that takes anatomy-based constraints into account for accurate segmentation of fiber bundles on the tracer sections from macaque brains. Also, given the limited availability of manual labels, we use a semi-supervised training technique for efficiently using unlabeled data to improve the performance, and location constraints for further reduction of false positives. Evaluation of our method on unseen sections from a different macaque yields promising results with a true positive rate of ~0.90. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.
GLASS: Global to Local Attention for Scene-Text Spotting

In recent years, the dominant paradigm for text spotting is to combine the tasks of text detection and recognition into a single end-to-end framework. Under this paradigm, both tasks are accomplished by operating over a shared global feature map extracted from the input image. Among the main challenges that end-to-end approaches face is the performance degradation when recognizing text across scale variations (smaller or larger text), and arbitrary word rotation angles. In this work, we address these challenges by proposing a novel global-to-local attention mechanism for text spotting, termed GLASS, that fuses together global and local features. The global features are extracted from the shared backbone, preserving contextual information from the entire image, while the local features are computed individually on resized, high-resolution rotated word crops. The information extracted from the local crops alleviates much of the inherent difficulties with scale and word rotation. We show a performance analysis across scales and angles, highlighting improvement over scale and angle extremities. In addition, we introduce an orientation-aware loss term supervising the detection task, and show its contribution to both detection and recognition performance across all angles. Finally, we show that GLASS is general by incorporating it into other leading text spotting architectures, improving their text spotting performance. Our method achieves state-of-the-art results on multiple benchmarks, including the newly released TextOCR.
Towards Fair Conversational Recommender Systems

Conversational recommender systems have demonstrated great success. They can accurately capture a user’s current detailed preference – through a multi-round interaction cycle – to effectively guide users to a more personalized recommendation. Alas, conversational recommender systems can be plagued by the adverse effects of bias, much like traditional recommenders. In this work, we argue for increased attention on the presence of and methods for counteracting bias in these emerging systems. As a starting point, we propose three fundamental questions that should be deeply ex-amined to enable fairness in conversational recommender systems.
Completing Networks by Learning Local Connection Patterns

Network completion is a harder problem than link prediction because it does not only try to infer missing links but also nodes. Diﬀerent methods have been proposed to solve this problem, but few of them employed structural information - the similarity of local connection patterns. In this paper, we propose a model named C-GIN to capture the local structural patterns from the observed part of a network based on the Graph Auto-Encoder framework equipped with Graph Isomorphism Network model and generalize these patterns to complete the whole graph. Experiments and analysis on synthetic and real-world networks from diﬀerent domains show that competitive performance can be achieved by C-GIN with less information being needed, and higher accuracy compared with baseline prediction models in most cases can be obtained. We further proposed a metric ”Reachable Clustering Coeﬃcient(CC)” based on network structure. And experiments show that our model perform better on a network with higher Reachable CC.
Active Galactic Nuclei signatures in Red Geyser galaxies from Gemini GMOS-IFU observations

Red Geysers are quiescent galaxies with galactic scale ionised outflows, likely due to low-luminosity Active Galactic Nuclei (AGN). We used Gemini GMOS-IFU observations of the inner ∼1–3 kpc of nine Red Geysers selected from the MaNGA survey to study the gas ionisation and kinematics. The emission-line ratios suggest the presence of Seyfert/LINER (Low Ionisation Nuclear Emission Region) nuclei in all sources. Two galaxies show Hα equivalent width (Hα EW) larger than 3 Å (indicative of AGN ionisation) within an aperture 2${_{. }^{\prime\prime}}$5 of diameter (1.3–3.7 kpc at the distance of galaxies) for MaNGA data, while with the higher resolution GMOS data, four galaxies present Hα EW>3 Å within an aperture equal to the angular resolution (0.3–0.9 kpc). For two objects with GMOS-IFU data, the Hα EW is lower than 3 Å but larger than 1.5 Å, most probably due to a faint AGN. The spatially resolved electron density maps show values between 100–3000 cm−3 and are consistent with those determined in other studies. The large (MaNGA) and the nuclear scale (GMOS-IFU) gas velocity fields are misaligned, with a kinematic position angle difference between 12○ and 60○. The [N ii] λ6583 emission-line profiles are asymmetrical, with blue wings on the redshifted side of the velocity field and red wings on the blueshifted side. Our results support previous indications that the gas in Red Geysers is ionised by an AGN, at least in their central region, with the presence of outflows, likely originating in a precessing accretion disc.
A Newton derivative scheme for shape optimization problems constrained by variational inequalities

Shape optimization problems constrained by variational inequalities (VI) are non-smooth and non-convex optimization problems. The non-smoothness arises due to the variational inequality constraint, which makes it challenging to derive optimality conditions. Besides the non-smoothness there are complementary aspects due to the VIs as well as distributed, non-linear, non-convex and inﬁnite-dimensional aspects due to the shapes which complicate to set up an optimality system and, thus, to develop fast and higher order solution algorithms. In this paper, we consider Newton-derivatives in order to formulate optimality conditions. In this context, we set up a Newton-shape derivative scheme. Examples show the application of the proposed scheme.
Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations

This paper introduces the Beer2Vec model that allows the most popular alcoholic beverage in the world to be encoded into vectors enabling flavorful recommendations. We present our algorithm using a unique dataset focused on the analysis of craft beers. We thoroughly explain how we encode the flavors and how useful, from an empirical point of view, the beer vectors are to generate meaningful recommendations. We also present three different ways to use Beer2Vec in a real-world environment to enlighten the pool of craft beer consumers. Finally, we make our model and functionalities available to everybody through a web application.