VAEs consist of two main parts: a content Voice conversion (VC) modifies voice characteristics while encoder and a decoder. The content encoder processes source preserving linguistic content. This paper presents the Stepback speech, transforms it into a latent representation, and removes network, a novel model for converting speaker identity using speaker information. The decoder takes the speaker identity, non-parallel data. Unlike traditional VC methods that rely on combines it with the latent representation, and reconstructs the parallel data, our approach leverages deep learning techniques speech[5]. A notable VAE approach is disentangling speaker to enhance disentanglement completion and linguistic content and content representations using instance normalization, which preservation.

We study 3D point cloud attribute compression via a volumetric approach: assuming point cloud geometry is known at both encoder and decoder, parameters $\theta$ of a continuous attribute function $f: \mathbb{R}^3 \mapsto \mathbb{R}$ are quantized to $\hat{\theta}$ and encoded, so that discrete samples $f_{\hat{\theta}}(\mathbf{x}_i)$ can be recovered at known 3D points $\mathbf{x}_i \in \mathbb{R}^3$ at the decoder. Specifically, we consider a nested sequences of function subspaces $\mathcal{F}^{(p)}_{l_0} \subseteq \cdots \subseteq \mathcal{F}^{(p)}_L$, where $\mathcal{F}_l^{(p)}$ is a family of functions spanned by B-spline basis functions of order $p$, $f_l^*$ is the projection of $f$ on $\mathcal{F}_l^{(p)}$ and encoded as low-pass coefficients $F_l^*$, and $g_l^*$ is the residual function in orthogonal subspace $\mathcal{G}_l^{(p)}$ (where $\mathcal{G}_l^{(p)} \oplus \mathcal{F}_l^{(p)} = \mathcal{F}_{l+1}^{(p)}$) and encoded as high-pass coefficients $G_l^*$. In this paper, to improve coding performance over [1], we study predicting $f_{l+1}^*$ at level $l+1$ given $f_l^*$ at level $l$ and encoding of $G_l^*$ for the $p=1$ case (RAHT($1$)). For the prediction, we formalize RAHT(1) linear prediction in MPEG-PCC in a theoretical framework, and propose a new nonlinear predictor using a polynomial of bilateral filter. We derive equations to efficiently compute the critically sampled high-pass coefficients $G_l^*$ amenable to encoding. We optimize parameters in our resulting feed-forward network on a large training set of point clouds by minimizing a rate-distortion Lagrangian. Experimental results show that our improved framework outperformed the MPEG G-PCC predictor by $11$ to $12\%$ in bit rate reduction.

Tracy Chou launched the Twitter app Block Party in 2021 to help users escape targeted harassment campaigns that she--as an Asian American woman--knew from personal experience could ostracize vulnerable voices from the public conversation. But on Wednesday Block Party closed its doors, becoming the latest victim of soaring new bills imposed by a struggling Twitter under new owner Elon Musk. Under Twitter's former ownership, Chou struck a deal with the company for free access to data--a win-win arrangement that would allow Block Party to grow and provide Twitter with a valuable anti-harassment tool to which it didn't have to devote expensive engineering time. But Chou tells TIME that following the recent expiration of that contract, Twitter wanted Block Party to pay $42,000 per month for access to enough data to keep the app running. There was no way Block Party could afford the figure, she says.

After a pandemic-driven cloud adoption boom in the enterprise, costs are finally coming under a microscope. More than a third of businesses report having cloud budget overruns of up to 40%, according to a recent poll by observability software vendor Pepperdata. A separate survey from Flexera found that optimizing the existing use of cloud services is a top initiative at 59% of companies -- cost being the main motivation. An entire cottage industry of startups has sprung up around optimizing cloud compute. But one in the race, Sync Computing, claims to uniquely tie business objectives like cost and runtime reduction directly to low-level infrastructure configurations.

It feels like DevOps principles are permeating every discipline, creating new buzzwords by the minute. This "JargonOps" is clearly encouraged by marketing campaigns (and bloggers, wink, wink). Yet, the phrases do depict a real trend: all industries are getting an efficiency overhaul in the wake of increased automation. As I've covered before, low-code and no-code tools lower the barrier to entry to application development, enabling field experts to construct workflows as they see fit. For tech-savvy non-engineers, this could be a huge boon to transform copy-and-paste stopgaps into efficient workflow automations.

Sean Chou thinks robots are stupid. "All you have to do is type in'YouTube robot fail,' says Chou, CEO of Chicago-based AI startup Catalytic. Here, we'll make it easier: click to see robots fail. And even though they're getting smarter all the time and serving industry in novel ways, Chou is firm in his belief that "we're pretty far from'Terminator.'" It's that they're rising much more slowly than some of the more breathless media coverage might have you believe -- which is great news for most of those who think robots and other AI-powered technology will soon steal their jobs. The consensus among many experts is that a number of professions will be totally automated in the next five to 10 years. A group of senior-level tech executives who comprise the Forbes Technology Council named 13, including insurance underwriting, warehouse and manufacturing jobs, customer service, research and data entry, long haul trucking and a somewhat disconcertingly broad category titled "Any Tasks That ...

For some people, a single event can be a missed opportunity to build wealth. That opportunity comes at the moment of exiting a company, and whether or not that individual exercises their stock options. M&A events, IPOs or even investment rounds are typically disclosed in huge packets of printed material. One of the key points of disclosure was the schedule of exceptions. In many ways, these were full of warnings for buyers or investors, copies of canceled options agreements, etc. Businesses published these for many reasons, a small one being protection against employees who left and didn't exercise.

The World Economic Forum says technologies like artificial intelligence (AI) will displace 75 million jobs by 2022 but will also create 133 million new roles. To prepare workers for these new jobs, organizations will have to provide significant resources for upskilling their workforces. And employees will need to take personal responsibility for their career development in a context of rapid technological change. How can HR professionals prepare employees and organizations for a present and future where AI is increasingly working with humans to drive business outcomes? "HR professionals need to begin by shifting their mindsets about AI," said Jeff Schwartz, a principal with Deloitte Consulting.

DARQ stands for Distributed ledger technology (DLT), Artificial intelligence (AI), extended Reality and Quantum computing. Though some of these technologies are on the horizon, some are already impacting industry. "DARQ is a set of emerging, and highly disruptive, technologies that will be a driver of competitive advantage and key differentiator in a world where digital is everywhere," said Ari Bernstein, R&D principal at Accenture Labs, said these technologies will be an important catalyst for change. Blockchain, on the cutting edge of credentialing, will soon be a boon to recruitment, said Alex Kaplan, IBM's global leader for blockchain and AI for industry credentials. The "next step in democratizing education," the technology will allow hiring professionals a one-stop-shop to verify academic credentials and more.

Markov networks are widely used in many Machine Learning applications including natural language processing, computer vision, and bioinformatics . Learning Markov networks have many complications ranging from intractable computations involved to the possibility of learning a model with a huge number of parameters. In this report, we provide a computationally tractable greedy heuristic for learning Markov networks structure. The proposed heuristic results in a model with a limited predefined number of parameters. We ran our method on 3 fully-observed real datasets, and we observed that our method is doing comparably good to the state of the art methods.