Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A. Collider data must be corrected for detector effects ("unfolded") to be compared with theoretical calculations and measurements from other experiments. Unfolding is traditionally done for individual, binned observables without including all information relevant for characterizing the detector response. We introduce OmniFold, an unfolding method that iteratively reweights a simulated dataset, using machine learning to capitalize on all available information. Our approach is un-binned, works for arbitrarily high-dimensional data, and naturally incorporates information from the full phase space. We illustrate this technique on a realistic jet substructure example from the Large Hadron Collider and compare it to standard binned unfolding methods. This new paradigm enables the simultaneous measurement of all observables, including those not yet invented at the time of the analysis.

Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully-connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN have three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.

The transfer learning toolkit wraps the codes of 17 transfer learn ing models and provides integrated interfaces, allowing users to use those models by calling a simple function. It is easy for primary researchers to use this toolkit and to choose proper models for real-world applica tions. The toolkit is written in Python and distributed under MIT open source license. In this pape r, the current state of this toolkit is described and the necessary environment setting and usage are in troduced. Keywords: Transfer Learning, Toolkit 1. Introduction Transfer learning is a promising and important direction in machine lear ning, which attempts to leverage the knowledge contained in a source domain to improve the le arning performance or minimize the number of labeled samples required in a target domain.

ABSTRACT In this paper, we present a new open source toolkit for automatic speech recognition (ASR), named CA T (CRF-based ASR Toolkit). A key feature of CA T is discriminative training in the framework of conditional random field (CRF), particularly with connectionist temporal classification (CTC) inspired state topology. CA T contains a full-fledged implementation of CTC-CRF and provides a complete workflow for CRF-based end-to-end speech recognition. Evaluation results on Chinese and English benchmarks such as Switchboard and Aishell show that CA T obtains the state-of-the-art results among existing end-to-end models with less parameters, and is competitive compared with the hybrid DNN-HMM models. Towards flexibility, we show that i-vector based speaker-adapted recognition and latency control mechanism can be explored easily and effectively in CA T. We hope CA T, especially the CRF-based framework and software, will be of broad interest to the community, and can be further explored and improved. Index T erms-- speech recognition, open source toolkit, conditional random field, end-to-end 1. INTRODUCTION In addition to theories and algorithms, open source toolkits make substantial contributions to automatic speech recognition (ASR) technologies.

Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance. Enriching the trust between these systems and people will accelerate this integration process. Many medical and retail banking/finance applications use state-of-the-art machine learning techniques to predict certain aspects of new instances. Tree ensembles, like random forests, are widely acceptable solutions on these tasks, while at the same time they are avoided due to their black-box uninterpretable nature, creating an unreasonable paradox. In this paper, we provide a sequence of actions for shedding light on the predictions of the misjudged family of tree ensemble algorithms. Using classic unsupervised learning techniques and an enhanced similarity metric, to wander among transparent trees inside a forest following breadcrumbs, the interpretable essence of tree ensembles arises. An explanation provided by these systems using our approach, which we call "LionForests", can be a simple, comprehensive rule.

Reasoning semantically in first-order logic is notoriously a challenge. This paper surveys a selection of semantically-guided or model-based methods that aim at meeting aspects of this challenge. For first-order logic we touch upon resolution-based methods, tableaux-based methods, DPLL-inspired methods, and we give a preview of a new method called SGGS, for Semantically-Guided Goal-Sensitive reasoning. For first-order theories we highlight hierarchical and locality-based methods, concluding with the recent Model-Constructing satisfiability calculus.

Omar Costilla Reyes reels off all the ways that artificial intelligence might benefit his native Mexico. It could raise living standards, he says, lower health care costs, improve literacy and promote greater transparency and accountability in government. But Mexico, like many of its Latin American neighbors, has failed to invest as heavily in AI as other developing countries. That worries Costilla Reyes, a postdoc at MIT's Department of Brain and Cognitive Sciences. To give the region a nudge, Costilla Reyes and three other MIT graduate students -- Guillermo Bernal, Emilia Simison and Pedro Colon-Hernandez -- have spent the last six months putting together a three-day event that will bring together policymakers and AI researchers in Latin America with AI researchers in the United States. The AI Latin American sumMIT will take place in January at the MIT Media Lab.

"Death Stranding" earned the most nominations in the Game Awards, which will be given out Dec. 12 in Los Angeles. The new game from famed designer Hideo Kojima ("Metal Gear Solid") collected nine nominations, including Game of the Year, Best Action/Adventure Game, Best Game Direction, Best Art Direction and Best Score. Actors Norman Reedus and Mads Mikkelsen, who portray Sam Porter Bridges and Cliff, respectively, in the PlayStation 4 game, earned nominations for Best Performance. They were joined by Ashly Burch as Parvati Holcomb in "The Outer Worlds," Courtney Hope as Jesse Faden in "Control," Laura Bailey as Kait Diaz in "Gears 5" and Matthew Porretta as Dr. Casper Darling in "Control." Considered the Oscars of the video game industry, the Game Awards began in 2014, established by longtime video game journalist Geoff Keighley.

The Nazca lines of Peru have fascinated archaeologists for centuries and now scientists say they have discovered 143 new drawings previously hidden in the soil. Featuring hundreds of pictures of animals, plants and patterns, the miles of figurative drawings are only visible from nearby clifftops and the air. Researchers from Yamagata University in Japan spent years searching through high-resolution images of the lines taken from space and studying them on site in Peru. This led to the discovery of previously unknown figures depicting a range of living creatures including birds, monkeys, fish, reptiles and humanoid characters. Researchers worked with IBM's Artificial Intelligence team to discover a humanoid character within the soil (pictured) Known as geoglyphs, they were created about 2,000 years ago, at the same time as the previously discovered lines and are between 16ft and 320ft across.

Throughout her life, Eileen Lach has been a leader. Growing up in northeast Minneapolis, she knew at a young age she wanted to move to New York City and travel the world. Lach accomplished that and more, carving out a position on Wall Street early in her career and later serving as the first general counsel and chief compliance officer for The Institute of Electrical and Electronics Engineers (IEEE). She is considered a thought leader, especially in the area of ethics and artificial intelligence (AI). During a conversation with Lach last summer, it was hard not to be wowed by her achievements and admire her dedication to philanthropic causes.