Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion

Temporal Knowledge graph completion (TKGC) is a crucial task that involves reasoning at known timestamps to complete the missing part of facts and has attracted more and more attention in recent years. Most existing methods focus on learning representations based on graph neural networks while inaccurately extracting information from timestamps and insufficiently utilizing the implied information in relations. To address these problems, we propose a novel TKGC model, namely Pre-trained Language Model with Prompts for TKGC (PPT). We convert a series of sampled quadruples into pre-trained language model inputs and convert intervals between timestamps into different prompts to make coherent sentences with implicit semantic information. We train our model with a masking strategy to convert TKGC task into a masked token prediction task, which can leverage the semantic information in pre-trained language models. Experiments on three benchmark datasets and extensive analysis demonstrate that our model has great competitiveness compared to other models with four metrics. Our model can effectively incorporate information from temporal knowledge graphs into the language models.
Rely-Guarantee Reasoning for Causally Consistent Shared Memory (Extended Version)

Rely-guarantee (RG) is a highly influential compositional proof technique for concurrent programs, which was originally developed assuming a sequentially consistent shared memory. In this paper, we first generalize RG to make it parametric with respect to the underlying memory model by introducing an RG framework that is applicable to any model axiomatically characterized by Hoare triples. Second, we instantiate this framework for reasoning about concurrent programs under causally consistent memory, which is formulated using a recently proposed potential-based operational semantics, thereby providing the first reasoning technique for such semantics. The proposed program logic, which we call Piccolo, employs a novel assertion language allowing one to specify ordered sequences of states that each thread may reach. We employ Piccolo for multiple litmus tests, as well as for an adaptation of Peterson's algorithm for mutual exclusion to causally consistent memory.
Contractads -- operads with inputs indexed by graphs

We introduce a generalization of the notion of an operad that we call a contractad, whose set of operations are indexed by connected graphs and composition rules are numbered by contractions of connected subgraphs, what explains the nature of terminology. We show that many classical operads such as the operad of commutative algebras, Lie algebras, associative algebras, pre-Lie algebras, the little discs operad and the operad of the moduli spaces $\overline{\mathcal{M}}_{0,n}$ of stable curves admits generalizations to contractads. We explain that standard tools like Koszul duality and Gr\"obner bases machinery can be easily generalized to contractads. We verify the koszulness of the commutative, Lie, associative, and Gerstenhaber contractads.
ReSDF: Redistancing Implicit Surfaces using Neural Networks

This paper proposes a deep-learning-based method for recovering a signed distance function (SDF) of a given hypersurface represented by an implicit level set function. Using the flexibility of constructing a neural network, we use an augmented network by defining an auxiliary output to represent the gradient of the SDF. There are three advantages of the augmented network; (i) the target interface is accurately captured, (ii) the gradient has a unit norm, and (iii) two outputs are approximated by a single network. Moreover, unlike a conventional loss term which uses a residual of the eikonal equation, a novel training objective consisting of three loss terms is designed. The first loss function enforces a pointwise matching between two outputs of the augmented network. The second loss function leveraged by a geometric characteristic of the SDF imposes the shortest path obtained by the gradient. The third loss function regularizes a singularity of the SDF caused by discontinuities of the gradient. Numerical results across a wide range of complex and irregular interfaces in two and three-dimensional domains confirm the effectiveness and accuracy of the proposed method. We also compare the results of the proposed method with physics-informed neural networks approaches and the fast marching method.
European 5G Security in the Wild: Reality versus Expectations

5G cellular systems are slowly being deployed worldwide delivering the promised unprecedented levels of throughput and latency to hundreds of millions of users. At such scale security is crucial, and consequently, the 5G standard includes a new series of features to improve the security of its predecessors (i.e., 3G and 4G). In this work, we evaluate the actual deployment in practice of the promised 5G security features by analysing current commercial 5G networks from several European operators. By collecting 5G signalling traffic in the wild in several cities in Spain, we i) fact-check which 5G security enhancements are actually implemented in current deployments, ii) provide a rich overview of the implementation status of each 5G security feature in a wide range of 5G commercial networks in Europe and compare it with previous results in China, iii) analyse the implications of optional features not being deployed, and iv) discuss on the still remaining 4G-inherited vulnerabilities. Our results show that in European 5G commercial networks, the deployment of the 5G security features is still on the works. This is well aligned with results previously reported from China [16] and keeps these networks vulnerable to some 4G attacks, during their migration period from 4G to 5G.
University of Birmingham Experimental study on interaction and coalescence of synchronized multiple bubbles

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Sentence Level Curriculum Learning for Improved Neural Conversational Models

Designing machine intelligence to converse with a human user necessarily requires an understanding of how humans participate in conversation, and thus conversation modeling is an important task in natural language processing. New breakthroughs in architecture and data gathering continue to push the performance of such conversational AI models. However, designs neglect the gradual buildup in sentence structure and complexity experienced by humans as we learn to communicate. During training, our model accepts one or more sentences as input and attempts to predict the next sentence in the conversation one word at a time, so our goal is to separate training into segments, with each segment's corpus comprised of longer sentence pairs than the previous one. This will mimic the desired"buildup"component of human learning. We begin with only"short"length sentence pairs, then only"medium"length pairs, and so on. A majority of our experiments were toward optimizing this technique, ensuring a proper representation of the technique's potential, since many of the details were new questions. Our segment-trained models were then able to achieve lower validation loss at the end of training than models trained with standard text preparation. This segmented training is straightforward to implement and our results provide a general direction for future research to implement and improve it.
On $\ell_2$-performance of weakly-hard real-time control systems

This paper considers control systems with failures in the feedback channel, that occasionally lead to loss of the control input signal. A useful approach for modeling such failures is to consider window-based constraints on possible loss sequences, for example that at least r control attempts in every window of s are successful. A powerful framework to model such constraints are weakly-hard real-time constraints. Various approaches for stability analysis and the synthesis of stabilizing controllers for such systems have been presented in the past. However, existing results are limited to asymptotic stability and do not consider performance measures such as the resulting $\ell_2$-gain. To address this problem, we adapt a switched system description where the switching sequence is constrained by a graph that captures the loss information. We present an approach for $\ell_2$-performance analysis involving linear matrix inequalities (LMI). Further, leveraging a system lifting method, we propose an LMI-based approach for synthesizing state-feedback controllers with guaranteed $\ell_2$-performance. The results are illustrated by a numerical example.
ML-Based Teaching Systems: A Conceptual Framework

As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and pass it on to novices. While this knowledge transfer has traditionally occurred through personal interaction, it lacks scalability and requires significant resources and time. IT-based teaching systems have addressed this scalability issue, but their development is still tedious and time-consuming. In this work, we investigate the potential of machine learning (ML) models to facilitate knowledge transfer in an organizational context, leading to more cost-effective IT-based teaching systems. Through a systematic literature review, we examine key concepts, themes, and dimensions to understand better and design ML-based teaching systems. To do so, we capture and consolidate the capabilities of ML models in IT-based teaching systems, inductively analyze relevant concepts in this context, and determine their interrelationships. We present our findings in the form of a review of the key concepts, themes, and dimensions to understand and inform on ML-based teaching systems. Building on these results, our work contributes to research on computer-supported cooperative work by conceptualizing how ML-based teaching systems can preserve expert knowledge and facilitate its transfer from SMEs to human novices. In this way, we shed light on this emerging subfield of human-computer interaction and serve to build an interdisciplinary research agenda.
SmartProbe: A Virtual Moderator for Market Research Surveys

Market research surveys are a powerful methodology for understanding consumer perspectives at scale, but are limited by depth of understanding and insights. A virtual moderator can introduce elements of qualitative research into surveys, developing a rapport with survey participants and dynamically asking probing questions, ultimately to elicit more useful information for market researchers. In this work, we introduce SmartProbe , an API which leverages the adaptive capabilities of large language models (LLMs), and incorporates domain knowledge from market research, in order to generate effective probing questions in any market research survey. We outline the modular processing ﬂow of SmartProbe , and evaluate the quality and effectiveness of its generated probing questions. We believe our efforts will inspire industry practitioners to build real-world applications based on the latest advances in LLMs. Our demo is publicly available at https://nexxt. in/smartprobe-demo .
Double-Weighting for Covariate Shift Adaptation

Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates $x$) of training and testing samples $\mathrm{p}_\text{tr}(x)$ and $\mathrm{p}_\text{te}(x)$ are different but the label conditionals coincide. Existing approaches address such covariate shift by either using the ratio $\mathrm{p}_\text{te}(x)/\mathrm{p}_\text{tr}(x)$ to weight training samples (reweighted methods) or using the ratio $\mathrm{p}_\text{tr}(x)/\mathrm{p}_\text{te}(x)$ to weight testing samples (robust methods). However, the performance of such approaches can be poor under support mismatch or when the above ratios take large values. We propose a minimax risk classification (MRC) approach for covariate shift adaptation that avoids such limitations by weighting both training and testing samples. In addition, we develop effective techniques that obtain both sets of weights and generalize the conventional kernel mean matching method. We provide novel generalization bounds for our method that show a significant increase in the effective sample size compared with reweighted methods. The proposed method also achieves enhanced classification performance in both synthetic and empirical experiments.
Géométrie et suites récurrentes

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Depletion of ESCRT ameliorates APP‐induced AD‐like symptoms in Drosophila

The amyloid‐β (Aβ) peptide, produced from amyloid precursor protein (APP) by β and γ‐secretases, has been implicated in the etiology of Alzheimer's disease (AD). However, the precise intracellular trafficking pathway of APP and its subcellular locations to produce Aβ have remained unclear. To address these issues, we established fly AD models that recapitulated multiple AD‐like symptoms by expressing human APP in the Drosophila nerve system. The ESCRT (endosomal sorting complexes required for transport) machinery regulates the sorting and trafficking of endocytosed proteins, yet its role in AD pathogenesis has not been explored in vivo. We found that knockdown of distinct ESCRT components ameliorated APP‐induced morphological and behavioral defects, including impaired wing expansion, eye degeneration, dopamine neuron loss, locomotor disability, lifespan shortening, and cognitive deficits. Mechanistically, we showed that impaired ESCRT impeded APP's intracellular transportation from early endosomes to late endosomes, resulting in reduced Aβ production and amyloid deposit load. These data suggest that APP undergoes ESCRT‐mediated endocytic trafficking, and Aβ is generated mainly in late endosomes. Our data provide the first in vivo evidence to support a physiological role of ESCRT in AD pathogenesis, suggesting that interfering with ESCRT machinery might be an alternative therapeutic strategy for AD.
Venous Thromboembolism Chemoprophylaxis Compliance in the Surgical Intensive Care Unit

Introduction Early initiation of venous thromboembolism chemoprophylaxis (VTEp) decreases VTE risk in trauma patients in the Surgical Intensive Care Unit (SICU). The frequency and variation of VTEp interruption by different surgical subspecialties in the SICU is incompletely described in the literature. The objective of this study was to examine VTEp compliance in the SICU in terms of uninterrupted VTEp following initiation, both by surgical service and time of year, to identify opportunities for improvement. Methods This single-center quality improvement (QI) study examined all SICU patients, which are almost exclusively trauma patients, at our institution (1/2021-04/2022). Exclusions were therapeutic anticoagulation. Type of VTEp, calendar month of SICU stay, perceived indications for interruption, and primary service were collected. Results Of 5 434 patient days (PD), VTEp was not administered in 1879 (35%). Common reasons for VTEp interruption were ongoing bleeding (n = 964 PD, 51%) and periprocedural status (n = 651 PD, 35%). Periprocedural interruption was highest in July. Acute Care Surgery (ACS) (n = 208 PD, 32%) and Orthopedics (n = 188 PD, 29%) interrupted VTEp most often. ACS most commonly withheld VTEp for second look laparotomies while Orthopedics withheld VTEp for intramedullary nailing or external fixator application. Conclusion Missed VTEp doses occurred most frequently at the beginning of the residency year, with a high percentage held for periprocedural status. Because the necessity of periprocedural VTEp holds is unclear, the appropriateness of these holds and any impact on VTE rates will be assessed as the next steps. In the interim, our findings provide targets for multidisciplinary QI endeavors.
Adipose tissue area is a predictive biomarker for the efficacy of pegylated liposomal doxorubicin in platinum‐refractory/resistant ovarian cancer

Pegylated liposomal doxorubicin (PLD), an anthracycline agent, is widely used as a treatment option for platinum‐refractory/resistant epithelial ovarian cancer (EOC). Although only a subset of patients with platinum‐refractory/resistant EOC derive benefit from PLD, predictive biomarkers for patients who will respond to the drug have not yet been established. Here, we evaluated the relationship between adipose tissue status and PLD efficacy in patients with platinum‐refractory/resistant EOC.
Special issue on chemical exchange saturation transfer MRI

MRI with limitations on this front of in vivo MRS. 15 The glutamate CEST (gluCEST) advantages, challenges, and limitations are explored thoroughly, beginning with phantom experiment results demonstrated in their initial publication, through the latest approaches to correct human brain images for B 1 inhomogeneity. Knutsson et al. review efforts to use sugars and sugar polymers as exogenous contrast agents, which is possible based on the exchange of their hydroxyl protons with water protons. 16 After an overview of spectral properties that determine the contrast as a function of field strength and a section on several agents that are already
Cellulase mediated stress triggers the mutations of oleaginous yeast Trichosporon cutaneum with super‐large spindle morphology and high lipid accumulation

Accumulation of intracellular lipid bodies in oleaginous yeast cells is highly restricted by their natural intracellular space. Here we show a cellulase mediated adaptive evolution with ultra‐centrifugation fractionation of oleaginous yeast Trichosporon cutaneum to obtain the favorable cell structure for lipid accumulation. Cellulase was added to the wheat straw hydrolysate during long‐term adaptive evolution for disruption of cell wall integrity of T. cutaneum cells. The cellulase, together with ultracentrifugation force, triggered multiple mutations and transcriptional expression changes of the functional genes associated with cell wall integrity and lipid synthesis metabolism. The fractionated mutant T. cutaneum YY52 demonstrated the heavily weakened cell wall and high lipid accumulation by the super‐large expanded spindle cells (two orders of magnitude greater than the parental). A record‐high lipid production by T. cutaneum YY52 was achieved (55.4 ± 0.5 g L−1 from wheat straw and 58.4 ± 0.1 g L−1 from corn stover). This study not only obtained an oleaginous yeast strain with industrial application potential for lipid production but also provided a new method for generation of mutant cells with high intracellular metabolite accumulation.
Well-Being and Professional Fulfillment in Mohs Surgeons: An Explanatory Sequential Mixed-Method Design Study

Supplemental Digital Content is Available in the Text. BACKGROUND There are limited data evaluating specific themes of well-being and professional fulfillment in Mohs surgeons. OBJECTIVE To identify factors that drive occupational distress and those that promote well-being and professional fulfillment among Mohs surgeons. METHODS This is an explanatory sequential mixed-method study, using semistructured individual interviews. Common drivers of physician well-being and fulfillment were identified based on the independent assessment of the coding in the interview transcripts. RESULTS This study reports the following qualitative themes: (1) gratitude for the chosen profession and relationships, (2) unrealistic standards of perfection that may have contributed to past career success but are unattainable and create emotional burden, and (3) ability to practice in a manner aligned with personal values promotes professional fulfillment. CONCLUSION This study suggests that gratitude, self-compassion, and ability to practice in a manner aligned with personal values promote well-being and professional fulfillment in Mohs surgeons. Notably, we found that unrealistic standards of perfection and personal-organization practice incongruences contribute to burnout.
“Arterial stiffness is not associated with changes in the circadian pattern of blood pressure in patients with type 1 diabetes mellitus and cardiovascular autonomic dysfunction”

Introduction Cardiovascular autonomic neuropathy (CAN) associates an abnormal circadian pattern in blood pressure (BP) regulation that might be aggravated by the coexistence of arterial stiffness. We aimed to evaluate the effect of arterial stiffness in the circadian rhythm of BP in patients with type 1 diabetes and CAN. Methods Cross-sectional study including 56 consecutive patients with type 1 diabetes and CAN, with (n = 28) or without (n = 24) arterial stiffness as defined by an ankle-brachial index above 1.2. CAN was diagnosed by BP and heart rate responses to active standing and cardiovascular autonomic reflex tests. Absence of nocturnal decrease in BP–“non-dipping” pattern– was defined by a daytime to nighttime decrease in mean BP smaller than 10%. Results The study’s subjects mean age was 40 ± 11 years-old, their mean duration of diabetes was 22 ± 10 years, and their mean A1c was 7.9 ± 1.5%. A “non-dipping” pattern was observed in 28 patients (54%) regardless of the presence or absence of arterial stiffness. Age, waist circumference, body mass index, and A1c, were introduced as independent variables into a multiple regression analysis. The stepwise model (R2: 0.113, p = 0.016) retained only A1c levels (β: ‒ 0.333, 95% confidence interval [CI]: −3.10 to −0.33) as significant predictor of the percentage of nighttime decrease in mean BP. Conclusions A non-dipping pattern in BP is very common in patients with type 1 diabetes presenting with subclinical CAN and is associated with a poorer metabolic control. On the contrary, coexistence of arterial stiffness is not associated with abnormalities in circadian BP regulation.
POT1 mutations cause differential effects on telomere length leading to opposing disease phenotypes

The protection of telomere protein (POT1) is a telomere‐binding protein and is an essential component of the six‐membered shelterin complex, which is associated with the telomeres. POT1 directly binds to the 3′ single‐stranded telomeric overhang and prevents the activation of DNA damage response at telomeres thus preventing the telomere–telomere fusions and genomic instability. POT1 also plays a pivotal role in maintaining telomere length by regulating telomerase‐mediated telomere elongation. Mutations in POT1 proteins result in three different telomere phenotypes, which include long, short, or aberrant telomere length. Long telomeres predispose individuals to cancer, while short or aberrant telomere phenotypes result in pro‐aging diseases referred to as telomeropathies. Here, we review the function of POT1 proteins in telomere length hemostasis and how the spectrum of mutations reported in POT1 can be segregated toward developing very distinct disease phenotypes of cancer and telomeropathies.