Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanics of Materials and Structures

Hard-particle models assume that the collision between rigid bodies is instantaneous and they propose algebraic equations for the jumps in the motions of the rigid bodies based on an assumed functional form for the impulsive force acting during the collision process. These hard-particle models have the advantage that they are signiﬁcantly less computationally expensive than soft-particle models. Moreover, they are sufﬁciently accurate for some applications and can also be used for preliminary analysis of complicated processes with large numbers of particle interactions. Here, a simple three parameter hard-particle collision model is developed which predicts a smooth transition from normal collisions with no slip to oblique collisions with full slip. This model automatically satisﬁes a number of physical restrictions on the impulsive force. An additional physical restriction on non-negative energy dissipation places nontrivial restrictions on the model parameters. Examples show the importance of improved modeling of the transition region from normal to oblique collisions with full slip.
Observations of the conjugate SAR arcs of September 28-30, 1967. [subauroral red arcs]

Stable subauroral red arcs (SAR arcs) were observed in both the northern and the southern hemispheres on September 28–30, 1967. This particular event has been the object of numerous observations, both from the ground and from various spacecraft. Photometric data regarding various emissions were obtained from Fritz Peak, Colorado, and Richland, Washington [Hoch et al., 1968], from Moscow, Idaho [Ichikawa and Kim, 1969], and from the Ogo 4 spacecraft [Reed and Blamont, 1968]. Related ionospheric parameters were obtained from Alouette 1 and 2 [Clark et al., 1969; Norton and Marovich, 1969; Norton and Findlay, 1969], from the ion traps on Explorer 31 and Ogo 4 [Chandra et al., 1971], and, less directly, from the VLF experiment on Ogo 4 [Carpenter, 1971].
HUVECs-encapsulation via Millimeter-sized Alginate Droplets

Droplet microfluidics are a powerful approach for hydrogel cell encapsulations. Much of the field has focused on single-cell encapsulations with pico-nanoliter droplet volumes necessary for single-cell sequencing or high-throughput screening. These small volumes, however, limit the use of hydrogel droplets for tissue engineering or cell therapies. We describe simple droplet microfluidics to generate millimeter-sized alginate droplets and demonstrate their use for cell encapsulations. This effort builds on our recent efforts, specifically by replacing the glass slide forming the bottom layer of the chamber with a more hydrophobic acrylic (PMMA) layer to improve the alginate-in-oil droplet formation. Using glass layer and PMMA layer devices, we characterized the tunable production of water-in-oil droplets (average droplet lengths ranged from 0.8 to 3.7 mm). Next, PMMA layer devices were used to demonstrate the tunable generation of alginate-in-oil droplets (average droplet lengths ranged from 3-6 mm). Increasing the flow ratio (Q.ratio = Q.oil/Q.alginate) led to more uniform droplets as measured by the coefficient of variance, which was approximately 5%. Finally, a proof-of-use experiment used HUVEC-encapsulated alginate droplets as part of a scratch-healing assay. Specifically, HUVEC-encapsulated droplets (AH droplets) led to the recovery of 3T3 fibroblast monolayers compared to no droplets or cell-free droplets (A droplets). Our results extended the use of simple microfluidics to generate and retrieve millimeter-sized alginate droplets for effective cell encapsulations.
Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation

Modern approaches have proved the huge potential of addressing semantic segmentation as a mask classification task which is widely used in instance-level segmentation. This paradigm trains models by assigning part of object queries to ground truths via conventional one-to-one matching. However, we observe that the popular video semantic segmentation (VSS) dataset has limited categories per video, meaning less than 10% of queries could be matched to receive meaningful gradient updates during VSS training. This inefficiency limits the full expressive potential of all queries.Thus, we present a novel solution THE-Mask for VSS, which introduces temporal-aware hierarchical object queries for the first time. Specifically, we propose to use a simple two-round matching mechanism to involve more queries matched with minimal cost during training while without any extra cost during inference. To support our more-to-one assignment, in terms of the matching results, we further design a hierarchical loss to train queries with their corresponding hierarchy of primary or secondary. Moreover, to effectively capture temporal information across frames, we propose a temporal aggregation decoder that fits seamlessly into the mask-classification paradigm for VSS. Utilizing temporal-sensitive multi-level queries, our method achieves state-of-the-art performance on the latest challenging VSS benchmark VSPW without bells and whistles.
Research on Joint Representation Learning Methods for Entity Neighborhood Information and Description Information

To address the issue of poor embedding performance in the knowledge graph of a programming design course, a joint represen-tation learning model that combines entity neighborhood infor-mation and description information is proposed. Firstly, a graph at-tention network is employed to obtain the features of entity neigh-boring nodes, incorporating relationship features to enrich the structural information. Next, the BERT-WWM model is utilized in conjunction with attention mechanisms to obtain the representation of entity description information. Finally, the final entity vector representation is obtained by combining the vector representations of entity neighborhood information and description information. Experimental results demonstrate that the proposed model achieves favorable performance on the knowledge graph dataset of the pro-gramming design course, outperforming other baseline models.
On Induced Versions of Menger's Theorem on Sparse Graphs

Let $A$ and $B$ be sets of vertices in a graph $G$. Menger's theorem states that for every positive integer $k$, either there exists a collection of $k$ vertex-disjoint paths between $A$ and $B$, or $A$ can be separated from $B$ by a set of at most $k-1$ vertices. Let $\Delta$ be the maximum degree of $G$. We show that there exists a function $f(\Delta) = (\Delta+1)^{\Delta^2+1}$, so that for every positive integer $k$, either there exists a collection of $k$ vertex-disjoint and pairwise anticomplete paths between $A$ and $B$, or $A$ can be separated from $B$ by a set of at most $k \cdot f(\Delta)$ vertices. We also show that the result can be generalized from bounded-degree graphs to graphs excluding a topological minor. On the negative side, we show that no such relation holds on graphs that have degeneracy 2 and arbitrarily large girth, even when $k = 2$. Similar results were obtained independently and concurrently by Hendrey, Norin, Steiner, and Turcotte [arXiv:2309.07905].
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition

We study a streamable attention-based encoder-decoder model in which either the decoder, or both the encoder and decoder, operate on pre-defined, fixed-size windows called chunks. A special end-of-chunk (EOC) symbol advances from one chunk to the next chunk, effectively replacing the conventional end-of-sequence symbol. This modification, while minor, situates our model as equivalent to a transducer model that operates on chunks instead of frames, where EOC corresponds to the blank symbol. We further explore the remaining differences between a standard transducer and our model. Additionally, we examine relevant aspects such as long-form speech generalization, beam size, and length normalization. Through experiments on Librispeech and TED-LIUM-v2, and by concatenating consecutive sequences for long-form trials, we find that our streamable model maintains competitive performance compared to the non-streamable variant and generalizes very well to long-form speech.
Fiber Loop Quantum Buffer for Photonic Qubits

We report a fiber loop quantum buffer based on a low-loss 2$\times$2 switch and a unit delay made of a fiber delay line. We characterize the device by using a two-photon polarization entangled state in which one photon of the entangled photon pair is stored and retrieved at a repetition rate up to 78$\,\rm{kHz}$. The device, which enables integer multiples of a unit delay, can store the qubit state in a unit of fiber delay line up to 5.4$\,\rm{km}$ and the number of loop round-trips up to 3. Furthermore, we configure the device with other active elements to realize integer multiplies and divider of a unit delay of a qubit. The quantum state tomography is performed on the retrieved photon and its entangled photon. We obtain a state fidelity $>94\%$ with a maximum storage time of 52$\,\mu\rm{sec}$. To further characterize the storing and retrieving processes of the device, we perform entanglement-assisted quantum process tomography on the buffered qubit state. The process fidelity of the device is $>$ 0.98. Our result implies that the device preserves the superposition and entanglement of a qubit state from a two-photon polarization-entangled state. This is a significant step towards facilitating applications in optical asynchronous transfer mode (ATM) based quantum networks.
An Automated and Efficient Aerodynamic Design and Analysis Framework Integrated to PANAIR

Aircraft design is an iterative process that requires an estimation of aerodynamic characteristics including drag and lift coefficients, stall behavior, velocity, and pressure profiles repeatedly, especially in the conceptual design phase. PanAir is a high-order aerodynamic panel method-based algorithm developed as a part of the Public Domain Aeronautical Software program mostly with NASA sponsorship. It is based upon potential flow theory and is used to numerically compute lift, induced drag, and moment coefficients of the aircraft in both subsonic and supersonic flight regimes. Quoting from developers it is the most versatile and accurate of all the linear theory panel codes. PanAir is a classical software that requires geometric data as an input in the form of a PanAir-compatible format; however, commonly used Computer-Aided Design Software packages no longer conform to the PanAir input format. Likewise, PanAir produces its output in a PanAir-specific output file which is not compatible with commonly used visualization software. The input geometry required by PanAir and its output, therefore, involves significant manual preand post-processing using intermediary software. The work proposed here is an automated pre and post-processor to be used together with PanAir. With the environment proposed in this work, manipulation of input and output data using several intermediary software to and from PanAir is bypassed successfully. The proposed environment is validated over a Cessna 210 aircraft geometry with a modified NLF (1)-0414 airfoil. The aircraft is numerically analyzed using PanAir together
Overpressure and seals in the North Sea - a model approach

Overpressure prediction in the North Sea is complicated by fault seal assumptions. The computer code, BasinMod-2D, is used to simulate overpressure for a small-scale case study, the Puffin structure, located on the western flank of the Central Graben. This area is characterized by tilted fault blocks and syn-rift sedimentation of Upper Jurassic Fulmar sandstones with a post-rift cover of Cretaceous chalk and Tertiary mudstones. The model, a 10 km W-E section based on published seismic data, is tied to detailed well stratigraphy in 29/10 -2 with pressure data from RFT and mudweight. The model fails to replicate observed overpressure data. The simulated overpressure is 20 MPa lower than the measured pressure of 85 MPa in the Puffin structure. The model fails to simulate hydrocarbon accumulation in the Fulmar sandstone reservoir, in contradiction to the observed bitumen indication of paleoaccumulation. The model also raises important questions as to the validity of uncalibrated parameter manipulation as a means of forward modelling fault seal, with specific reference to shale permeability. We contend that these failures are not due to model limitations, but are the result of the size-scale of this case study. The inference is that overpressure and hydrocarbon creation and transmission ismore » on a size scale fault seal at one time can leak hydrocarbons at another time. We consider that the drive for this comes from pressure increase during hydrocarbon generation in the deep kitchen. « less
Graph IRs for Impure Higher-Order Languages (Technical Report)

This is a companion report for the OOPSLA 2023 paper of the same title, presenting a detailed end-to-end account of the $\lambda^*_{\mathsf{G}}$ graph IR, at a level of detail beyond a regular conference paper. Our first concern is adequacy and soundness of $\lambda^*_{\mathsf{G}}$, which we derive from a direct-style imperative functional language (a variant of Bao et al.'s $\lambda^*$-calculus with reachability types and a simple effect system) by a series of type-preserving translations into a calculus in monadic normalform (MNF). Static reachability types and effects entirely inform $\lambda^*_{\mathsf{G}}$'s dependency synthesis. We argue for its adequacy by proving its functional properties along with dependency safety via progress and preservation lemmas with respect to a notion of call-by-value (CBV) reduction that checks the observed order of effects. Our second concern is establishing the correctness of $\lambda^*_{\mathsf{G}}$'s equational rules that drive compiler optimizations (e.g., DCE, $\lambda$-hoisting, etc.), by proving contextual equivalence using logical relations. A key insight is that the functional properties of dependency synthesis permit a logical relation on $\lambda^*_{\mathsf{G}}$ in MNF in terms of previously developed logical relations for the direct-style $\lambda^*$-calculus. Finally, we also include a longer version of the conference paper's section on code generation and code motion for $\lambda^*_{\mathsf{G}}$ as implemented in Scala~LMS.
Convergence of ADAM with Constant Step Size in Non-Convex Settings: A Simple Proof

In neural network training, RMSProp and ADAM remain widely favoured optimization algorithms. One of the keys to their performance lies in selecting the correct step size, which can significantly influence their effectiveness. It is worth noting that these algorithms performance can vary considerably, depending on the chosen step sizes. Additionally, questions about their theoretical convergence properties continue to be a subject of interest. In this paper, we theoretically analyze a constant stepsize version of ADAM in the non-convex setting. We show sufficient conditions for the stepsize to achieve almost sure asymptotic convergence of the gradients to zero with minimal assumptions. We also provide runtime bounds for deterministic ADAM to reach approximate criticality when working with smooth, non-convex functions.
De novo drug designing coupled with brute force screening and structure guided lead optimization gives highly specific inhibitor of METTL3: a potential cure for Acute Myeloid Leukaemia

Expression of METTL3, a SAM dependent methyltransferase, which deposits m6A on mRNA is linked to poor prognosis in Acute Myeloid Leukaemia and other type of cancers. Down regulation of this epitranscriptomic regulator has been found to inhibit cancer progression. Silencing the methyltransferase activity of METTL3 is a lucrative strategy to design anticancer drugs. In this study 3600 commercially available molecules were screened against METTL3 using brute force screening approach. However, none of these compounds take advantage of the unique Y-shaped binding cavity of the protein, raising the need for de novo drug designing strategies. As such, 125 branched, Y-shaped molecules were designed by “stitching” together the chemical fragments of the best inhibitors that interact strongly with the METTL3 binding pocket. This results in molecules that have the three-dimensional structure and functional groups which enable it to fit in the METTL3 cavity like fingers in a glove, having unprecedented selectivity and binding affinities. The designed compounds were further refined based on Lipinski’s rule, docking score and synthetic accessibility. The molecules faring well in these criteria were simulated for 100ns to check the stability of the protein inhibitor complex followed by binding free energy calculation.
Defining the SPCA2C interactome identifies unique links to Store-Operated Ca2+ Entry

Calcium (Ca2+) is critical for normal cell function and several protein networks are required for Ca2+ signaling. In the pancreas, regulated changes in cytosolic Ca2+ allow for the exocytosis of zymogen granules and altered Ca2+ signaling underlies pancreatic pathologies. Previously, our laboratory showed a pancreas-specific isoform of secretory pathway Ca2+-ATPase 2 (SPCA2C) affects multiple pathways involved in Ca2+ homeostasis. The goal of this study was to define the SPCA2C interactome that contributes to these processes. Using proximity-dependent biotin identification, BioID, we expressed SPCA2C-BirA*HA in HEK293 cells with constitutive Orai1 expression. In silico modeling of SPCA2C showed a highly dynamic cytosolic C-terminus, revealing a putative site for interactions and was selected for BirA* fusion. 150 candidate SPCA2C interactors were identified. Gene Ontology and KEGG Pathway analyses supported localization of SPCA2C to the endoplasmic reticulum as well as function in Ca2+ signaling and suggested roles in vesicular transport. SPCA2C interactions with stromal interaction molecule 1 (STIM1), extended synaptotagmin-1 (ESYT1), and aspartate beta-hydroxylase (ASPH) were validated. Coiled-coil domain containing protein 47 (CCDC47) ranked as a high confidence SPCA2C-interactor which was confirmed through immunoprecipitation and co-localization with SPCA2C. CCDC47 interactions with SPCA2C, STIM1 and Orai1 were maintained following deletion of the CCDC47 luminal and cytosolic domains while deletion of only the coiled-coil domain altered CCDC47 localization and decreased interactions with STIM1 and Orai1. Overall, this study defines several novel protein interactions for SPCA2C and suggests it may be involved in affecting CCDC47, STIM1 and Orai1 function.
A Real-Time Active Speaker Detection System Integrating an Audio-Visual Signal with a Spatial Querying Mechanism

We introduce a distinctive real-time, causal, neural network-based active speaker detection system optimized for low-power edge computing. This system drives a virtual cinematography module and is deployed on a commercial device. The system uses data originating from a microphone array and a 360-degree camera. Our network requires only 127 MFLOPs per participant, for a meeting with 14 participants. Unlike previous work, we examine the error rate of our network when the computational budget is exhausted, and find that it exhibits graceful degradation, allowing the system to operate reasonably well even in this case. Departing from conventional DOA estimation approaches, our network learns to query the available acoustic data, considering the detected head locations. We train and evaluate our algorithm on a realistic meetings dataset featuring up to 14 participants in the same meeting, overlapped speech, and other challenging scenarios.
Empowering Visually Impaired Individuals: A Novel Use of Apple Live Photos and Android Motion Photos

Numerous applications have been developed to assist visually impaired individuals that employ a machine learning unit to process visual input. However, a critical challenge with these applications is the sub-optimal quality of images captured by the users. Given the complexity of operating a camera for visually impaired individuals, we advocate for the use of Apple Live Photos and Android Motion Photos technologies. In this study, we introduce a straightforward methodology to evaluate and contrast the efficacy of Live/Motion Photos against traditional image-based approaches. Our findings reveal that both Live Photos and Motion Photos outperform single-frame images in common visual assisting tasks, specifically in object classification and VideoQA. We validate our results through extensive experiments on the ORBIT dataset, which consists of videos collected by visually impaired individuals. Furthermore, we conduct a series of ablation studies to delve deeper into the impact of deblurring and longer temporal crops.
Test Case Generation and Test Oracle Support for Testing CPSs using Hybrid Models

Cyber-Physical Systems (CPSs) play a central role in the behavior of a wide range of autonomous physical systems such as medical devices, autonomous vehicles, and smart homes, many of which are safety-critical. CPSs are often specified iteratively as a sequence of models at different levels that can be tested via simulation systems at early stages of their development cycle. One such model is a hybrid automaton; these are used frequently for CPS applications and have the advantage of encapsulating both continuous and discrete CPS behaviors. When testing CPSs, engineers can take advantage of these models to generate test cases that target both types of these behaviors. Moreover, since these models are constructed early in the development process for CPSs, they allow test cases to be generated early in that process for those CPSs, even before simulation models of the CPSs have been designed. One challenge when testing CPSs is that these systems may operate differently even under an identically applied test scenario. In such cases, we cannot employ test oracles that use predetermined deterministic behaviors; instead, test oracles should consider sets of desired behaviors in order to determine whether the CPS has behaved appropriately. In this paper we present a test case generation technique, HYTEST, that generates test cases based on hybrid models, accompanied by appropriate test oracles, for use in testing CPSs early in their development cycle. To evaluate the effectiveness and efficiency of HYTEST, we conducted an empirical study in which we applied the technique to several CPSs and measured its ability to detect faults in those CPSs and the amount of time required to perform the testing process. The results of the study show that HYTEST was able to detect faults more effectively and efficiently than the baseline techniques we compare it to.
DEVELOPING SPATIALLY-EXPLICIT ASSESSMENT TOOLS FOR EASTERN OYSTER IN CHESAPEAKE BAY

Title of Document: DEVELOPING SPATIALLY-EXPLICIT ASSESSMENT TOOLS FOR EASTERN OYSTER IN CHESAPEAKE BAY Maude Elizabeth Livings, M.S, 2011 Directed By: Dr. Michael Wilberg Marine, Estuarine, and Environmental Sciences Decreasing abundance of the eastern oyster Crassostrea virginica in Chesapeake Bay is of concern because of its ecological, economic, and cultural importance. The objective of my study was to develop methods for conducting stock assessments of eastern oysters in Maryland waters of Chesapeake Bay that estimate abundance, recruitment, and fishing mortality at regional scales. First, I evaluated how spatial patterns in autocorrelation of recruitment and adult relativity density varied over time by fitting semivariogram models to survey data for each year. This information was then used to determine appropriate scales for my second objective which was to develop a stage-based model for the lower Potomac River using data from the Maryland Department of Natural Resources fall dredge survey and fishery data from the Potomac River Fisheries Commission. Estimated abundance declined to approximately 39% of that in 1990. The analyses will provide a platform for regional management of eastern oysters. DEVELOPING SPATIALLY-EXPLICIT ASSESSMENT TOOLS FOR EASTERN OYSTER IN CHESAPEAKE BAY