Miracle-dz/UniFAR-FTUs huggingface dataset:
Governance Capture in a Self-Governing Community: A Qualitative Analysis of Serbo-Croatian Wikipedias

What institutional features make some language projects on Wikipedia vulnerable to governance capture by a small group of ideologically motivated users? In this interview study, we examine the factors that facilitated the well-documented capture of the Croatian Wikipedia by a cohort of far-right editors, and discuss why adjacent language editions, such as the Serbian Wikipedia, did not fail in the same way. Our work highlights the role of institutional design in fostering resilience in self-governed online groups to knowledge integrity threats.
RETROSPECTIVE: Proﬁling a warehouse-scale computer

We wrote “Profiling a WSC” for ISCA 2015 in order to summarize and share our experiences extracting performance information from Google’s datacenter fleet. The paper outlined performance bottlenecks and trends that we observed, both in software and hardware, and sketched out a handful of areas for future research. It was intentionally light on solutions and meant to inspire directions for others’ work. Almost ten years later, we reflect on the predictions the paper made, check back on which ones panned out, and which of the trends have caught on in industry or academia.
Lifelong 3D object recognition: A comparison of deep features and handcrafted descriptors

: Over the past few decades we have been ﬁnding more and more uses for service robots. While the early robots that were employed by humans were often stationary and operating in static environments, nowadays the demand for service robots capable of more complex behaviour in dynamic environments has been increasing. Object recognition has come a long way the past decade with the improvements in software and hardware allowing widespread implementation using Convolutional Neural Networks (CNN). Unfortunately, the batch training that is required of CNNs is hard to employ in a system that has to be capable of lifelong learning while operating in a dynamic open-ended domain at the same time. Using OrthographicNet, a system designed speciﬁcally towards the purpose of achieving lifelong 3D object recognition in open-ended domains, we aim to compare the performances of deep features (MobileNetV2, VGG19 fc1) and handcrafted descriptors (GOOD, ESF). We performed oﬄine experiments for our preliminary comparisons and found during the online testing phase that the deep feature descriptor MobileNetV2 is capable of learning the most object categories whilst providing decent accuracy and learning speed.
Temporally Aligning Long Audio Interviews with Questions: A Case Study in Multimodal Data Integration

The problem of audio-to-text alignment has seen significant amount of research using complete supervision during training. However, this is typically not in the context of long audio recordings wherein the text being queried does not appear verbatim within the audio file. This work is a collaboration with a non-governmental organization called CARE India that collects long audio health surveys from young mothers residing in rural parts of Bihar, India. Given a question drawn from a questionnaire that is used to guide these surveys, we aim to locate where the question is asked within a long audio recording. This is of great value to African and Asian organizations that would otherwise have to painstakingly go through long and noisy audio recordings to locate questions (and answers) of interest. Our proposed framework, INDENT, uses a cross-attention-based model and prior information on the temporal ordering of sentences to learn speech embeddings that capture the semantics of the underlying spoken text. These learnt embeddings are used to retrieve the corresponding audio segment based on text queries at inference time. We empirically demonstrate the significant effectiveness (improvement in R-avg of about 3%) of our model over those obtained using text-based heuristics. We also show how noisy ASR, generated using state-of-the-art ASR models for Indian languages, yields better results when used in place of speech. INDENT, trained only on Hindi data is able to cater to all languages supported by the (semantically) shared text space. We illustrate this empirically on 11 Indic languages.
Toward Fair and Efficient Congestion Control: Machine Learning Aided Congestion Control (MLACC)

Emerging inter-datacenter applications require massive loads of data transfer which makes them sensitive to packet drops, high latency, and fair resource sharing. However, current congestion control (CC) protocols do not guarantee the optimal outcome of these metrics. In this paper, we introduce a new CC technique, Machine Learning Aided Congestion Control (MLACC), that combines heuristics and machine learning (ML) to improve these three network metrics. The proposed technique achieves a high level of fairness, minimum latency, and minimum drop rate. ML is utilized to estimate the ratio of the available bandwidth of the bottleneck link while the heuristic uses this ratio to enable end-points to cooperatively limit the shared bottleneck link utilization under a predefined threshold in order to minimize latency and drop rate. The key to achieving the desired fairness is using the gradient of the link utilization to control the sending rate. We compared MLACC to BBR (which is at least on par with the state-of-the-art ML-based techniques) as a base case in different network settings. The results show that MLACC can achieve lower and more stable end-to-end latency (25% to 52% latency saving). It also significantly reduces packet drop rates while attaining a higher fairness level. The only cost for these advantages is a small throughput reduction of less than 3.5%.
Exploring the joint probability of precipitation and soil moisture over Europe using copulas

Abstract. The joint probability of precipitation and soil moisture is here investigated over Europe with the goal to extrapolate meaningful insights into the potential joint use of these variables for the detection of agricultural droughts within a multivariate probabilistic modeling framework. The use of copulas is explored, being the framework often used in hydrological studies for the analysis of bivariate distributions. The analysis is performed for the period 1996–2020 on the empirical frequencies derived from ERA5 precipitation and LISFLOOD soil moisture datasets, both available as part of the Copernicus European Drought Observatory. The results show an overall good correlation between the two standardized series (Kendall's τ= 0.42±0.1) but also clear spatial patterns in the tail dependence derived with both non-parametric and parametric approaches. About half of the domain shows symmetric tail dependence, well reproduced by the Student's t copula, whereas the rest of the domain is almost equally split between low- and high-tail dependences (both modeled with the Gumbel family of copulas). These spatial patterns are reasonably reproduced by a random forest classifier, suggesting that this outcome is not driven by chance. This study stresses how a joint use of standardized precipitation and soil moisture for agriculture drought characterization may be beneficial in areas with strong low-tail dependence and how this behavior should be carefully considered in multivariate drought studies.
Applying Effective Theories to Collider Phenomenology

Author(s): Ovanesyan, Grigol | Advisor(s): Bauer, Christian W; Nomura, Yasunori | Abstract: Collinear fields in soft collinear effective theory (SCET) can be made invariant under collinear gauge transformations by multiplying them with collinear Wilson lines. We discuss how we can quantize SCET directly in terms of these gauge invariant fields, allowing to directly calculate S matrix elements using the gauge invariant collinear fields. We also show how for each collinear direction SCET can be written in terms of fields whose interactions are given by the usual QCD Lagrangian, and how external operators coupling these different directions can be constructed.Using SCET, which provides a unified framework for factorization, resummation of logarithms, and incorporation of universal nonperturbative functions in hard-scattering QCD cross-sections, we present a new prediction of angularity distributions in e+e- annihilation. Angularities τa are an infinite class of event shapes which vary in their sensitivity to the substructure of jets in the final state, controlled by a continuous parameter al2. We calculate angularity distributions for all al1 to first order in the strong coupling αs and resum large logarithms in these distributions to next-to-leading logarithmic (NLL) accuracy. Our expressions for the next-to-leading order (NLO) O(αs) partonic jet and soft functions in the factorization theorem for angularity distributions are given for the first time. We employ a model for the nonperturbative soft function with a gap parameter which cancels the renormalon ambiguity in the partonic soft function. We explore the relation between the SCET approach to resummation and past approaches in QCD, and discuss the advantages of the effective theory approach. In addition, we draw from the NLO calculations of the jet and soft functions an intuitive lesson about how factorization breaks down in the effective theory as a → 1.A matching calculation for SCET is performed using exotic external states, which mimic the topology of Drell-Yan amplitude. It is found that for the consistency of effective theory, more specifically for the fact that the matching coefficient C2 is independent of external states involved in the matching, a new mode needs to be added to SCET, which is the Glauber mode. Connections with Coleman-Norton theorem and Landau equations are discussed.
Impact of shallow sills on heat transport and stratification regimes in proglacial fjords

The increased melting and rapid retreat of glaciers is a main contributor to sea level rise. In shallow-silled fjords common in Patagonia, Alaska, and other systems, these bathymetric features may act as the first-order control on the dynamics, constraining fjord-shelf exchange and thereby modulating glacial melting. However, we still lack a clear understanding of how circulation and associated heat transport in shallow-silled glacial fjords are modulated by fjord-glacier geometry and fjord-shelf properties. To address this, idealized numerical simulations are conducted using a coupled plume-ocean fjord model. 5 The steady-state fjord exhibits strong mixing and vertical transport over the sill. Relatively colder water from the upper-layer outflow is refluxed into the deeper layer, cooling the incoming warm oceanic water and modifying water properties near the glacier front. Driven by a shallow sill, up to ∼ 70% of the outflow is refluxed downward and leads to ∼ 10% cooling of the inflow and the deep fjord. A range of sensitivity experiments indicate that sill depth, subglacial discharge, ambient fjord temperature and stratification are key parameters that modulate the heat transport to the glacier terminus. In particular, the 10 relative depth of the fjord, the sill, and the terminal height of meltwater plume are used to characterize four circulation and heat transport regimes. The sill-driven reflux is found to result in a decrease of both deep fjord temperature and stratification, which have opposite effects on the glacial melt rate. These results underscore the importance of sill bathymetry and associated fjord processes in the variability of oceanic heat supply to melting glaciers.
KINETIC EVOLUTION OF CORONAL HOLE PROTONS BY IMBALANCED ION-CYCLOTRON WAVES: IMPLICATIONS FOR MEASUREMENTS BY SOLAR PROBE PLUS

We extend the kinetic guiding-center model of collisionless coronal hole protons presented in Isenberg & Vasquez to consider driving by imbalanced spectra of obliquely propagating ion-cyclotron waves. These waves are assumed to be a small by-product of the imbalanced turbulent cascade to high perpendicular wavenumber, and their total intensity is taken to be 1% of the total fluctuation energy. We also extend the kinetic solutions for the proton distribution function in the resulting fast solar wind to heliocentric distances of 20 solar radii, which will be attainable by the Solar Probe Plus spacecraft. We consider three ratios of outward-propagating to inward-propagating resonant intensities: 1, 4, and 9. The self-consistent bulk flow speed reaches fast solar wind values in all cases, and these speeds are basically independent of the intensity ratio. The steady-state proton distribution is highly organized into nested constant-density shells by the resonant wave-particle interaction. The radial evolution of this kinetic distribution as the coronal hole plasma flows outward is understood as a competition between the inward- and outward-directed large-scale forces, causing an effective circulation of particles through the (v∥, v⊥) phase space and a characteristic asymmetric shape to the distribution. These asymmetries are substantial and persist to the outer limit of the model computation, where they should be observable by the Solar Probe Plus instruments.
The core of traveling salesman games: computational experiments

Let G = (V,E) be a complete, undirected graph with vertices V = {0, 1, . , n} and edges E. A weight or cost ce is associated with each edge e ∈ E. The aim of the traveling salesman problem (TSP) is to nd a tour with minimum cost, visiting all vertices in V exactly once. Now consider the traveling salesman game (TSG) from cooperative game theory, introduced in Potters et al. [1992] and Tamir [1989]. In the version of the TSG considered in this talk, vertex 0 is denoted the home vertex and Vu = {1, . , n} are user vertices. For any set S ⊆ Vu, let c(S) be the cost of the minimum TSP tour on the vertices S∪{0}. In the TSG one wants to distribute the cost of the TSP tour visiting all vertices in V among the user vertices. An application (from Potters et al. [1992]) of the problem is the following: a researcher is giving talks at a number of universities and wants to distribute his travel costs among the universities in a fair way. For the distribution to be fair it makes sense to require that no subset S of Vu would be better o by leaving the coalition and arranging a separate trip for the researcher that only visits the universities in S. To formalize the concepts, let xi be the cost charged to user i ∈ Vu. The vector x should satisfy
M ACHINE L EARNING R EPORTS New Variants of the Counter Propagation Network for Classiﬁcation Learning - A Formal Description of Strategies and Respective Approaches

In this paper we present new variants of the counter propagation network introduced already in 1987 by Robert Hecht-Nielsen, which is a network consisting of a vector quantization layer and a subsequent classi(cid:28)cation layer. In particular, we discuss several vector quantization layers and how to transmit the information to the classi(cid:28)cation layer. This is discussed in relation to the information-bottleneck-paradigm and regarding perspectives for network training are provided. Thereby, both layers are not longer handled independently during training as in the original approach. More precisely, we explain how the vector quantization layer can be optimized in dependence on the following classi(cid:28)cation layer. The mathematical formulations of the models are described in detail.
A Comparative Study of Cache Side Channels across AES Implementations and Modes of Operation

AES applied in diﬀerent modes of operation is widely considered secure and used in practice. However, it is well known that many software implementations of AES are vulnerable to cache side channels. In this article we investigate the eﬀect of diﬀerent modes of operation on the potential cache-side-channel leakage of lookup-table-based AES-128 software implementations. To this end, we apply quantitative static side-channel analysis to obtain upper bounds on the leakage of AES-128 in CBC mode, CTR mode and CCM from OpenSSL 1.1. 1d, mbedTLS 2.16. 5 and Nettle 3.5. By shedding light on how the security wrt. each mode relates to the security of the other modes and the underlying AES implementation, we provide the ﬁrst systematic study of the cache side channel leakage of AES across modes of operation.
Learning to grasp objects in highly cluttered environments using Deep Convolutional Neural Networks

: This paper evaluates the performance of the Generative Residual Convolutional Neural Network (GR-ConvNet), proposed by Kumra et al. (2020), as a grasp inference method. The performance is evaluated by testing the network’s predictive accuracy on a subset of the Cornell grasping dataset, and by conducting several rounds of the isolated, pack, and pile grasping experiments, as proposed by Kasaei et al. (2021), using a UR5e robotic arm. For this purpose, a simulation was developed in PyBullet (Coumans et al. , 2020). The GR-ConvNet achieved an accuracy of 97.7% on the Cornell grasping dataset. A grasping success rate of 91.9%, 73.2%, and 55.2% was achieved, and a target success rate of 91.9%, 73.2%, and 55.2% was achieved for the isolated, pack, and pile experiments respectively.
A dense station-based, long-term and high-accuracy dataset of daily surface solar radiation in China

Abstract. The lack of long-term and high-quality solar radiation data has been an obstacle for scientific and industrial fields. In this study, a dense station-based, long-term and high-accuracy dataset of daily surface solar radiation was developed using two surface radiation models. One is the model developed by Yang et al. (2006) for global radiation estimation, and the other is the model developed by Tang et al. (2018) for direct radiation estimation. The main inputs for the development of the dataset are surface pressure, air temperature, relative humidity, horizontal visibility and sunshine duration, which are the routine meteorological variables observed at the 2743 China Meteorological Administration (CMA) weather stations. Validation against in situ observations and comparisons with two satellite-based radiation products shows that our station-based radiation dataset clearly outperforms the satellite-based radiation products at both daily and monthly scales. In addition, our dataset is available for more than 60 years and includes three radiation components of global, direct and diffuse radiation, which is not possible with satellite products. This station-based radiation dataset will contribute to the climate change research and solar energy engineering applications in the future. The station-based dataset is now available at https://doi.org/10.11888/Atmos.tpdc. 300461 (Tang, 2023).
Rejuvenating the ocean: mean ocean radiocarbon, CO2 release, and radiocarbon budget closure across the last deglaciation

Abstract. Radiocarbon is a tracer that provides unique insights into the ocean's ability to sequester CO2 from the atmosphere. While spatial patterns of radiocarbon in the ocean interior can indicate the vectors and timescales for carbon transport through the ocean, estimates of the global average ocean–atmosphere radiocarbon age offset (B-Atm) place constraints on the closure of the global carbon cycle. Here, we apply a Bayesian interpolation method to compiled B-Atm data to generate global interpolated fields and mean ocean B-Atm estimates for a suite of time slices across the last deglaciation. The compiled data and interpolations confirm a stepwise and spatially heterogeneous “rejuvenation” of the ocean, suggesting that carbon was released to the atmosphere through two swings of a “ventilation seesaw” operating between the North Atlantic and both the Southern Ocean and the North Pacific. Sensitivity tests using the Bern3D model of intermediate complexity demonstrate that a portion of the reconstructed deglacial B-Atm changes may reflect “phase-attenuation” biases that are unrelated to ocean ventilation and that arise from independent atmospheric radiocarbon dynamics instead. A deglacial minimum in B-Atm offsets during the Bølling–Allerød could partly reflect such a bias. However, the sensitivity tests further demonstrate that when correcting for such biases, ocean “ventilation” could still account for at least one-third of deglacial atmospheric CO2 rise. This contribution to CO2 rise appears to have continued through the Younger Dryas, though much of the impact was likely achieved by the end of the Bølling–Allerød, indicating a key role for marine carbon cycle adjustment early in the deglacial process. Our global average B-Atm estimates place further new constraints on the long-standing mystery of global radiocarbon budget closure across the last deglaciation and suggest that glacial radiocarbon production levels are likely underestimated on average by existing reconstructions.
A Climate Data Record of Year-Round Global Sea Ice Drift from the EUMETSAT OSI SAF

Sea ice in the polar regions can move several tens of kilometers per day under the actions of winds, ocean currents, and internal stresses. Long-term observations of the rate and patterns of this motion are needed to characterize the full response of the polar environment to climate change. Here, we introduce a new climate data record (CDR) of year-round, global, daily sea-ice drift vectors covering 1991-2020. The motion vectors are computed from series of passive microwave imagery in the 5 winter months, and from a parametric free-drift model in the summer months. An evaluation against on-ice buoy trajectories reveals that the RMSEs of the sea-ice drift CDR are small and vary with hemisphere and seasons (2.1 km for Arctic winters, 2.6 km for Arctic summer, 3 to 4 km for the Antarctic sea ice). The CDR is un-biased for Arctic winter conditions. The bias is larger for Antarctic and for summer sea ice motion. The CDR consists of daily product ﬁles holding the d X and d Y components of the drift vectors on an EASE2 grid with 75 km spacing, as well as associated uncertainties and ﬂags. It is 10 prepared in the context of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) and is readily available as https://doi.org/10.15770/EUM_SAF_OSI_0012 (EUMETSAT Ocean and Sea Ice Satellite Application Facility, 2022).
GENERATING FREQUENT PATTERNS BY IMPROVING THE EFFICIENCY OF APRIORI ALGORITHM

-Association rule mining is considered as main technique in the data mining area. Association rule mining presents associations among items in transaction databases, relational databases, frequent patterns, correlations and other information of warehouses. Frequent pattern mining is one of the active areas in data mining. It takes part in every data mining task such as prediction, classification, clustering, and association assessment. Finding all frequent patterns is time-consuming process because of its vast number of patterns generated. In this paper, frequent patterns are generated in faster way by improving the efficiency of Apriori algorithm to speed up the process.