Some of the problems we work on as UX researchers are simple and are easily solved by getting users in front of our product. But other problems can be complex and it's hard to know how to start solving them. In situations like that, a simple 2x2 diagram can cut through the 'what ifs', the 'how abouts' and the edge cases and provide a simple way of looking at the problem. Here are 10 examples of 2x2 diagrams to simplify UX research discussions.

Are you a logical individual? Do you carefully consider all options before making a decision? Are opinions shaped primarily through facts and reasoning? If you answered yes to these questions, you’d be wrong. We are all emotional beings, and our emotions are the root cause of our thoughts and behaviors. Our logical, conscious thoughts simply […]

The post Fight or Flight: Which Emotion Does Your Website Evoke? appeared first on Psychology of Web Design | 3.7 Blog.

A set of key visuals for Nike Shanghai

Designer Spotlight: Burnt Toast

They say that a picture is worth a thousand words, but have you ever stopped to think what your Ecommerce images are saying about the products you’re trying to sell online? Are your photos helping your products to jump off the screen and convince shoppers to buy them? Or, are your product images quite simply […]

Original post: 5 Product Image Tips For High Converting Landing Pages

The post 5 Product Image Tips For High Converting Landing Pages appeared first on Daily Blog Tips.

For the first time, it seems, the entire world knows what it’s like to live inside my head.

Prizes: Grand prize winner X100V camera from our main sponsor Fujifilm, worth $1,700.00 A cash prize of $5,000.00 Category Winner Prizes: Every category winner will [...]

The post 2020 Spotlight Awards appeared first on Photocompete.

The Republican National Committee and President Donald Trump's reelection campaign have doubled their litigation budget to $20 million, Politico reported Thursday. RNC chief of staff Richard Walters told Politico that the GOP is prepared to sue Democrats "into oblivion" by spending "whatever is necessary" to prevail in legal fights against its rivals leading up to the November election.

Corey Rich is a director, writer, and one of the top action sports photographers of our time. He has built a life and a career around his passions for travel and adventure by documenting some of the world’s greatest athletes in extreme locations spanning the globe. From alpine climbing in Pakistan’s Karakoram Mountains to ultramarathon racing in the Sahara Desert of Morocco, freight-train hopping in the American West, underwater cave exploration in the Yucatan and snowboarding in Papua New Guinea – if you’re an outdoor adventure lover, you’ve likely seen his work gracing the pages of your favorite magazines and video screens. Corey is a long-time friend and a master storyteller, so when he released his latest book, Stories Behind the Images, I couldn’t wait to have him on the show. In this episode, Corey takes us on a journey behind the scenes of his life and career. We get into: Doing what you love. The process of chasing the things we know we’re supposed to be doing in our heart. Sacrifice. I don’t know a single person at the top of their game who hasn’t sacrificed something to be where they are today. Corey reflects on what what he’s sacrificed […]

The post Corey Rich: Good Enough is Never Good Enough appeared first on Chase Jarvis Photography.

As I stated in my Best of 2017 post, I didn’t get to take many photos this year – which also multiple times throughout the year made me think: why am I paying so much money for Lightroom for how […]

May is here! And even though the current situation makes this a different kind of May, with a new routine and different things on our minds as in the years before, luckily some things never change. Like the fact that we start into the new month with some fresh inspiration. Since more than nine years already, we challenge you, the design community, to get creative and produce wallpaper designs for our monthly posts.

Aputure’s been coming pretty thick and fast on the announcements lately, and now they’ve announced their new Light Storm 60D daylight and 60X bi-colour adjustable focusing LED lights. As the name suggests, these are 60 Watt LEDs, and everything is built inside the head, meaning there’s no external control unit to have to deal with. […]

The post Aputure announces new LS-60D daylight and LX-60X bicolour LED lights appeared first on DIY Photography.

Are there still folks among you who, like me, prefer handwriting to typing? If you’re in this group, you’ll love this new feature on Google Lens. The app now lets you scan your handwritten notes, copy them, and paste them straight to your computer. I gave it a spin, and I bring you my impressions […]

The post Google Lens now copies handwritten text and pastes it straight to your computer appeared first on DIY Photography.

DJI has announced their new Matrice 300 RTK “flying platform” (big drone) and the Zenmuse H20 hybrid camera series, to provide “a safer and smarter solution” to their enterprise customers. The M300 RTK, DJI says, is their first to integrate modern aviation features, advanced AI, 6-direction sensing and positioning, a UAV health management system and […]

The post DJI’s new Matrice 300 RTK drone offers a ridiculous 55-minutes of flight time and 2.7kg payload appeared first on DIY Photography.

The Godox SL series LED lights have proven to be extremely popular due to their low cost. Two of the models in that range, the SL150 and SL200 have seen a Mark II update today, according to an email that Godox has been sending out today. One of the features of the new SL150II and […]

The post Godox’s new SL150/SL200 Mark II LED lights offer fanless “silent mode” operation appeared first on DIY Photography.

We show how to efficiently compute the derivative (when it exists) of the solution map of log-log convex programs (LLCPs). These are nonconvex, nonsmooth optimization problems with positive variables that become convex when the variables, objective functions, and constraint functions are replaced with their logs. We focus specifically on LLCPs generated by disciplined geometric programming, a grammar consisting of a set of atomic functions with known log-log curvature and a composition rule for combining them. We represent a parametrized LLCP as the composition of a smooth transformation of parameters, a convex optimization problem, and an exponential transformation of the convex optimization problem's solution. The derivative of this composition can be computed efficiently, using recently developed methods for differentiating through convex optimization problems. We implement our method in CVXPY, a Python-embedded modeling language and rewriting system for convex optimization. In just a few lines of code, a user can specify a parametrized LLCP, solve it, and evaluate the derivative or its adjoint at a vector. This makes it possible to conduct sensitivity analyses of solutions, given perturbations to the parameters, and to compute the gradient of a function of the solution with respect to the parameters. We use the adjoint of the derivative to implement differentiable log-log convex optimization layers in PyTorch and TensorFlow. Finally, we present applications to designing queuing systems and fitting structured prediction models.

High dimensional expanders is a vibrant emerging field of study. Nevertheless, the only known construction of bounded degree high dimensional expanders is based on Ramanujan complexes, whereas one dimensional bounded degree expanders are abundant.

In this work, we construct new families of bounded degree high dimensional expanders obeying the local spectral expansion property. This property has a number of important consequences, including geometric overlapping, fast mixing of high dimensional random walks, agreement testing and agreement expansion. Our construction also yields new families of expander graphs which are close to the Ramanujan bound, i.e., their spectral gap is close to optimal.

The construction is quite elementary and it is presented in a self contained manner; This is in contrary to the highly involved previously known construction of the Ramanujan complexes. The construction is also very symmetric (such symmetry properties are not known for Ramanujan complexes) ; The symmetry of the construction could be used, for example, in order to obtain good symmetric LDPC codes that were previously based on Ramanujan graphs.

The main tool that we use for is the theory of coset geometries. Coset geometries arose as a tool for studying finite simple groups. Here, we show that coset geometries arise in a very natural manner for groups of elementary matrices over any finitely generated algebra over a commutative unital ring. In other words, we show that such groups act simply transitively on the top dimensional face of a pure, partite, clique complex.

In cite{CC} the authors, investigating a model of population dynamics, find the following result. Let $Omegasubset mathbb{R}^N$, $Ngeq 1$, be a bounded smooth domain. The weighted eigenvalue problem $-Delta u =lambda m u $ in $Omega$ under homogeneous Dirichlet boundary conditions, where $lambda in mathbb{R}$ and $min L^infty(Omega)$, is considered. The authors prove the existence of minimizers $check m$ of the principal positive eigenvalue $lambda_1(m)$ when $m$ varies in a class $mathcal{M}$ of functions where average, maximum, and minimum values are given. A similar result is obtained in cite{CCP} when $m$ is in the class $mathcal{G}(m_0)$ of rearrangements of a fixed $m_0in L^infty(Omega)$. In our work we establish that, if $Omega$ is Steiner symmetric, then every minimizer in cite{CC,CCP} inherits the same kind of symmetry.

We show that every 4-uniform hypergraph with $n$ vertices and minimum pair degree at least $(5/9+o(1))n^2/2$ contains a tight Hamiltonian cycle. This degree condition is asymptotically optimal.

In the spirit of very recent articles by J. Bonet, W. Lusky and J. Taskinen we are studying the so-called solid hulls and cores of spaces of weighted entire functions when the weights are given in terms of associated weight functions coming from weight sequences. These sequences are required to satisfy certain (standard) growth and regularity properties which are frequently arising and used in the theory of ultradifferentiable and ultraholomorphic function classes (where also the associated weight function plays a prominent role). Thanks to this additional information we are able to see which growth behavior the so-called "Lusky-numbers", arising in the representations of the solid hulls and cores, have to satisfy resp. if such numbers can exist.

We study SLE$_{kappa}$ theory with elements of Quasi-Sure Stochastic Analysis through Aggregation. Specifically, we show how the latter can be used to construct the SLE$_{kappa}$ traces quasi-surely (i.e. simultaneously for a family of probability measures with certain properties) for $kappa in mathcal{K}cap mathbb{R}_+ setminus ([0, epsilon) cup {8})$, for any $epsilon>0$ with $mathcal{K} subset mathbb{R}_{+}$ a nontrivial compact interval, i.e. for all $kappa$ that are not in a neighborhood of zero and are different from $8$. As a by-product of the analysis, we show in this language a version of the continuity in $kappa$ of the SLE$_{kappa}$ traces for all $kappa$ in compact intervals as above.

In this work we study the dynamical behavior Tonelli Lagrangian systems defined on the tangent bundle of the torus $mathbb{T}^2=mathbb{R}^2 / mathbb{Z}^2$. We prove that the Lagrangian flow restricted to a high energy level $ E_L^{-1}(c)$ (i.e $ c> c_0(L)$) has positive topological entropy if the flow satisfies the Kupka-Smale propriety in $ E_L^{-1}(c)$ (i.e, all closed orbit with energy $c$ are hyperbolic or elliptic and all heteroclinic intersections are transverse on $E_L^{-1}(c)$). The proof requires the use of well-known results in Aubry-Mather's Theory.

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs, because of three challenges: (1) difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based in sparse linear algebra, which will allow graph algorithms to be expressed in a performant, succinct, composable and portable manner. In this paper, we examine the performance challenges of a linear algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in "GraphBLAST", the first open-source linear algebra-based graph framework on GPU targeting high-performance computing. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.

Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.

We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in groups of more than two players. To remedy this, we introduce higher-order interactions, where a link can connect more than two individuals, and study their evolutionary dynamics. We first consider a public goods game on a uniform hypergraph, showing that it corresponds to the replicator dynamics in the well-mixed limit, and providing an exact theoretical foundation to study cooperation in networked groups. We also extend the analysis to heterogeneous hypergraphs that describe interactions of groups of different sizes and characterize the evolution of cooperation in such cases. Finally, we apply our new formulation to study the nature of group dynamics in real systems, showing how to extract the actual dependence of the synergy factor on the size of a group from real-world collaboration data in science and technology. Our work is a first step towards the implementation of new actions to boost cooperation in social groups.

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

When time-dependent partial differential equations (PDEs) are solved numerically in a domain with curved boundary or on a curved surface, mesh error and geometric approximation error caused by the inaccurate location of vertices and other interior grid points, respectively, could be the main source of the inaccuracy and instability of the numerical solutions of PDEs. The role of these geometric errors in deteriorating the stability and particularly the conservation properties are largely unknown, which seems to necessitate very fine meshes especially to remove geometric approximation error. This paper aims to investigate the effect of geometric approximation error by using a high-order mesh with negligible geometric approximation error, even for high order polynomial of order p. To achieve this goal, the high-order mesh generator from CAD geometry called NekMesh is adapted for surface mesh generation in comparison to traditional meshes with non-negligible geometric approximation error. Two types of numerical tests are considered. Firstly, the accuracy of differential operators is compared for various p on a curved element of the sphere. Secondly, by applying the method of moving frames, four different time-dependent PDEs on the sphere are numerically solved to investigate the impact of geometric approximation error on the accuracy and conservation properties of high-order numerical schemes for PDEs on the sphere.

Within the field of multilinear algebra, inverses and generalized inverses of tensors based on the Einstein product have been investigated over the past few years. In this paper, we explore the singular value decomposition and full-rank decomposition of arbitrary-order tensors using {it reshape} operation. Applying range and null space of tensors along with the reshape operation; we further study the Moore-Penrose inverse of tensors and their cancellation properties via the Einstein product. Then we discuss weighted Moore-Penrose inverses of arbitrary-order tensors using such product. Following a specific algebraic approach, a few characterizations and representations of these inverses are explored. In addition to this, we obtain a few necessary and sufficient conditions for the reverse-order law to hold for weighted Moore-Penrose inverses of arbitrary-order tensors.

Participatory Design's vision of democratic participation assumes participants' feelings of agency in envisioning a collective future. But this assumption may be leaky when dealing with vulnerable populations. We reflect on the results of a series of activities aimed at supporting agentic-future-envisionment with a group of sex-trafficking survivors in Nepal. We observed a growing sense among the survivors that they could play a role in bringing about change in their families. They also became aware of how they could interact with available institutional resources. Reflecting on the observations, we argue that building participant agency on the small and personal interactions is necessary before demanding larger Political participation. In particular, a value of PD, especially for vulnerable populations, can lie in the process itself if it helps participants position themselves as actors in the larger world.

In this paper, we propose a new nonlinear detector with improved interference suppression in Multi-User Multiple Input, Multiple Output (MU-MIMO) system. The proposed detector is a combination of the following parts: QR decomposition (QRD), low complexity users sorting before QRD, sorting-reduced (SR) K-best method and minimum mean square error (MMSE) pre-processing. Our method outperforms a linear interference rejection combining (IRC, i.e. MMSE naturally) method significantly in both strong interference and additive white noise scenarios with both ideal and real channel estimations. This result has wide application importance for scenarios with strong interference, i.e. when co-located users utilize the internet in stadium, highway, shopping center, etc. Simulation results are presented for the non-line of sight 3D-UMa model of 5G QuaDRiGa 2.0 channel for 16 highly correlated single-antenna users with QAM16 modulation in 64 antennas of Massive MIMO system. The performance was compared with MMSE and other detection approaches.

Diagnosis of high impedance fault (HIF) is a challenge for nowadays distribution network protections. The fault current of a HIF is much lower than that of a normal load, and fault feature is significantly affected by fault scenarios. A detection and feeder identification algorithm for HIFs is proposed in this paper, based on the high-resolution and synchronous waveform data. In the algorithm, an interval slope is defined to describe the waveform distortions, which guarantees a uniform feature description under various HIF nonlinearities and noise interferences. For three typical types of network neutrals, i.e.,isolated neutral, resonant neutral, and low-resistor-earthed neutral, differences of the distorted components between the zero-sequence currents of healthy and faulty feeders are mathematically deduced, respectively. As a result, the proposed criterion, which is based on the distortion relationships between zero-sequence currents of feeders and the zero-sequence voltage at the substation, is theoretically supported. 28 HIFs grounded to various materials are tested in a 10kV distribution networkwith three neutral types, and are utilized to verify the effectiveness of the proposed algorithm.

Rescue vessels are the main actors in maritime safety and rescue operations. At the same time, aerial drones bring a significant advantage into this scenario. This paper presents the research directions of the AutoSOS project, where we work in the development of an autonomous multi-robot search and rescue assistance platform capable of sensor fusion and object detection in embedded devices using novel lightweight AI models. The platform is meant to perform reconnaissance missions for initial assessment of the environment using novel adaptive deep learning algorithms that efficiently use the available sensors and computational resources on drones and rescue vessel. When drones find potential objects, they will send their sensor data to the vessel to verity the findings with increased accuracy. The actual rescue and treatment operation are left as the responsibility of the rescue personnel. The drones will autonomously reconfigure their spatial distribution to enable multi-hop communication, when a direct connection between a drone transmitting information and the vessel is unavailable.

Reinforcement learning methods have been used to compute dialog policies from language-based interaction experiences. Efficiency is of particular importance in dialog policy learning, because of the considerable cost of interacting with people, and the very poor user experience from low-quality conversations. Aiming at improving the efficiency of dialog policy learning, we develop algorithm LHUA (Learning with Hindsight, User modeling, and Adaptation) that, for the first time, enables dialog agents to adaptively learn with hindsight from both simulated and real users. Simulation and hindsight provide the dialog agent with more experience and more (positive) reinforcements respectively. Experimental results suggest that, in success rate and policy quality, LHUA outperforms competitive baselines from the literature, including its no-simulation, no-adaptation, and no-hindsight counterparts.

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.

We analyze the computational complexity of motion planning through local "input/output" gadgets with separate entrances and exits, and a subset of allowed traversals from entrances to exits, each of which changes the state of the gadget and thereby the allowed traversals. We study such gadgets in the 0-, 1-, and 2-player settings, in particular extending past motion-planning-through-gadgets work to 0-player games for the first time, by considering "branchless" connections between gadgets that route every gadget's exit to a unique gadget's entrance. Our complexity results include containment in L, NL, P, NP, and PSPACE; as well as hardness for NL, P, NP, and PSPACE. We apply these results to show PSPACE-completeness for certain mechanics in Factorio, [the Sequence], and a restricted version of Trainyard, improving prior results. This work strengthens prior results on switching graphs and reachability switching games.

We consider generic optimal Bayesian inference, namely, models of signal reconstruction where the posterior distribution and all hyperparameters are known. Under a standard assumption on the concentration of the free energy, we show how replica symmetry in the strong sense of concentration of all multioverlaps can be established as a consequence of the Franz-de Sanctis identities; the identities themselves in the current setting are obtained via a novel perturbation of the prior distribution of the signal. Concentration of multioverlaps means that asymptotically the posterior distribution has a particularly simple structure encoded by a random probability measure (or, in the case of binary signal, a non-random probability measure). We believe that such strong control of the model should be key in the study of inference problems with underlying sparse graphical structure (error correcting codes, block models, etc) and, in particular, in the derivation of replica symmetric formulas for the free energy and mutual information in this context.

Despite the improvements in helmet technology, helmets may prevent skull fractures, but they do not prevent concussions.

Competition is steep in games like football. The desire to win often trumps safety.

The decisions coaches make on the sidelines about returning a concussed player to the game or not can be a "game changer" for that athlete's life.

Research is showing that the accumulation of sub-concussive hits in sports like football can be just as damaging as one or two major concussions.

For many competitive high school football players like Garrett Harper, the intensity of this contact sport has its price.

The issues of sports-related concussions and chronic traumatic encephalopathy were intensified when the brain of a deceased 21-year-old football player was examined.

Why do some parents and coaches think it's okay to let 9-year-old kids get hit in the head over and over in football practices and games?

Bob Duncan talks about what happened to his son when he returned to college and to his midterm exams only 24 hours after his concussion.

The Cannabis Issue People have been enjoying cannabis for recreational purposes for centuries, including in the United States since the early 1900s. That means weed was in America a good 50 years or so before the invention of rock 'n' roll.…

The Cannabis Issue It's been almost a month since the COVID-19 pandemic forced folks inside and made "social distancing" part of our daily lexicons.…

How the coronavirus outbreak has upended people's lives across the Inland Northwest The numbers don't lie.…

Monty Spears didn't know it at the time, but the last time he'd see his father would be through the window at the Life Care Center of Lewiston.…

[IMAGE-1] I started sobbing from the opening moments of A Beautiful Day in the Neighborhood, and I didn't stop crying for two hours. And then after I left the cinema and ran into a fellow film critic who had also just seen it, I literally could not manage a word of discussion without bursting into tears again.…

[IMAGE-1] Watching Rian Johnson's Knives Out, I was reminded of my middle school English teacher Mrs. Soderbergh, who loved Agatha Christie books almost as much as she loved diagramming sentences. There was a week when she brought in a box stacked high with her own Christie paperbacks, set it down in front of the classroom and had each of us pick a book based solely on the plot summary on the back.…