Miracle-dz/UniFAR-FTUs huggingface dataset:
Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis

Caseworkers are trained to write detailed narratives about families in Child-Welfare (CW) which informs collaborative high-stakes decision-making. Unlike other administrative data, these narratives offer a more credible source of information with respect to workers’ interactions with families as well as underscore the role of systemic factors in decision-making. SIGCHI researchers have emphasized the need to understand human discretion at the street-level to be able to design human-centered algorithms for the public sector. In this study, we conducted computational text analysis of casenotes at a child-welfare agency in the midwestern United States and highlight patterns of invisible street-level discretionary work and latent power structures that have direct implications for algorithm design. Casenotes offer a unique lens for policymakers and CW leadership towards understanding the experiences of on-the-ground caseworkers. As a result of this study, we highlight how street-level discretionary work needs to be supported by sociotechnical systems developed through worker-centered design. This study offers the first computational inspection of casenotes and introduces them to the SIGCHI community as a critical data source for studying complex sociotechnical systems.
Pre-Calabi--Yau algebras and homotopy double Poisson gebras

We prove that the notion of a curved pre-Calabi--Yau algebra is equivalent to the notion of a curved homotopy double Poisson gebra, thereby settling the equivalence between the two ways to define derived noncommutative Poisson structures. We actually prove that the respective differential graded Lie algebras controlling both deformation theories are isomorphic.This allows us to apply the recent developments of the properadic calculus in order to establish the homotopical properties of curved pre-Calabi--Yau algebras: infinity-morphisms, homotopy transfer theorem, formality, Koszul hierarchy, and twisting procedure.
Deep Regression Ensembles

We introduce a methodology for designing and training deep neural networks (DNN) that we call “Deep Regression Ensembles" (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE has two components, randomly drawn input weights and output weights trained myopically (as if the final output layer) using linear ridge regression. Within a layer, each neuron uses a different subset of inputs and a different ridge penalty, constituting an ensemble of random feature ridge regressions. Our experiments show that a single DRE architecture is at par with or exceeds state-of-the-art DNN in many data sets. Yet, because DRE neural weights are either known in closed-form or randomly drawn, its computational cost is orders of magnitude smaller than DNN.
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing

With diverse presentation attacks emerging continually, generalizable face anti-spoofing (FAS) has drawn growing attention. Most existing methods implement domain generalization (DG) on the complete representations. However, different image statistics may have unique properties for the FAS tasks. In this work, we separate the complete representation into content and style ones. A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space. Then, to obtain a generalized representation, a contrastive learning strategy is developed to emphasize liveness-related style information while suppress the domain-specific one. Finally, the representations of the correct assemblies are used to distinguish between living and spoofing during the inferring. On the other hand, despite the decent performance, there still exists a gap between academia and industry, due to the difference in data quantity and distribution. Thus, a new large-scale benchmark for FAS is built up to further evaluate the performance of algorithms in reality. Both qualitative and quantitative results on existing and proposed benchmarks demonstrate the effectiveness of our methods. The codes will be available at https://github.com/wangzhuo2019/SSAN.
Constrained Structured Optimization and Augmented Lagrangian Proximal Methods

We investigate and develop numerical methods for ﬁnite-dimensional constrained structured optimization problems. O ﬀ ering a comprehensive yet simple and expressive language, this problem class provides a modeling framework for a variety of applications. A general and ﬂexible algorithm is proposed that interlaces proximal methods and safeguarded augmented Lagrangian schemes. We provide a theoretical character-ization of the algorithm and its asymptotic properties, deriving convergence results for fully nonconvex problems. Adopting a proximal gradient method with an oracle as a formal tool, it is demonstrated how the inner subproblems can be solved by o ﬀ -the-shelf methods for composite optimization, without introducing slack variables and despite the appearance of set-valued projections. Finally, we describe our open-source matrix-free implementation of the proposed algorithm and test it numerically. Illustrative examples show the versatility of constrained structured programs as a modeling tool, expose di ﬃ culties arising in this vast problem class and highlight beneﬁts of the implicit approach developed.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the second step of this procedure in the context of fine-tuning large pre-trained models, where fine-tuned models often appear to lie in a single low error basin. We show that averaging the weights of multiple models fine-tuned with different hyperparameter configurations often improves accuracy and robustness. Unlike a conventional ensemble, we may average many models without incurring any additional inference or memory costs -- we call the results"model soups. "When fine-tuning large pre-trained models such as CLIP, ALIGN, and a ViT-G pre-trained on JFT, our soup recipe provides significant improvements over the best model in a hyperparameter sweep on ImageNet. The resulting ViT-G model, which attains 90.94% top-1 accuracy on ImageNet, achieved a new state of the art. Furthermore, we show that the model soup approach extends to multiple image classification and natural language processing tasks, improves out-of-distribution performance, and improves zero-shot performance on new downstream tasks. Finally, we analytically relate the performance similarity of weight-averaging and logit-ensembling to flatness of the loss and confidence of the predictions, and validate this relation empirically. Code is available at https://github.com/mlfoundations/model-soups.
Controlling the Flexibility of Non-Gaussian Processes Through Shrinkage Priors

The normal inverse Gaussian (NIG) and generalized asymmetric Laplace (GAL) distributions can be seen as skewed and semi-heavy-tailed extensions of the Gaussian distribution. Models driven by these more ﬂexible noise distributions are then re-garded as ﬂexible extensions of simpler Gaussian models. Inferential procedures tend to overestimate the degree of non-Gaussianity in the data and therefore we propose controlling the ﬂexibility of these non-Gaussian models by adding sensible priors in the inferential framework that contract the model towards Gaussianity. In our venture to derive sensible priors, we also propose a new intuitive parameterization of the non-Gaussian models and discuss how to implement them eﬃciently in Stan . The methods are derived for a generic class of non-Gaussian models that include spatial Mat´ern ﬁelds, autoregressive models for time series, and simultaneous autoregressive models for aerial data. The results are illustrated with a simulation study and geostatistics application, where priors that penalize model complexity were shown to lead to more robust estimation and give preference to the Gaussian model, while at the same time allowing for non-Gaussianity if there is suﬃcient evidence in the data.
On Embeddings for Numerical Features in Tabular Deep Learning

Recently, Transformer-like deep architectures have shown strong performance on tabular data problems. Unlike traditional models, e.g., MLP, these architectures map scalar values of numerical features to high-dimensional embeddings before mixing them in the main backbone. In this work, we argue that embeddings for numerical features are an underexplored degree of freedom in tabular DL, which allows constructing more powerful DL models and competing with GBDT on some traditionally GBDT-friendly benchmarks. We start by describing two conceptually different approaches to building embedding modules: the first one is based on a piecewise linear encoding of scalar values, and the second one utilizes periodic activations. Then, we empirically demonstrate that these two approaches can lead to significant performance boosts compared to the embeddings based on conventional blocks such as linear layers and ReLU activations. Importantly, we also show that embedding numerical features is beneficial for many backbones, not only for Transformers. Specifically, after proper embeddings, simple MLP-like models can perform on par with the attention-based architectures. Overall, we highlight embeddings for numerical features as an important design aspect with good potential for further improvements in tabular DL.
The Forward Physics Facility at the High-Luminosity LHC

High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF’s physics potential.
Neural Data-Dependent Transform for Learned Image Compression

Learned image compression has achieved great success due to its excellent modeling capacity, but seldom further considers the Rate-Distortion Optimization (RDO) of each input image. To explore this potential in the learned codec, we make the first attempt to build a neural data-dependent transform and introduce a continuous online mode decision mechanism to jointly optimize the coding efficiency for each individual image. Specifically, apart from the image content stream, we employ an additional model stream to generate the transform parameters at the decoder side. The pres-ence of a model stream enables our model to learn more abstract neural-syntax, which helps cluster the latent repre-sentations of images more compactly. Beyond the transform stage, we also adopt neural-syntax based post-processing for the scenarios that require higher quality reconstructions regardless of extra decoding overhead. Moreover, the in-volvement of the model stream further makes it possible to optimize both the representation and the decoder in an on-line way, i. e. RDO at the testing time. It is equivalent to a continuous online mode decision, like coding modes in the traditional codecs, to improve the coding efficiency based on the individual input image. The experimental results show the effectiveness of the proposed neural-syntax de-sign and the continuous online mode decision mechanism, demonstrating the superiority of our method in coding effi-ciency. Our project is available at: https://dezhao-wang.github. io/Neural-Syntax-Website/.
Automatically Generating Counterfactuals for Relation Classification

The goal of relation classification (RC) is to extract the semantic relations between/among entities in the text. As a fundamental task in natural language processing, it is crucial to ensure the robustness of RC models. Despite the high accuracy current deep neural models have achieved in RC tasks, they are easily affected by spurious correlations. One solution to this problem is to train the model with counterfactually augmented data (CAD) such that it can learn the causation rather than the confounding. However, no attempt has been made on generating counterfactuals for RC tasks. In this paper, we formulate the problem of automatically generating CAD for RC tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. Specifically, we exploit two elementary topological properties, i.e., the centrality and the shortest path, in syntactic and semantic dependency graphs, to first identify and then intervene on the contextual causal features for entities. We conduct a comprehensive evaluation on four RC datasets by combining our proposed approach with a variety of backbone RC models. The results demonstrate that our approach not only improves the performance of the backbones, but also makes them more robust in the out-of-domain test.
LEARNING ORGANIZATIONS: CAN THEY BE SHOWN TO EXIST THROUGH THE REALIZATION OF QUANTIFIABLE BENEFITS?

Learning Organization concepts and their implementation have been debated for over fifty years. This paper constructs a position on how learning propagates within a knowledge intensive firm based on extant theory. The key purpose of the research is to establish how the effects of organizational learning can be measured in a knowledge intensive business in the support services sector. Learning Organization research areas reviewed included studies on Learning Organization strategy and implementation in the Construction/Project based sectors. Related key topic areas were; measurement; realization of benefits; and valuation of knowledge assets. The aim is to outline a bridge between Organizational Learning and the knowledge practitioners undertaking the learning. The review examines whether the quantifiable measurement of benefits from Learning Organization implementations can provide ‘evidence’ that organizations can learn in a meaningful and beneficial way. The paper reveals that this requires enabling learning as part of normal activity. The ongoing research is aimed at taking the learning from this review to develop strategies for implementing a learning organization culture in the sponsoring organization and quantifying the outcomes.
Natural History of Plutella armoraciae Busck, 1912, A Sympatric Congener of the Diamondback Moth, Plutella xylostella (L., 1758), in Southwestern Canada

ABSTRACT. The basic biology, seasonal ecology, and hymenopteran parasitoid community of Plutella armoraciae Busck, 1912 (Lepidoptera: Plutellidae) was investigated. The study area was a dry grasslands region of interior British Columbia, Canada, where P. armoraciae feeds on the introduced mustard Sisymbrium loeselii L. , 1755 (Brassicaceae) in partial sympatry with its congener, the diamondback moth Plutella xylostella (L., 1758) (Lepidoptera: Plutellidae). Sampling was conducted over five years, including one year of repeated, intensive sampling over a large region. The two Plutella species can be differentiated with morphology alone and we provide comparisons of features of the eggs, larvae, pupae and adults of each species. Plutella armoraciae was found to overwinter as eggs or early-instar larvae in S. loeselii rosettes, with adults emerging in April and May, and the first full generation each year taking place in May and June. Larvae fed within webbed flowers and buds of S. loeselii plants, giving rise to a second autumn generation of adults that laid eggs of the overwintering generation. The early summer generation of P. armoraciae larvae peaked earlier than, but partially overlapped with, the peak occurrence of P. xylostella larvae. Both Plutella species were relatively common across all surveyed sites, but not highly abundant at any site. At least two species of larval parasitoids, Diadegma insulare (Cresson) (Hymenoptera: Ichneumonidae) and Diolcogaster claritibia (Papp) (Hymenoptera: Braconidae), were shared between the two Plutella species, and a community of eight other species of parasitoids and hyperparasitoids were reared from the pupae of one or both Plutella species. The observations of D. claritibia emerging from P. armoraciae are the first records of this parasitoid from a host species other than P. xylostella. It was also discovered that one of the most important parasitoids of P. xylostella worldwide, D. insulare, appears to use P. armoraciae as an overwintering host. We hypothesize that in areas where P. xylostella is a seasonal migrant, year-round resident Plutella species such as P. armoraciae could provide important overwintering hosts and reservoirs for shared parasitoids that help to reduce populations of P. xylostella.
Incremental Graph Computations: Doable and Undoable

The incremental problem for a class \( {\mathcal {Q}} \) of graph queries aims to compute, given a query \( Q \in {\mathcal {Q}} \) , graph G, answers Q(G) to Q in G and updates ΔG to G as input, changes ΔO to output Q(G) such that Q(G⊕ΔG) = Q(G)⊕ΔO. It is called bounded if its cost can be expressed as a polynomial function in the sizes of Q, ΔG and ΔO, which reduces the computations on possibly big G to small ΔG and ΔO. No matter how desirable, however, our first results are negative: For common graph queries such as traversal, connectivity, keyword search, pattern matching, and maximum cardinality matching, their incremental problems are unbounded. In light of the negative results, we propose two characterizations for the effectiveness of incremental graph computation: (a) localizable, if its cost is decided by small neighbors of nodes in ΔG instead of the entire G; and (b) bounded relative to a batch graph algorithm \( {\mathcal {T}} \) , if the cost is determined by the sizes of ΔG and changes to the affected area that is necessarily checked by any algorithms that incrementalize \( {\mathcal {T}} \) . We show that the incremental computations above are either localizable or relatively bounded by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize existing batch graph algorithms by minimizing unnecessary recomputation. Using real-life and synthetic data, we experimentally verify the effectiveness of our incremental algorithms.
Study of Positive and Negative Affect and Neurocognitive Functioning in Adolescents

Abstract The main purpose of the present work was to study neurocognitive performance of adolescents at risk for emotional difficulties. The sample included a total of 1,509 adolescents from stratified random cluster sampling. Derived from this sample, a group of high-risk (n = 92) and a comparison group (n = 92) were selected based on the short version of the Positive and Negative Affect Schedule (PANAS) for comparison on the University of Pennsylvania computerized neuropsychological test battery for children (PENN). A Multivariate analysis of covariance (MANCOVA) was performed taking the scores on the PENN as dependent variables and the two groups derived from the scores of the PANAS (at risk vs. comparison) as a fixed factor. Adolescents at high risk of presenting affectivity problems showed statistically significant differences in several different neurocognitive domains, in accuracy, λ = . 820, F (9, 160,000) = 3.913, p < . 01, partial η² = . 180; speed, λ = . 502, F (5, 88,000) = 17.493, p < . 01, partial η² = . 498; and efficiency, λ = . 485, F (4, 89,000) = 23.599, p <. 01, partial η² = . 515. The high risk group showed lower neurocognitive performance than the comparison group. In addition, a positive statistically significant correlation was found between all the neurocognitive competences (p < .05). Results found in this study reveal that neurocognitive impairments can be shown in adolescents at psychometric high risk for emotional problems before transition to more severe psychological problems.
Will computers blow up the school: or is our digital wisdom evolving?

ABSTRACT Following the unexpected lockdown of education in March 2020 and again in February 2021, this research looks at the subsequent online learning that took place during the second lockdown at a vocational further education college located in the south of England. These online sessions used Google Meet as a forum and teachers at the college suddenly had to adapt their planned teaching to suit this new environment, Observation data was collected using an assessment tool consisting of 11 criteria with which to assess these sessions. This was followed up with teacher self-assessment using the same criteria and students surveyed for their perspective after the lockdown had finished in March. The data shows some discrepancy between what the observer 'saw' and what the teachers thought about their own TPACK levels in terms of outcomes and the student data indicates that online learning was 'unengaging' in terms of activities. The conclusion looks to the concept of digital wisdom as a means of using technology as a meaningful learning tool rather than as simply a different way to cover content in the light of the changes to curriculum delivery.
Mass Deacidification 25 Years Later: Analysis of pH, Alkaline Reserve, DP and Usage Evaluation of Naturally Aged Books

ABSTRACT In 1994–1995 the same editions of printed books were treated by four mass deacidification methods: diethyl zinc, Wei T'o, Battelle, and Bookkeeper. Forty one titles from 1832 to 1991 were sampled from a dissertation collection and four sets sent to mass deacidification plants, while one set was untreated. In 2019, 155 paper samples were analysed for alkaline reserve and pH (cold extract) to compare the performance of treated books with untreated ones. Twenty samples were analysed for degree of polymerization by viscosity average. User experience has also been tested, in 1996 and in 2019. Results showed higher pH and AR for treated books compared to those untreated, and a greater experienced effect for the untreated compared to the treated today. For pH and AR there were differentiations between the deacidification methods, while the results for DP showed greater complexity, pointing to the need for on-going further studies of mass deacidified books.