Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Weighting Schemes for Auditing Instant-Runoff Voting Elections

Various risk-limiting audit (RLA) methods have been developed for instant-runoff voting (IRV) elections. A recent method, AWAIRE, is the first efficient approach that does not require cast vote records (CVRs). AWAIRE involves adaptively weighted averages of test statistics, essentially"learning"an effective set of hypotheses to test. However, the initial paper on AWAIRE only examined a few weighting schemes and parameter settings. We provide an extensive exploration of schemes and settings, to identify and recommend efficient choices for practical use. We focus only on the (hardest) case where CVRs are not available, using simulations based on real election data to assess performance. Across our comparisons, the most effective schemes are often those that place most or all of the weight on the apparent"best"hypotheses based on already seen data. Conversely, the optimal tuning parameters tended to vary based on the election margin. Nonetheless, we quantify the performance trade-offs for different choices across varying election margins, aiding in selecting the most desirable trade-off if a default option is needed. A limitation of the current AWAIRE implementation is its restriction to handling a small number of candidates (previously demonstrated up to six candidates). One path to a more computationally efficient implementation would be to use lazy evaluation and avoid considering all possible hypotheses. Our findings suggest that such an approach could be done without substantially comprising statistical performance.
Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts

Tasks such as semantic search and clustering on crisis-related social media texts enhance our comprehension of crisis discourse, aiding decision-making and targeted interventions. Pre-trained language models have advanced performance in crisis informatics, but their contextual embeddings lack semantic meaningfulness. Although the CrisisTransformers family includes a sentence encoder to address the semanticity issue, it remains monolingual, processing only English texts. Furthermore, employing separate models for different languages leads to embeddings in distinct vector spaces, introducing challenges when comparing semantic similarities between multi-lingual texts. Therefore, we propose multi-lingual sentence encoders (CT-XLMR-SE and CT-mBERT-SE) that embed crisis-related social media texts for over 50 languages, such that texts with similar meanings are in close proximity within the same vector space, irrespective of language diversity. Results in sentence encoding and sentence matching tasks are promising, suggesting these models could serve as robust baselines when embedding multi-lingual crisis-related social media texts. The models are publicly available at: https://huggingface.co/crisistransformers.
Stochastic Inertial Dynamics Via Time Scaling and Averaging

Our work is part of the close link between continuous-time dissipative dynamical systems and optimization algorithms, and more precisely here, in the stochastic setting. We aim to study stochastic convex minimization problems through the lens of stochastic inertial differential inclusions that are driven by the subgradient of a convex objective function. This will provide a general mathematical framework for analyzing the convergence properties of stochastic second-order inertial continuous-time dynamics involving vanishing viscous damping and measurable stochastic subgradient selections. Our chief goal in this paper is to develop a systematic and unified way that transfers the properties recently studied for first-order stochastic differential equations to second-order ones involving even subgradients in lieu of gradients. This program will rely on two tenets: time scaling and averaging, following an approach recently developed in the literature by one of the co-authors in the deterministic case. Under a mild integrability assumption involving the diffusion term and the viscous damping, our first main result shows that almost surely, there is weak convergence of the trajectory towards a minimizer of the objective function and fast convergence of the values and gradients. We also provide a comprehensive complexity analysis by establishing several new pointwise and ergodic convergence rates in expectation for the convex, strongly convex, and (local) Polyak-Lojasiewicz case. Finally, using Tikhonov regularization with a properly tuned vanishing parameter, we can obtain almost sure strong convergence of the trajectory towards the minimum norm solution.
MixRED: A Mix-lingual Relation Extraction Dataset

Relation extraction is a critical task in the field of natural language processing with numerous real-world applications. Existing research primarily focuses on monolingual relation extraction or cross-lingual enhancement for relation extraction. Yet, there remains a significant gap in understanding relation extraction in the mix-lingual (or code-switching) scenario, where individuals intermix contents from different languages within sentences, generating mix-lingual content. Due to the lack of a dedicated dataset, the effectiveness of existing relation extraction models in such a scenario is largely unexplored. To address this issue, we introduce a novel task of considering relation extraction in the mix-lingual scenario called MixRE and constructing the human-annotated dataset MixRED to support this task. In addition to constructing the MixRED dataset, we evaluate both state-of-the-art supervised models and large language models (LLMs) on MixRED, revealing their respective advantages and limitations in the mix-lingual scenario. Furthermore, we delve into factors influencing model performance within the MixRE task and uncover promising directions for enhancing the performance of both supervised models and LLMs in this novel task.
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition

Despite living in a multi-sensory world, most AI models are limited to textual and visual understanding of human motion and behavior. In fact, full situational awareness of human motion could best be understood through a combination of sensors. In this survey we investigate how knowledge can be transferred and utilized amongst modalities for Human Activity/Action Recognition (HAR), i.e. cross-modality transfer learning. We motivate the importance and potential of IMU data and its applicability in cross-modality learning as well as the importance of studying the HAR problem. We categorize HAR related tasks by time and abstractness and then compare various types of multimodal HAR datasets. We also distinguish and expound on many related but inconsistently used terms in the literature, such as transfer learning, domain adaptation, representation learning, sensor fusion, and multimodal learning, and describe how cross-modal learning fits with all these concepts. We then review the literature in IMU-based cross-modal transfer for HAR. The two main approaches for cross-modal transfer are instance-based transfer, where instances of one modality are mapped to another (e.g. knowledge is transferred in the input space), or feature-based transfer, where the model relates the modalities in an intermediate latent space (e.g. knowledge is transferred in the feature space). Finally, we discuss future research directions and applications in cross-modal HAR.
Distance Adjustment of a Graph Drawing Stress Model

Stress models are a promising approach for graph drawing. They minimize the weighted sum of the squared errors of the Euclidean and desired distances for each node pair. The desired distance typically uses the graph-theoretic distances obtained from the all-node pair shortest path problem. In a minimized stress function, the obtained coordinates are affected by the non-Euclidean property and the high-dimensionality of the graph-theoretic distance matrix. Therefore, the graph-theoretic distances used in stress models may not necessarily be the best metric for determining the node coordinates. In this study, we propose two different methods of adjusting the graph-theoretical distance matrix to a distance matrix suitable for graph drawing while preserving its structure. The first method is the application of eigenvalue decomposition to the inner product matrix obtained from the distance matrix and the obtainment of a new distance matrix by setting some eigenvalues with small absolute values to zero. The second approach is the usage of a stress model modified by adding a term that minimizes the Frobenius norm between the adjusted and original distance matrices. We perform computational experiments using several benchmark graphs to demonstrate that the proposed method improves some quality metrics, including the node resolution and the Gabriel graph property, when compared to conventional stress models.
Advances in Bayesian Network Learning using Integer Programming

We consider the problem of learning Bayesian networks (BNs) from complete discrete data. This problem of discrete optimisation is formulated as an integer program (IP). We describe the various steps we have taken to allow efficient solving of this IP. These are (i) efficient search for cutting planes, (ii) a fast greedy algorithm to find high-scoring (perhaps not optimal) BNs and (iii) tightening the linear relaxation of the IP. After relating this BN learning problem to set covering and the multidimensional 0-1 knapsack problem, we present our empirical results. These show improvements, sometimes dramatic, over earlier results.
A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

MOTIVATION An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. RESULTS By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.
Critical role for Ser20 of human p53 in the negative regulation of p53 by Mdm2

In response to environmental stress, the p53 phosphoprotein is stabilized and activated to inhibit cell growth. p53 stability and activity are negatively regulated by the murine double minute (Mdm2) oncoprotein in an autoregulatory feedback loop. The inhibitory effect of Mdm2 on p53 has to be tightly regulated for proper p53 activity. Phosphorylation is an important level of p53 regulation. In response to DNA damage, p53 is phosphorylated at several N‐terminal serines. In this study we examined the role of Ser20, a potential phosphorylation site in human p53, in the regulation of p53 stability and function. Substitution of Ser20 by Ala (p53‐Ala20) significantly increases the susceptibility of human p53 to negative regulation by Mdm2 in vivo, as measured by apoptosis and transcription activation assays. Mutation of Ser20 to Ala renders p53 less stable and more prone to Mdm2‐mediated degradation. While the in vitro binding of p53 to Mdm2 is not increased by the Ala20 mutation, the same mutation results in a markedly enhanced binding in vivo. This is consistent with the conclusion that phosphorylation of Ser20 in vivo attenuates the binding of wild‐type p53 to Mdm2. Peptides bearing non‐phosphorylated Ser20 or Ala20 compete with p53 for Mdm2 binding, while a similar peptide with phosphorylated Ser20 does not. This implies a critical role for Ser20 in modulating the negative regulation of p53 by Mdm2, probably through phosphorylation‐dependent inhibition of p53–Mdm2 interaction.
Topical Administration of Allogeneic Mesenchymal Stromal Cells Seeded in a Collagen Scaffold Augments Wound Healing and Increases Angiogenesis in the Diabetic Rabbit Ulcer

There is a critical clinical need to develop therapies for nonhealing diabetic foot ulcers. Topically applied mesenchymal stromal cells (MSCs) provide a novel treatment to augment diabetic wound healing. A central pathological factor in nonhealing diabetic ulcers is an impaired blood supply. It was hypothesized that topically applied allogeneic MSCs would improve wound healing by augmenting angiogenesis. Allogeneic nondiabetic bone-marrow derived MSCs were seeded in a collagen scaffold. The cells were applied to a full-thickness cutaneous wound in the alloxan-induced diabetic rabbit ear ulcer model in a dose escalation fashion. Percentage wound closure and angiogenesis at 1 week was assessed using wound tracings and stereology, respectively. The topical application of 1,000,000 MSCs on a collagen scaffold demonstrated increased percentage wound closure when compared with lower doses. The collagen and collagen seeded with MSCs treatments result in increased angiogenesis when compared with untreated wounds. An improvement in wound healing as assessed by percentage wound closure was observed only at the highest cell dose. This cell-based therapy provides a novel therapeutic strategy for increasing wound closure and augmenting angiogenesis, which is a central pathophysiological deficit in the nonhealing diabetic foot ulcer.
Sequential Decision-Making for Inline Text Autocomplete

Autocomplete suggestions are fundamental to modern text entry systems, with applications in domains such as messaging and email composition. Typically, autocomplete suggestions are generated from a language model with a confidence threshold. However, this threshold does not directly take into account the cognitive load imposed on the user by surfacing suggestions, such as the effort to switch contexts from typing to reading the suggestion, and the time to decide whether to accept the suggestion. In this paper, we study the problem of improving inline autocomplete suggestions in text entry systems via a sequential decision-making formulation, and use reinforcement learning to learn suggestion policies through repeated interactions with a target user over time. This formulation allows us to factor cognitive load into the objective of training an autocomplete model, through a reward function based on text entry speed. We acquired theoretical and experimental evidence that, under certain objectives, the sequential decision-making formulation of the autocomplete problem provides a better suggestion policy than myopic single-step reasoning. However, aligning these objectives with real users requires further exploration. In particular, we hypothesize that the objectives under which sequential decision-making can improve autocomplete systems are not tailored solely to text entry speed, but more broadly to metrics such as user satisfaction and convenience.
Kinetics of Lethal Factor and Poly-d-Glutamic Acid Antigenemia during Inhalation Anthrax in Rhesus Macaques

ABSTRACT Systemic anthrax manifests as toxemia, rapidly disseminating septicemia, immune collapse, and death. Virulence factors include the anti-phagocytic γ-linked poly-d-glutamic acid (PGA) capsule and two binary toxins, complexes of protective antigen (PA) with lethal factor (LF) and edema factor. We report the characterization of LF, PA, and PGA levels during the course of inhalation anthrax in five rhesus macaques. We describe bacteremia, blood differentials, and detection of the PA gene (pagA) by PCR analysis of the blood as confirmation of infection. For four of five animals tested, LF exhibited a triphasic kinetic profile. LF levels (mean ± standard error [SE] between animals) were low at 24 h postchallenge (0.03 ± 1.82 ng/ml), increased at 48 h to 39.53 ± 0.12 ng/ml (phase 1), declined at 72 h to 13.31 ± 0.24 ng/ml (phase 2), and increased at 96 h (82.78 ± 2.01 ng/ml) and 120 h (185.12 ± 5.68 ng/ml; phase 3). The fifth animal had an extended phase 2. PGA levels were triphasic; they were nondetectable at 24 h, increased at 48 h (2,037 ± 2 ng/ml), declined at 72 h (14 ± 0.2 ng/ml), and then increased at 96 h (3,401 ± 8 ng/ml) and 120 h (6,004 ± 187 ng/ml). Bacteremia was also triphasic: positive at 48 h, negative at 72 h, and positive at euthanasia. Blood neutrophils increased from preexposure (34.4% ± 0.13%) to 48 h (75.6% ± 0.08%) and declined at 72 h (62.4% ± 0.05%). The 72-h declines may establish a “go/no go” turning point in infection, after which systemic bacteremia ensues and the host's condition deteriorates. This study emphasizes the value of LF detection as a tool for early diagnosis of inhalation anthrax before the onset of fulminant systemic infection.
Survey of the morphology of macaque retinal ganglion cells that project to the pretectum, superior colliculus, and parvicellular laminae of the lateral geniculate nucleus

In common with other vertebrates, the primate retina contains a number of different ganglion cell types that project to different regions in the brain. We wanted to determine how the different ganglion cell types, distinguished morphologically, mapped to these regions of the brain. We injected a fluorescent dye into one of three regions of a macaque brain: the superior colliculus (SC), the pretectal region, and the parvicellular laminae of the lateral geniculate nucleus. By means of an in vitro preparation, the retrogradely labelled ganglion cells were intracellularly injected with horseradish peroxidase, so as to reveal their dendritic morphology. When the dendritic‐field diameters of the injected cells were plotted against retinal eccentricity, each of the three regions was found to receive input from a distinctive population of cells. The pretectal projection was dominated by cells with large dendritic fields. The SC projection was composed of a number of distinct types, with smaller dendritic fields. Parasol cells project to SC but are extremely rare.
itmBench: generalized API for Internet traffic managers

Internet traffic managers (ITMs) are special machines placed at strategic places in the Internet. itmBench is an interface that allows users (e.g. network managers, service providers, or experimental researchers) to register different traffic control functionalities to run on one ITM or an overlay of ITMs. Thus itmBench offers a tool that is extensible and powerful yet easy to maintain. ITM traffic control applications could be developed either using a kernel API so they run in kernel space, or using a user-space API so they run in user space. We demonstrate the flexibility of itmBench by showing the implementation of a kernel module that provides a differentiated network service. Due to space limitations, we refer the reader to Gali Diamant et al. (December 16, 2003) for a user-space module that provides an overlay routing service. Our itmBench Linux-based prototype is free software and can be obtained from http: //www.cs. bu.edu/groups/itm/.
Active protection of patient data by reverse cloud approach

Experiments to move sensitive patient data into cloud based solutions, and their usage in mobile applications, were seen skeptically by users and authorities, as concerns about long-term safe storage as well as protected communication could not been addressed convincingly in these initial approaches. This paper introduces a new approach of a “hybrid” or “reverse” cloud, which combines the advantage of centralised distribution and maintenance of application-oriented software, while at the same time sensitive data remain in already certified, protected areas. The approach also uses an innovative modular software concept based on “Generic Enablers”, and is being tested currently in seven clinical field trials in seven European countries.
Bi-objective Optimization in Role Mining

Role mining is a technique used to derive a role-based authorization policy from an existing policy. Given a set of users $U$, a set of permissions $P$ and a user-permission authorization relation $\mahtit{UPA}\subseteq U\times P$, a role mining algorithm seeks to compute a set of roles $R$, a user-role authorization relation $\mathit{UA}\subseteq U\times R$ and a permission-role authorization relation $\mathit{PA}\subseteq R\times P$, such that the composition of $\mathit{UA}$ and $\mathit{PA}$ is close (in some appropriate sense) to $\mathit{UPA}$. In this paper, we first introduce the Generalized Noise Role Mining problem (GNRM) -- a generalization of the MinNoise Role Mining problem -- which we believe has considerable practical relevance. Extending work of Fomin et al., we show that GNRM is fixed parameter tractable, with parameter $r + k$, where $r$ is the number of roles in the solution and $k$ is the number of discrepancies between $\mathit{UPA}$ and the relation defined by the composition of $\mathit{UA}$ and $\mathit{PA}$. We further introduce a bi-objective optimization variant of GNRM, where we wish to minimize both $r$ and $k$ subject to upper bounds $r\le \bar{r}$ and $k\le \bar{k}$, where $\bar{r}$ and $\bar{k}$ are constants. We show that the Pareto front of this bi-objective optimization problem (BO-GNRM) can be computed in fixed-parameter tractable time with parameter $\bar{r}+\bar{k}$. We then report the results of our experimental work using the integer programming solver Gurobi to solve instances of BO-GNRM. Our key findings are that (a) we obtained strong support that Gurobi's performance is fixed-parameter tractable, (b) our results suggest that our techniques may be useful for role mining in practice, based on our experiments in the context of three well-known real-world authorization policies.
EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World

Being able to map the activities of others into one's own point of view is one fundamental human skill even from a very early age. Taking a step toward understanding this human ability, we introduce EgoExoLearn, a large-scale dataset that emulates the human demonstration following process, in which individuals record egocentric videos as they execute tasks guided by demonstration videos. Focusing on the potential applications in daily assistance and professional support, EgoExoLearn contains egocentric and demonstration video data spanning 120 hours captured in daily life scenarios and specialized laboratories. Along with the videos we record high-quality gaze data and provide detailed multimodal annotations, formulating a playground for modeling the human ability to bridge asynchronous procedural actions from different viewpoints. To this end, we present benchmarks such as cross-view association, cross-view action planning, and cross-view referenced skill assessment, along with detailed analysis. We expect EgoExoLearn can serve as an important resource for bridging the actions across views, thus paving the way for creating AI agents capable of seamlessly learning by observing humans in the real world. Code and data can be found at: https://github.com/OpenGVLab/EgoExoLearn