Miracle-dz/UniFAR-FTUs huggingface dataset:
Bikesharing in Europe, the Americas, and Asia: Past, Present, and Future

Growing concerns over global motorization and climate change have led to increasing interest in sustainable transportation alternatives, such as bikesharing (the shared use of a bicycle fleet). Since 1965, bikesharing has grown across the globe on four continents including: Europe, North America, South America, and Asia (including Australia). Today, there are approximately 100 bikesharing programs operating in an estimated 125 cities around the world with over 139,300 bicycles. Bikesharing’s evolution is categorized into three generations: 1) White Bikes (or Free Bike Systems); 2) Coin-Deposit Systems; and 3) IT-Based Systems. In this paper, the authors propose a fourth-generation: “Demand-Responsive, Multi-Modal Systems. ” A range of existing bikesharing business models (e.g., advertising) and lessons learned are discussed including: 1) bicycle theft and vandalism; 2) bicycle redistribution; 3) information systems (e.g., real-time information); 4) insurance and liability concerns; and 5) pre-launch considerations. While limited in number, several studies have documented bikesharing’s social and environmental benefits including reduced auto use, increased bicycle use, and a growing awareness of bikesharing as a daily mobility option. Despite bikesharing’s ongoing growth, obstacles and uncertainty remain, including: future demand; safety; sustainability of business models; limited cycling infrastructure; challenges to integrating with public transportation systems; technology costs; and user convenience (e.g., limited height adjustment on bicycles, lack of cargo space, and exposure to weather conditions). In the future, more research is needed to better understand bikesharing’s impacts, operations, and business models in light of its reported growth and benefits.
IEEE 802.11be Wi-Fi 7: New Challenges and Opportunities

With the emergence of 4k/8k video, the throughput requirement of video delivery will keep grow to tens of Gbps. Other new high-throughput and low-latency video applications including augmented reality (AR), virtual reality (VR), and online gaming, are also proliferating. Due to the related stringent requirements, supporting these applications over wireless local area network (WLAN) is far beyond the capabilities of the new WLAN standard – IEEE 802.11ax. To meet these emerging demands, the IEEE 802.11 will release a new amendment standard IEEE 802.11be – Extremely High Throughput (EHT), also known as Wireless-Fidelity (Wi-Fi) 7. This article provides the comprehensive survey on the key medium access control (MAC) layer techniques and physical layer (PHY) techniques being discussed in the EHT task group, including the channelization and tone plan, multiple resource units (multi-RU) support, 4096 quadrature amplitude modulation (4096-QAM), preamble designs, multiple link operations (e.g., multi-link aggregation and channel access), multiple input multiple output (MIMO) enhancement, multiple access point (multi-AP) coordination (e.g., multi-AP joint transmission), enhanced link adaptation and retransmission protocols (e.g., hybrid automatic repeat request (HARQ)). This survey covers both the critical technologies being discussed in EHT standard and the related latest progresses from worldwide research. Besides, the potential developments beyond EHT are discussed to provide some possible future research directions for WLAN.
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues

We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake". We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale deepfake detection datasets, DeepFake-TIMIT Dataset and DFDC. We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets, respectively. To the best of our knowledge, ours is the first approach that simultaneously exploits audio and video modalities and also perceived emotions from the two modalities for deepfake detection.
An amenability-like property of finite energy path and loop groups

We show that the groups of finite energy loops and paths (that is, those of Sobolev class $H^1$) with values in a compact connected Lie group, as well as their central extensions, satisfy an amenability-like property: they admit a left-invariant mean on the space of bounded functions uniformly continuous with regard to a left-invariant metric. Every strongly continuous unitary representation $\pi$ of such a group (which we call skew-amenable) has a conjugation-invariant state on $B({\mathcal H}_{\pi})$.
Assessing gravitational-wave binary black hole candidates with Bayesian odds

Gravitational waves from the coalescence of binary black holes can be distinguished from noise transients in a detector network through Bayesian model selection by exploiting the coherence of the signal across the network. We present a Bayesian framework for calculating the posterior probability that a signal is of astrophysical origin, agnostic to the specific search strategy, pipeline or search domain with which a candidate is identified. We apply this framework under \textit{identical} assumptions to all events reported in the LIGO-Virgo GWTC-1 catalog, GW190412 and numerous event candidates reported by independent search pipelines by other authors. With the exception of GW170818, we find that all GWTC-1 candidates, and GW190412, have odds overwhelmingly in favour of the astrophysical hypothesis, including GW170729, which was assigned significantly different astrophysical probabilities by the different search pipelines used in GWTC-1. GW170818 is de-facto a single detector trigger, and is therefore of no surprise that it is disfavoured as being produced by an astrophysical source in our framework. We find \textit{three} additional event candidates, GW170121, GW170425 and GW170727, that have significant support for the astrophysical hypothesis, with a probability that the signal is of astrophysical origin of 0.53, 0.74 and 0.64 respectively. We carry out a hierarchical population study which includes these three events in addition to those reported in GWTC-1, finding that the main astrophysical results are unaffected.
Superlinear Precision and Memory in Simple Population Codes

The brain constructs population codes to represent stimuli through widely distributed patterns of activity across neurons. An important figure of merit of population codes is how much information about the original stimulus can be decoded from them. Fisher information is widely used to quantify coding precision and specify optimal codes, because of its relationship to mean squared error (MSE) under certain assumptions. When neural firing is sparse, however, optimizing Fisher information can result in codes that are highly sub-optimal in terms of MSE. We find that this discrepancy arises from the non-local component of error not accounted for by the Fisher information. Using this insight, we construct optimal population codes by directly minimizing the MSE. We study the scaling properties of MSE with coding parameters, focusing on the tuning curve width. We find that the optimal tuning curve width for coding no longer scales as the inverse population size, and the quadratic scaling of precision with system size predicted by Fisher information alone no longer holds. However, superlinearity is still preserved with only a logarithmic slowdown. We derive analogous results for networks storing the memory of a stimulus through continuous attractor dynamics, and show that similar scaling properties optimize memory and representation.
Contextual Document Similarity for Content-based Literature Recommender Systems

To cope with the ever-growing information overload, an increasing number of digital libraries employ content-based recommender systems. These systems traditionally recommend related documents with the help of similarity measures. However, current document similarity measures simply distinguish between similar and dissimilar documents. This simplification is especially crucial for extensive documents, which cover various facets of a topic and are often found in digital libraries. Still, these similarity measures neglect to what facet the similarity relates. Therefore, the context of the similarity remains ill-defined. In this doctoral thesis, we explore contextual document similarity measures, i.e., methods that determine document similarity as a triple of two documents and the context of their similarity. The context is here a further specification of the similarity. For example, in the scientific domain, research papers can be similar with respect to their background, methodology, or findings. The measurement of similarity in regards to one or more given contexts will enhance recommender systems. Namely, users will be able to explore document collections by formulating queries in terms of documents and their contextual similarities. Thus, our research objective is the development and evaluation of a recommender system based on contextual similarity. The underlying techniques will apply established similarity measures and as well as neural approaches while utilizing semantic features obtained from links between documents and their text.
Dissipating with Relations: Implication for the Entity-Relationship Model

Difficulties arise when conceptual modeling lacks ontological clarity and rigorous definitions, which is especially the case in the relationship construct. Evidence shows that use of relationships is often problematic when it comes to communicating the form of meaning of an application domain. Research on this topic is important because relationships are central to a number of approaches and commonly used by practitioners. In this paper, we study the notion of relation or relationship in the context of conceptual modeling. Specifically, we focus on the notion of relationship used in the entity-relationship (ER) model. The ER model is scrutinized through a new form of conceptual modeling called the thinging machine (TM) to pursue further understanding of the semantics of the relationship concept. The ER model is composed of three fundamental categories (i.e., entity, relationship and attribute), whereas TM is built from one ontological category called the thing/machine (thimac). Several ER diagrams are re-casted as TM diagrams, creating a categorical collision with interesting implications regarding the status of the conception of relationship in a conceptual model. The re-modeling shows that the relational construct is dissipated into TM flows of things and chronology of events.
On a probabilistic model for martensitic avalanches incorporating mechanical compatibility

Building on the work by Ball et al (2015 MATEC Web of Conf. 33 02008), Cesana and Hambly (2018 A probabilistic model for interfaces in a martensitic phase transition arXiv:1810.04380), Torrents et al (2017 Phys. Rev. E 95 013001), in this article we propose and study a simple, geometrically constrained, probabilistic algorithm geared towards capturing some aspects of the nucleation in shape-memory alloys. As a main novelty with respect to the algorithms by Ball et al (2015 MATEC Web of Conf. 33 02008), Cesana and Hambly (2018 A probabilistic model for interfaces in a martensitic phase transition arXiv:1810.04380), Torrents et al (2017 Phys. Rev. E 95 013001) we include mechanical compatibility. The mechanical compatibility here is guaranteed by using convex integration building blocks in the nucleation steps. We analytically investigate the algorithm’s convergence and the solutions’ regularity, viewing the latter as a measure for the fractality of the resulting microstructure. We complement our analysis with a numerical implementation of the scheme and compare it to the numerical results by Ball et al (2015 MATEC Web of Conf. 33 02008), Cesana and Hambly (2018 A probabilistic model for interfaces in a martensitic phase transition arXiv:1810.04380), Torrents et al (2017 Phys. Rev. E 95 013001).
Unsupervised Discovery of Recurring Speech Patterns Using Probabilistic Adaptive Metrics

Unsupervised spoken term discovery (UTD) aims at finding recurring segments of speech from a corpus of acoustic speech data. One potential approach to this problem is to use dynamic time warping (DTW) to find well-aligning patterns from the speech data. However, automatic selection of initial candidate segments for the DTW-alignment and detection of "sufficiently good" alignments among those require some type of pre-defined criteria, often operationalized as threshold parameters for pair-wise distance metrics between signal representations. In the existing UTD systems, the optimal hyperparameters may differ across datasets, limiting their applicability to new corpora and truly low-resource scenarios. In this paper, we propose a novel probabilistic approach to DTW-based UTD named as PDTW. In PDTW, distributional characteristics of the processed corpus are utilized for adaptive evaluation of alignment quality, thereby enabling systematic discovery of pattern pairs that have similarity what would be expected by coincidence. We test PDTW on Zero Resource Speech Challenge 2017 datasets as a part of 2020 implementation of the challenge. The results show that the system performs consistently on all five tested languages using fixed hyperparameters, clearly outperforming the earlier DTW-based system in terms of coverage of the detected patterns.
Markovian And Non-Markovian Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic

We study and predict the evolution of Covid-19 in six US states from the period May 1 through August 31 using a discrete compartment-based model and prescribe active intervention policies, like lockdowns, on the basis of minimizing a loss function, within the broad framework of partially observed Markov decision processes. For each state, Covid-19 data for 40 days (starting from May 1 for two northern states and June 1 for four southern states) are analyzed to estimate the transition probabilities between compartments and other parameters associated with the evolution of the epidemic. These quantities are then used to predict the course of the epidemic in the given state for the next 50 days (test period) under various policy allocations, leading to different values of the loss function over the training horizon. The optimal policy allocation is the one corresponding to the smallest loss. Our analysis shows that none of the six states need lockdowns over the test period, though the no lockdown prescription is to be interpreted with caution: responsible mask use and social distancing of course need to be continued. The caveats involved in modeling epidemic propagation of this sort are discussed at length. A sketch of a non-Markovian formulation of Covid-19 propagation (and more general epidemic propagation) is presented as an attractive avenue for future research in this area.
SemEval-2020 Task 7: Assessing Humor in Edited News Headlines

This paper describes the SemEval-2020 shared task “Assessing Humor in Edited News Headlines. ” The task’s dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.
Asynchronous Periodic Distributed Event-Triggered Frequency Control of Microgrids

In this paper, we introduce a distributed secondary frequency control scheme for an islanded ac microgrid under event-triggered communication. An integral type event-triggered mechanism is proposed by which each distributed generator (DG) asynchronously and periodically checks its triggering condition and determines whether to update its control inputs and broadcast its states to neighboring DGs. In contrast to existing event-triggered strategies on secondary control of microgrids, under the proposed sampled-data based event-triggered mechanism, DGs need not be synchronized to a common clock and each individual DG checks its triggering condition periodically, relying on its own clock. Furthermore, the proposed method efficiently reduces communication and computation complexity. We provide sufficient conditions under which all DGs' frequencies asymptotically converge to the common reference frequency value. Finally, effectiveness of our proposed method is verified by simulating different scenarios on a well-established islanded ac microgrid benchmark in the MATLAB/Simulink environment.
Getting chirality right: Single scalar leptoquark solutions to the 
(g−2)e,μ
 puzzle

We identify the two scalar leptoquarks capable of generating sign-dependent contributions to leptonic magnetic moments, $R_2\sim (\mathbf{3}, \mathbf{2}, 7/6)$ and $S_1\sim (\mathbf{3}, \mathbf{1}, -1/3)$, as favoured by current measurements. We consider the case in which the electron and muon sectors are decoupled, and real-valued Yukawa couplings are specified using an up-type quark mass-diagonal basis. Contributions to $\Delta a_e$ arise from charm-containing loops and $\Delta a_\mu$ from top-containing loops -- hence avoiding dangerous LFV constraints, particularly from $\mu \to e \gamma$. The strongest constraints on these models arise from contributions to the Z leptonic decay widths, high-$p_T$ leptonic tails at the LHC, and from (semi)leptonic kaon decays. To be a comprehensive solution to the $(g-2)_{e/\mu}$ puzzle we find that the mass of either leptoquark must be $\lesssim 65$ TeV. This analysis can be embedded within broader flavour anomaly studies, including those of hierarchical leptoquark coupling structures. It can also be straightforwardly adapted to accommodate future measurements of leptonic magnetic moments, such as those expected from the Muon $g-2$ collaboration in the near future.
A refinement of Baillon’s theorem on maximal regularity

By Baillon's result, it is known that maximal regularity with respect to the space of continuous functions is rare; it implies that either the involved semigroup generator is a bounded operator or the considered space contains $c_{0}$. We show that the latter alternative can be excluded under a refined condition resembling maximal regularity with respect to $\mathrm{L}^{\infty}$.
KAPLAN: A 3D Point Descriptor for Shape Completion

We present a novel 3D shape completion method that operates directly on unstructured point clouds, thus avoiding resource-intensive data structures like voxel grids. To this end, we introduce KAPLAN, a 3D point descriptor that aggregates local shape information via a series of 2D convolutions. The key idea is to project the points in a local neighborhood onto multiple planes with different orientations. In each of those planes, point properties like normals or point-to-plane distances are aggregated into a 2D grid and abstracted into a feature representation with an efficient 2D convolutional encoder. Since all planes are encoded jointly, the resulting representation nevertheless can capture their correlations and retains knowledge about the underlying 3D shape, without expensive 3D convolutions. Experiments on public datasets show that KAPLAN achieves state-of-the-art performance for 3D shape completion.
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances

In this paper, we propose a sub-utterance unit selection framework to remove acoustic segments in audio recordings that carry little information for acoustic scene classification (ASC). Our approach is built upon a universal set of acoustic segment units covering the overall acoustic scene space. First, those units are modeled with acoustic segment models (ASMs) used to tokenize acoustic scene utterances into sequences of acoustic segment units. Next, paralleling the idea of stop words in information retrieval, stop ASMs are automatically detected. Finally, acoustic segments associated with the stop ASMs are blocked, because of their low indexing power in retrieval of most acoustic scenes. In contrast to building scene models with whole utterances, the ASM-removed sub-utterances, i.e., acoustic utterances without stop acoustic segments, are then used as inputs to the AlexNet-L back-end for final classification. On the DCASE 2018 dataset, scene classification accuracy increases from 68%, with whole utterances, to 72.1%, with segment selection. This represents a competitive accuracy without any data augmentation, and/or ensemble strategy. Moreover, our approach compares favourably to AlexNet-L with attention.
MySpace and Facebook: Identifying dimensions of uses and gratifications for friend networking sites.

- Research examining friend networking sites has greatly increased in the past several months exploring topics related to general use, psychological well being, and content analysis of profiles to name a few. The current study sought to identify dimensions of uses and gratifications for users of friend networking sites. Results identified the following three dimensions: the Information Dimension, the Friendship Dimension, and the Connection Dimension. Sex differences also existed with regards to use of friend networking sites. For example, men were more likely to use the sites for dating purposes and women were more likely to set their accounts to private. A discussion of the results is presented in light of the uses and gratifications obtained from the use of friend networking sites.