By Hannah Hickey UWNEWS While winter sea ice in the Arctic is declining so dramatically that ships can now navigate those waters without any icebreaker escort, the scene in the Southern Hemisphere is very different. Sea ice area around Antarctica … Continue reading →

By Jessica Corbett Common Dreams “The most robust study of the ice mass balance of Antarctica to date,” scientists say, “now puts Antarctica in the frame as one of the largest contributors to sea-level rise.” Scientists are expressing alarm over … Continue reading →

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.

If you compare the UX research methods we use today with the methods we used 16 years ago, something interesting emerges. We see that UX research is becoming increasingly remote and increasingly unmoderated. In other words, we're moving to a world where UX research is becoming automated. We can learn a lot from automated research. But it comes at the price of understanding our users.

When carried out in a lab, user experience research is gear heavy. You need technology to record audio, video and the screen of the device under test. In contrast, when carried out in the field, user experience research is more lightweight. Even so, there are a few non-obvious items of kit that I find essential on a field visit.

Doing UX research in a university is very different to doing UX research in a business setting. If you're an academic making the leap, what are the main differences you need to keep in mind?

Whether you call it a field visit, a contextual inquiry or a customer discovery interview, the goal of early stage research is the same: to uncover users' needs. Here are 5 mistakes I've seen crop up time and again in this kind of research.

Robots operating in household environments must find objects on shelves, under tables, and in cupboards. Previous work often formulate the object search problem as a POMDP (Partially Observable Markov Decision Process), yet constrain the search space in 2D. We propose a new approach that enables the robot to efficiently search for objects in 3D, taking occlusions into account. We model the problem as an object-oriented POMDP, where the robot receives a volumetric observation from a viewing frustum and must produce a policy to efficiently search for objects. To address the challenge of large state and observation spaces, we first propose a per-voxel observation model which drastically reduces the observation size necessary for planning. Then, we present a novel octree-based belief representation which captures beliefs at different resolutions and supports efficient exact belief update. Finally, we design an online multi-resolution planning algorithm that leverages the resolution layers in the octree structure as levels of abstractions to the original POMDP problem. Our evaluation in a simulated 3D domain shows that, as the problem scales, our approach significantly outperforms baselines without resolution hierarchy by 25%-35% in cumulative reward. We demonstrate the practicality of our approach on a torso-actuated mobile robot searching for objects in areas of a cluttered lab environment where objects appear on surfaces at different heights.

Deep neural networks have exhibited promising performance in image super-resolution (SR). Most SR models follow a hierarchical architecture that contains both the cell-level design of computational blocks and the network-level design of the positions of upsampling blocks. However, designing SR models heavily relies on human expertise and is very labor-intensive. More critically, these SR models often contain a huge number of parameters and may not meet the requirements of computation resources in real-world applications. To address the above issues, we propose a Hierarchical Neural Architecture Search (HNAS) method to automatically design promising architectures with different requirements of computation cost. To this end, we design a hierarchical SR search space and propose a hierarchical controller for architecture search. Such a hierarchical controller is able to simultaneously find promising cell-level blocks and network-level positions of upsampling layers. Moreover, to design compact architectures with promising performance, we build a joint reward by considering both the performance and computation cost to guide the search process. Extensive experiments on five benchmark datasets demonstrate the superiority of our method over existing methods.

For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points. We propose an approach based on log barriers to find a local solution of a non-convex non-smooth black-box optimization problem $min f^0(x)$ subject to $f^i(x)leq 0,~ i = 1,ldots, m$, at the same time, guaranteeing constraint satisfaction while learning an optimal solution with high probability. Our proposed algorithm exploits noisy observations to iteratively improve on an initial safe point until convergence. We derive the convergence rate and prove safety of our algorithm. We demonstrate its performance in an application to an iterative control design problem.

Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

This work deals with the efficient numerical solution of the time-fractional heat equation discretized on non-uniform temporal meshes. Non-uniform grids are essential to capture the singularities of "typical" solutions of time-fractional problems. We propose an efficient space-time multigrid method based on the waveform relaxation technique, which accounts for the nonlocal character of the fractional differential operator. To maintain an optimal complexity, which can be obtained for the case of uniform grids, we approximate the coefficient matrix corresponding to the temporal discretization by its hierarchical matrix (${cal H}$-matrix) representation. In particular, the proposed method has a computational cost of ${cal O}(k N M log(M))$, where $M$ is the number of time steps, $N$ is the number of spatial grid points, and $k$ is a parameter which controls the accuracy of the ${cal H}$-matrix approximation. The efficiency and the good convergence of the algorithm, which can be theoretically justified by a semi-algebraic mode analysis, are demonstrated through numerical experiments in both one- and two-dimensional spaces.

Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain. In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier. We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples. This not only makes the classifier more robust but also boosts the overall ranking performance. Our model achieves a relative gain compared to baselines by over 26% in F-score, and over 17% in Area Under PR curve. On live search traffic, our model gains significant improvement in multiple countries.

Passive optical networks (PON) can play a vital role in data centres and access fog solutions by providing scalable, cost and energy efficient architectures. This paper proposes a Mixed Integer Linear Programming (MILP) model to optimize the placement of virtual machines (VMs) over an energy efficient WDM-TDM AWGR PON based data centre architecture. In this optimization, the use of VMs and their requirements affect the optimum number of servers utilized in the data centre when minimizing the power consumption and enabling more efficient utilization of servers is considered. Two power consumption minimization objectives were examined for up to 20 VMs with different computing and networking requirements. The results indicate that considering the minimization of the processing and networking power consumption in the allocation of VMs in the WDM-TDM AWGR PON can reduce the networking power consumption by up to 70% compared to the minimization of the processing power consumption.

This paper proposes a faceted information exploration model that supports coarse-grained and fine-grained focusing of geographic maps by offering a graphical representation of data attributes within interactive widgets. The proposed approach enables (i) a multi-category projection of long-lasting geographic maps, based on the proposal of efficient facets for data exploration in sparse and noisy datasets, and (ii) an interactive representation of the search context based on widgets that support data visualization, faceted exploration, category-based information hiding and transparency of results at the same time. The integration of our model with a semantic representation of geographical knowledge supports the exploration of information retrieved from heterogeneous data sources, such as Public Open Data and OpenStreetMap. We evaluated our model with users in the OnToMap collaborative Web GIS. The experimental results show that, when working on geographic maps populated with multiple data categories, it outperforms simple category-based map projection and traditional faceted search tools, such as checkboxes, in both user performance and experience.

With the continued digitalization of societal processes, we are seeing an explosion in available data. This is referred to as big data. In a research setting, three aspects of the data are often viewed as the main sources of challenges when attempting to enable value creation from big data: volume, velocity and variety. Many studies address volume or velocity, while much fewer studies concern the variety. Metric space is ideal for addressing variety because it can accommodate any type of data as long as its associated distance notion satisfies the triangle inequality. To accelerate search in metric space, a collection of indexing techniques for metric data have been proposed. However, existing surveys each offers only a narrow coverage, and no comprehensive empirical study of those techniques exists. We offer a survey of all the existing metric indexes that can support exact similarity search, by i) summarizing all the existing partitioning, pruning and validation techniques used for metric indexes, ii) providing the time and storage complexity analysis on the index construction, and iii) report on a comprehensive empirical comparison of their similarity query processing performance. Here, empirical comparisons are used to evaluate the index performance during search as it is hard to see the complexity analysis differences on the similarity query processing and the query performance depends on the pruning and validation abilities related to the data distribution. This article aims at revealing different strengths and weaknesses of different indexing techniques in order to offer guidance on selecting an appropriate indexing technique for a given setting, and directing the future research for metric indexes.

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.

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.

Despite recent progress on computer vision and natural language processing, developing video understanding intelligence is still hard to achieve due to the intrinsic difficulty of story in video. Moreover, there is not a theoretical metric for evaluating the degree of video understanding. In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. The DramaQA focused on two perspectives: 1) hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) character-centered video annotations to model local coherence of the story. Our dataset is built upon the TV drama "Another Miss Oh" and it contains 16,191 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels. We provide 217,308 annotated images with rich character-centered annotations, including visual bounding boxes, behaviors, and emotions of main characters, and coreference resolved scripts. Additionally, we provide analyses of the dataset as well as Dual Matching Multistream model which effectively learns character-centered representations of video to answer questions about the video. We are planning to release our dataset and model publicly for research purposes and expect that our work will provide a new perspective on video story understanding research.

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.

The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture frame-level salient aspects through attention but they lack the capacity to effectively map the temporal relationships in between the frames as they only capture a limited span of temporal dependencies. To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time, thus improving the overall segmentation performance. The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales, to form embeddings at frame level and segment level, and perform fine-grained action segmentation. This generates a simple, lightweight, yet extremely effective architecture for segmenting continuous video streams and has multiple application domains. We evaluate our system on multiple challenging public benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech Egocentric datasets, and achieves state-of-the-art performance. The evaluated datasets encompass numerous video capture settings which are inclusive of static overhead camera views and dynamic, ego-centric head-mounted camera views, demonstrating the direct applicability of the proposed framework in a variety of settings.

In this paper, we continue our prior work on using imitation learning (IL) and model free reinforcement learning (RL) to learn driving policies for autonomous driving in urban scenarios, by introducing a model based RL method to drive the autonomous vehicle in the Carla urban driving simulator. Although IL and model free RL methods have been proved to be capable of solving lots of challenging tasks, including playing video games, robots, and, in our prior work, urban driving, the low sample efficiency of such methods greatly limits their applications on actual autonomous driving. In this work, we developed a model based RL algorithm of guided policy search (GPS) for urban driving tasks. The algorithm iteratively learns a parameterized dynamic model to approximate the complex and interactive driving task, and optimizes the driving policy under the nonlinear approximate dynamic model. As a model based RL approach, when applied in urban autonomous driving, the GPS has the advantages of higher sample efficiency, better interpretability, and greater stability. We provide extensive experiments validating the effectiveness of the proposed method to learn robust driving policy for urban driving in Carla. We also compare the proposed method with other policy search and model free RL baselines, showing 100x better sample efficiency of the GPS based RL method, and also that the GPS based method can learn policies for harder tasks that the baseline methods can hardly learn.

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.

Calling all fearless freaks! Mark your calendars: The Flaming Lips have rescheduled their now-canceled April gig at the Fox Theater for March 19, 2021.…

Methods and arrangements for conducting a search using query logs. A query log is consulted and query rewrite rules are learned automatically based on data in the query log. The learning includes obtaining click-through data present in the query log.

Methods, systems, and computer-readable media for providing topical search suggestions are provided. Topical search suggestions allow a user to receive search results related to the designated topic or subject matter. The present invention may generate multiple topics based on search input provided by a user. The search input may be a search prefix that includes one or more words entered into the search query box before the completed search query is submitted to the search engine. A search interface then presents the topics derived from the search prefix to a user before the user submits the query. In another embodiment, the user designates multiple search inputs. The present invention generates search results based on the search inputs and then presents topics extracted from the search results. In one embodiment, the topics are extracted by performing a natural language analysis of search result metadata.

Data is compressed using content addressable memory without disruption despite error using a plurality of content addressable memories to detect sequentially repeating data elements of the data. Compression information is generated for each sequence of repeating data elements that repeat for at least a compression threshold without any one of the plurality of content addressable memories generating an indication of an error for a matching content addressable memory entry. Individual data elements are output for each of the data elements that do not repeat for the compression threshold. Compression information is generated for each sequence of repeating data elements that repeat for at least the compression threshold and then generating a currently searched data element that matches the repeating data elements when any one of the plurality of content addressable memories generates an indication of an error for a content addressable memory entry that matches the currently searched data element.

A two step process for the destruction of a precursor material using a steam plasma in a three zone reactor wherein the precursor material is hydrolyzed as a first step in the high temperature zone of the reactor, followed by a second step of medium temperature oxidation of the reactant stream in the combustion zone of the reactor where combustion oxygen or air is injected and immediate quenching of the resulting gas stream to avoid the formation of unwanted by-products. A related apparatus includes a non transferred direct current steam plasma torch, an externally cooled three zone steam plasma reactor means for introducing the precursor material into the plasma plume of the plasma torch, means for introducing the combustion air or oxygen into the combustion zone, means for exiting the reactant mixture from the reactor and means for quenching the reactant mixture located at the exit end of the reactor.

Efficient computation of complex multiplication results and very efficient fast Fourier transforms (FFTs) are provided. A parallel array VLIW digital signal processor is employed along with specialized complex multiplication instructions and communication operations between the processing elements which are overlapped with computation to provide very high performance operation. Successive iterations of a loop of tightly packed VLIWs are used allowing the complex multiplication pipeline hardware to be efficiently used. In addition, efficient techniques for supporting combined multiply accumulate operations are described.

An adaptable integrated circuit is disclosed having a plurality of heterogeneous computational elements coupled to an interconnection network. The interconnection network changes interconnections between the plurality of heterogeneous computational elements in response to configuration information. A first group of computational elements is allocated to form a first version of a functional unit to perform a first function by changing interconnections in the interconnection network between the first group of heterogeneous computational elements. A second group of computational elements is allocated to form a second version of a functional unit to perform the first function by changing interconnections in the interconnection network between the second group of heterogeneous computational elements. One or more of the first or second group of heterogeneous computational elements are reallocated to perform a second function by changing the interconnections between the one or more of the first or second group of heterogeneous computational elements.

A method of monitoring thread execution within a multicore processor architecture which comprises a plurality of interconnected processor elements for processing the threads, the method comprising receiving a plurality of thread parameter indicators of one or more parameters relating to the function and/or identity and/or execution location of a thread or threads, comparing at least one of the thread parameter indicators with a first plurality of predefined criteria each representative of an indicator of interest, and generating an output consequential upon thread parameter indicators which have been identified to be of interest as a result of the said comparison.

A microprocessor capable of running both x86 instruction set architecture (ISA) machine language programs and Advanced RISC Machines (ARM) ISA machine language programs. The microprocessor includes a mode indicator that indicates whether the microprocessor is currently fetching instructions of an x86 ISA or ARM ISA machine language program. The microprocessor also includes a plurality of model-specific registers (MSRs) that control aspects of the operation of the microprocessor. When the mode indicator indicates the microprocessor is currently fetching x86 ISA machine language program instructions, each of the plurality of MSRs is accessible via an x86 ISA RDMSR/WRMSR instruction that specifies an address of the MSR. When the mode indicator indicates the microprocessor is currently fetching ARM ISA machine language program instructions, each of the plurality of MSRs is accessible via an ARM ISA MRRC/MCRR instruction that specifies the address of the MSR.

A framework for implementing multitenant architecture is provided. The framework comprises a framework services module which is configured to provide framework services that facilitate abstraction of Software-as-a-Service (SaaS) services and crosscutting services for a Greenfield application and a non SaaS based web application. Further the abstraction results in a SaaS based multitenant web application. The framework further comprises a runtime module configured to automatically integrate and consume the framework services and APIs to facilitate monitoring and controlling of features associated with the SaaS based multitenant web application. The framework further comprises a metadata services module configured to provide a plurality of metadata services to facilitate abstraction of storage structure of metadata associated with the framework and act as APIs for managing the metadata. The framework further comprises a role based administration module that facilitates management of the metadata through a tenant administrator and a product administrator.

The present invention relates generally to client-server architectures for allowing generic upload and download functionality between a web application at a server and a client. One exemplary method includes sending a download/upload request to a web application at the server, where the download/upload request specifies at least one file to download/upload; receiving a transmission from the server; parsing the transmission to identify a download/upload command and an associated download/upload manifest, where the download/upload manifest includes executable code that, when executed on the client, will perform the download/upload of the at least one file.

A navigation system and various methods of using the system are described herein. Search query results are refined by the system and are prioritized based at least in part upon sub-search categories selected during the searching process. Sub-searches can be represented by graphical icons displayed on the user interface.

A method and apparatus for partitioning of the input design into repeating patterns called template cores for the application of reticle enhancement methods, design verification for manufacturability and design corrections for optical and process effects is accomplished by hierarchy analysis to extract cell overlap information. Also hierarchy analysis is performed to extract hierarchy statistics. Finally template core candidates are identified. This allows to the design to be made amenable for design corrections or other analyses or modifications that are able to leverage the hierarchy of the design since the cell hierarchy could otherwise be very deep or cells could have significant overlap with each other.

An elongated segmented soap bar is segmented longitudinally into a plurality of soap bodies separate and discrete from one another. Adjacent soap bodies are movable with respect to one another between at least two different configurations including at least an arc configuration with the plurality of soap bodies disposed in an arc. At least one coupler couples the plurality of soap bodies together to allow the adjacent soap bodies to move with respect to one another between the at least two different configurations.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating search results. In one aspect, a method includes obtaining a transcription of a voice query, and data that identifies an accent of the voice query, submitting the transcription and the data that identifies the accent of the voice query to a search engine to generate one or more accent-influenced results of the voice query, and providing the accent-influenced results to a client device for display.

A system and methods for matching at least one word of an utterance against a set of template hierarchies to select the best matching template or set of templates corresponding to the utterance. Certain embodiments of the system and methods determines at least one exact, inexact, and partial match between the at least one word of the utterance and at least one term within the template hierarchy to select and populate a template or set of templates corresponding to the utterance. The populated template or set of templates may then be used to generate a narrative template or a report template.

A system and method for implementing a dynamic menu characterized by a real-time menu architecture. An example method includes determining a menu structure, with reference to a data set, for use with user interface software. A menu is then displayed, wherein the menu includes menu items arranged according to the menu structure. An initial drop-down menu list of the menu includes one or more of the menu items. Each menu item is associated with a data object of the data set. A signal is generated when a change is made to a data set corresponding to a menu item. The menu is then updated based on the signal. In an illustrative embodiment, the example method further includes employing a browser to trigger implementation of the method. The browser communicates with a web server, which communicates with an application that is adapted to analyze the data set and associated data structure and provide an update in response thereto via, the signal, to the web server. The web server includes software for enabling the browser to render an updated menu or graphical representation thereof.

Disclosed is a method of preparing cationic starch. The method includes preparing a starch suspension containing a cationization agent; performing ultrahigh pressure treatment on the starch suspension; and obtaining cationic starch from the starch suspension subjected to ultrahigh pressure treatment.

An α-amylase from Bacillus subtilis (AmyE) produces significant amounts of glucose from various carbohydrate substrates, including vegetable starch, maltoheptaose, and maltotriose. Among other things, this advantageous property allows AmyE or variants thereof to be used in a saccharification reaction having a reduced or eliminated requirement for glucoamylase. The reduction or elimination of the glucoamylase requirement significantly improves the efficiency of the production of ethanol or high fructose corn syrup, for example.

The present invention provides an acrylic resin composition containing a polycrystal of colloidal particles of silicon oxide in an acrylic resin that is formed by curing an acrylic monomer liquid at room temperature and/or an acrylic oligomer liquid at room temperature, wherein a mean distance between the colloidal particles in the polycrystal is 140 to 330 nm. The size of the single crystal that constitutes the polycrystal can be controlled by adjusting the content of silicon oxide and/or the additive amount of impurities. An architectural material, a fashion accessory, and an optical material are provided that are formed by using the acrylic resin composition.

The present invention relates to methodology and constructs for modifying plant architecture and enhancing plant biomass and/or sucrose yield.

An arc melting furnace apparatus is provided which reduces an operation burden on a worker and shortens working hours. An arc melting furnace apparatus 1 includes a housing 2 having formed therein a melting chamber 2a, a hearth 4 provided within the melting chamber 2a and having a recessed portion 4a, and a heating mechanism 10 for heating and melting a metal material supplied into the recessed portion 4 to generate an alloy ingot. The apparatus comprises a turning member 23 rotatably supported on a supporting member 21 standing within the melting chamber 2a, a perimeter edge of the turning member 23 rotating and moving along the inner surface of the recessed portion 4a to lift the alloy ingot generated in the recessed portion 4a above the hearth 4 and turn it over, and a resilient turn-over assisting member 24 provided above an upper end of the recessed portion 4a. Further, the turn-over assisting member 24 is arranged to flex by a predetermined amount when the alloy ingot abuts it, and to return to its original state from the flexed state so that the alloy ingot is urged to drop into the recessed portion 4a.

A sensor system for monitoring and controlling the performance of the bottom electrode and the deflection of an electric arc in an electric steel making furnace includes an organized matrix of anode pins interspersed with refractory material and extending toward an electrically conductive plate secured to distal ends of the anode pins. A sensing device includes two temperature sensors at spaced apart locations along each of a distributed select group of anode pins for providing corresponding electrical signals and a current sensor responsive to electrical current flowing through the anode pins of the distributed select group of anode pins for providing a corresponding electrical signal. A controller responsive to the electrical signals derived at the anode pins of the select group operates the power supply and a display for monitoring the electrical performance of the elongated anode pins for heating by the electric arc in the furnace.

A roof system for an electric arc furnace includes a skew removably attached to the electric arc furnace, a lining of refractory material affixed to the skew, and a delta composed of a refractory material. The delta has at least one aperture capable of receiving an electrode. The delta fits onto and is supported by the refractory lining that is affixed to the skew.

A movable device for injecting oxygen and other technical materials into an electric arc furnace includes a housing situated above a portion of a step of a crucible and equipped with a cooling coil, an injection lance of oxygen and other technical materials, a supporting and moving system of the lance between minimum and maximum range points of the liquid bath level contained therein, positioned in the housing, an opening situated in the housing and facing the inside of the crucible in which the lance is transferably guided, and a scraping member disposed between the opening and the lance.