Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Adversarial Learning of Sinkhorn Algorithm Initializations

The Sinkhorn algorithm is the state-of-the-art to approximate solutions of entropic optimal transport (OT) distances between discrete probability distributions. We show that meticulously training a neural network to learn initializations to the algorithm via the entropic OT dual problem can significantly speed up convergence, while maintaining desirable properties of the Sinkhorn algorithm, such as differentiability and parallelizability. We train our predictive network in an adversarial fashion using a second, generating network and a self-supervised bootstrapping loss. The predictive network is universal in the sense that it is able to generalize to any pair of distributions of fixed dimension and cost at inference, and we prove that we can make the generating network universal in the sense that it is capable of producing any pair of distributions during training. Furthermore, we show that our network can even be used as a standalone OT solver to approximate regularized transport distances to a few percent error, which makes it the first meta neural OT solver.
Face Animation with Multiple Source Images

Face animation has received a lot of attention from researchers in recent years due to its wide range of promising applications. Many face animation models based on optical flow or deep neural networks have achieved great success. However, these models are likely to fail in animated scenarios with significant view changes, resulting in unrealistic or distorted faces. One of the possible reasons is that such models lack prior knowledge of human faces and are not proficient to imagine facial regions they have never seen before. In this paper, we propose a flexible and generic approach to improve the performance of face animation without additional training. We use multiple source images as input as compensation for the lack of prior knowledge of faces. The effectiveness of our method is experimentally demonstrated, where the proposed method successfully supplements the baseline method.
Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion

Point clouds captured by scanning devices are often incomplete due to occlusion. To overcome this limitation, point cloud completion methods have been developed to predict the complete shape of an object based on its partial input. These methods can be broadly classified as supervised or unsupervised. However, both categories require a large number of 3D complete point clouds, which may be difficult to capture. In this paper, we propose Cross-PCC, an unsupervised point cloud completion method without requiring any 3D complete point clouds. We only utilize 2D images of the complete objects, which are easier to capture than 3D complete and clean point clouds. Specifically, to take advantage of the complementary information from 2D images, we use a single-view RGB image to extract 2D features and design a fusion module to fuse the 2D and 3D features extracted from the partial point cloud. To guide the shape of predicted point clouds, we project the predicted points of the object to the 2D plane and use the foreground pixels of its silhouette maps to constrain the position of the projected points. To reduce the outliers of the predicted point clouds, we propose a view calibrator to move the points projected to the background into the foreground by the single-view silhouette image. To the best of our knowledge, our approach is the first point cloud completion method that does not require any 3D supervision. The experimental results of our method are superior to those of the state-of-the-art unsupervised methods by a large margin. Moreover, our method even achieves comparable performance to some supervised methods. We will make the source code publicly available at https://github.com/ltwu6/cross-pcc.
Simultaneously Transmitting and Reflecting Surface (STARS) for Terahertz Communications

A simultaneously transmitting and reflecting surface (STARS) aided terahertz (THz) communication system is proposed. A novel power consumption model is proposed that depends on the type and resolution of the STARS elements. The spectral efficiency (SE) and energy efficiency (EE) are maximized in both narrowband and wideband THz systems by jointly optimizing the hybrid beamforming at the base station (BS) and the passive beamforming at the STARS. 1) For narrowband systems, independent phase-shift STARSs are investigated first. The resulting complex joint optimization problem is decoupled into a series of subproblems using penalty dual decomposition. Low-complexity element-wise algorithms are proposed to optimize the analog beamforming at the BS and the passive beamforming at the STARS. The proposed algorithm is then extended to the case of coupled phase-shift STARS. 2) For wideband systems, the spatial wideband effect at the BS and STARS leads to significant performance degradation due to the beam split issue. To address this, true time delayers (TTDs) are introduced into the conventional hybrid beamforming structure for facilitating wideband beamforming. An iterative algorithm based on the quasi-Newton method is proposed to design the coefficients of the TTDs. Finally, our numerical results confirm the superiority of the STARS over the conventional reconfigurable intelligent surface (RIS). It is also revealed that i) there is only a slight performance loss in terms of SE and EE caused by coupled phase shifts of the STARS in both narrowband and wideband systems, and ii) the conventional hybrid beamforming achieves comparable SE performance and much higher EE performance compared with the full-digital beamforming in narrowband systems but not in wideband systems, where the TTD-based hybrid beamforming is required for mitigating wideband beam split.
Influence of initial conditions on the flow patterns of a shock‐accelerated thin fluid layer

Previous observations of three flow patterns generated by shock acceleration of a thin perturbed, fluid layer are now correlated with asymmetries in the initial conditions. Using a different diagnostic (planar laser Rayleigh scattering) than the previous experiments, upstream mushrooms, downstream mushrooms, and sinuous patterns are still observed. For each experiment the initial perturbation amplitude on one side of the layer can either be larger, smaller, or the same as the amplitude on the other side, as observed with two images per experiment, and these differences lead to the formation of the different patterns.
Proceedings of the 2nd International Workshop on Reading Music Systems

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 2nd International Workshop on Reading Music Systems, held in Delft on the 2nd of November 2019.
Bendamustine Plus Rituximab for Mantle Cell Lymphoma: A Korean, Multicenter Retrospective Analysis

Background/Aim: The combination of bendamustine and rituximab (BR) is highly effective in both treatment-naïve and relapsed or refractory mantle cell lymphoma (MCL). Due to the rarity of MCL and limited accessibility of BR, clinical outcome from BR in the routine clinical practice in Korean patients are limited. Patients and Methods: To evaluate the real-world outcomes of BR treatment for MCL in Korea, medical records from 37 patients were retrospectively analyzed. Results: Twenty-five patients received BR as first-line treatment, and ten, eight, and seven patients were classified as low-, intermediate-, and high-risk by MIPI-classification, respectively. With the follow-up duration of 24.3 months, the three-year progression-free survival (PFS) rate was 80.5%±11.8%. PFS significantly differed according to MIPI-classification (p=0.002) and TP53 status (p=0.042). The three-year overall survival (OS) rate was 92.0%±5.4%. In 12 patients who received BR as salvage treatment, the median age was 66. The median PFS was 12.8 months, and the three-year OS rate was 66.8%±16.2%. Conclusion: BR is an effective regimen for both newly-diagnosed and relapsed or refractory MCL patients in Korea, with favorable response rates and outcomes.
Controllability of multi-input positive discrete-time systems

Linear discrete-time positive systems arise in various fields of science, such as economic modelling, behavioural science, stochastic processes, etc. Recently, some authors have examined the problem of the controllability of such systems for the single-input case. This paper provides necessary and sufficient conditions for characterizing the controllability properties of multi-input systems. The results here developed show that only a narrow class of these systems enjoys the properties of reachability and controllability.
Towards disaggregated P4 pipelines with information exchange minimization

With the advent of programmable data plane hardware and the use of P4 as a high-level programming language, switches not only implement existing protocols but can also offload other tasks easily. However, hardware switches introduce several constraints on the number of computational stages, available SRAM/TCAM, ALUs, etc. In future use cases, such a device may potentially host multiple pipelines at the same time, resulting in additional constraints on the available resources for a single pipeline. The limitations of a single switch can however be handled by disaggregating the pipeline and deploying the resulted subpipelines at different switches in the network. In such approach, in addition to handling routing and packet classification, we also have to solve the information exchange between the depending subpipeline elements, adding extra headers transmitting metadata and variables needed for the continuation of the pipeline execution on another switch in the network. This paper explores the disaggregation of P4 pipelines with long-dependency sequences into subpipelines that minimizes the information exchange overhead and considers constraints on the maximum length of dependency chains in each subpipelines in a way to reach a fully automated process eventually.
ETHOLOGICAL MODELS AND THE CONCEPT OF ‘DRIVE’*

IN recent years considerable advances in the study of animal behaviour have been made possible by the work of Lorenz, Tinbergen • and others influenced by them. Their ethological* approach to behaviour study has been fertile partly because it has not been hobbled by an over-rigid theoretical scheme. This has allowed due attention to be paid to the initial descriptive phase, essential in the development of any natural science, but neglected so often by behaviour students; and has also ensured that the explanatory concepts have been chosen to suit the phenomena studied, rather than vice versa. It does not seem over-optimistic to suggest that ethology is now entering a period of rapid expansion—a process which may, however, require a thorough revision of some of the concepts which have grown up with it and seen it through its teething troubles. The purpose of this paper is to consider the models used hitherto by ethologists in the light of some of the known properties of scientific models, and to elucidate their relationship to the ethologist's conception of' drive '. The two models concerned arc Lorenz's ' Hydraulic reservoir * * and Tinbergen's ' Hierarchical system of centres '. ' Lorenz postulates for each instinctive act, a particular ' reaction specific energy' which he pictures as accumulating in a reservoir with a spring-valve at its base. In an appropriate stimulus situation, the spring-valve is released partly by the hydrostatic pressure of the reservoir's contents and partly by the action of the external stimulus, which is pictured as
Measurement-based blockage and intra-cluster interference analysis in mmWave multi-point connectivity networks

Millimeter-wave (mmWave) is one key enabler for high data rates in (beyond-)5G wireless communication networks. The use of directive beams and the shorter transmission range in mmWave communications make it easily obstructed or blocked by humans in dynamic real-world scenarios. Connectivity diversity, a solution to link disruption and blockage, could be achieved by deploying distributed multi-point infrastructure. To this end, a mmWave User Equipment (UE) requires a multi-reception capability. This paper investigates how different multi-point connectivity schemes, established by a set of non-cooperative and cooperative serving Base Stations (BSs), perform in the ideal Line-of-Sight (LoS) and the worst-case blocked scenarios. The results and analysis found that the impact of intra-cluster interference on spectral efficiency is much more significant than the blockage. Without any interference cancellation, regardless of LoS or blocked scenarios, adding extra serving BSs does not necessarily improve the spectral efficiency due to the dominant inter-stream interference within a cluster of services.
FLOREnce: A Hybrid Logic-Functional Reactive Programming Language

Functional reactive programming (FRP) languages allow programmers to write declarative expressions to denote event-streams. They feature operators (e.g., map, filter, merge, .) to build compositions of events. Among these operators we would expect to find the traditional Boolean operators to express complex events. To some extent this is true. However, a remarkable absent in existing FRP languages is the logical conjunction, i.e., the and operator. In this paper, we describe the difficulty of introducing logical conjunction in FRP. We then present a new experimental language called FLOREnce that reconciles functional reactive programming with ‘logic reactive programming’ abstractions. The latter emerge naturally when adding a reactive conjunction operator to an event-based FRP language. We present a prototypical implementation of FLOREnce and we study its expressiveness by conducting an experiment.
The Use of Organic Coatings to Promote Dropwise Condensation of Steam

Fourteen polymer coatings were evaluated for their ability to promote and sustain dropwise condensation of steam. Nine of the coatings employed a fluoropolymer as a major constituent; four employed hydrocarbons and one a silicone. Each coating was applied to 25-mm-square by approximately 1-mm-thick metal substrates of brass, copper, copper-nickel, and titanium. While exposed to steam at atmospheric pressure, each coating was visually evaluated for its ability to promote dropwise condensation. Observations were also conduction over a period of 22,000 hr. Hardness and adhesion tests were performed on selected specimens, On the basis of sustained performance, six coatings were selected for application to the outside of 19-mm-dia copper tubes in order to perform a heat transfer evaluation. These tubes were mounted horizontally in a separate apparatus through which steam flowed vertically downward. Steam-side heat transfer coefficients were inferred from overall measurements. Test results indicate that the steam-side heat transfer coefficient can be increased by a factor of five to eight through the use of polymer coatings to promote dropwise condensation.
Inlining-Benefit Prediction with Interprocedural Partial Escape Analysis

Inlining is the primary facilitating mechanism for intraprocedural Partial Escape Analysis (PEA), which allows for the removal of object allocations on a branch-by-branch basis and is critical for performance in object-oriented languages. Prior work used interprocedural Escape Analysis to make inlining decisions, but it discarded control-flow-sensitivity when crossing procedure boundaries, and did not weigh other metrics to model the cost-benefit of inlining, resulting in unpredictable inlining decisions and suboptimal performance. Our work addresses these issues and introduces a novel Interprocedural Partial Escape Analysis algorithm (IPEA) to predict the inlining benefits, and improve the cost-benefit model of an existing optimization-driven inliner. We evaluate the implementation of IPEA in GraalVM Native Image, on industry-standard benchmark suites Dacapo, ScalaBench, and Renaissance. Out of 36 benchmarks with a geometric mean runtime improvement of 1.79%, 6 benchmarks achieve an improvement of over 5% with a geomean of 9.10% and up to 24.62%, while also reducing code size and compilation times compared to existing approaches.
Improving Vectorization Heuristics in a Dynamic Compiler with Machine Learning Models

Optimizing compilers rely on many hand-crafted heuristics to guide the optimization process. However, the interactions between different optimizations makes their design a difficult task. We propose using machine learning models to either replace such heuristics or to support their development process, for example, by identifying important code features. Especially in static compilation, machine learning has been shown to outperform hand-crafted heuristics. We applied our approach in a state-of-the-art dynamic compiler, the GraalVM compiler. Our models predict an unroll factor for vectorized loops for which the GraalVM compiler developers have not been able to design satisfactory heuristics. Thereby, we identified features to describe vectorized loops and empirically evaluated the impact of different training data, features or model parameters on the accuracy of the learned models. When deployed in the GraalVM dynamic compiler, our models produce significant speedups of 8-11%, on average. Furthermore, large speedups unveiled a performance bug in the compiler which was fixed after our report. Our work shows that machine learning can be used to improve a dynamic compiler directly by replacing existing vectorization heuristics or indirectly by helping compiler developers to design better hand-crafted heuristics.
jGuard: Programming Misuse-Resilient APIs

APIs provide access to valuable features, but studies have shown that they are hard to use correctly. Misuses of these APIs can be quite costly. Even though documentations and usage manuals exist, developers find it hard to integrate these in practice. Several static and dynamic analysis tools exist to detect and mitigate API misuses. But it is natural to wonder if APIs can be made more difficult to misuse by capturing the knowledge of domain experts (, API designers). Approaches like CogniCrypt have made inroads into this direction by offering API specification languages like CrySL which are then consumed by static analysis tools. But studies have shown that developers do not enjoy installing new tools into their pipeline. In this paper, we present jGuard, an extension to Java that allows API designers to directly encode their specifications while implementing their APIs. Code written in jGuard is then compiled to regular Java with the checks encoded as exceptions, thereby making sure the API user does not need to install any new tooling. Our evaluation shows that jGuard can be used to express the most commonly occuring misuses in practice, matches the accuracy of state of the art in API misuse detection tools, and introduces negligible performance overhead.
Flow-based network intrusion detection based on BERT masked language model

A Network Intrusion Detection System (NIDS) is an important tool that identifies potential threats to a network. Recently, different flow-based NIDS designs utilizing Machine Learning (ML) algorithms have been proposed as potential solutions to detect intrusions efficiently. However, conventional ML-based classifiers have not seen widespread adoption in the real-world due to their poor domain adaptation capability. In this research, our goal is to explore the possibility of improve the domain adaptation capability of NIDS. Our proposal employs Natural Language Processing (NLP) techniques and Bidirectional Encoder Representations from Transformers (BERT) framework. The proposed method achieved positive results when tested on data from different domains.
Towards a decentralized infrastructure for data marketplaces: narrowing the gap between academia and industry

One big challenge for Industry 4.0 is leveraging the large amount of data that remain unused after collection. A variety of commercial data marketplaces have emerged in recent years to tackle this task. Despite their different business models and target markets, such marketplaces share a number of common issues that slow the growth of the industry, including data discovery, transparency, data privacy and data valuation. Many academic designs have been proposed to address these issues, yet most of them remain unimplemented, due to complexity or inefficiency. We argue that these issues can be addressed with a combination of blockchain-based infrastructure, privacy-preserving computing and machine learning-based valuation metrics. Furthermore, we discuss key enabling technologies in each of these areas that are feasible to deploy at scale and could thus be implemented in real-world marketplaces in the near future. We select such technologies based on their current maturity and their industrial prominence.
Solar wind temperature and speed.

When it is averaged on a time scale of several solar rotations, the solar wind proton temperature T increases monotonically with the bulk speed V (Hundhausen et al., 1970; Burlaga and Ogilvie, 1970). This macroscale T-V relation does not change appreciably with solar cycle. The temperatures corresponding to intervals of increasing speed are only ≈15% higher than those corresponding to decreasing speeds, and thus it is indicated that the macroscale T-V relation is not appreciably affected by stream interactions. On a time scale of a tenth of a solar rotation there are time dependent T(t)-V(t) relations that are closely related to the stream profiles, as was noted by Hundhausen (1973). These T(t)-V(t) relations can meaningfully be resolved into two components—the macroscale T-V relation and systematic time dependent deviations from the macroscale relation. These results support the view that the macroscale T-V relation is not appreciably affected by nonsteady interplanetary processes but is determined rather by the proton heating mechanism within ≈50 RS. The systematic time dependent deviations between temperatures measured at streams and those given by the macroscale T-V relation are probably due primarily to nonlinear nonsteady processes in the interplanetary medium.
Automatic building identification under bomb damage conditions

Given the vast amount of image intelligence utilized in support of planning and executing military operations, a passive automated image processing capability for target identification is urgently required. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive Resonance Theory approach to cluster templates of target buildings. The results show that the network successfully classifies targets from non-targets in a virtual test bed environment.