Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictors of events of violence or aggression against nurses in the workplace: A scoping review

Abstract Aim To identify predictors and consequences of violence or aggression events against nurses and nursing students in different work contexts. Background Workplace violence against nurses and nursing students is a very common and widespread phenomenon. Actions to manage or prevent violent events could be implemented knowing the risk factors and consequences. However, there is a lack of systematic reviews that summarize knowledge on the predictors and consequences of workplace violence. Evaluation A scoping review was conducted using electronic databases including APA PsycInfo, CINAHL, Cochrane, Ovid Medline, PubMed and Scopus. Key issues After full text analysis, 87 papers were included in the current scoping review. Risk factors of horizontal violence were grouped into ‘personal’ and ‘Environmental and organizational’, and for violence perpetrated by patients into ‘personal’, ‘Environmental and organizational’ and ‘Characteristics of the perpetrators’. Conclusions The results of this scoping review uncover problems that often remain unaddressed, especially where these episodes are very frequent. Workplace violence prevention and management programmes are essential to counter it. Implications for Nursing Management The predictors and the consequents identified constitute the body of knowledge necessary for nurse managers to develop and implement policy and system actions to effectively manage or prevent violent events.
Edge Labelled Graphs and Property Graphs; a comparison from the user perspective

This study compares participant acceptance of the property graph and edge-labelled graph paradigms, as represented by Cypher and the proposed extensions to the W3C standards, RDF* and SPARQL*. In general, modelling preferences are consistent across the two paradigms. When presented with location information, participants preferred to create nodes to represent cities, rather than use metadata; although the preference was less marked for Cypher. In Cypher, participants showed little difference in preference between representing dates or population size as nodes. In RDF*, this choice was not necessary since both could be represented as literals. However, there was a significant preference for using the date as metadata to describe a triple containing population size, rather than vice versa. There was no significant difference overall in accuracy of interpretation of queries in the two paradigms; although in one specific case, the use of a reverse arrow in Cypher was interpreted significantly more accurately than the ^ symbol in SPARQL. Based on our results and on the comments of participants, we make some recommendations for modellers. Techniques for reifing RDF have attracted a great deal of research. Recently, a hybrid approach, employing some of the features of property graphs, has claimed to offer an improved technique for RDF reification. Query-time reasoning is also a requirement which has prompted a number of proposed extensions to SPARQL and which is only possible to a limited extent in the property graph paradigm. Another recent development, the hypergraph paradigm enables more powerful query-time reasoning. There is a need for more research into the user acceptance of these various more powerful approaches to modelling and querying. Such research should take account of complex modelling situations.
InCoder: A Generative Model for Code Infilling and Synthesis

Code is seldom written in a single left-to-right pass and is instead repeatedly edited and refined. We introduce InCoder, a unified generative model that can perform program synthesis (via left-to-right generation) as well as editing (via infilling). InCoder is trained to generate code files from a large corpus of permissively licensed code, where regions of code have been randomly masked and moved to the end of each file, allowing code infilling with bidirectional context. Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming. We find that the ability to condition on bidirectional context substantially improves performance on these tasks, while still performing comparably on standard program synthesis benchmarks in comparison to left-to-right only models pretrained at similar scale. The InCoder models and code are publicly released. https://sites.google. com/view/incoder-code-models
Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization: Anonymous submission Paper ID 73

Due to its high societal impact, deepfake detection is getting active attention in the computer vision community. Most deepfake detection methods rely on identity, facial attributes, and adversarial perturbation-based spatio-temporal modifications at the whole video or random locations while keeping the meaning of the content intact. However, a sophisticated deepfake may contain only a small segment of video/audio manipulation, through which the meaning of the content can be, for example, completely inverted from a sentiment perspective. We introduce a content-driven audio-visual deepfake dataset, termed Localized Audio Visual DeepFake (LAV-DF), explicitly designed for the task of learning temporal forgery localization. Specifically, the content-driven audio-visual manipulations are performed strategically to change the sentiment polarity of the whole video. Our baseline method for benchmarking the proposed dataset is a 3DCNN model, termed as Boundary Aware Temporal Forgery Detection (BA-TFD), which is guided via contrastive, boundary matching, and frame classification loss functions. Our extensive quantitative and qualitative analysis demonstrates the proposed method's strong performance for temporal forgery localization and deepfake detection tasks.
Can Question Rewriting Help Conversational Question Answering?

Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA.We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.
Optimal Cybersecurity Investments Using SIS Model: Weakly Connected Networks

We study the problem of minimizing the (time) average security costs in large systems comprising many interdependent subsystems, where the state evolution is captured by a susceptible-infected-susceptible (SIS) model. The security costs reflect security investments, economic losses and recovery costs from infections and failures following successful attacks. However, unlike in existing studies, we assume that the underlying dependence graph is only weakly connected, but not necessarily strongly connected. When the dependence graph is not strongly connected, existing approaches to computing optimal security investments cannot be applied. Instead, we show that it is still possible to find a good solution by perturbing the problem and establishing necessary continuity results that then allow us to leverage the existing algorithms.
Deep Learning Model with GA based Feature Selection and Context Integration

— Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Many top-performing methods for image segmentation are based on deep CNN models. However, deep CNN models fail to integrate global and local context alongside visual features despite having complex multi-layer architectures. We propose a novel three-layered deep learning model that assiminlate or learns independently global and local contextual information alongside visual features. The novelty of the proposed model is that One-vs-All binary class-based learners are introduced to learn Genetic Algorithm (GA) optimized features in the visual layer, followed by the contextual layer that learns global and local contexts of an image, and finally the third layer integrates all the information optimally to obtain the final class label. Stanford Background and CamVid benchmark image parsing datasets were used for our model evaluation, and our model shows promising results. The empirical analysis reveals that optimized visual features with global and local contextual information play a significant role to improve accuracy and produce stable predictions comparable to state-of-the-art deep CNN models .
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU

The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerating CTR prediction model training is critical to ensuring an up-to-date model and reducing the training cost. One approach to increase the training speed is to apply large batch training. However, as shown in computer vision and natural language processing tasks, training with a large batch easily suffers from the loss of accuracy. Our experiments show that previous scaling rules fail in the training of CTR prediction neural networks. To tackle this problem, we first theoretically show that different frequencies of ids make it challenging to scale hyperparameters when scaling the batch size. To stabilize the training process in a large batch size setting, we develop the adaptive Column-wise Clipping (CowClip). It enables an easy and effective scaling rule for the embeddings, which keeps the learning rate unchanged and scales the L2 loss. We conduct extensive experiments with four CTR prediction networks on two real-world datasets and successfully scaled 128 times the original batch size without accuracy loss. In particular, for CTR prediction model DeepFM training on the Criteo dataset, our optimization framework enlarges the batch size from 1K to 128K with over 0.1% AUC improvement and reduces training time from 12 hours to 10 minutes on a single V100 GPU. Our code locates at github.com/bytedance/LargeBatchCTR.
Energy Complexity of Regular Language Recognition

The erasure of each bit of information by a computing device has an intrinsic energy cost. Although any Turing machine can be rewrit-ten to be thermodynamically reversible without changing the recognized language, ﬁnite automata that are restricted to scan their input once in “real-time” fashion can only recognize the members of a proper subset of the class of regular languages in this reversible manner. We use a general quantum ﬁnite automaton model to study the thermodynamic cost per step associated with the recognition of diﬀerent regular languages. We show that zero-error quantum ﬁnite automata have no energy cost advantage over their classical deterministic counterparts, and prove an upper bound for the cost that holds for all regular languages. We also demonstrate languages for which “error can be traded for energy”, i.e. whose zero-error recognition is associated with provably bigger energy cost per step when compared to their bounded-error recognition by real-time ﬁnite-memory quantum devices.
Multi-band D-TRILEX approach to materials with strong electronic correlations

We present the multi-band dual triply irreducible local expansion (D-TRILEX) approach to interacting electronic systems and discuss its numerical implementation. This method is designed for a self-consistent description of multi-orbital systems that can also have several atoms in the unit cell. The current implementation of the D-TRILEX approach is able to account for the frequency- and channel-dependent long-ranged electronic interactions. We show that our method is accurate when applied to small multi-band systems such as the Hubbard-Kanamori dimer. Calculations for the extended Hubbard, the two-orbital Hubbard-Kanamori, and the bilayer Hubbard models are also discussed.
The sparse Polynomial Chaos expansion: a fully Bayesian approach with joint priors on the coeﬃcients and global selection of terms

Polynomial chaos expansion (PCE) is a versatile tool widely used in uncertainty quantiﬁcation and machine learning, but its successful application depends strongly on the accuracy and reliability of the resulting PCE-based response surface. High accuracy typically requires high polynomial degrees, demanding many training points especially in high-dimensional problems through the curse of dimensionality. So-called sparse PCE concepts work with a much smaller selection of basis polynomials compared to conventional PCE approaches and can overcome the curse of dimensionality very eﬃciently, but have to pay speciﬁc attention to their strategies of choosing training points. Furthermore, the approximation error resembles an uncertainty that most existing PCE-based methods do not estimate. In this study, we develop and evaluate a fully Bayesian approach to establish the PCE representation via joint shrinkage priors and Markov chain Monte Carlo. The suggested Bayesian PCE model directly aims to solve the two challenges named above: achieving a sparse PCE representation and estimating uncertainty of the PCE itself. The embedded Bayesian regularizing via the joint shrinkage prior allows using higher polynomial degrees for given training points due to its ability to handle underdetermined situations, where the number of considered PCE coeﬃcients could be much larger than the number of available training points. We also explore multiple variable selection methods to construct sparse PCE expansions based on the established Bayesian representations, while globally selecting the most meaningful orthonormal polynomials given the available training data. We demonstrate the advantages of our Bayesian PCE and the corresponding sparsity-inducing methods on several benchmarks.
Context and Attribute-Aware Sequential Recommendation via Cross-Attention

In sparse recommender settings, users’ context and item attributes play a crucial role in deciding which items to recommend next. Despite that, recent works in sequential and time-aware recommendations usually either ignore both aspects or only consider one of them, limiting their predictive performance. In this paper, we address these limitations by proposing a context and attribute-aware recommender model (CARCA) that can capture the dynamic nature of the user profiles in terms of contextual features and item attributes via dedicated multi-head self-attention blocks that extract profile-level features and predict item scores. Also, unlike many of the current state-of-the-art sequential item recommendation approaches that use a simple dot-product between the most recent item’s latent features and the target items embeddings for scoring, CARCA uses cross-attention between all profile items and the target items to predict their final scores. This cross-attention allows CARCA to harness the correlation between old and recent items in the user profile and their influence on deciding which item to recommend next. Experiments on four real-world recommender system datasets show that the proposed model significantly outperforms all state-of-the-art models in the task of item recommendation and achieving improvements of up to 53% in Normalized Discounted Cumulative Gain (NDCG) and Hit-Ratio. Results also show that CARCA outperformed several state-of-the-art dedicated image-based recommender systems by merely utilizing image attributes extracted from a pre-trained ResNet50 in a black-box fashion.
Software Supply Chain Map: How Reuse Networks Expand

Clone-and-own is a typical code reuse approach because of its simplicity and efficiency. Cloned software components are maintained independently by a new owner. These clone-and-own operations can be occurred sequentially, that is, cloned components can be cloned again and owned by other new owners on the supply chain. In general, code reuse is not documented well, consequently, appropriate changes like security patches cannot be propagated to descendant software projects. However, the OpenChain Project defined identifying and tracking source code reuses as responsibilities of FLOSS software staffs. Hence supporting source code reuse awareness is in a real need. This paper studies software reuse relations in FLOSS ecosystem. Technically, clone-and-own reuses of source code can be identified by file-level clone set detection. Since change histories are associated with files, we can determine origins and destinations in reusing across multiple software by considering times. By building software supply chain maps, we find that clone-and-own is prevalent in FLOSS development, and set of files are reused widely and repeatedly. These observations open up future challenges of maintaining and tracking global software genealogies.
Improving Differential-Neural Distinguisher Model For DES, Chaskey, and PRESENT

In CRYPTO'19, Gohr proposed a new cryptanalysis strategy using machine learning algorithms. Combining the differential-neural distinguisher with a differential path and integrating the advanced key recovery procedure, Gohr achieved a 12-round key recovery attack on Speck32/64. Chen and Yu improved prediction accuracy of differential-neural distinguisher considering derived features from multiple-ciphertext pairs instead of single-ciphertext pairs. By modifying the kernel size of initial convolutional layer to capture more dimensional information, the prediction accuracy of differential-neural distinguisher can be improved for for three reduced symmetric ciphers. For DES, we improve the prediction accuracy of (5-6)-round differential-neural distinguisher and train a new 7-round differential-neural distinguisher. For Chaskey, we improve the prediction accuracy of (3-4)-round differential-neural distinguisher. For PRESENT, we improve the prediction accuracy of (6-7)-round differential-neural distinguisher. The source codes are available in https://drive.google. com/drive/folders/1i0RciZlGZsEpCyW-wQAy7zzJeOLJNWqL? usp=sharing.
Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets

Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization frameworks that largely emerge from general-purpose time series features for data science and, more precisely, from a large variety of such features. We focused on 28 features that included (partial) autocorrelation, entropy, temporal variation, seasonality, trend, lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others. We estimated these features for daily temperature, precipitation and streamflow time series from 511 catchments and then merged them within regionalization contexts with traditional topographic, land cover, soil and geologic attributes. Precipitation and temperature features (e.g., the spectral entropy, seasonality strength and lag-1 autocorrelation of the precipitation time series, and the stability and trend strength of the temperature time series) were found to be useful predictors of many streamflow features. The same applies to traditional attributes such as the catchment mean elevation. Relationships between predictor and dependent variables were also revealed, while the spectral entropy, the seasonality strength and several autocorrelation features of the streamflow time series were found to be more regionalizable than others.
3D Morphology of Open Clusters in the Solar Neighborhood with Gaia EDR 3. II. Hierarchical Star Formation Revealed by Spatial and Kinematic Substructures

We identify members of 65 open clusters in the solar neighborhood using the machine-learning algorithm StarGO based on Gaia EDR3 data. After adding members of 20 clusters from previous studies we obtain 85 clusters, and study their morphology and kinematics. We classify the substructures outside the tidal radius into four categories: filamentary (f1) and fractal (f2) for clusters <100 Myr, and halo (h) and tidal tail (t) for clusters >100 Myr. The kinematical substructures of f1-type clusters are elongated; these resemble the disrupted cluster Group X. Kinematic tails are distinct in t-type clusters, especially Pleiades. We identify 29 hierarchical groups in four young regions (Alessi 20, IC 348, LP 2373, LP 2442); 10 among these are new. The hierarchical groups form filament networks. Two regions (Alessi 20, LP 2373) exhibit global orthogonal expansion (stellar motion perpendicular to the filament), which might cause complete dispersal. Infalling-like flows (stellar motion along the filament) are found in UBC 31 and related hierarchical groups in the IC 348 region. Stellar groups in the LP 2442 region (LP 2442 gp 1–5) are spatially well mixed but kinematically coherent. A merging process might be ongoing in the LP 2442 subgroups. For younger systems (≲30 Myr), the mean axis ratio, cluster mass, and half-mass–radius tend to increase with age values. These correlations between structural parameters may imply two dynamical processes occurring in the hierarchical formation scenario in young stellar groups: (1) filament dissolution and (2) subgroup mergers.
Entanglement statistics of polymers in a lattice tube and unknotting of 4-plats

It has been conjectured that the exponential growth rate of the number of lattice polygons with knot-type $K$ is the same as that for unknot polygons, and that the entropic critical exponent increases by one for each prime knot in the knot decomposition of $K$. We provide the first proof of this conjecture by considering knots and non-split links in tube $\mathbb{T}^*$, the $\infty \times 2\times 1$ sublattice of the simple cubic lattice. We establish upper and lower bounds relating the asymptotics of the number of $n$-edge polygons with fixed link-type in $\mathbb{T}^*$ to that of the number of $n$-edge unknots. For the upper bounds, we prove that polygons can be unknotted by braid insertions. For the lower bounds, we prove a pattern theorem for unknots using information from exact transfer-matrices. We prove new results for 4-plats and highlight connections to modelling DNA in nanochannels.
Lepton flavor violating decays of vector mesons in the MRSSM

In this study, we analyze the rare decays of the neutral vector mesons and in the scenario of the minimal R-symmetric supersymmetric standard model using the effective Lagrangian method. The predicted branching ratios are dominated by the mass insertion parameters , i.e., the off-diagonal inputs, and the contributions of different parts are comparable. Taking into account the experimental constraints on the mass insertion parameters, the predicted branching ratios for the most promising processes are ten orders of magnitude smaller than the present experimental bounds.
Remarkable Correspondence of the Sagittarius A* Submillimeter Variability with a Stellar-wind-fed Accretion Flow Model

We compare the 230 GHz near-horizon emission from Sagittarius A* to simulations representing three classes of accretion flows. Using the structure function to capture the variability statistics of the light curve, we find a noticeable discrepancy between the observations and models based on torus-fed accretion disks, whether those disks bring in a small or large amount of net magnetic flux. On the other hand, the simulations that are fed more realistically by stellar winds match the observed structure function very well. We describe the differences between models, arguing that feeding by stellar winds may be a critical component in constructing theoretical models for accretion in the Galactic Center.