Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Fine-Grained Entity Types with Box Embeddings

Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types’ complex interdependencies. We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchies of types even when these relationships are not defined explicitly in the ontology. Our model represents both types and entity mentions as boxes. Each mention and its context are fed into a BERT-based model to embed that mention in our box space; essentially, this model leverages typological clues present in the surface text to hypothesize a type representation for the mention. Box containment can then be used to derive both the posterior probability of a mention exhibiting a given type and the conditional probability relations between types themselves. We compare our approach with a vector-based typing model and observe state-of-the-art performance on several entity typing benchmarks. In addition to competitive typing performance, our box-based model shows better performance in prediction consistency (predicting a supertype and a subtype together) and confidence (i.e., calibration), demonstrating that the box-based model captures the latent type hierarchies better than the vector-based model does.
Minimizing L 1 over L 2 norms on the gradient

In this paper, we study the L 1/L 2 minimization on the gradient for imaging applications. Several recent works have demonstrated that L 1/L 2 is better than the L 1 norm when approximating the L 0 norm to promote sparsity. Consequently, we postulate that applying L 1/L 2 on the gradient is better than the classic total variation (the L 1 norm on the gradient) to enforce the sparsity of the image gradient. Numerically, we design a specific splitting scheme, under which we can prove subsequential and global convergence for the alternating direction method of multipliers (ADMM) under certain conditions. Experimentally, we demonstrate visible improvements of L 1/L 2 over L 1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of magnetic resonance imaging and computed tomography reconstruction. Finally, we reveal some empirical evidence on the superiority of L 1/L 2 over L 1 when recovering piecewise constant signals from low-frequency measurements to shed light on future works.
Effect of methanol on the gas-phase reaction of trifluoromethane with methane

The reaction of CHF3 with CH4 in the presence of small amounts of CH3OH (5% of CHF3 concentration) was investigated in a tubular alumina reactor at temperatures between 900 K and 1150 K. The presence of CH3OH has a significant influence on both the conversion and selectivity of the reaction. Increasing the CH3OH concentration enhances the conversion of CHF3 by a factor of 1−4 times at temperatures between 873 K and 1123 K, although the rate of formation of the major products (CH2═CF2) does not increase. A reaction scheme, based on NIST HFC and GRI-Mech mechanisms, is developed to model the reactions. Satisfactory agreement between experimental results and predictions is achieved for the conversion of reactants and formation of products. Based on experimental measurements and modeling results, a detailed reaction mechanism is proposed, and insight into how CH3OH influences the reaction of CHF3 with CH4 is presented.
Ecological Assessment of Water Quality by Three-species Acute Toxicity Test and GC/MS Analysis - A Case Study of Agricultural Drains -

The water quality of environmental waters from the viewpoint of aquatic ecotoxicity was investigated using a three-species ecotoxicity test (algae, daphnia and fish). Water samples were collected, concentrated with a solid-phase extraction technique and exposed to each test species. The growth inhibition, immobilization (swimming inhibition) and mortality ratios in acute toxicity tests for algae, daphnia and fish, respectively, were used as water quality indexes. For the river waters, 38% of the monitoring sites showed good water quality from the viewpoint of long-term ecotoxicity for all the three test species because no toxicity effects were observed at the concentration factors of 10, 50 and 50 for algae, daphnia and fish, respectively. For the agricultural drains, the ecotoxicity level responded sensitively especially when agricultural chemicals were applied. The GC/MS analysis also confirmed that the detection index (DI) in the agricultural drains was often raised significantly by the agricultural chemicals, but the period with high ecotoxicity did not continue for long.
A Mathematical Model of SOFC: A Proposal

The mathematical model of the solid oxide fuel cell (SOFC) is presented. The new approach for modeling the voltage of SOFC is proposed. Electrochemical, thermal, electrical, and flow parameters are collected in the 0D mathematical model. The aim was to combine all cell working conditions in as a low number of factors as possible and to have the factors relatively easy to determine. A validation process for various experimental data was made and adequate results are shown. The presented model was validated for various fuel mixtures in relatively wide ranges of parameters as well as for various cell design parameters (e.g. electrolyte thickness, anode porosity, etc.). A distinction is made between the “design‐point” and “off‐design operation”.
Anticoagulant surface prepared by the heparinization of ionic polyurethane film

We present a new method for heparinization on the surface of polyurethane. The segmented polyurethane was first modified with an epoxide monomer and followed by a ring-opening reaction with diethanolamine to introduce sufficient hydroxyl groups on the surface of cast film. On this film surface, a cationic monomer was grafted by using tetravalent Cerium salt as an initiator. Heparin was immobilized in high efficiency on the ionized surface through static interactions in aqueous solution. The structure of ionized and heparinized surfaces were characterized by attenuated total reflectance infrared spectroscopy (ATR–FTIR) and electron spectroscopy for chemical analysis (ESCA) spectra. The platelet-rich plasma (PRP) contacting test and the platelet-poor plasma (PPP) clotting time measurements showed that the immobilized heparin retained its strong anticoagulant property. The release of heparin from film into salt solution was also studied, and it was found that only a small portion of heparin (10–20%) was released over a period as long as 10 h. It is expected that this new method for surface heparinization can be used to prepare antithrombogenic materials with long-term stability. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 76: 382–390, 2000
Observations of the evolution of the aerosol, cloud and boundary-layer characteristics during the 1st ACE-2 Lagrangian experiment

During the 1st Lagrangian experiment of the North Atlantic Regional Aerosol Characterisation Experiment (ACE-2), a parcel of air was tagged by releasing a smart, constant level balloon into it from the Research Vessel Vodyanitskiy. The Meteorological Research Flight’s C-130 aircraft then followed this parcel over a period of 30 h characterising the marine boundary layer (MBL), the cloud and the physical and chemical aerosol evolution. The air mass had originated over the northern North Atlantic and thus was clean and had low aerosol concentrations. At the beginning of the experiment the MBL was over 1500 m deep and made up of a surface mixed layer (SML) underlying a layer containing cloud beneath a subsidence inversion. Subsidence in the free troposphere caused the depth of the MBL to almost halve during the experiment and, after 26 h, the MBL became well mixed throughout its whole depth. Salt particle mass in the MBL increased as the surface wind speed increased from 8 m s-1 to 16 m s-1 and the accumulation mode (0.1μm to 3.0 μm) aerosol concentrations quadrupled from 50 cm-3 to 200 cm-3. However, at the same time the total condensation nuclei (>3 nm) decreased from over 1000 cm-3 to 750 cm-3. The changes in the accumulation mode aerosol concentrations had a significant effect on the observed cloud microphysics. Observational evidence suggests that the important processes in controlling the Aitken mode concentration which, dominated the total CN concentration, included, scavenging of interstitial aerosol by cloud droplets, enhanced coagulation of Aitken mode aerosol and accumulation mode aerosol due to the increased sea salt aerosol surface area, and dilution of the MBL by free tropospheric air.
On a Mass Spectrometric Angular Distribution Study of the Effusion of the Potassium Chloride Vapor System from Cylindrical Orifices

A mass spectrometric investigation has been made of the angular number distributions of the KCl and K2Cl2 molecules effusing from cylindrical orifices with geometries of L/R = 1−4 at temperatures of 565, 585, and 605°C. It was found that the angular distributions for neither the KCl nor the K2Cl2 molecule agrees with the predictions of the classical Clausing model. The angular distribution of the monomer is more diffuse than predicted by theory, whereas the dimer distribution is more highly focused than the experimental monomer distribution or the theoretical distribution. For a given orifice geometry, progressive defocusing of both beams was observed as the temperature was increased. None of the presently proposed theoretical models is capable of predicting the experimental angular distributions. However, substantial evidence exists to suggest that dissociation of the dimer on the orifice walls is an important factor.
A Model for Flue Gas Desulfurization with Lime Absorbent at Middle Temperature

A model to simulate the desulfurization process for reaction of flue gas with lime at middle temperature (400-800dC) has been established based on the grain model. The rationality of the model has been examined by making comparison between the calculated values of the model and the experimental results. The effects of certain parameters on conversion of CaO have been analyzed. Establishment of this model provides the initial foundation for the development of flue gas desulfurization technology with lime absorbent at middle temperature.
Multidimensional Numerical Dispersion

Numerical dispersion can cause a smearing of otherwise sharp saturation fronts. The usual methods of estimating the magnitude of the smearing effect in one dimension (1D) are shown to apply in two and three dimensions (2 and 3D) as well. Besides the smearing effect, numerical dispersion affects the finite-difference solution of a multidimensional flow problem by rotating the principal flow axes. A method for determining the importance of the rotation effect is discussed. Numerical illustrations are included.
Encoding sinusoidal functions in hybrid automata formalism

Hybrid systems can express a plethora of physical phenomena and systems as they can combine continuous and discrete dynamics. There exist several tools which enable the reachability analysis of hybrid systems modeled as hybrid automata. However, these tools exhibit certain limitations in the type of mathematical operations that they natively support. For example, SpaceEx, a well established tool in the hybrid verification community, supports the use of linear ODEs in the flow of each discrete location. Mathematical functions like alegbraic equations or trigonometric functions have to be encoded as the solutions of a set of ODEs. In this article, we provide a mechanism to define sinusoidal functions that is supported by SpaceEx. We also note how certain Simulink blocks can be translated in hybrid automata.
Zombie Account Detection Based on Community Detection and Uneven Assignation PageRank

In the social media, there are a large amount of potential zombie accounts which may has negative impact on the public opinion. In tradition, PageRank algorithm is used to detect zombie accounts. However, problems such as it requires a large RAM to store adjacent matrix or adjacent list and the value of importance may approximately to zero for large graph exist. To solve the first problem, since the structure of social media makes the graph divisible, we conducted a community detection algorithm – Louvain to decompose the whole graph into 1,002 subgraphs. The modularity of 0.58 shows the result is effective. To solve the second problem, we performed the uneven assignation PageRank algorithm to calculate the importance of node in each community. Then, a threshold is set to distinguish the zombie account and normal accounts. The result shows that about 20% accounts in the dataset are zombie accounts and they center in tier-one cities in China such as Beijing, Shanghai, and Guangzhou. In the future, a classification algorithm with semi-supervised learning can be used to detect zombie accounts.
The Query Complexity of Local Search and Brouwer in Rounds

We consider the query complexity of finding a local minimum of a function defined on a graph, where at most $k$ rounds of interaction with the oracle are allowed. Rounds model parallel settings, where each query takes resources to complete and is executed on a separate processor. Thus the query complexity in $k$ rounds informs how many processors are needed to achieve a parallel time of $k$. We focus on the d-dimensional grid $[n]^d$, where the dimension $d$ is a constant, and consider two regimes for the number of rounds: constant and polynomial in n. We give algorithms and lower bounds that characterize the trade-off between the number of rounds of adaptivity and the query complexity of local search. When the number of rounds $k$ is constant, we show that the query complexity of local search in $k$ rounds is $\Theta\bigl(n^{\frac{d^{k+1} - d^k}{d^k - 1}}\bigl)$, for both deterministic and randomized algorithms. When the number of rounds is polynomial, i.e. $k = n^{\alpha}$ for $0<\alpha<d/2$, the randomized query complexity is $\Theta\left(n^{d-1 - \frac{d-2}{d}\alpha}\right)$ for all $d \geq 5$. For $d=3$ and $d=4$, we show the same upper bound expression holds and give almost matching lower bounds. The local search analysis also enables us to characterize the query complexity of computing a Brouwer fixed point in rounds. Our proof technique for lower bounding the query complexity in rounds may be of independent interest as an alternative to the classical relational adversary method of Aaronson from the fully adaptive setting.
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs

In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control and route planning. As both spatial and temporal patterns are indispensable passenger demand prediction, relevant research has evolved from pure time series to graph-structured data for modeling historical passenger demand data, where a snapshot graph is constructed for each time slot by connecting region nodes via different relational edges (origin-destination relationship, geographical distance, etc.). Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i.e., weights) of passenger demands between two connected regions. aspects in the graph-structure data. representation for DDW is the key to solve the prediction problem. However, existing graph-based solutions fail to simultaneously consider those three crucial aspects of dynamic, directed, and weighted graphs, leading to limited expressiveness when learning graph representations for passenger demand prediction. Therefore, we propose a novel spatiotemporal graph attention network, namely Gallat (Graph prediction with all attention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of dynamic directed and weighted graphs, we build three attention layers to fully capture the spatiotemporal dependencies among different regions across all historical time slots. Moreover, the model employs a subtask to conduct pretraining so that it can obtain accurate results more quickly. We evaluate the proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.
Optical Variability Power Spectrum Analysis of Blazar Sources on Intranight Timescales

We report the first results of a systematic investigation to characterize blazar variability power spectral densities (PSDs) at optical frequencies using densely sampled (5–15 minutes of integration time), high photometric accuracy (≲0.2%–0.5%) R-band intranight light curves, covering timescales ranging from several hours to ∼15 minutes. Our sample consists of 14 optically bright blazars—nine BL Lacertae objects and five flat-spectrum radio quasars (FSRQs)—which have shown statistically significant variability during 29 monitoring sessions. We model the intranight PSDs as simple power laws and derive the best-fit slope along with the uncertainty using the “power spectral response” method. Our main results are as follows: (1) in 19 out of 29 monitoring sessions, the intranight PSDs show an acceptable fit to simple power laws at a rejection confidence ≤90%; (2) for these 19 instances, the PSD slopes show a wide range of 1.4 to 4.0, consistent with the statistical characters of red-noise (slope ∼ 2) and black-noise (slope ≥ 3) stochastic processes; (3) the average PSD slopes for the BL Lac objects and FSRQs are indistinguishable from one another; and (4) the normalization of intranight PSDs for individual blazar sources monitored on more than one occasion turns out to be consistent with one another with a few exceptions. The average PSD slope, 2.9 ± 0.3 (1σ uncertainty), is steeper than that of red noise–type variability found on longer timescales (many decades to days), indicative of a cutoff in the variability spectrum on timescales around a few days at the synchrotron frequencies of the emission spectrum.
The Truth is Out There: Investigating Conspiracy Theories in Text Generation

With the growing adoption of text generation models in today’s society, users are increasingly exposed to machine-generated text. This in turn can leave users vulnerable to the generation of harmful information such as con-spiracy theories. While the propagation of conspiracy theories through social media has been studied, previous work has not evaluated their diffusion through text generation. In this work, we investigate the propensity for language models to generate conspiracy theory text. Our study focuses on testing these models for the elicitation of conspiracy theories and comparing these generations to human-written theories from Reddit. We also introduce a new dataset consisting of conspiracy theory topics, machine-generated conspiracy theories, and human-written conspiracy theories. Our experiments show that many well-known conspiracy theory topics are deeply rooted in the pre-trained language models, and can become more prevalent through different model settings.
Milnor K-theory of p-adic rings

Abstract We study the mod p r {p^{r}} Milnor K-groups of p-adically complete and p-henselian rings, establishing in particular a Nesterenko–Suslin-style description in terms of the Milnor range of syntomic cohomology. In the case of smooth schemes over complete discrete valuation rings we prove the mod p r {p^{r}} Gersten conjecture for Milnor K-theory locally in the Nisnevich topology. In characteristic p we show that the Bloch–Kato–Gabber theorem remains true for valuation rings, and for regular formal schemes in a pro sense.
Dynamical phase error in interacting topological quantum memories

A local Hamiltonian with topological quantum order (TQO) has a robust ground-state degeneracy that makes it an excellent quantum memory candidate. This memory can be corrupted however if part of the state leaves the protected ground-state manifold and returns later with a dynamically accrued phase error. Here we analyze how TQO suppresses this process and use this to quantify the degree to which spectral densities in different topological sectors are correlated. We provide numerical verification of our results by modeling an interacting p-wave superconducting wire.
Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter

General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene. In this work, we design and train a network that predicts 6 DOF grasps from 3D scene information gathered from an on-board sensor such as a wrist-mounted depth camera. Our proposed Volumetric Grasping Network (VGN) accepts a Truncated Signed Distance Function (TSDF) representation of the scene and directly outputs the predicted grasp quality and the associated gripper orientation and opening width for each voxel in the queried 3D volume. We show that our approach can plan grasps in only 10 ms and is able to clear 92% of the objects in real-world clutter removal experiments without the need for explicit collision checking. The real-time capability opens up the possibility for closed-loop grasp planning, allowing robots to handle disturbances, recover from errors and provide increased robustness. Code is available at https://github.com/ethz-asl/vgn.
Decoding Time Lexical Domain Adaptationfor Neural Machine Translation

Machine translation systems are vulnerable to domain mismatch, especially when the task is low-resource. In this setting, out of domain translations are often of poor quality and prone to hallucinations, due to the translation model preferring to predict common words it has seen during training, as opposed to the more uncommon ones from a different domain. We present two simple methods for improving translation quality in this particular setting: First, we use lexical shortlisting in order to restrict the neural network predictions by IBM model computed alignments. Second, we perform n - best list reordering by reranking all translations based on the amount they overlap with each other. Our methods are computationally simpler and faster than alternative approaches, and show a moderate success on low-resource settings with explicit out of domain test sets. However, our methods lose their effectiveness when the domain mismatch is too great, or in high resource setting.