Miracle-dz/UniFAR-FTUs huggingface dataset:
cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

Nonuniform fast Fourier transforms dominate the computational cost in many applications including image reconstruction and signal processing. We thus present a general-purpose GPU-based CUDA library for type 1 (nonuniform to uniform) and type 2 (uniform to nonuniform) transforms in dimensions 2 and 3, in single or double precision. It achieves high performance for a given user-requested accuracy, regardless of the distribution of nonuniform points, via cache-aware point reordering, and load-balanced blocked spreading in shared memory. At low accuracies, this gives on-GPU throughputs around 109 nonuniform points per second, and (even including host-device transfer) is typically 4–10× faster than the latest parallel CPU code FINUFFT (at 28 threads). It is competitive with two established GPU codes, being up to 90× faster at high accuracy and/or type 1 clustered point distributions. Finally we demonstrate a 5–12× speedup versus CPU in an X-ray diffraction 3D iterative reconstruction task at 10−12 accuracy, observing excellent multi-GPU weak scaling up to one rank per GPU.
An asymptotic analysis of probabilistic logic programming with implications for expressing projective families of distributions

Probabilistic logic programming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in a setting of uncertainty. However, the behaviour of statistical relational representations across variable domain sizes is complex, and scaling inference and learning to large domains remains a significant challenge. In recent years, connections have emerged between domain size dependence, lifted inference and learning from sampled subpopulations. The asymptotic behaviour of statistical relational representations has come under scrutiny, and projectivity was investigated as the strongest form of domain size dependence, in which query marginals are completely independent of the domain size. In this contribution we show that every probabilistic logic program under the distribution semantics is asymptotically equivalent to an acyclic probabilistic logic program consisting only of determinate clauses over probabilistic facts. We conclude that every probabilistic logic program inducing a projective family of distributions is in fact everywhere equivalent to a program from this fragment, and we investigate the consequences for the projective families of distributions expressible by probabilistic logic programs.
Removal of Dissolved Organic Carbon in Sanitary Gravity Sewer

Utilization of dissolved organic matter in a 1.5 km section of a sanitary gravity concrete sewer with an inner diameter of 450 mm constructed on a slope of 0.0075 was studied. Continuous measurements of dissolved organic carbon (DOC), suspended solids, dissolved oxygen, and flow rate were performed along the sewer. About 14% of the DOC was removed in an 18-min retention time. A slower flow rate in the sewer would favor higher DOC removal efficiency because it offers a longer retention time. Oxygen was not a limiting factor as the dissolved oxygen level was at least 1 mg/L. Batch tests using raw sewage and either suspended solids or settled sediments yielded specific DOC rates of 1.3 mg and 2.6 mg DOC/mg dry wt/day for the sewage phase and sediment phase, respectively. Adenosine triphosphate content analysis of the suspended solids and sediment samples confirmed that both contained substantial amounts of active biomass. In the 1.5-km sewer system, it is estimated that 39.13 kg of DOC can be stabilized/day; the sewage phase contributes 40% while the sediment phase contributes 60%.
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n Parameters

Recent works have demonstrated reasonable success of representation learning in hypercomplex space. Specifically,"fully-connected layers with Quaternions"(4D hypercomplex numbers), which replace real-valued matrix multiplications in fully-connected layers with Hamilton products of Quaternions, both enjoy parameter savings with only 1/4 learnable parameters and achieve comparable performance in various applications. However, one key caveat is that hypercomplex space only exists at very few predefined dimensions (4D, 8D, and 16D). This restricts the flexibility of models that leverage hypercomplex multiplications. To this end, we propose parameterizing hypercomplex multiplications, allowing models to learn multiplication rules from data regardless of whether such rules are predefined. As a result, our method not only subsumes the Hamilton product, but also learns to operate on any arbitrary nD hypercomplex space, providing more architectural flexibility using arbitrarily $1/n$ learnable parameters compared with the fully-connected layer counterpart. Experiments of applications to the LSTM and Transformer models on natural language inference, machine translation, text style transfer, and subject verb agreement demonstrate architectural flexibility and effectiveness of the proposed approach.
C60 Fullerene-based materials as singlet oxygen O2(1Δg) photosensitizers: a time-resolved near-IR luminescence and optoacoustic study

The triplet properties of a series of fullerene dihydroderivatives of the o-quinodimethane type and of three representative inclusion complexes with γ-cyclodextrin and calix[8]arenes have been studied. It is found that chemical modification results in a decrease of the singlet oxygen quantum yield relative to C60, with an average ΦΔ = 0.76. On the other hand, encapsulation in either γ-cyclodextrin or calix[8]arene produces greatly varying results depending on host and solvent. Thus, in D2O the γ-cyclodextrin complex of C60 sensitizes O2(1Δg) with ΦΔ = 0.77, while the calixarene complex is totally ineffective, probably due to the occurrence of a fast charge transfer-mediated deactivation process. In CCl4 the calixarene complex acts very much like C60, i.e., ΦΔ = 0.84.
Constructing a Distance Sensitivity Oracle in O(n2.5794M) Time

We continue the study of distance sensitivity oracles (DSOs). Given a directed graph G with n vertices and edge weights in {1, 2, . , M}, we want to build a data structure such that given any source vertex u, any target vertex v, and any failure f (which is either a vertex or an edge), it outputs the length of the shortest path from u to v not going through f . Our main result is a DSO with preprocessing time O(n2.5794M) and constant query time. Previously, the best preprocessing time of DSOs for directed graphs is O(n2.7233M), and even in the easier case of undirected graphs, the best preprocessing time is O(n2.6865M) [Ren, ESA 2020]. One drawback of our DSOs, though, is that it only supports distance queries but not path queries. Our main technical ingredient is an algorithm that computes the inverse of a degree-d polynomial matrix (i.e. a matrix whose entries are degree-d univariate polynomials) modulo x. The algorithm is adapted from [Zhou, Labahn and Storjohann, Journal of Complexity, 2015], and we replace some of its intermediate steps with faster rectangular matrix multiplication algorithms. We also show how to compute unique shortest paths in a directed graph with edge weights in {1, 2, . , M}, in O(n2.5286M) time. This algorithm is crucial in the preprocessing algorithm of our DSO. Our solution improves the O(n2.6865M) time bound in [Ren, ESA 2020], and matches the current best time bound for computing all-pairs shortest paths. 2012 ACM Subject Classification Theory of computation → Shortest paths; Theory of computation → Design and analysis of algorithms
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection

Research on sound event detection (SED) in environmental settings has seen increased attention in recent years. The large amounts of (private) domestic or urban audio data needed raise significant logistical and privacy concerns. The inherently distributed nature of these tasks, make federated learning (FL) a promising approach to take advantage of large-scale data while mitigating privacy issues. While FL has also seen increased attention recently, to the best of our knowledge there is no research towards FL for SED. To address this gap and foster further research in this field, we create and publish novel FL datasets for SED in domestic and urban environments. Furthermore, we conduct baseline results on the datasets in a FL context for three deep neural network architectures. The results indicate that FL is a promising approach for SED, but faces challenges with divergent data distributions inherent to distributed client edge devices.
The Relationship of National and International Environmental NGOs in Bangladesh and Their Role in Wetland Conservation

Biodiversity degradation, lose of wetlands and arsenic pollution are becoming the major environmental threats of Bangladesh. Huge number of population, corruption and lack of capacity of the governmental agencies are deteriorating the over environmental condition of Bangladesh. This paper reports the findings of NGOs and their role in protection of wetlands of Bangladesh. Ministry of Environment and Forestry (MoEF) and its daughter organization Department of Environment (DoE) are comparatively weak in power and capacity in comparison to other ministries. To fill up this gap, a number of environmental NGOs (ENGOs) have emerged in Bangladesh after the Rio Earth Summit in 1992. ENGOs are actively involved in the environmental sectors of Bangladesh by doing research, advocacy, companying activities and as pressure group as well. ENGOs are implementing several projects with government and international donor agencies. Their role to protect and manage wetland ecosystem and biodiversity is very praise-worthy. Bangladeshi ENGOs have very good relations with international ENGOs and donor agencies. Most of the Bangladeshi ENGOs (research based) are dependent on the foreign aids. But the prominent companying ENGOs are not interested to seek governmental and international donorsâ€™ supports to uphold their independence and to raise their voice against environmental damages taking by governmental and private projects.
Smoothed Analysis with Adaptive Adversaries

We prove novel algorithmic guarantees for several online problems in the smoothed analysis model. In this model, at each time step an adversary chooses an input distribution with density function bounded above pointwise by a multiplicative factor from the uniform distribution; nature then samples an input from this distribution. This interpolates between the extremes of worst-case and average case analysis. Crucially, our results hold for adaptive adversaries that can base their choice of an input distribution on the decisions of the algorithm and the realizations of the inputs in the previous time steps. An adaptive adversary can nontrivially correlate inputs at different time steps with each other and with the algorithm's current state; this appears to rule out the standard proof approaches in smoothed analysis. This paper presents a general technique for proving smoothed algorithmic guarantees against adaptive adversaries, in effect reducing the setting of an adaptive adversary to the much simpler case of an oblivious adversary (i.e., an adversary that commits in advance to the entire sequence of input distributions). We apply this technique to prove strong smoothed guarantees for three different problems: Online learning, Online discrepancy and Dispersion in online optimization. We show that in these setting, we can get bounds that match bounds we can get for non-adaptive adversaries.
Fast Graphical Population Protocols

Let $G$ be a graph on $n$ nodes. In the stochastic population protocol model, a collection of $n$ indistinguishable, resource-limited nodes collectively solve tasks via pairwise interactions. In each interaction, two randomly chosen neighbors first read each other's states, and then update their local states. A rich line of research has established tight upper and lower bounds on the complexity of fundamental tasks, such as majority and leader election, in this model, when $G$ is a clique. Specifically, in the clique, these tasks can be solved fast, i.e., in $n \operatorname{polylog} n$ pairwise interactions, with high probability, using at most $\operatorname{polylog} n$ states per node. In this work, we consider the more general setting where $G$ is an arbitrary graph, and present a technique for simulating protocols designed for fully-connected networks in any connected regular graph. Our main result is a simulation that is efficient on many interesting graph families: roughly, the simulation overhead is polylogarithmic in the number of nodes, and quadratic in the conductance of the graph. As a sample application, we show that, in any regular graph with conductance $\phi$, both leader election and exact majority can be solved in $\phi^{-2} \cdot n \operatorname{polylog} n$ pairwise interactions, with high probability, using at most $\phi^{-2} \cdot \operatorname{polylog} n$ states per node. This shows that there are fast and space-efficient population protocols for leader election and exact majority on graphs with good expansion properties. We believe our results will prove generally useful, as they allow efficient technology transfer between the well-mixed (clique) case, and the under-explored spatial setting.
Pathloss modeling of reconfigurable intelligent surface assisted THz wireless systems

This paper presents an analytical pathloss model for reconfigurable intelligent surface (RIS) assisted terahertz (THz) wireless systems. Specifically, the model accommodates both the THz link and the RIS particularities. Finally, we derive a closed-form expression that returns the optimal phase shifting of each RIS reflection unit. The derived pathloss model is validated through extensive electromagnetic simulations and is expected to play a key role in the design of RIS-assisted THz wireless systems.
LambdaNetworks: Modeling Long-Range Interactions Without Attention

We present lambda layers -- an alternative framework to self-attention -- for capturing long-range interactions between an input and structured contextual information (e.g. a pixel surrounded by other pixels). Lambda layers capture such interactions by transforming available contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Similar to linear attention, lambda layers bypass expensive attention maps, but in contrast, they model both content and position-based interactions which enables their application to large structured inputs such as images. The resulting neural network architectures, LambdaNetworks, significantly outperform their convolutional and attentional counterparts on ImageNet classification, COCO object detection and COCO instance segmentation, while being more computationally efficient. Additionally, we design LambdaResNets, a family of hybrid architectures across different scales, that considerably improves the speed-accuracy tradeoff of image classification models. LambdaResNets reach excellent accuracies on ImageNet while being 3.2 - 4.4x faster than the popular EfficientNets on modern machine learning accelerators. When training with an additional 130M pseudo-labeled images, LambdaResNets achieve up to a 9.5x speed-up over the corresponding EfficientNet checkpoints.
Near-infrared Coronal Line Observations of Dwarf Galaxies Hosting AGN-driven Outflows

We have obtained Keck near-infrared spectroscopy of a sample of nine M ⋆ < 1010 M ⊙ dwarf galaxies to confirm active galactic nuclei (AGNs) activity and the presence of galaxy-wide, AGN-driven outflows through coronal line (CL) emission. We find strong CL detections in 5/9 galaxies (55%) with line ratios incompatible with shocks, confirming the presence of AGNs in these galaxies. Similar CL detection rates are found in larger samples of more massive galaxies hosting type 1 and 2 AGNs. We investigate the connection between the CLs and galaxy-wide outflows by analyzing the kinematics of the CL region as well as the scaling of gas velocity with ionization potential of different CLs. In addition, using complementary Keck Cosmic Web Imager observations of these objects, we find that the outflow velocities measured in [Si vi] are generally faster than those seen in [O iii]. The galaxies with the fastest outflows seen in [O iii] also have the highest [Si vi] luminosity. The lack of J-band CN absorption features, which are often associated with younger stellar populations, provides further evidence that these outflows are driven by AGNs in low-mass galaxies.
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the image synthesis setup, exhibit a limited capacity for diagnosing the different modes of failure of generative models across broader application domains. In this paper, we introduce a 3-dimensional evaluation metric, ($\alpha$-Precision, $\beta$-Recall, Authenticity), that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion. Our metric unifies statistical divergence measures with precision-recall analysis, enabling sample- and distribution-level diagnoses of model fidelity and diversity. We introduce generalization as an additional, independent dimension (to the fidelity-diversity trade-off) that quantifies the extent to which a model copies training data -- a crucial performance indicator when modeling sensitive data with requirements on privacy. The three metric components correspond to (interpretable) probabilistic quantities, and are estimated via sample-level binary classification. The sample-level nature of our metric inspires a novel use case which we call model auditing, wherein we judge the quality of individual samples generated by a (black-box) model, discarding low-quality samples and hence improving the overall model performance in a post-hoc manner.
Complex Momentum for Learning in Games

We generalize gradient descent with momentum for learning in differentiable games to have complex-valued momentum. We give theoretical motivation for our method by proving convergence on bilinear zero-sum games for simultaneous and alternating updates. Our method gives real-valued parameter updates, making it a drop-in replacement for standard optimizers. We empirically demonstrate that complex-valued momentum can improve convergence in adversarial games—like generative adversarial networks—by showing we can ﬁnd better solutions with an almost identical computational cost. We also show a practical generalization to a complex-valued Adam variant, which we use to train BigGAN to better inception scores on CIFAR-10.
Organic and inorganic carbon stocks and balance of Adana city soils in Turkey

Carbon stocks are calculated between from 0 - 100 cm depths of Adana great soil groups (GSG). Total carbon stock is 567.19 Tg in 0 - 100 cm, 168.37 Tg of which is organic carbon (OC) and 398.83 Tg of which is inorganic carbon (IC). While 77.81% of soil organic carbon consists of alluvial, brown and non-calcareous brown forest soils, 59.77% of soil inorganic carbon (SIC) consists of alluvial, colluvial and brown forest soils. Organic carbon stocks are mostly seen in non-calcareous brown forest soils (53.75 Tg or 31.92% of OC stocks, 22.61% of all area). The least stock is determined in alluvial coast soils (0.02 Tg or 0.01% of OC stocks, 0.01% of all area). Inorganic carbon stocks are mostly found in alluvial soils (165.7 Tg or 41.65% of SIC stocks, 19.88% of all area) and it is least seen in alluvial coast soils (0.12 Tg or 0.03% of SIC stocks, 0.01% of all area). While soil organic carbon amount is between 6.01 - 13.78 kg C m-2, soil inorganic carbon amount changes between 26.27-60.01 kg C m-2. Generally, it is seen that soil organic carbon amount is low in the area where intense agriculture techniques are used and it is high in meadow and forest areas which form high areas. Key words: Adana soils, soil organic carbon amounts and stocks, soil inorganic carbon amounts and stocks.
Emerging recognition of the complement system in hepatic ischemia/reperfusion injury, liver regeneration and recovery (Review)

Hepatic ischemia/reperfusion injury (IRI) is a result of the ischemic cascade and may occur in the settings of liver trauma, resection and transplantation. Components of the complement system have been indicated to be mediators of hepatic IRI and regulators of liver regeneration. As such, their potential to mediate both beneficial and harmful effects render them key targets for therapy. In the present study, the mechanisms of complement mediating hepatic IRI were discussed with a focus on the different functions of complement in hepatic injury and liver recovery, and an explanation for this apparent paradox is provided, i.e. that the complement products C3a and C5a have an important role in liver damage; however, C3a and C5a are also necessary for liver regeneration. Furthermore, situated at the end of the complement activation cascade, the membrane attack complex is crucial in hepatic IRI and inhibiting the complex with a site-targeted murine complement inhibitor, complement receptor 2-CD59, may improve liver regeneration after partial hepatectomy, even when hepatectomy is combined with ischemia and reperfusion.
Kahook Dual Blade Goniotomy vs iStent inject: Long-Term Results in Patients with Open-Angle Glaucoma

Purpose Efficacy and safety evaluation of Kahook Dual Blade (KDB) goniotomy vs iStent inject implantation. Materials and Methods Retrospective study in patients that underwent goniotomy with KDB or iStent inject implantation, stand-alone or combined with cataract surgery. Main outcome parameters were intraocular pressure (IOP), number of glaucoma medications, proportion of eyes achieving >20% IOP reduction and number of eyes with postoperative IOP <19 mmHg at last follow-up. Results A total of 29 patients (30 eyes) were included in the iStent inject group and 30 patients (32 eyes) in the KDB group. Mean follow-ups were 20.9±6.5 (KDB-alone) to 29.5±7.6 (phaco-iStent inject) months. Pre- and post-operative IOPs were 22.2±5.8 mmHg and 15.9±4.3 mmHg (P=0.004) in the KDB-alone, 24.2±6.8 mmHg and 16.2±6.7 mmHg (P=0.001) in the phaco-KDB, 20.6±5.4 mmHg and 20.9±6.8 mmHg (P=0.598) in the iStent inject-alone as well as 20.9±5.5 mmHg and 15.6±3.4 mmHg (P=0.003) in the phaco-iStent inject subgroups. No major complications occurred. Conclusion All KDB and iStent subgroups except the stand-alone iStent inject subgroup showed a clinically significant IOP-lowering effect as a stand-alone procedure or combined with cataract surgery. Goniotomy with KDB in this setting seems to offer an advantageous IOP reduction compared to iStent inject.