Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal for Codification of a DSM Design Approach for Cold-Formed Steel Short-to Intermediate Angle Columns

This paper presents a proposal for the codification of an efficient design approach for cold-formed steel short-to-intermediate equal-leg angle columns, consisting of a slight modification of a design approach developed by Dinis & Camotim (2015) and based on the Direct Strength Method (DSM). After (i) collecting the available experimental and numerical failure load data, comprising fixed-ended and pin-ended columns with several geometries (cross-section dimensions and length) and reported by various researchers, and (ii) briefly reviewing the mechanical reasoning behind the proposed procedures, the search for new/simpler expressions to provide the DSM design curves is addressed. Their merits are assessed through (i) the quality of the estimates of the available failure load data and (ii) the determination of the corresponding LRFD resistance factors. Concerning the latter, it is shown that the value recommended, for compression members, by the North American Specification (NAS) for the Design of Cold-Formed Steel Structural Members (AISI 2012), namely φc=0.85, can also be adopted for angle columns.
Genome-Wide Characterization Analysis of CCT Genes in Raphanus sativus and Their Potential Role in Flowering and Abiotic Stress Response

: CCT genes play vital roles in ﬂowering, plant growth, development, and response to abiotic stresses. Although they have been reported in many plants, the characterization and expression pattern of CCT genes is still limited in R. sativus . In this study, a total of 58 CCT genes were identiﬁed in R. sativus . Phylogenetic tree, gene structure, and conserved domains revealed that all CCT genes were classiﬁed into three groups: COL , CMF , and PRR . Genome-wide identiﬁcation and evolutionary analysis showed that segmental duplication expanded the CCT gene families considerably, with the LF subgenome retaining more CCT genes. We observed strong purifying selection pressure for CCT genes. RsCCT genes showed tissue speciﬁcity, and some genes (such as RsCCT22 , RsCCT36 , RsCCT42 and RsCCT51 ) were highly expressed in ﬂowers. Promoter cis-elements and RNA-seq data analysis showed that RsCCT genes could play roles in controlling ﬂowering through the photoperiodic pathway and vernalization pathway. The expression proﬁles of RsCCT genes under Cd, Cr, Pb, and heat and salt stresses revealed that many RsCCT genes could respond to one or more abiotic stresses. Our ﬁndings could provide essential information for further studies on the function of RsCCT genes.
Wave-current interactions in coastal tidal currents

Various methods for approximating the nonlinear advection terms in a high resolution tidal model with complex coastal boundaries have been implemented and tested. The model, driven by the dominant M2 tidal component at the open boundaries, has been applied to a model domain with 100 meter grid resolution for the Tjeldsundet channel in northern Norway. Overtides, intensivated jets and eddy structures appear in the current fields of the full nonlinear simulations. How these flow features depend on the way the friction terms are calculated and the way the nonlinear advection terms are calculated in a zone near the coastal boundaries, are discussed. Some comparison with field measurements have also been made.
Development of Students' Spatial Thinking in a Unit on Geometric Motions and Area

In this study we investigated the application and development of spatial thinking in an instructional unit on area and motions, part of a largescale curriculum development project funded by the National Science Foundation. We also investigated the role of noncomputer and computer interactions in that development. We collected data from paper-and-pencil assessments and case studies as we pilot tested the unit with 2 third graders and field tested it in 3 third-grade classrooms. Results revealed strong positive effects on spatial abilities and the establishment of spatial-numeric connections; they also provided information about students' strategies for solving the unit's spatial and spatial-numeric problems. A distinguishing feature of these strategies was the degree to which students applied a unitizing operation to construct spatial and numerical units and "units of units. "
TRUNCATED CODES AND TERNARY CUBE COMPOUNDS

Perfect codes in the n -dimensional grid Λ n of Z n (0 < n ∈ Z ) and its quotient toroidal grids were obtained via the truncated distance ρ ( u, v ) in Z n given between u = ( u 1 , · · · , u n ) and v = ( v 1 , . . . , v n ) in Z n as the graph distance h ( u, v ) in Λ n if | u i − v i | ≤ 1, for all i ∈ { 1 , . , n } , and as n + 1, otherwise. Such perfect codes are extended to non-lattice superlattice graphs Γ n obtained by glueing ternary n -cubes along each of their codimension 1 ternary subcubes. We ascertain the existence of an inﬁnite number of isolated perfect truncated-metric codes of radius 2 in Γ n for n = 2 and conjecture such existence for n > 2 with radius n . We ask whether there are suitable notions replacing quotient toroidal grids of Λ n for the case of Γ n .
An empirical study of CTC based models for OCR of Indian languages

Recognition of text on word or line images, without the need for sub-word segmentation has become the mainstream of research and development of text recognition for Indian languages. Modelling unsegmented sequences using Connectionist Temporal Classification (CTC) is the most commonly used approach for segmentation-free OCR. In this work we present a comprehensive empirical study of various neural network models that uses CTC for transcribing step-wise predictions in the neural network output to a Unicode sequence. The study is conducted for 13 Indian languages, using an internal dataset that has around 1000 pages per language. We study the choice of line vs word as the recognition unit, and use of synthetic data to train the models. We compare our models with popular publicly available OCR tools for end-to-end document image recognition. Our end-to-end pipeline that employ our recognition models and existing text segmentation tools outperform these public OCR tools for 8 out of the 13 languages. We also introduce a new public dataset called Mozhi for word and line recognition in Indian language. The dataset contains more than 1.2 million annotated word images (120 thousand text lines) across 13 Indian languages. Our code, trained models and the Mozhi dataset will be made available at http://cvit.iiit. ac.in/research/projects/cvit-projects/
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference

This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting. Impala builds upon recent solutions that combine the complementary strengths of homomorphic encryption (HE) and secure multi-party computation (MPC). A series of protocol optimizations are developed to reduce both communication and performance bottlenecks. First, we remove MPC's overwhelmingly high communication cost from the client by introducing a proxy server and developing a low-overhead key switching technique. Key switching reduces the clients bandwidth by multiple orders of magnitude, however the communication between the proxy and cloud is still excessive. Second, to we develop an optimized garbled circuit that leverages truncated secret shares for faster evaluation and less proxy-cloud communication. Finally, we propose sparse HE convolution to reduce the computational bottleneck of using HE. Compared to the state-of-the-art, these optimizations provide a bandwidth savings of over 3X and speedup of 4X for private deep learning inference.
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning

Contrastive learning pre-trains an image encoder using a large amount of unlabeled data such that the image encoder can be used as a general-purpose feature extractor for various downstream tasks. In this work, we propose PoisonedEncoder , a data poisoning attack to contrastive learning. In particular, an attacker injects carefully crafted poisoning inputs into the unlabeled pre-training data, such that the downstream classifiers built based on the poisoned encoder for multiple target downstream tasks simultaneously classify attacker-chosen, arbitrary clean inputs as attacker-chosen, arbitrary classes. We formulate our data poisoning attack as a bilevel optimization problem, whose solution is the set of poisoning inputs; and we propose a contrastive-learning-tailored method to approximately solve it. Our evaluation on multiple datasets shows that PoisonedEncoder achieves high attack success rates while maintaining the testing accuracy of the downstream classifiers built upon the poisoned encoder for non-attacker-chosen inputs. We also evaluate five defenses against PoisonedEncoder, including one pre-processing , three in-processing , and one post-processing defenses. Our results show that these defenses can decrease the attack success rate of PoisonedEncoder, but they also sacrifice the utility of the encoder or require a large clean pre-training dataset.
Virtually Full-duplex Cell-Free Massive MIMO with Access Point Mode Assignment

We consider a cell-free massive multiple-input multiple-output (MIMO) network utilizing a virtually full-duplex (vFD) mode, where access points (APs) with a downlink (DL) mode and those with an uplink (UL) mode simultaneously serve DL and UL users (UEs). In order to maximize the sum spectral efficiency (SE) of the DL and UL transmissions, we formulate a mixed-integer optimization problem to jointly design the AP mode assignment and power control. This problem is subject to minimum per-UE SE requirements, per-AP power control, and per-UL UE power constraints. By employing the successive convex approximation technique, we propose an algorithm to obtain a stationary solution of the formulated problem. Numerical results show that the proposed vFD approach can provide a sum SE that is 2.5 and 1.5 times larger than the traditional half-duplex and heuristic baseline schemes, respectively, in terms of 95%-likely sum SE.
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation

Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference. The chosen set is typically determined by separately trained alignment model parameters, independent of the source-sentence context at inference time. While vocabulary selection appears competitive with respect to automatic quality metrics in prior work, we show that it can fail to select the right set of output words, particularly for semantically non-compositional linguistic phenomena such as idiomatic expressions, leading to reduced translation quality as perceived by humans. Trading off latency for quality by increasing the size of the allowed set is often not an option in real-world scenarios. We propose a model of vocabulary selection, integrated into the neural translation model, that predicts the set of allowed output words from contextualized encoder representations. This restores translation quality of an unconstrained system, as measured by human evaluations on WMT newstest2020 and idiomatic expressions, at an inference latency competitive with alignment-based selection using aggressive thresholds, thereby removing the dependency on separately trained alignment models.
Task Allocation for Energy Optimization in Fog Computing Networks With Latency Constraints

Fog networks offer computing resources of varying capacities at different distances from end users. A Fog Node (FN) closer to the network edge may have less powerful computing resources compared to the cloud, but the processing of computational tasks in FN limits long-distance transmission. How should the tasks be distributed between fog and cloud nodes? We formulate a universal non-convex Mixed-Integer Nonlinear Programming (MINLP) problem minimizing task transmission- and processing-related energy with delay constraints to answer this question. It is transformed with Successive Convex Approximation (SCA) and decomposed using the primal and dual decomposition techniques. Two practical algorithms called Energy-EFFicient Resource Allocation (EEFFRA) and Low-Complexity (LC)-EEFFRA are proposed and their effectiveness is tested for various network and traffic scenarios. Using EEFFRA/LC-EEFFRA can significantly decrease the number of computational requests with unmet delay requirements when compared with baseline solutions (from 48% to 24% for 10 MB requests). Utilizing Dynamic Voltage and Frequency Scaling (DVFS) minimizes energy consumption (by one-third) while satisfying delay requirements.
Selective Evaporation at the Nozzle Exit in Piezoacoustic Inkjet Printing

In practical applications of inkjet printing the nozzles in a printhead have intermittent idle periods, during which ink can evaporate from the nozzle exit. Inks are usually multicomponent where each component has its own characteristic evaporation rate resulting in concentration gradients within the ink. These gradients may directly and indirectly (via Marangoni flows) alter the jetting process and thereby its reproducibility and the resulting print quality. In the present work, we study selective evaporation from an inkjet nozzle for water-glycerol mixtures. Through experiments, analytical modeling, and numerical simulations, we investigate changes in mixture composition with drying time. By monitoring the acoustics within the printhead, and subsequently modeling the system as a mass-spring-damper system, the composition of the mixture can be obtained as a function of drying time. The results from the analytical model are validated using numerical simulations of the full fluid mechanical equations governing the printhead flows and pressure fields. Furthermore, the numerical simulations reveal that the time independent concentration gradient we observe in the experiments is due to the steady state of water flux through the printhead. Finally, we measure the number of drop formation events required in this system before the mixture concentration within the nozzle attains the initial (pre-drying) value, and find a stronger than exponential trend in the number of drop formations required. These results shed light on the complex physiochemical hydrodynamics associated with the drying of ink at a printhead nozzle, and help in increasing the stability and reproducibility of inkjet printing.
Characterization of Lipschitz Functions via the Commutators of Maximal Function on Stratified Lie Groups

In this paper, the main aim is to consider the boundedness of the Hardy-Littlewood maximal commutator M b and the nonlinear commutator [ b, M ] on the Lebesgue spaces and Morrey spaces over some stratiﬁed Lie group G when b belongs to the Lipschitz space, by which some new characterizations of the Lipschitz spaces on Lie group are given.
Lithium depletion boundary, stellar associations, and Gaia

Stellar ages are key to improving our understanding of different astrophysical phenomena. However, many techniques to estimate stellar ages are highly model-dependent. The lithium depletion boundary (LDB), based on the presence or absence of lithium in low-mass stars, can be used to derive ages in stellar associations of between 20 and 500~Ma. The purpose of this work is to revise former LDB ages in stellar associations in a consistent way, taking advantage of the homogeneous \textit{Gaia} parallaxes as well as bolometric luminosity estimations that do not rely on monochromatic bolometric corrections. We studied nine open clusters and three moving groups characterised by a previous determination of the LDB age. We gathered all the available information from our data and the literature: membership, distances, photometric data, reddening, metallicity, and surface gravity. We re-assigned membership and calculated bolometric luminosities and effective temperatures using distances derived from Gaia DR2 and multi-wavelength photometry for individual objects around the former LDB. We located the LDB using a homogeneous method for all the stellar associations. Finally, we estimated the age by comparing it with different evolutionary models. We located the LDB for the twelve stellar associations and derived their ages using several theoretical evolutionary models. We compared the LDB ages among them, along with data obtained with other techniques, such as isochrone fitting, ultimately finding some discrepancies among the various approaches. Finally, we remark that the 32 Ori MG is likely to be composed of at least two populations of different ages.
Accelerometry-Based Classification of Circulatory States During Out-of-Hospital Cardiac Arrest

Objective: Exploit accelerometry data for an automatic, reliable, and prompt detection of spontaneous circulation during cardiac arrest, as this is both vital for patient survival and practically challenging. Methods: We developed a machine learning algorithm to automatically predict the circulatory state during cardiopulmonary resuscitation from 4-second-long snippets of accelerometry and electrocardiogram (ECG) data from pauses of chest compressions of real-world defibrillator records. The algorithm was trained based on 422 cases from the German Resuscitation Registry, for which ground truth labels were created by a manual annotation of physicians. It uses a kernelized Support Vector Machine classifier based on 49 features, which partially reflect the correlation between accelerometry and electrocardiogram data. Results: Evaluating 50 different test-training data splits, the proposed algorithm exhibits a balanced accuracy of 81.2%, a sensitivity of 80.6%, and a specificity of 81.8%, whereas using only ECG leads to a balanced accuracy of 76.5%, a sensitivity of 80.2%, and a specificity of 72.8%. Conclusion: The first method employing accelerometry for pulse/no-pulse decision yields a significant increase in performance compared to single ECG-signal usage. Significance: This shows that accelerometry provides relevant information for pulse/no-pulse decisions. In application, such an algorithm may be used to simplify retrospective annotation for quality management and, moreover, to support clinicians to assess circulatory state during cardiac arrest treatment.
Continuous Interior Penalty stabilization for divergence-free finite element methods

In this paper, we propose, analyze and test numerically a pressure-robust stabilized finite element for a linearized problem in incompressible fluid mechanics, namely, the steady Oseen equation with low viscosity. Stabilization terms are defined by jumps of different combinations of derivatives for the convective term over the element faces of the triangulation of the domain. With the help of these stabilizing terms, and the fact the finite element space is assumed to provide a point-wise divergence-free velocity, an $\mathcal O\big(h^{k+\frac 12}\big)$ error estimate in the $L^2$-norm is proved for the method (in the convection-dominated regime), and optimal order estimates in the remaining norms of the error. Numerical results supporting the theoretical findings are provided.
dewolf: Improving Decompilation by leveraging User Surveys

Analyzing third-party software such as malware or firmware is a crucial task for security analysts. Although various approaches for automatic analysis exist and are the subject of ongoing research, analysts often have to resort to manual static analysis to get a deep understanding of a given binary sample. Since the source code of encountered samples is rarely available, analysts regularly employ decompilers for easier and faster comprehension than analyzing a binary's disassembly. In this paper, we introduce our decompilation approach dewolf. We developed a variety of improvements over the previous academic state-of-the-art decompiler and some novel algorithms to enhance readability and comprehension, focusing on manual analysis. To evaluate our approach and to obtain a better insight into the analysts' needs, we conducted three user surveys. The results indicate that dewolf is suitable for malware comprehension and that its output quality noticeably exceeds Ghidra and Hex-Rays in certain aspects. Furthermore, our results imply that decompilers aiming at manual analysis should be highly configurable to respect individual user preferences. Additionally, future decompilers should not necessarily follow the unwritten rule to stick to the code-structure dictated by the assembly in order to produce readable output. In fact, the few cases where dewolf already cracks this rule lead to its results considerably exceeding other decompilers. We publish a prototype implementation of dewolf and all survey results on GitHub.
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder

Although generative facial prior and geometric prior have recently demonstrated high-quality results for blind face restoration, producing fine-grained facial details faithful to inputs remains a challenging problem. Motivated by the classical dictionary-based methods and the recent vector quantization (VQ) technique, we propose a VQ-based face restoration method - VQFR. VQFR takes advantage of high-quality low-level feature banks extracted from high-quality faces and can thus help recover realistic facial details. However, the simple application of the VQ codebook cannot achieve good results with faithful details and identity preservation. Therefore, we further introduce two special network designs. 1). We first investigate the compression patch size in the VQ codebook and find that the VQ codebook designed with a proper compression patch size is crucial to balance the quality and fidelity. 2). To further fuse low-level features from inputs while not"contaminating"the realistic details generated from the VQ codebook, we proposed a parallel decoder consisting of a texture decoder and a main decoder. Those two decoders then interact with a texture warping module with deformable convolution. Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods.
Addressing Confounding Feature Issue for Causal Recommendation

In recommender systems, some features directly affect whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, short videos are objectively easier to finish even though the user may not like the video. We term such feature as confounding feature, and video length is a confounding feature in video recommendation. If we fit a model on such interaction data, just as done by most data-driven recommender systems, the model will be biased to recommend short videos more, and deviate from user actual requirement. This work formulates and addresses the problem from the causal perspective. Assuming there are some factors affecting both the confounding feature and other item features, e.g., the video creator, we find the confounding feature opens a backdoor path behind user-item matching and introduces spurious correlation. To remove the effect of backdoor path, we propose a framework named Deconfounding Causal Recommendation(DCR), which performs intervened inference with do-calculus. Nevertheless, evaluating do-calculus requires to sum over the prediction on all possible values of confounding feature, significantly increasing the time cost. To address the efficiency challenge, we further propose a mixture-of-experts (MoE) model architecture, modeling each value of confounding feature with a separate expert module. Through this way, we retain the model expressiveness with few additional costs. We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost. We release our code at: https://github.com/zyang1580/DCR.