In this paper, we present three distributed algorithms to solve a class of generalized Nash equilibrium (GNE) seeking problems in strongly monotone games. The first one (SD-GENO) is based on synchronous updates of the agents, while the second and the third (AD-GEED and AD-GENO) represent asynchronous solutions that are robust to communication delays. AD-GENO can be seen as a refinement of AD-GEED, since it only requires node auxiliary variables, enhancing the scalability of the algorithm. Our main contribution is to prove converge to a variational GNE of the game via an operator-theoretic approach. Finally, we apply the algorithms to network Cournot games and show how different activation sequences and delays affect convergence. We also compare the proposed algorithms to the only other in the literature (ADAGNES), and observe that AD-GENO outperforms the alternative.

For any finite Galois field extension $mathsf{K}/mathsf{F}$, with Galois group $G = mathrm{Gal}(mathsf{K}/mathsf{F})$, there exists an element $alpha in mathsf{K}$ whose orbit $Gcdotalpha$ forms an $mathsf{F}$-basis of $mathsf{K}$. Such an $alpha$ is called a normal element and $Gcdotalpha$ is a normal basis. We introduce a probabilistic algorithm for testing whether a given $alpha in mathsf{K}$ is normal, when $G$ is either a finite abelian or a metacyclic group. The algorithm is based on the fact that deciding whether $alpha$ is normal can be reduced to deciding whether $sum_{g in G} g(alpha)g in mathsf{K}[G]$ is invertible; it requires a slightly subquadratic number of operations. Once we know that $alpha$ is normal, we show how to perform conversions between the working basis of $mathsf{K}/mathsf{F}$ and the normal basis with the same asymptotic cost.

One of the main open problems in the qualitative theory of real planar differential systems is the study of limit cycles. In this article, we present an algorithmic approach for detecting how many limit cycles can bifurcate from the periodic orbits of a given polynomial differential center when it is perturbed inside a class of polynomial differential systems via the averaging method. We propose four symbolic algorithms to implement the averaging method. The first algorithm is based on the change of polar coordinates that allows one to transform a considered differential system to the normal form of averaging. The second algorithm is used to derive the solutions of certain differential systems associated to the unperturbed term of the normal of averaging. The third algorithm exploits the partial Bell polynomials and allows one to compute the integral formula of the averaged functions at any order. The last algorithm is based on the aforementioned algorithms and determines the exact expressions of the averaged functions for the considered differential systems. The implementation of our algorithms is discussed and evaluated using several examples. The experimental results have extended the existing relevant results for certain classes of differential systems.

Graph convolution network (GCN) have achieved state-of-the-art performance in the task of node prediction in the graph structure. However, with the gradual various of graph attack methods, there are lack of research on the robustness of GCN. At this paper, we will design a robust GCN method for node prediction tasks. Considering the graph structure contains two types of information: node information and connection information, and attackers usually modify the connection information to complete the interference with the prediction results of the node, we first proposed a method to hide the connection information in the generator, named Anonymized GCN (AN-GCN). By hiding the connection information in the graph structure in the generator through adversarial training, the accurate node prediction can be completed only by the node number rather than its specific position in the graph. Specifically, we first demonstrated the key to determine the embedding of a specific node: the row corresponding to the node of the eigenmatrix of the Laplace matrix, by target it as the output of the generator, we designed a method to hide the node number in the noise. Take the corresponding noise as input, we will obtain the connection structure of the node instead of directly obtaining. Then the encoder and decoder are spliced both in discriminator, so that after adversarial training, the generator and discriminator can cooperate to complete the encoding and decoding of the graph, then complete the node prediction. Finally, All node positions can generated by noise at the same time, that is to say, the generator will hides all the connection information of the graph structure. The evaluation shows that we only need to obtain the initial features and node numbers of the nodes to complete the node prediction, and the accuracy did not decrease, but increased by 0.0293.

In this paper, we study the problem of fair classification in the presence of prior probability shifts, where the training set distribution differs from the test set. This phenomenon can be observed in the yearly records of several real-world datasets, such as recidivism records and medical expenditure surveys. If unaccounted for, such shifts can cause the predictions of a classifier to become unfair towards specific population subgroups. While the fairness notion called Proportional Equality (PE) accounts for such shifts, a procedure to ensure PE-fairness was unknown.

In this work, we propose a method, called CAPE, which provides a comprehensive solution to the aforementioned problem. CAPE makes novel use of prevalence estimation techniques, sampling and an ensemble of classifiers to ensure fair predictions under prior probability shifts. We introduce a metric, called prevalence difference (PD), which CAPE attempts to minimize in order to ensure PE-fairness. We theoretically establish that this metric exhibits several desirable properties.

We evaluate the efficacy of CAPE via a thorough empirical evaluation on synthetic datasets. We also compare the performance of CAPE with several popular fair classifiers on real-world datasets like COMPAS (criminal risk assessment) and MEPS (medical expenditure panel survey). The results indicate that CAPE ensures PE-fair predictions, while performing well on other performance metrics.

Consider the following prediction problem. Assume that there is a block box that produces bits according to some unknown computable distribution on the binary tree. We know first $n$ bits $x_1 x_2 ldots x_n$. We want to know the probability of the event that that the next bit is equal to $1$. Solomonoff suggested to use universal semimeasure $m$ for solving this task. He proved that for every computable distribution $P$ and for every $b in {0,1}$ the following holds: $$sum_{n=1}^{infty}sum_{x: l(x)=n} P(x) (P(b | x) - m(b | x))^2 < infty .$$ However, Solomonoff's method has a negative aspect: Hutter and Muchnik proved that there are an universal semimeasure $m$, computable distribution $P$ and a random (in Martin-L{"o}f sense) sequence $x_1 x_2ldots$ such that $lim_{n o infty} P(x_{n+1} | x_1ldots x_n) - m(x_{n+1} | x_1ldots x_n) rightarrow 0$. We suggest a new way for prediction. For every finite string $x$ we predict the new bit according to the best (in some sence) distribution for $x$. We prove the similar result as Solomonoff theorem for our way of prediction. Also we show that our method of prediction has no that negative aspect as Solomonoff's method.

In recent years, deep neural networks have been applied to obtain high performance of prediction, classification, and pattern recognition. However, the weights in these deep neural networks are difficult to be explained. Although a linear regression method can provide explainable results, the method is not suitable in the case of input interaction. Therefore, an explainable deep neural network (ExpDNN) with explainable layers is proposed to obtain explainable results in the case of input interaction. Three cases were given to evaluate the proposed ExpDNN, and the results showed that the absolute value of weight in an explainable layer can be used to explain the weight of corresponding input for feature extraction.

Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computations using neural networks. The stabilized lottery ticket hypothesis states that networks can be pruned after none or few training iterations, using a mask computed based on the unpruned converged model. On the transformer architecture and the WMT 2014 English-to-German and English-to-French tasks, we show that stabilized lottery ticket pruning performs similar to magnitude pruning for sparsity levels of up to 85%, and propose a new combination of pruning techniques that outperforms all other techniques for even higher levels of sparsity. Furthermore, we confirm that the parameter's initial sign and not its specific value is the primary factor for successful training, and show that magnitude pruning cannot be used to find winning lottery tickets.

We show that combining a prediction model (based on neural networks), with a new method of test pooling (better than the original Dorfman method, and better than double-pooling) called 'Grid', we can reduce the number of Covid-19 tests by 73%.

In this work we propose lifted regression/reconstruction networks (LRRNs), which combine lifted neural networks with a guaranteed Lipschitz continuity property for the output layer. Lifted neural networks explicitly optimize an energy model to infer the unit activations and therefore---in contrast to standard feed-forward neural networks---allow bidirectional feedback between layers. So far lifted neural networks have been modelled around standard feed-forward architectures. We propose to take further advantage of the feedback property by letting the layers simultaneously perform regression and reconstruction. The resulting lifted network architecture allows to control the desired amount of Lipschitz continuity, which is an important feature to obtain adversarially robust regression and classification methods. We analyse and numerically demonstrate applications for unsupervised and supervised learning.

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.

An important goal in reinforcement learning is to create agents that can quickly adapt to new goals while avoiding situations that might cause damage to themselves or their environments. One way agents learn is through exploration mechanisms, which are needed to discover new policies. However, in deep reinforcement learning, exploration is normally done by injecting noise in the action space. While performing well in many domains, this setup has the inherent risk that the noisy actions performed by the agent lead to unsafe states in the environment. Here we introduce a novel approach called Meta-Learned Instinctual Networks (MLIN) that allows agents to safely learn during their lifetime while avoiding potentially hazardous states. At the core of the approach is a plastic network trained through reinforcement learning and an evolved "instinctual" network, which does not change during the agent's lifetime but can modulate the noisy output of the plastic network. We test our idea on a simple 2D navigation task with no-go zones, in which the agent has to learn to approach new targets during deployment. MLIN outperforms standard meta-trained networks and allows agents to learn to navigate to new targets without colliding with any of the no-go zones. These results suggest that meta-learning augmented with an instinctual network is a promising new approach for safe AI, which may enable progress in this area on a variety of different domains.

Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.

Randomized Numerical Linear Algebra (RandNLA) uses randomness to develop improved algorithms for matrix problems that arise in scientific computing, data science, machine learning, etc. Determinantal Point Processes (DPPs), a seemingly unrelated topic in pure and applied mathematics, is a class of stochastic point processes with probability distribution characterized by sub-determinants of a kernel matrix. Recent work has uncovered deep and fruitful connections between DPPs and RandNLA which lead to new guarantees and improved algorithms that are of interest to both areas. We provide an overview of this exciting new line of research, including brief introductions to RandNLA and DPPs, as well as applications of DPPs to classical linear algebra tasks such as least squares regression, low-rank approximation and the Nystr"om method. For example, random sampling with a DPP leads to new kinds of unbiased estimators for least squares, enabling more refined statistical and inferential understanding of these algorithms; a DPP is, in some sense, an optimal randomized algorithm for the Nystr"om method; and a RandNLA technique called leverage score sampling can be derived as the marginal distribution of a DPP. We also discuss recent algorithmic developments, illustrating that, while not quite as efficient as standard RandNLA techniques, DPP-based algorithms are only moderately more expensive.

Most optimization-based community detection approaches formulate the problem in a single or bi-objective framework. In this paper, we propose two variants of a three-objective formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network. In the first variant, named NSGA-III-KRM, we considered Kernel k means, Ratio cut, and Modularity, as the three objectives, whereas the second variant, named NSGA-III-CCM, considers Community score, Community fitness and Modularity, as three objective functions. Experiments are conducted on four benchmark network datasets. Comparison with state-of-the-art approaches along with decomposition-based multi-objective evolutionary algorithm variants (MOEA/D-KRM and MOEA/D-CCM) indicates that the proposed variants yield comparable or better results. This is particularly significant because the addition of the third objective does not worsen the results of the other two objectives. We also propose a simple method to rank the Pareto solutions so obtained by proposing a new measure, namely the ratio of the hyper-volume and inverted generational distance (IGD). The higher the ratio, the better is the Pareto set. This strategy is particularly useful in the absence of empirical attainment function in the multi-objective framework, where the number of objectives is more than two.

In this technical report we give an elementary introduction to Quantum Computing for non-physicists. In this introduction we describe in detail some of the foundational Quantum Algorithms including: the Deutsch-Jozsa Algorithm, Shor's Algorithm, Grocer Search, and Quantum Counting Algorithm and briefly the Harrow-Lloyd Algorithm. Additionally we give an introduction to Solomonoff Induction, a theoretically optimal method for prediction. We then attempt to use Quantum computing to find better algorithms for the approximation of Solomonoff Induction. This is done by using techniques from other Quantum computing algorithms to achieve a speedup in computing the speed prior, which is an approximation of Solomonoff's prior, a key part of Solomonoff Induction. The major limiting factors are that the probabilities being computed are often so small that without a sufficient (often large) amount of trials, the error may be larger than the result. If a substantial speedup in the computation of an approximation of Solomonoff Induction can be achieved through quantum computing, then this can be applied to the field of intelligent agents as a key part of an approximation of the agent AIXI.

Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables. LTGA performs well in various kinds of single-task optimization and yields promising results in comparison with the canonical genetic algorithm. However, LTGA is an unsuitable method for dealing with multi-task optimization problems. On the other hand, Multifactorial Optimization (MFO) can simultaneously solve independent optimization problems, which are encoded in a unified representation to take advantage of the process of knowledge transfer. In this paper, we introduce Multifactorial Linkage Tree Genetic Algorithm (MF-LTGA) by combining the main features of both LTGA and MFO. MF-LTGA is able to tackle multiple optimization tasks at the same time, each task learns the dependency between problem variables from the shared representation. This knowledge serves to determine the high-quality partial solutions for supporting other tasks in exploring the search space. Moreover, MF-LTGA speeds up convergence because of knowledge transfer of relevant problems. We demonstrate the effectiveness of the proposed algorithm on two benchmark problems: Clustered Shortest-Path Tree Problem and Deceptive Trap Function. In comparison to LTGA and existing methods, MF-LTGA outperforms in quality of the solution or in computation time.

Making changes to a program to optimize its performance is an unscalable task that relies entirely upon human intuition and experience. In addition, companies operating at large scale are at a stage where no single individual understands the code controlling its systems, and for this reason, making changes to improve performance can become intractably difficult. In this paper, a learning system is introduced that provides AI assistance for finding recommended changes to a program. Specifically, it is shown how the evaluative feedback, delayed-reward performance programming domain can be effectively formulated via the Monte Carlo tree search (MCTS) framework. It is then shown that established methods from computational games for using learning to expedite tree-search computation can be adapted to speed up computing recommended program alterations. Estimates of expected utility from MCTS trees built for previous problems are used to learn a sampling policy that remains effective across new problems, thus demonstrating transferability of optimization knowledge. This formulation is applied to the Apache Spark distributed computing environment, and a preliminary result is observed that the time required to build a search tree for finding recommendations is reduced by up to a factor of 10x.

A method of automating the deployment of a number of enterprise applications on one or more computer data processing systems. Each enterprise application or update is stored in a dynamic distribution directory and is provided with identifying indicia, such as stage information, target information, and settings information. When automated enterprise deployment is invoked, computer instructions in a computer readable medium provide for initializing deployment, performing deployment, and finalizing deployment of the enterprise applications or updates.

An electronic device for text prediction in a virtual keyboard. The device includes a memory including an input determination module for execution by the microprocessor, the input determination module being configured to: receive signals representing input at the virtual keyboard, the virtual keyboard being divided into a plurality of subregions, the plurality of subregions including at least one subregion being associated with two or more characters and/or symbols of the virtual keyboard; identify a subregion on the virtual keyboard corresponding to the input; determine any character or symbol associated with the identified subregion; and if there is at least one determined character or symbol, provide the at least one determined character or symbol to a text prediction algorithm.

Described is a microfluidic process for manufacturing partially cross-linked alginate solution, wherein the alginate solution is a homogenous liquid which exhibits an elastic response (G') which is equal to or greater than its viscous response (G″). In particular, the process may comprise microfluidic mixing of sodium alginate and calcium gluconate solutions to provide an injectable partially cross-linked alginate solution.

A region growing algorithm for controlling leakage is presented including a processor configured to select a starting point for segmentation of data, initiate a propagation process by designating adjacent voxels around the starting point, determine whether any new voxels are segmented, count and analyze the segmented new voxels to determine leakage levels, and identify and record segmented new voxels from a previous iteration when the leakage levels exceed a predetermined threshold. The processor is further configured to perform labeling of the segmented new voxels of the previous iteration, select the segmented new voxels from the previous iteration when the leakage levels fall below the predetermined threshold, and create a voxel list based on acceptable segmented voxels found in the previous iteration.

Methods and devices for determining optimal Atrial to Ventricular (AV) pacing intervals and Ventricular to Ventricular (VV) delay intervals in order to optimize cardiac output. Impedance, preferably sub-threshold impedance, is measured across the heart at selected cardiac cycle times as a measure of chamber expansion or contraction. One embodiment measures impedance over a long AV interval to obtain the minimum impedance, indicative of maximum ventricular expansion, in order to set the AV interval. Another embodiment measures impedance change over a cycle and varies the AV pace interval in a binary search to converge on the AV interval causing maximum impedance change indicative of maximum ventricular output. Another method varies the right ventricle to left ventricle (VV) interval to converge on an impedance maximum indicative of minimum cardiac volume at end systole. Another embodiment varies the VV interval to maximize impedance change.

Many GTPases such as Ras, Ral and Rho require post-translational farnestylation or geranylgeranylation for mediating malignant transformation. Dual farnesyltransferase (FT) (FTI) and geranylgeranyltransferase-I (GGT-1) inhibitors (GGTI) were developed as anticancer agents from based on an ethylenediamine scaffold. On the basis of a 4-fold substituted ethylenediamine scaffold, the inhibitors are structurally simple and readily derivatized, facilitating extensive structure-activity relationship studies. The most potent inhibitor is compound exhibited an in vitro hFTase IC50 value of 25 nM and a whole cell H-Ras processing IC50 value of 90 nM. Several of the inhibitors proved highly selective for hFTase over the related prenyltransferase enzyme geranylgeranyltransferase-I (GGTase-I). A crystal structure of an inhibitor cocrystallized with farnesyl pyrophosphate in the active site of rat FTase illustrates that the para-benzonitrile moiety is stabilized by a π-π stacking interaction with the Y361β residue, suggesting an importance of this component of the inhibitors.

Disclosed is a drug effective in the treatment of fibromyalgia. Basically, the disclosed therapeutic agent was created on the basis of experiments showing improvement in symptoms when etanercept was administered to patients suffering from fibromyalgia. Etanercept is known as a therapeutic agent for rheumatoid arthritis, and the JFIQ score of patients not suffering from fibromyalgia improved considerably in the preferred embodiment. In other words, a therapeutic agent for fibromyalgia is disclosed that contains etanercept as an active ingredient in an effective amount.

A device for filtering liquids has a container, units for introducing a liquid to be filtered into the container, a container outlet for unfiltered liquid to be discharged from the container, and at least one rotor, which is drivable to rotate around the container axis. The rotor has a hollow shaft mounted in an end wall and a support device fastened thereon for filter elements, which are arranged with a clearance to the container axis or rotate around their own axis. The interior of the filter elements opens via the support device and the hollow shaft out of the container as the discharge for filtered liquid. To provide improved filtration conditions, the container internal radius of the inner wall of the container circumferential shell is enlarged up to a maximum in the container circumferential direction while bulging the inner wall between two minima. The bulge forms a guide unit which guides the liquid toward the filter elements.

A method for creating laboratory-scale reactive distillation apparatus from provided modular components is described. At least two types of modular distillation column stages are provided. A first type of modular stage comprises two physical interfaces for connection with a respective physical interface of another modular stage. A second type modular stage comprises one such physical interface. At least one type of tray is provided for insertion into the first type of modular stage. A clamping arrangement is provided for joining together two modular stages at their respective physical interfaces for connection to form a joint. The invention provides for at least three modular stages can be joined. At least one sensor or sensor array can be inserted into each modular stage. At least one controllable element can be inserted into each modular stage. The invention provides for study of traditional, advanced, and photochemical types of reactive distillation.

A system for culturing photosynthesizing microorganisms includes a source of a gaseous fluid a mixer that creates micron bubbles within an aqueous medium using the gaseous fluid. The mixing chamber holds the aqueous medium including the micron bubbles before the micron bubbles and aqueous medium are mixed with a culture of photosynthesizing microorganism in a reaction chamber.

A method for cultivating algae can include providing a body of water in a substantially enclosed system. The enclosed system can have a length of channel and a cover. The method can optionally include circulating the body of water through the enclosed system under positive pressure conditions. The positive pressure should prevent ingress of any external atmosphere or material. Further, the method can include cultivating the algae in the body of water at conditions which promote growth. Likewise, a system for cultivating algae can include a channel with a cover, water in the channel, and a pump to introduce positive pressure into the system.

The present invention discloses a system and method for monitoring an infusion pump capable of intelligently easing pain. Each infusion pump control terminal is connected with a monitoring server through a wireless AP and a local area network respectively; each human body vital sign sensor is connected with the signal input end of a field programmable gate array FPGA through a sensor interface circuit respectively, an infusion control device is connected with the control signal output end of the field programmable gate array FPGA, the field programmable gate array FPGA is in communication with an ARM processor in a bus coding mode, and the ARM processor is in communication connection with the wireless AP through a WIFI communication module. By means of the system and method for monitoring infusion pump capable of intelligently easing pain, a plurality of basic vital sign data of a patient is collected in real time, corresponding infusion schemes are generated through analysis of the data, the infusion pump is controlled to achieve automatic infusion, monitoring and pain-easing infusion are combined together for coordinative work, and infusion control is more scientific and reliable; patient online perception and feedback is supported, self-improvement of a system is facilitated, and more accurate and reliable infusion schemes can be acquired.

A process for converting triacylglycerides-containing oils into crude oil precursors and/or distillate hydrocarbon fuels is disclosed. The process may include reacting a triacylglycerides-containing oil-carbon dioxide mixture at a temperature in the range from about 250° C. to about 525° C. and a pressure greater than about 75 bar to convert at least a portion of the triacylglycerides to a hydrocarbon or mixture of hydrocarbons comprising one or more of isoolefins, isoparaffins, cycloolefins, cycloparaffins, and aromatics.

The invention relates to a product for pilgrims performing the rites of Haj and Umrah. Used to reduce the crushes resulting from pilgrims colliding because, for example, pilgrims places their shoes close to the gate from which they entered thinking that it will be easy to return to the same place once they have performed the rites of circumambulation (tawaf) and running (sa'ay). However, in reality the pilgrims must walk against the flow, causing collisions and severe crushes, or he is forced to leave his shoes behind, which is a burden for cleaning staff, looks untidy and is a nuisance for pilgrims, the main figures for the inventions are FIG. 7 before wearing the shoes and FIGS. 8,9,10 and 11 after wearing it.

The sparsely-populated world created as the result of a plague in The Surge 2 needed a suitably dystopian sound for the music. I talked with composer Helge Borgarts of BowsToHymns who, with his colleague Thomas Stanger, crafted their music for the soundtrack that's inspired by the striking visuals and unique sound design by developers Deck 13. Helge and BowsToHymns also worked on the soundtrack for The Kraken , an expansion for The Surge 2 set during the 1980s on an aircraft carrier that's been turned into a cruise ship. Helge says it was really fun to recreate a grunge rock sound from some of his 1980s heroes. The Surge 2 Soundtrack, including the Kraken expansion is available in Apple Music, and many other sources. Episode tracklist : All tracks composed and performed by Helge Borgarts and Thomas Stanger (BowsToHymns) The Surge 2: Plane Crash; Infiltration; City Exploration; University; The Wall; Dangerous Harbour; Black Market; The Escape (feat. Alina Lesnik, vocals); Kraken Electro

A new study from Yale University and Oregon Health and Science University looks at how doctor’s prejudices toward LGBT patients change during medical school.

A panel of appeals court judges has ordered Suffolk County Police to release the 911 call that led authorities to discover 11 bodies near Gilgo Beach a decade ago.

Southside celebrates LGBT History Festival.

Morris has unveiled a four-song Amazon Live EP, 'Maren Morris Live From Chicago.' Continue reading…

Milestone marked by new productions.

Acclaimed local writer added to theatre board of directors.

UVI has launched a sale on the Sparkverb algorithmic reverb effect plugin, offering 60% off for a few days only. Sparkverb features an advanced design with stunning sound and CPU efficiency, and intuitive controls and ergonomics for phenomenal ease-of-use. Easily traverse everything from natural sounding spaces to infinite, shimmering ambiences with stunning depth and fidelity […]

The post Sparkverb algorithmic reverb by UVI on sale for $49 USD appeared first on rekkerd.org.

United Plugins has announced the release of MorphVerb reverb plugin with an introductory 87% discount for a few days only. MorphVerb lets you blend smoothly between reverb types. It features ducking, a real-time spectrogram, and controls for the reverb algorithm, modulation, saturation and compression of the reflections. MorphVerb covers all reverb types you could think […]

The post United Plugins launches MorphVerb algorithmic reverb at intro offer appeared first on rekkerd.org.

Over the past few weeks, chief executives of Unilever, Mondelez, Hershey's Procter & Gamble, Coca-Cola and Kimberly Clark have said India’s Covid-19 lockdown protocols had led to severe supply chain disruptions and labour shortages, hurting business in the key market.

“While during the early days of lockdown basic essentials got sold, with time some discretionary sales are coming back,” said Devendra Chawla, MD at Nature’s Basket and Spencer’s Retail that saw 30-60% growth in sales for cold cuts, exotic vegetables, assorted breads and cakes, cookies, international sauces, organic range of staples in last ten days.

Jessie Sage is a sex worker who writes and speaks publicly on issues related to sex work, feminism, and social justice. James Tison is a stand-up comedian in New York who uses humor to fight stigma against his LGBTQ identity and life with HIV. Sage and Tison recently spoke at an event at Penn State called “Facts not Fear: A Night to Fight Stigma,” and talked with WPSU about fighting the sigma their communities face. This Take Note interview talks about sex work and might not be suitable for children to hear.

Both LG and Samsung have opened bookings for various products on their websites for limited periods and are offering gifts of up to Rs 10,000 on pre-bookings made during the lockdown period. LG has opened pre-bookings till May 15 and Samsung by May 8.

"The journey actually began in June when we did well due to marketing, though we were actually not ready operationally."

It was six years ago that Catriona Rilley and Aby Watson had their lightbulb moment, while mopping the floors of Glasgow's Flying Duck after a shift.

Sarah McBride made history as the first transgender person to speak at a national political convention in 2016.

Heung-min Son nutzte die Corona-Zwangspause der Premier League, um sich in Südkorea militärisch ausbilden zu lassen. Tottenham Hotspurs Stürmer tat sich in den Gefechtsübungen hervor. Das machte Eindruck bei seinen Vorgesetzten.

Viele Anleger wähnen sich und ihre Börseninvestments bereits in einem neuen Aufschwung. Doch einige der bekanntesten Investoren mahnen zur Vorsicht. Ihre Szenarien sind deutlich negativer. Dabei haben sie die Historie der vergangenen Crashs auf ihrer Seite.