Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Anomaly Detection in Edge Streams

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose Midas , which focuses on detecting microcluster anomalies , or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. We further propose Midas -F, to solve the problem by which anomalies are incorporated into the algorithm’s internal states, creating a “poisoning” effect that can allow future anomalies to slip through undetected. Midas -F introduces two modifications: (1) we modify the anomaly scoring function, aiming to reduce the “poisoning” effect of newly arriving edges; (2) we introduce a conditional merge step, which updates the algorithm’s data structures after each time tick, but only if the anomaly score is below a threshold value, also to reduce the “poisoning” effect. Experiments show that Midas -F has significantly higher accuracy than Midas . In general, the algorithms proposed in this work have the following properties: (a) they detects microcluster anomalies while providing theoretical guarantees about the false positive probability; (b) they are online, thus processing each edge in constant time and constant memory, and also processes the data orders-of-magnitude faster than state-of-the-art approaches; and (c) they provides up to 62% higher area under the receiver operating characteristic curve than state-of-the-art approaches.
O ct 2 02 1 Interatomic interaction at the aluminum-fullerene C 60 interface

We propose a model describing the interatomic interaction at the interface between fullerene C60 and aluminum. Using the density functional theory, we calculate the binding energy and the fullerene’s position on the Al(111) slab. The obtained data are applied to estimate the parameters of the Lennard-Jones potential for carbon and aluminum atoms, which is then used in molecular dynamics simulations. The results of the theoretical study of desorption of fullerenes from an aluminum substrate are in good agreement with those of the experiments from the literature. We also investigate the capillary effects in an aluminum melt with submerged fullerenes. The positive interface surface energy indicates the poor wettability of C60 by the melt. The calculated value of the diffusion relaxation time is approximately two orders of magnitude less than the characteristic coagulation time of fullerenes. The activation character of the coagulation process and the capillary nature of the interaction between fullerenes are discussed.
Sharp convergence to steady states of Allen-Cahn

In our recent work we found a surprising breakdown of symmetry conservation: using standard numerical discretization with very high precision the computed numerical solutions corresponding to very nice initial data may converge to completely incorrect steady states due to the gradual accumulation of machine round-off error. We solved this issue by introducing a new Fourier filter technique for solutions with certain band gap properties. To further investigate the attracting basin of steady states we classify in this work all possible bounded nontrivial steady states for the Allen-Cahn equation. We characterize sharp dependence of nontrivial steady states on the diffusion coefficient and prove strict monotonicity of the associated energy. In particular, we establish a certain self-replicating property amongst the hierarchy of steady states and give a full classification of their energies and profiles. We develop a new modulation theory and prove sharp convergence to the steady state with explicit rates and profiles.
Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems

Dialogue policy optimisation via reinforcement learning requires a large number of training interactions, which makes learning with real users time consuming and expensive. Many set-ups therefore rely on a user simulator instead of humans. These user simulators have their own problems. While hand-coded, rule-based user simulators have been shown to be sufficient in small, simple domains, for complex domains the number of rules quickly becomes intractable. State-of-the-art data-driven user simulators, on the other hand, are still domain-dependent. This means that adaptation to each new domain requires redesigning and retraining. In this work, we propose a domain-independent transformer-based user simulator (TUS). The structure of TUS is not tied to a specific domain, enabling domain generalization and the learning of cross-domain user behaviour from data. We compare TUS with the state-of-the-art using automatic as well as human evaluations. TUS can compete with rule-based user simulators on pre-defined domains and is able to generalize to unseen domains in a zero-shot fashion.
Dynamic Recompilation of Software Network Services with Morpheus

State-of-the-art approaches to design, develop and optimize software packet-processing programs are based on static compilation: the compiler's input is a description of the forwarding plane semantics and the output is a binary that can accommodate any control plane configuration or input traffic. In this paper, we demonstrate that tracking control plane actions and packet-level traffic dynamics at run time opens up new opportunities for code specialization. We present Morpheus, a system working alongside static compilers that continuously optimizes the targeted networking code. We introduce a number of new techniques, from static code analysis to adaptive code instrumentation, and we implement a toolbox of domain specific optimizations that are not restricted to a specific data plane framework or programming language. We apply Morpheus to several eBPF and DPDK programs including Katran, Facebook's production-grade load balancer. We compare Morpheus against state-of-the-art optimization frameworks and show that it can bring up to 2x throughput improvement, while halving the 99th percentile latency.
Counting H-free orientations of graphs

Abstract In 1974, Erdős posed the following problem. Given an oriented graph H, determine or estimate the maximum possible number of H-free orientations of an n-vertex graph. When H is a tournament, the answer was determined precisely for sufficiently large n by Alon and Yuster. In general, when the underlying undirected graph of H contains a cycle, one can obtain accurate bounds by combining an observation of Kozma and Moran with celebrated results on the number of F-free graphs. As the main contribution of the paper, we resolve all remaining cases in an asymptotic sense, thereby giving a rather complete answer to Erdős’s question. Moreover, we determine the answer exactly when H is an odd cycle and n is sufficiently large, answering a question of Araújo, Botler and Mota.
Towards Automated Attack Simulations of BPMN-based Processes

Process digitization and integration is an increasing need for enterprises, while cyber-attacks denote a growing threat. Using the Business Process Model and Notation (BPMN) is common to handle the digital and integration focus within and across organizations. In other parts of the same companies, threat modeling and attack graphs are used for analyzing the security posture and resilience.In this paper, we propose a novel approach to use attack graph simulations on processes represented in BPMN. Our contributions are the identification of BPMN’s attack surface, a mapping of BPMN elements to concepts in a Meta Attack Language (MAL)-based Domain-Specific Language (DSL), called coreLang, and a prototype to demonstrate our approach in a case study using a real-world invoice integration process. The study shows that non-invasively enriching BPMN instances with cybersecurity analysis through attack graphs is possible without much human expert input. The resulting insights into potential vulnerabilities could be beneficial for the process modelers.
Charge Carrier Transport in Iron Pyrite Thin Films: Disorder-Induced Variable-Range Hopping

The origin of p-type conductivity and the mechanism responsible for low carrier mobility was investigated in pyrite (FeS2) thin films. Temperature dependent resistivity measurements were performed on polycrystalline and nanostructured thin films prepared by three different methods. Films have a high hole density and low mobility regardless of the method used for their preparation. The charge transport mechanism is determined to be nearest neighbour hopping (NNH) at near room temperature with Mott-type variable range hopping (VRH) of holes via localized states occurring at lower temperatures. Density functional theory (DFT) predicts that sulfur vacancy induced localized defect states will be situated within the band gap with the charge remaining localized around the defect. The data indicate that the electronic properties including hopping transport in pyrite thin films can be correlated to sulfur vacancy related defect. The results provide insights on electronic properties of pyrite thin films and its implications for charge transport
Model-Based Counterfactual Synthesizer for Interpretation

Counterfactuals, serving as one of the emerging type of model interpretations, have recently received attention from both researchers and practitioners. Counterfactual explanations formalize the exploration of "what-if'' scenarios, and are an instance of example-based reasoning using a set of hypothetical data samples. Counterfactuals essentially show how the model decision alters with input perturbations. Existing methods for generating counterfactuals are mainly algorithm-based, which are time-inefficient and assume the same counterfactual universe for different queries. To address these limitations, we propose a Model-based Counterfactual Synthesizer (MCS) framework for interpreting machine learning models. We first analyze the model-based counterfactual process and construct a base synthesizer using a conditional generative adversarial net (CGAN). To better approximate the counterfactual universe for those rare queries, we novelly employ the umbrella sampling technique to conduct the MCS framework training. Besides, we also enhance the MCS framework by incorporating the causal dependence among attributes with model inductive bias, and validate its design correctness from the causality identification perspective. Experimental results on several datasets demonstrate the effectiveness as well as efficiency of our proposed MCS framework, and verify the advantages compared with other alternatives.
GKNet: Grasp keypoint network for grasp candidates detection

Contemporary grasp detection approaches employ deep learning to achieve robustness to sensor and object model uncertainty. The two dominant approaches design either grasp-quality scoring or anchor-based grasp recognition networks. This paper presents a different approach to grasp detection by treating it as keypoint detection in image-space. The deep network detects each grasp candidate as a pair of keypoints, convertible to the grasp representation g = {x,y,w,θ} T , rather than a triplet or quartet of corner points. Decreasing the detection difficulty by grouping keypoints into pairs boosts performance. To promote capturing dependencies between keypoints, a non-local module is incorporated into the network design. A final filtering strategy based on discrete and continuous orientation prediction removes false correspondences and further improves grasp detection performance. GKNet, the approach presented here, achieves a good balance between accuracy and speed on the Cornell and the abridged Jacquard datasets (96.9% and 98.39% at 41.67 and 23.26 fps). Follow-up experiments on a manipulator evaluate GKNet using four types of grasping experiments reflecting different nuisance sources: static grasping, dynamic grasping, grasping at varied camera angles, and bin picking. GKNet outperforms reference baselines in static and dynamic grasping experiments while showing robustness to varied camera viewpoints and moderate clutter. The results confirm the hypothesis that grasp keypoints are an effective output representation for deep grasp networks that provide robustness to expected nuisance factors.
Strategic Behavior is Bliss: Iterative Voting Improves Social Welfare

Recent work in iterative voting has defined the additive dynamic price of anarchy (ADPoA) as the difference in social welfare between the truthful and worst-case equilibrium profiles resulting from repeated strategic manipulations. While iterative plurality has been shown to only return alternatives with at most one less initial votes than the truthful winner, it is less understood how agents' welfare changes in equilibrium. To this end, we differentiate agents' utility from their manipulation mechanism and determine iterative plurality's ADPoA in the worst- and average-cases. We first prove that the worst-case ADPoA is linear in the number of agents. To overcome this negative result, we study the average-case ADPoA and prove that equilibrium winners have a constant order welfare advantage over the truthful winner in expectation. Our positive results illustrate the prospect for social welfare to increase due to strategic manipulation.
Coreference-Aware Dialogue Summarization

Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions. Examples of such challenges include unstructured information exchange in dialogues, informal interactions between speakers, and dynamic role changes of speakers as the dialogue evolves. Many of such challenges result in complex coreference links. Therefore, in this work, we investigate different approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models to tackle the aforementioned challenges. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. Evaluation results on factual correctness suggest such coreference-aware models are better at tracing the information flow among interlocutors and associating accurate status/actions with the corresponding interlocutors and person mentions.
The Oxford Road Boundaries Dataset

In this paper we present The Oxford Road Boundaries Dataset, designed for training and testing machine-learning-based road-boundary detection and inference approaches. We have hand-annotated two of the 10 km-long forays from the Oxford Robotcar Dataset and generated from other forays several thousand further examples with semi-annotated road-boundary masks. To boost the number of training samples in this way, we used a vision-based localiser to project labels from the annotated datasets to other traversals at different times and weather conditions. As a result, we release 62 605 labelled samples, of which 47 639 samples are curated. Each of these samples contain both raw and classified masks for left and right lenses. Our data contains images from a diverse set of scenarios such as straight roads, parked cars, junctions, etc. Files for download and tools for manipulating the labelled data are available at: oxford-robotics-institute.github. io/road-boundaries-dataset
Time Series Momentum Predictability via Dynamic Binary Classiﬁcation *

Time series momentum strategies are widely applied in the quantitative ﬁnancial industry and its academic research has grown rapidly since the work of Moskowitz, Ooi, and Pedersen (2012). However, trading signals are usually obtained via simple observation of past return measurements. In this article we study the beneﬁts of incorporating dynamic econometric models to sequentially learn the time-varying importance of different look-back periods for individual assets. By the use of a dynamic binary classiﬁer model, the investor is able to switch between time-varying or constant relations between past momentum and future returns, dynamically combining different look-back periods and improving trading signals accuracy and portfolio performance. Using data from 56 future contracts we show that a mean-variance investor will be willing to pay a considerable managment fee to switch from the traditional naive time series momentum strategy to the dynamic classiﬁer approach.
Privacy Preference Signals: Past, Present and Future

Abstract Privacy preference signals are digital representations of how users want their personal data to be processed. Such signals must be adopted by both the sender (users) and intended recipients (data processors). Adoption represents a coordination problem that remains unsolved despite efforts dating back to the 1990s. Browsers implemented standards like the Platform for Privacy Preferences (P3P) and Do Not Track (DNT), but vendors profiting from personal data faced few incentives to receive and respect the expressed wishes of data subjects. In the wake of recent privacy laws, a coalition of AdTech firms published the Transparency and Consent Framework (TCF), which defines an optin consent signal. This paper integrates post-GDPR developments into the wider history of privacy preference signals. Our main contribution is a high-frequency longitudinal study describing how TCF signal gained dominance as of February 2021. We explore which factors correlate with adoption at the website level. Both the number of third parties on a website and the presence of Google Ads are associated with higher adoption of TCF. Further, we show that vendors acted as early adopters of TCF 2.0 and provide two case-studies describing how Consent Management Providers shifted existing customers to TCF 2.0. We sketch ways forward for a pro-privacy signal.
TUTA: Tree-based Transformers for Generally Structured Table Pre-training

We propose TUTA, a unified pre-training architecture for understanding generally structured tables. Noticing that understanding a table requires spatial, hierarchical, and semantic information, we enhance transformers with three novel structure-aware mechanisms. First, we devise a unified tree-based structure, called a bi-dimensional coordinate tree, to describe both the spatial and hierarchical information of generally structured tables. Upon this, we propose tree-based attention and position embedding to better capture the spatial and hierarchical information. Moreover, we devise three progressive pre-training objectives to enable representations at the token, cell, and table levels. We pre-train TUTA on a wide range of unlabeled web and spreadsheet tables and fine-tune it on two critical tasks in the field of table structure understanding: cell type classification and table type classification. Experiments show that TUTA is highly effective, achieving state-of-the-art on five widely-studied datasets.
Bridge Networks Relating Information Bottlenecks through Vector-Symbolic Manipulations

Despite rapid progress, current deep learning methods face a number of critical challenges. These include high energy consumption, catastrophic forgetting, dependance on global losses, and an inability to reason symbolically. By combining concepts from information bottleneck theory and vector-symbolic architectures, we propose and implement a novel information processing architecture, the ‘Bridge network. ’ We show this architecture provides unique advantages which can address the problem of global losses and catastrophic forgetting. Furthermore, we argue that it provides a further basis for increasing energy efficiency of execution and the ability to reason symbolically
A New Schiff Base Derivatives Designed to Bind Metal Ion (Cu, Co): Thermodynamics and Biological Activity Studies

Schiff base synthesis is usually acid catalyzed and it usually requires refluxing the mixture of aldehydes and amine in ethanolic solution. Synthesis and characterization of Schiff base ligands derived from substituted amine and salicylaldehyde and their complexes (Cu2+, Co2+) are reported. The ligands and ligand-complexes were characterized by melting point, FTIR, CHN-elemental analysis and UV-Visible analysis. The UV-Visible and elemental analysis of complexes established (1:2) mole ratio (M:L). The stability constant and thermodynamic parameters (K, ΔG, ΔH, ΔS) were determined at different temperature (30 - 40)°C which established that the metal-complexes were very stable. The review describes the promising biological activities of Schiff base and their metal complexes.
First‐in‐human intracochlear application of human stromal cell‐derived extracellular vesicles

Abstract Extracellular vesicles (EVs) derived from the secretome of human mesenchymal stromal cells (MSC) contain numerous factors that are known to exert anti‐inflammatory effects. MSC‐EVs may serve as promising cell‐based therapeutics for the inner ear to attenuate inflammation‐based side effects from cochlear implantation which represents an unmet clinical need. In an individual treatment performed on a ‘named patient basis’, we intraoperatively applied allogeneic umbilical cord‐derived MSC‐EVs (UC‐MSC‐EVs) produced according to good manufacturing practice. A 55‐year‐old patient suffering from Menière's disease was treated with intracochlear delivery of EVs prior to the insertion of a cochlear implant. This first‐in‐human use of UC‐MSC‐EVs demonstrates the feasibility of this novel adjuvant therapeutic approach. The safety and efficacy of intracochlear EV‐application to attenuate side effects of cochlea implants have to be determined in controlled clinical trials.
Role of Semisulcospira gottschei extract as medicinal food on reflux esophagitis in rats

Abstract Gastroesophageal reflux disease (GERD) is a globally prevalent disease and results from a reflux of gastric contents into the esophagus. Existing synthetic drug‐based treatments for GERD have various drawbacks including refractory symptoms, relapse, or resistance due to long‐term use or may result in mucosal degeneration, polyps, and osteoporosis. Semisulcospira gottschei (SE), a freshwater snail, has been generally consumed as a food source due to its excellent flavor and nutritional value in Korea and considered to have therapeutic properties for various diseases including dyspepsia, stomachache, and hepatic diseases. The present study aims to investigate whether Semisulcospira gottschei extract (SGE) has a protective effect on reflux esophagitis‐induced rat models. The anti‐inflammatory effects of SGE were evaluated via NO production in LPS‐induced Raw 264.7 macrophage. And the protection effects of SGE were analyzed by assessing the amelioration of mucosal damage and expression of inflammation‐associated proteins in reflux esophagitis (RE) rats. Our results indicate that SGE significantly suppressed NO production in LPS‐induced raw 264.7 cells without any cytotoxicity. We observed mucosal lesions and histological changes in the esophagus of RE control rats. However, SGE treatment markedly ameliorated mucosal lesion ratio indicated through histological changes. SGE administration suppressed the expression of proteins related to inflammation, such as p‐NF‐κB, p‐IκBα, COX‐2, and TNF‐α, in esophageal tissue. Moreover, SGE elevated the expression of claudin‐5, which is a tight junction protein, involved in barrier function of epithelium and endothelium. The results suggest that SGE is useful as a medicinal food in esophagitis and may be helpful in developing effective treatment protocols for GERD.