Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying distinct neural features between the initial and corrective phases of precise reaching using AutoLFADS.

Many initial movements require subsequent corrective movements, but how motor cortex transitions to make corrections and how similar the encoding is to initial movements is unclear. In our study, we explored how the brain's motor cortex signals both initial and corrective movements during a precision reaching task. We recorded a large population of neurons from two male rhesus macaques across multiple sessions to examine the neural firing rates during not only initial movements but also subsequent corrective movements. AutoLFADS, an auto-encoder-based deep-learning model, was applied to provide a clearer picture of neurons' activity on individual corrective movements across sessions. Decoding of reach velocity generalized poorly from initial to corrective submovements. Unlike initial movements, it was challenging to predict the velocity of corrective movements using traditional linear methods in a single, global neural space. We identified several locations in the neural space where corrective submovements originated after the initial reaches, signifying firing rates different than the baseline before initial movements. To improve corrective movement decoding, we demonstrate that a state-dependent decoder incorporating the population firing rates at the initiation of correction improved performance, highlighting the diverse neural features of corrective movements. In summary, we show neural differences between initial and corrective submovements and how the neural activity encodes specific combinations of velocity and position. These findings are inconsistent with assumptions that neural correlations with kinematic features are global and independent, emphasizing that traditional methods often fall short in describing these diverse neural processes for online corrective movements.Significance Statement We analyzed submovement neural population dynamics during precision reaching. Using an auto-encoder-based deep-learning model, AutoLFADS, we examined neural activity on a single-trial basis. Our study shows distinct neural dynamics between initial and corrective submovements. We demonstrate the existence of unique neural features within each submovement class that encode complex combinations of position and reach direction. Our study also highlights the benefit of state-specific decoding strategies, which consider the neural firing rates at the onset of any given submovement, when decoding complex motor tasks such as corrective submovements.
On Computational Complexity of Basic Decision Problems of Finite Tree Automata

This report focuses on the following basic decision problems of nite tree au-tomata: nonemptiness and intersection nonemptiness. There is a comprehensive proof of EXPTIME-completeness of the intersection nonemptiness problem, and it is shown that the nonemptiness problem is P-complete. A notion of succinct-ness is considered with respect to which the intersection nonemptiness problem is in fact a succinct version of the nonemptiness problem. The report includes a short survey of closely related problems which shows that there is a rule of thumb: if a decision problem for (deterministic) nite automata is complete for a certain space complexity then the same decision problem for (deterministic) nite tree automata is complete for the corresponding alternating space complexity , but alternating space is precisely deterministic time, only one exponential higher.
On boundary recognition without location information in wireless sensor networks

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This article tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. We explain the challenges of accurately defining the boundary of a wireless sensor network with and without node positions and provide a new definition of network boundary in the discrete domain. Our solution for boundary recognition approximates the boundary of the sensor network by determining the majority of inner nodes using geometric constructions, which guarantee that for a given d, a node lies inside of the construction for a d-quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We present a fully distributed algorithm for boundary recognition based on these concepts and perform a detailed complexity analysis. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.
Threshold values of characteristics of atmosphere instability during thunderstorms

The peculiarities of the development of strong convection in the atmosphere of Western Siberia, Gorny Altay and the Far East were studied on grounds of some characteristics. First of all, it is dynamics of parameters describing temperature stratification of the atmosphere up to the height of 5 km. The integrated characteristics, which, in addition to temperature stratification, include the estimation of humidity of different atmospheric layers, make up the second group. The third group of characteristics is indices evaluating the energy of convective potential of atmosphere. The forth group comprises indices describing vertical and horizontal flows in the atmosphere. The threshold values of some indices at which the thunderstorms are formed are defined.
Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety

We present a framework and empirical estimates that quantify the economic impact of increased product variety made available through electronic markets. Recent research has focused on the effect of increased competition on Internet market efficiency. While these efficiency gains significantly enhance consumer welfare, for instance by leading to lower average selling prices, our present research shows that increased product variety made available through electronic markets can be a significantly larger source of consumer welfare gains. One reason for increased product variety on the Internet is the ability of online retailers to stock, display, and sell a large number of products. For example, the number of book titles available at Amazon.com is over 18 times larger than the number at a typical Barnes & Noble superstore, and 45 times greater than the number of books available at a typical large independent bookstore. Our analysis indicates that the increased product variety of online bookstores enhanced consumer welfare by $756 million to $971 million dollars in the year 2000, which is at least five times as large as the consumer welfare gain from increased competition and lower prices in this market. There may also be large welfare gains in other SKU-intensive consumer goods such as music, movies, consumer electronics, and computer software and hardware.
RFID-Based Facility Maintenance at Frankfurt Airport

Frankfurt airport's operating company, Fraport AG, integrated RFID and a mobile application with its asset management systems. The benefits include better planning, control, and documentation of technicians' work as well as improved process quality. This article summarizes not only the technology Fraport tested at Frankfurt airport but also the process-flow and facility-management changes it adopted.
Semantic Enablement for Spatial Data Infrastructures

Building on abstract reference models, the Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information. These standards act as a basis for the implementation of specific services and Spatial Data Infrastructures (SDI). Research on geo‐semantics plays an increasing role to support complex queries and retrieval across heterogeneous information sources, as well as for service orchestration, semantic translation, and on‐the‐fly integration. So far, this research targets individual solutions or focuses on the Semantic Web, leaving the integration into SDI aside. What is missing is a shared and transparent Semantic Enablement Layer for SDI which also integrates reasoning services known from the Semantic Web. Instead of developing new semantically enabled services from scratch, we propose to create profiles of existing services that implement a transparent mapping between the OGC and the Semantic Web world. Finally, we point out how to combine SDI with linked data.
On-Chip Communication Architecture Exploration for Processor-Pool-based MPSoC

MPSoC is evolving towards processor-pool (PP)-based architectures, which employ hierarchical on-chip network for interand intra-PP communication. Since the design space of PPbased MPSoC is extremely wide, application-specific optimization of on-chip communication is a nontrivial task. This paper presents a systematic methodology for on-chip network design of PP-based MPSoC. The proposed approach allows independent configurations of PPs, which leads to efficient solutions than previous work. Since time-consuming simulation is inevitable to evaluate complicated on-chip network during exploration, we do early pruning of design space by a bandwidth analysis technique that considers task execution dependencies. Our approach yields the Pareto-optimal solutions between clock frequency and area requirements. The experiments show that the proposed technique finds more efficient architectures compared with the previous approaches.
Modeling Carbon Fluxes Using Multi-Temporal MODIS Imagery and CO2 Eddy Flux Tower Data in Zoige Alpine Wetland, South-West China

An accurate and synoptic quantification of gross primary production (GPP) in wetland ecosystems is essential for assessing carbon budgets at regional or global scales. In this study, a satellite-based Vegetation Photosynthesis Model (VPM) integrated with observed eddy tower and remote sensing data was employed and adapted to evaluate the feasibility and dependability of the model for estimating GPP in an alpine wetland, located in Zoige, Southwestern China. Eddy flux data from 2-year observations showed that temperature explained most of the seasonal variability in carbon fluxes and that warming increased GPP and ecosystem respiration, and hence affected the carbon balance of alpine wetlands. The comparison between modeled and observed GPP fluxes indicated that simulated values were largely in agreement with tower-based values ( P <0.0001). 12-year long-term simulations (2000 – 2011) found that (1) there was significantly increasing trends at rate of 17.01 gCm − 2 year − 1 for annual GPP ( R 2 =0.62, P =0.002); (2) the inter-annual variation in GPP was highly sensitive to climate warming; and (3) a warmer climate can prolong the plant growing season and, by that, increase wetland productivity. Our results demonstrated that the satellite-driven VPM model has the potential to be applied at large spatial and temporal scales for scaling-up carbon fluxes of alpine wetlands.
Tractability of interpretability via selection of group-sparse models

Group-based sparsity models [1], [2] are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. A promise of these models is to lead to “interpretable” signals for which we identify its constituent groups, however we show that, in general, claims of correctly identifying the groups with convex relaxations would lead to polynomial time solution algorithms for an NP-hard problem. Instead, leveraging a graph-based understanding of group models, we describe group structures which enable correct model identification in polynomial time via dynamic programming. We also show that group structures that lead to totally unimodular constraints have tractable relaxations. Finally, we highlight the non-convexity of the Pareto frontier of group-sparse approximations.
Monitoring tropical peatland ecosystem in regional scale using multi-temporal MODIS data: Present possibilities and future challenges

Many studies on peatland ecosystem have been focused on forest conversion or forest degradation as a single pathway, meanwhile; in the context of peatland ecosystem, the change in land surface is more complicated since it can be categorized into two types and mechanisms: 1) gradual change, caused by interannual climate variability and forestland degradation, and 2) abrupt change, caused by disturbances such as deforestation and wildfires. Understanding this change types is needed for conservation and management, particularly to improve understanding of terrestrial environmental change in peatland ecosystem. In such situation, simultaneous analysis of land surface attributes from long-term datasets and seasonal variation seems to be a way to monitor the tropical peatland ecosystem. This analysis provides information about how the changes occurred accurately as well as how big are these affected areas. In this article, the feasibility of using long-term MODIS data for monitoring the dynamics change in peatland ecosystem is examined. The temporal vegetation dynamics of long-term MODIS datasets offer great promise for characterizing gradual change as well as abrupt change at large scale, however, the mixed pixel issue and some residual noises in temporal sequences are quite problematic when using MODIS data.
Towards a reconfigurable hardware architecture for implementing a LDPC module suitable for software radio systems

Forward Error Correction is a key piece in modern digital communications. When a signal is transmitted over a noisy channel, multiple errors are generated. FEC techniques are directed towards the recovery of such errors. In last years, LDPC (Low Density Parity Check) codes have attracted attention of researchers because of their excellent error correction capabilities, but for actual radios high performance is not enough since they require to communicate with other multiple radios too. In general, communication between multiple radios requires the use of different standards. In this sense, Software Defined Radio (SDR) approach allows building multi standard radios based on reconfigurability abilities which means that base components including recovery errors block must provide reconfigurable options. In this paper, some open problems in designing and implementing reconfigurable LDPC components are presented and discussed. Some features of works in the state of the art are commented and possible research lines proposed.
Colour, usability and security: a case study

The use of colour in user interfaces is extensive. It is typically a usability issue, and has rarely caused any security concerns. In this ar ticle, we show that the use of colours in the design of CAPTCHA, a standard security technology that has found widespread applications in commercial websites, can have inter esting but critical implications on both security and usability.
LISFLOOD: a GIS‐based distributed model for river basin scale water balance and flood simulation

In this paper we describe the spatially distributed LISFLOOD model, which is a hydrological model specifically developed for the simulation of hydrological processes in large European river basins. The model was designed to make the best possible use of existing data sets on soils, land cover, topography and meteorology. We give a detailed description of the simulation of hydrological processes in LISFLOOD, and discuss how the model is parameterized. We also describe how the model was implemented technically using a combination of the PCRaster GIS system and the Python programming language, and discuss the management of in‐ and output data. Finally, we review some recent applications of LISFLOOD, and we present a case study for the Elbe river.
Title : An Object Oriented Shared Data Model for GIS and Distributed Hydrologic Models

Distributed physical models for the space-time distribution of water, energy, vegetation, and mass flow require new strategies for data representation, model domain decomposition, a-priori parameterization, and visualization. The Geographic Information System (GIS) has been traditionally used to accomplish these data management functionalities in hydrologic applications. However, the interaction between the data management tools and the physical model are often loosely integrated and non-dynamic. This is because a) the data types, semantics, resolutions and formats for the physical model system and the distributed data or parameters may be different, with significant data preprocessing required before they can be shared, b) the management tools may not be accessible or shared by the GIS and physical model c) the individual systems may be operating system dependent or are driven by proprietary data structures. The impediment to seamless data flow between the two software components has the effect of increasing the model setup time and analysis time of model output results, and also makes it restrictive to perform sophisticated numerical modeling procedures (real time forecasting, sensitivity analysis etc.) that utilize extensive GIS data. These limitations can be offset to a large degree by developing an integrated software component that shares data between the (hydrologic) model and the GIS modules. We contend that the pre-requisite for the development of such an integrated software component is a “shared data-model” that is designed using an Object Oriented Strategy. Here we present the design of such a shared data model taking into consideration the data type descriptions, identification of dataclasses, relationships and constraints. The developed data model has been used as a method base for developing a coupled GIS interface to Penn State Integrated Hydrologic Model (PIHM) called PIHMgis.
A practical implementation of the timing attack

When the running time of a cryptographic algorithm is non-constant, timing measurements can leak information about the secret key. This idea, first publicly introduced by Kocher, is developed here to attack an earlier version of the CASCADE smart card(1). We propose several improvements on Kocher's ideas, leading to a practical implementation that is able to break a 512-bit key in few hours, provided we, are able to collect 300 000 timing measurements (128-bit keys can be recovered in few seconds using a personal computer and less than 10 000 samples). We therefore show that the timing attack represents an important threat against cryptosystems, which must be very seriously taken into account.
A Document Tagging Support System for Nursing Care Experts

We present the findings of an interdisciplinary project that implemented a document tagging support system for nursing care experts. We evaluate its performance and provide lessons learned. This project was particularly marked by a low inter-rater reliability of the document labels and use case understanding issues, but also of a good performance of a simple, BERT-based binary relevance approach.
Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images

Accurate segmentation of magnetic resonance (MR) images of the brain is of interest in the study of many brain disorders. In this paper, we provide a review of some of the current approaches in the tissue segmentation of MR brain images. We broadly divided current MR brain image segmentation algorithms into three categories: classification- based, region-based, and contour-based, and discuss the advantages and disadvantages of these approaches. We also briefly review our recent work in this area. We show that by incorporating two key ideas into the conventional fuzzy c- means clustering algorithm, we are able to take into account the local spatial context and compensate for the intensity nonuniformity (INU) artifact during the clustering process. We conclude this review by pointing to some possible future directions in this area.
The State of the Art in Locally Distributed Webserver Systems

The overall increase in traÆc on the World Wide Web causes a lengthening of client requests to popular Web sites, especially in conjunction with special events. Single node platforms that do not replicate information content cannot provide the needed scalability to handle large traÆc volumes and to match rapid and dramatic changes in the number of clients. The need to improve the performance of Web-based services has produced a variety of novel content delivery architectures. This paper will focus on locally distributed Web systems, where the server nodes reside at a single location. After years of proposals of new routing mechanisms, policies and system solutions (the rst dated back to 1994 when the NCSA Web site had to face the rst million of requests per day) many problems concerning multiple server architectures for Web sites have been solved. Other issues remain to be addressed especially at the network application level, but the principle techniques and methodologies for building scalable Web content delivery architectures placed in a single location are settled now. This paper classi es and describes main mechanisms to split the traÆc load among the server nodes, discussing both the alternative architectures and the load sharing policies. To this purpose, it focuses on architectures, internal routing mechanisms, and dispatching request algorithms for designing and developing scalable Web-server systems and identi es some of the open research issues associated with the use of distributed systems for highly accessed Web sites. Corresponding author: Philip S. Yu IBM T.J. Watson Research Center 30 Saw Mill River Road Hawthorne, NY 10532 Phone: (914) 784 7141 Fax: (914) 784 7455 E-mail: psyu@us.ibm. com
How to Measure the Killer Microsecond

Datacenter-networking research requires tools to both generate traffic and accurately measure latency and throughput. While hardware-based tools have long existed commercially, they are primarily used to validate ASICs and lack flexibility, e.g. to study new protocols. They are also too expensive for academics. The recent development of kernel-bypass networking and advanced NIC features such as hardware timestamping have created new opportunities for accurate latency measurements. This paper compares these two approaches, and in particular whether commodity servers and NICs, when properly configured, can measure the latency distributions as precisely as specialized hardware. Our work shows that well-designed commodity solutions can capture subtle differences in the tail latency of stateless UDP traffic. We use hardware devices as the ground truth, both to measure latency and to forward traffic. We compare the ground truth with observations that combine five latency-measuring clients and five different port forwarding solutions and configurations. State-of-the-art software such as MoonGen that uses NIC hardware timestamping provides sufficient visibility into tail latencies to study the effect of subtle operating system configuration changes. We also observe that the kernel-bypass-based TRex software, that only relies on the CPU to timestamp traffic, can also provide solid results when NIC timestamps are not available for a particular protocol or device.
Recurrent neural networks for polyphonic sound event detection

TAMPERE UNIVERSITY OF TECHNOLOGY Master‘s Degree Programme in Signal Processing PARASCANDOLO, GIAMBATTISTA: Recurrent neural networks for polyphonic sound event detection Master of Science Thesis, 66 pages November 2015 Major: Signal Processing Minor: Learning and Intelligent Systems Examiners: Tuomas Virtanen, Heikki Huttunen