Miracle-dz/UniFAR-FTUs huggingface dataset:
TISEM: A Two-Stage Interval-Stochastic Evacuation Management Model

Traffic allocation planning is commonly required for mass evacuation management. It primarily relies on efficient coordination and appropriate utilization of roadway capacity and available traffic resources. However, traffic and evacuee information are usually difficult to be obtained and consequently of various uncertainties in data. Especially, stochastic information may often exist in evacuation management systems. In this study, a two-stage interval-stochastic evacuation management (TISEM) model was developed for supporting the evacuation planning under uncertainty, by which stochastic and interval evacuation information could be well reflected and communicated in the system. In addition, by adopting the proposed model, a case study abstracted from the City of Wuhan was introduced and solved through an interactive method. Results indicated that useful solutions for planning evacuation routes could be generated based on results of the model. As well, through the model, complex relationships between evacuation time, environmental influences and economic factors could be systematically analyzed. It demonstrated that the proposed TISP model is practical and applicable in real world, and is helpful for authorities to make decisions allocating vehicles before evacuation starts.
Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas.

Pancreatic cancer will soon be the second leading cause of cancer-related death in Western society. Imaging techniques such as CT, MRI and ultrasound typically help providing the initial diagnosis, but histopathological assessment is still the gold standard for final confirmation of disease presence and prognosis. In recent years machine learning approaches and pathomics pipelines have shown potential in improving diagnostics and prognostics in other cancerous entities, such as breast and prostate cancer. A crucial first step in these pipelines is typically identification and segmentation of the tumour area. Ideally this step is done automatically to prevent time consuming manual annotation. In this paper, we propose a multi-task convolutional neural network to balance disease detection and segmentation accuracy. We validated our approach on a dataset of 29 patients (for a total of 58 slides) at different resolutions. The best single task segmentation network achieved a median Dice of 0.885 (0.122) IQR at a resolution of 15.56 μm. Our multi-task network improved on that with a median Dice score of 0.934 (0.077) IQR.
Chronological Causal Bandits

This paper studies an instance of the multi-armed bandit (MAB) problem, specifically where several causal MABs operate chronologically in the same dynamical system. Practically the reward distribution of each bandit is governed by the same non-trivial dependence structure, which is a dynamic causal model. Dynamic because we allow for each causal MAB to depend on the preceding MAB and in doing so are able to transfer information between agents. Our contribution, the Chronological Causal Bandit (CCB), is useful in discrete decision-making settings where the causal effects are changing across time and can be informed by earlier interventions in the same system. In this paper, we present some early findings of the CCB as demonstrated on a toy problem.
Evaluating NLP Systems On a Novel Cloze Task: Judging the Plausibility of Possible Fillers in Instructional Texts

Cloze task is a widely used task to evaluate an NLP system's language understanding ability. However, most of the existing cloze tasks only require NLP systems to give the relative best prediction for each input data sample, rather than the absolute quality of all possible predictions, in a consistent way across the input domain. Thus a new task is proposed: predicting if a filler word in a cloze task is a good, neutral, or bad candidate. Complicated versions can be extended to predicting more discrete classes or continuous scores. We focus on subtask A in Semeval 2022 task 7, explored some possible architectures to solve this new task, provided a detailed comparison of them, and proposed an ensemble method to improve traditional models in this new task.
InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation

Assessing the quality of natural language generation (NLG) systems through human annotation is very expensive. Additionally, human annotation campaigns are time-consuming and include non-reusable human labour. In practice, researchers rely on automatic metrics as a proxy of quality. In the last decade, many string-based metrics (e.g., BLEU or ROUGE) have been introduced. However, such metrics usually rely on exact matches and thus, do not robustly handle synonyms. In this paper, we introduce InfoLM a family of untrained metrics that can be viewed as a string-based metric that addresses the aforementioned flaws thanks to a pre-trained masked language model. This family of metrics also makes use of information measures allowing the possibility to adapt InfoLM to different evaluation criteria. Using direct assessment, we demonstrate that InfoLM achieves statistically significant improvement and two figure correlation gains in many configurations compared to existing metrics on both summarization and data2text generation tasks.
The effect of sloshing in partially filled spherical LNG tanks on ship motions

This report describes two methods to analyse the effect of sloshing on ship motions. The first method applies linear potential theory on both the barge and the internal tanks. For the barge the radiation, diffraction and incoming wave potentials are solved. For the internal tanks only the radiation potential is calculated, as there are obviously no incoming or diffracted waves in the tanks. The potentials are then used to solve the added mass, damping and wave forces for the coupled barge-tank system in the frequency domain. By comparing the frequency domain results with the scale model tests results a good agreement was identified for all loading conditions. On top of this the frequency domain approach is very fast and relatively easy to set up. However, linearity is assumed while in the scale model test non-linear sloshing was observed. It is expected that non-linearity will damp out the roll motion of the vessel and it is therefore expected that for increasing wave height the frequency domain solution will overestimate the response. It was also found that both roll RAO peaks are very sensitive to the wave direction. By comparing the roll RAO’s for liquid and frozen cargo in the tanks it was found that sloshing significantly decreases the height of the main roll RAO peak and also creates a lower barge natural frequency. The second method is a more complex time domain model that is based on a coupling between Volume of Fluid solver ComFLOW and ship motions solver aNySIM. In the coupled model the motions of the barge due to waves, without internal tanks, are obtained by using linear potential flow and are calculated in aNySIM. The ship motions calculated in aNySIM are used as input in ComFLOW that calculates the more complex and potentially non-linear motions of the liquid in the spherical cargo tanks. The resulting sloshing loads are again used as input in aNySIM, creating a two-way coupling between the dynamics of the ship and cargo tanks. The time domain results showed a reasonably good agreement with the scale model tests. However, the second, sloshing induced, roll RAO peak is underestimated in the coupled model. This might be due to the type of waves used in the test, uncertainty in the RAO’s and due to the properties of the coupling between ComFLOW and aNySIM. The time domain method is time consuming and complex to set up, but offers more insight in what is really happening. It was shown that non-linear motions of the barge occur due to sloshing in the tanks. It was also found that with partially filled tanks the response of the barge is irregular when exposed regular waves. The influence of the pump tower was also investigated, which showed that due to the damping created by the pump tower the overall roll response is lower and the barge natural frequencies moves to a lower frequency.
Polarization-path-frequency entanglement using interferometry and frequency shifters

Higher dimensional Hilbert space along with ability to control multiple degrees of freedom of photon and entangle them has enabled new quantum protocols for various quantum information processing applications. Here, we propose a scheme to generate and control polarization-path-frequency entanglement using the operative elements required to implement a polarization-controlled quantum walk in the path (position) space and frequency domain. Hyperentangled states manifests in the controlled dynamics using an interferometric setup where half-wave plates, beam-splitters and frequency shifters such as those based on the electro-optic effect are used to manipulate the polarization, path and frequency degrees of freedom respectively. The emphasis is on utilizing the polarization to influence the movement to a specific value in the frequency and position space. Negativity between the subspaces is calculated to demonstrate the controllability of the entanglement between the three degrees of freedom and the effect of noise on the entanglement is modelled using the depolarizing channel. Progress reported with experimental demonstration of realization of quantum walk using quantum states of light makes quantum walks a practical approach to generate hyperentangled states.
Energy-Proportional Data Center Network Architecture Through OS, Switch and Laser Co-design

Optical interconnects are already the dominant technology in large-scale data center networks. However, the high optical loss of many optical components coupled with the low efficiency of laser sources result in high aggregate power requirements for the thousands of optical transceivers used by these networks. As optical interconnects stay always on even as traffic demands ebb and flow, most of this power is wasted. We present LC/DC, a data center network system architecture in which the operating system, the switch, and the optical components are co-designed to achieve energy proportionality. LC/DC capitalizes on the path divergence of data center networks to turn on and off redundant paths according to traffic demand, while maintaining full connectivity. Turning off redundant paths allows the optical transceivers and their electronic drivers to power down and save energy. Maintaining full connectivity hides the laser turn-on delay. At the node layer, intercepting send requests within the OS allows for the NIC's laser turn-on delay to be fully overlapped with TCP/IP packet processing, and thus egress links can remain powered off until needed with zero performance penalty. We demonstrate the feasibility of LC/DC by i) implementing the necessary modifications in the Linux kernel and device drivers, ii) implementing a 10Gbit/s FPGA switch, and iii) performing physical experiments with optical devices and circuit simulations. Our results on university data center traces and models of Facebook and Microsoft data center traffic show that LC/DC saves on average 60% of the optical transceivers power (68% max) at the cost of 6% higher packet delay.
Career Preference for Retail Formats Among College Students: A Socialization Perspective

College students majoring in retailing, fashion/merchandising or clothing/textiles/design (N = 387) from 12 land-grant universities were surveyed to identify their career preferences for five retail formats. An expectancy theory was adopted in assessing students' career preferences, and a socialization theory was adopted in identifying factors that influence the career socialization processes. Overall, students expressed a clear pattern of preference for upscale department stores, whereas discount stores were the least preferred venue. Socialization conditioned by shopping at various types of retailers had the most significant impact on student preferences for the retail formats. Other socialization factors (i. e., working at a specific retail store format and general socialization agents) as well as social structural variables had limited impact on the career socialization process. Implications for college educators, career counselors, retail recruiters, and students are discussed.
The UniNAS framework: combining modules in arbitrarily complex configurations with argument trees

Designing code to be simplistic yet to offer choice is a tightrope walk. Additional modules such as optimizers and data sets make a framework useful to a broader audience, but the added complexity quickly becomes a problem. Framework parameters may apply only to some modules but not others, be mutually exclusive or depend on each other, often in unclear ways. Even so, many frameworks are limited to a few specific use cases. This paper presents the underlying concept of UniNAS, a framework designed to incorporate a variety of Neural Architecture Search approaches. Since they differ in the number of optimizers and networks, hyper-parameter optimization, network designs, candidate operations, and more, a traditional approach can not solve the task. Instead, every module defines its own hyper-parameters and a local tree structure of module requirements. A configuration file specifies which modules are used, their used parameters, and which other modules they use in turn This concept of argument trees enables combining and reusing modules in complex configurations while avoiding many problems mentioned above. Argument trees can also be configured from a graphical user interface so that designing and changing experiments becomes possible without writing a single line of code. UniNAS is publicly available at https://github.com/cogsys-tuebingen/uninas
Optimal Locations for Plant Reintroductions in a Changing World

Of all conservation strategies currently practiced throughout the world, reintroductions require the most sophisticated understanding of species biology and ecology (Falk et al. 1996). Whether augmenting existing populations, reintroducing within a species’ known range, or introducing to a location outside the known range, finding optimal sites for long-term survival, growth, reproduction, and establishment of new populations is often “not as self-evident as it might otherwise seem” (Fiedler and Laven 1996, p. 157). Identifying appropriate habitat is essential to establish sustainable populations in existing or new locations, and yet for many species of conservation concern habitat needs are unknown. This uncertainty takes on even more importance in the context of contemporary and projected near-term changes in landscape and regional climate (Giorgi and Francisco 2000; Millar et al. 2007).
Machine Learning Subsystem for Autonomous Collision Avoidance on a small UAS with Embedded GPU

Interest in unmanned aerial system (UAS) powered solutions for 6G communication networks has grown immensely with the widespread availability of machine learning based autonomy modules and embedded graphical processing units (GPUs). While these technologies have revolutionized the possibilities of UAS solutions, designing an operable, robust autonomy framework for UAS remains a multi-faceted and difficult problem. In this work, we present our novel, modular framework for UAS autonomy, entitled MR-iFLY, and discuss how it may be extended to enable 6G swarm solutions. We begin by detailing the challenges associated with machine learning based UAS autonomy on resource constrained devices. Next, we describe in depth, how MR-iFLY’s novel depth estimation and collision avoidance technology meets these challenges. Lastly, we describe the various evaluation criteria we have used to measure performance, show how our optimized machine vision components provide up to 15X speedup over baseline models and present a flight demonstration video of MR-iFLY’s vision-based collision avoidance technology. We argue that these empirical results substantiate MR-iFLY as a candidate for use in reducing communication overhead between nodes in 6G communication swarms by providing standalone collision avoidance and navigation capabilities.
A generalized hypothesis test for community structure and homophily in networks

Networks continue to be of great interest to statisticians, with an emphasis on community detection. Less work, however, has addressed this question: given some network, does it exhibit meaningful community structure? We propose to answer this question in a principled manner by framing it as a statistical hypothesis in terms of a formal and general homophily metric. Homophily is a well-studied network property where intra-community edges are more likely than between-community edges. We use the homophily metric to identify and distinguish between three concepts: nominal, collateral, and intrinsic homophily. We propose a simple and interpretable test statistic leveraging this homophily parameter and formulate both asymptotic and bootstrap-based rejection thresholds. We prove its asymptotic properties and demonstrate it outperforms benchmark methods on both simulated and real world data. Furthermore, the proposed method yields rich, provocative insights on four classic data sets; namely, that meany well-studied networks do not actually have intrinsic homophily.
Asymptotic Properties of the Maximum Likelihood Estimator in Regime-Switching Models with Time-Varying Transition Probabilities

Time-varying transition probability (TVTP) regime-switching models extend the constant regime transition probability in Markov-switching models to include information from observations. We show consistency and asymptotic normality of the maximum likelihood estimator (MLE) in general TVTP regime-switching models where the conditional distribution of Yt depends on lagged regimes. Consistency of the MLE is also shown under misspecification. The assumptions are verified in regime-switching autoregressive models with widely-applied TVTP specifications. A simulation study examines the finite-sample distributions of the MLE and compares the asymptotic variance estimates constructed from the Hessian matrix and the outer product of the score. The simulation results favour the latter. As an empirical example, we compare three leading economic indicators in terms of describing U.S. industrial production.
Nonlinear Exciton‐Mie Coupling in Transition Metal Dichalcogenide Nanoresonators

Thanks to a high refractive index, giant optical anisotropy, and pronounced excitonic response, bulk transition metal dichalcogenides (TMDCs) have recently been discovered to be an ideal foundation for post‐silicon photonics. The inversion symmetry of bulk TMDCs, on the other hand, prevents their use in nonlinear‐optical processes such as second‐harmonic generation (SHG). To overcome this obstacle and broaden the application scope of TMDCs, MoS2 nanodisks are engineered to couple Mie resonances with C‐excitons. As a result, their alliance produces 23‐fold enhancement of SHG intensity with respect to the resonant SHG from a high‐quality exfoliated MoS2 monolayer under C‐exciton excitation. Furthermore, SHG demonstrates a strongly anisotropic response typical of a MoS2 monolayer due to the single‐crystal structure of the fabricated nanodisks, providing a polarization degree of freedom to manipulate SHG. Hence, these results significantly improve the potential of bulk TMDCs enabling an avenue for next‐generation nonlinear photonics.
Hamiltonian cycles in 2-tough $2K_2$-free graphs

A graph G is called a 2K2-free graph if it does not contain 2K2 as an induced subgraph. In 2014, Broersma, Patel and Pyatkin showed that every 25-tough 2K2free graph on at least three vertices is Hamiltonian. Recently, Shan improved this result by showing that 3-tough is sufficient instead of 25-tough. In this paper, we show that every 2-tough 2K2-free graph on at least three vertices is Hamiltonian, which was conjectured by Gao and Pasechnik.
Capacitive transmission lines in coplanar waveguide for millimeter-wave integrated circuit design

For use in millimeter-wave integrated circuits, theoretical and experimental design data for various useful capacitive transmission lines in coplanar waveguide on gallium arsenide are presented. Multifinger and metal-insulator-metal (MIM) lines enabling low characteristic impedance of the order of 10 /spl Omega/ as well as broadband capacitive coupled transmission lines are investigated. Simple design rules and accurate models validated with measurements up to 120 GHz demonstrate that such planar transmission lines can be used in monolithic microwave integrated circuits (MMICs) to extend the impedance range and the design flexibility with respect to the conventional CPW.
A concept of a beam prestressed by bending: Application to a footbridge in composite materials

This paper proposes a concept of a beam prestressed by bending. An application through an example of footbridge with three supports is shown and illustrated. By bending a beam with a central cable, this cable will be in tension. This cable is then able to act as column which is an advantage as it cannot buckle. In this example, the method of calculation is given and shows that composite materials are well suited for this application. Finally the construction of a scale one model is shown and the authors conclude on the interest of such concept.
ZO-1 Intracellular Localization Organizes Immune Response in Non-Small Cell Lung Cancer

Delocalization of zonula occludens-1 (ZO-1) from tight junctions plays a substantial role in epithelial cell plasticity observed during tumor progression. In vitro, we reported an impact of ZO-1 cyto-nuclear content in modulating the secretion of several pro-inflammatory chemokines. In vivo, we demonstrated that it promotes the recruitment of immune cells in mouse ear sponge assays. Examining lung cancers, we showed that a high density of CD8 cytotoxic T cells and Foxp3 immunosuppressive regulatory T cells in the tumor microenvironment correlated with a cyto-nuclear expression of ZO-1. Taken together, our results support that, by affecting tumor cell secretome, the cyto-nuclear ZO-1 pool may recruit immune cells, which could be permissive for tumor progression.
Heat modeling of wastewater in sewer networks: Determination of thermal energy content from sewage with modeling tools

This text presents an approach to assess the energy content (flow and temperature) at any point of the sewer system based on the coupling of a stochastic model to predict drinking water demand from households and a model to estimate the temperature distribution in the sewer network. An application example is given where the developed tool is used to estimate the implications of temperature drop in some points of an existing sewer network.