Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal Strategies for Long-Term FPGA Resource Management

The deployment of increasingly large and capable FPGAs has motivated mechanisms for sharing them, but system support for FPGAs is not yet mature. Traditional scheduling algorithms do not account for the unique characteristics of FPGAs, leading to infeasible or inefficient allocations. We propose a novel scheduling policy, called Spatiotemporal FPGA Scheduling, that overcomes these challenges to achieve long-term target allocations by tracking and correcting deviations from targets across management time periods. Compared to traditional algorithms, Spatiotemporal FPGA Scheduling produces allocations that are up to 32% closer to targets, improves average throughput by up to 44%, and improves average FPGA utilization by up to 23%.
Laws of Human Memory

Despite the complexity of memory and its diverse manifestations in our daily lives, certain mnemonic e ﬀ ects appear to hold across a wide range of conditions. We identify the e ﬀ ects of recency, contiguity, similarity, primacy, and repetition as potential laws of memory, evaluating their explanatory scope and discussing their theoretical signiﬁcance. We show that apparent violations of these laws occur when di ﬀ erent e ﬀ ects come into conﬂict, as in the situation of opposing physical forces. We see the search for law-like phenomena as guiding the develop-ment and reﬁnement of integrative memory theories.
Machine learning based alignment of mass spectrometry data for improved clinical biomarker discovery

Challenges: To date, although few algorithms have been proposed to do so, there does not exist any generic methodological framework allowing for the alignment of several samples, in order to subsequently provide a robust identification transfer between samples. An innovation in this direction will therefore strongly impact the reproducibility of MS-based molecular profiling, and subsequently, clinical research tools.
Aircraft-Based Satellite Navigation Augmentation to Enable Automated Landing and Movement on the Airport Surface

This work identifies a series of shortcomings in conventional aircraft navigation systems. These shortcomings can be overcome by using satellite-based navigation systems; however, even state-of-the-art satellite-based systems are not well suited for operations like precision approach, landing, and taxiing. The proposed solutions require no new ground-based hardware; rather the proposed solutions require primarily software (and communication) upgrades be made to aircraft. The three major contributions of the thesis are: 1. A monitor to autonomously identify satellite clock faults on moving satellite receivers 2. A collaborative method to detect ionospheric activity that is potentially hazardous to aviation users of satellite navigation systems 3. A novel approach to estimating bounds on position error distributions in real time All three contributions have the potential to tighten the confidence intervals in satellite navigation position estimates, which could potentially enhance the efficiency of use of the airspace by operating aircraft closer together and allow them to fly routes that are closer to the optimal path than currently possible.
ARCTiC LawE: Armed robotic control for training in civilian law enforcement

Much of this thesis looked at performing a cogent literature review of exoskeletons to determine the state-of-the-art and to determine the remaining needs in exoskeletal design. The literature review of over 80 journals, allowed the researcher to determine the lack of research in upper body exoskeletons for training in civilian, military, and law enforcement personnel. Thus the genesis of the Armed Robotic Control for Training in Civilian Law Enforcement, or ARCTiC LawE, an upper body exoskeleton designed to assist civilian, military, and law enforcement personnel in accurate, precise, and reliable handgun techniques. This exoskeleton training utilizes a laser based handgun with similar dimensions, trigger pull, and break action to a Glock ® 19 pistol, common to both public and private security sectors. The project aims to train and test subjects with no handgun training/experience with the ARCTiC LawE, and without, and compare the results of accuracy, precision, and speed. Ultimately, the exoskeleton greatly impacts sensory motor learning and the biomechanical implications are confirmed via both performance and physiological measurements. The researchers believe the ARCTiC LawE to be a viable substitute for training with live fire hand guns to reduce the cost of training time and munitions and will increase accuracy and precisions for typical law enforcement and military live fire drills. Additionally, this project increases the breadth of knowledge for exoskeletons as a tool for training.
A multiple scattering theoretical approach to time delay in high energy core-level photoemission of heteronuclear diatomic molecules

We present analytical expressions of momentum-resolved core-level photoemission time delay in a molecular frame of a heteronuclear diatomic molecule upon photoionization by a linearly polarized soft x-ray attosecond pulse. For this purpose, we start to derive a general expression of photoemission time delay based on the first order time dependent perturbation theory within the one electron and single channel model in the fixed-in-space system (atoms, molecules and crystals) and apply it to the core-level photoemission within the electric dipole approximation. By using multiple scattering theory and applying series expansion, plane wave and muffin-tin approximations, the core-level photoemission time delay t is divided into three components, tabs , tpath and tsc , which are atomic photoemission time delay, delays caused by the propagation of photoelectron among the surrounding atoms and the scattering of photoelectron by them, respectively. We applied a single scattering approximation to tpath and obtained tpath(1)(k,θ) for a linearly polarized soft x-ray field with polarization vector parallel to the molecular axis for a heteronuclear diatomic molecule, where θ is the angle of measured photoelectron from the molecular axis. The core-level photoemission time delay t is approximated well with this simplified expression tpath(1)(k,θ) in the high energy regime ( k≳3.5 a.u.−1, where k is the amplitude of photoelectron momentum k ), and the validity of this estimated result is confirmed by comparing it with multiple scattering calculations for C 1s core-level photoemission time delay of CO molecules. tpath(1)(k,θ) shows a characteristic dependence on θ, it becomes zero at θ = 0°, exhibits extended x-ray absorption fine structure type oscillation with period 2kR at θ = 180°, where R is the bondlength, and gives just the travelling time of photoelectron from the absorbing atom to the neighbouring atom at θ = 90°. We confirmed these features also in the numerical results performed by multiple scattering calculations.
Understanding Signed Distance Fields

Ground-breaking hardware advances from the past decade or two have blurred the dividing line between the real and virtual worlds beyond recognition. Nowadays, one can scan a real-world object using a simple smartphone app and render it in a virtual scene, and even predict how a musical instrument will sound just from a 2D image of it. The inverse direction of this challenge has been even more dramatically revolutionized by the democratization of 3D printing technology, accessible today to many consumers.
Surgical Workflow Anticipation

As a non-robotic minimally invasive surgery, endoscopic surgery is one of the widely used surgeries for the medical domain to reduce the risk of infection, incisions, and the discomfort of the patient. The endoscopic surgery procedure, also named surgical workﬂow in this work, can be divided into diﬀerent sub-phases. During the procedure, the surgeon inserts a thin, ﬂexible tube with a video camera through a small incision or a natural oriﬁce like the mouth or nostrils. The surgeon can utilize tiny surgical instruments while viewing organs on the computer monitor through these tubes. The surgery only allows a limited number of instruments simultaneously appearing in the body, requiring a suﬃcient instrument preparation method. Therefore, surgical workﬂow anticipation, including surgical instrument and phase anticipation, is essential for an intra-operative decision-support system. It deciphers the surgeon’s behaviors and the patient’s status to forecast surgical instrument and phase occurrence before they appear, supporting instrument preparation and computer-assisted intervention (CAI) systems. In this work, we investigate an unexplored surgical workﬂow anticipation problem by proposing an Instrument Interaction Aware Anticipation Network (IIA-Net). Spatially, it utilizes rich visual features about the context information around the instrument, i.e., instrument interaction with their surroundings. Temporally, it allows for a large receptive ﬁeld to capture the long-term dependency in the long and untrimmed surgical videos through a causal dilated multi-stage temporal convolutional network. Our model enforces an online inference with reliable predictions even with severe noise and artifacts in the recorded videos. Extensive experiments on Cholec80 dataset demonstrate the performance of our proposed method exceeds the state-of-the-art method by a large margin (1 . 40 v.s. 1 . 75 for inMAE and 2 . 14 v.s. 2 . 68 for eMAE).
Investor Protections and Stock Market Participation: An Evaluation of Financial Advisor Oversight ∗

We examine a regulatory change in Canada that increased the oversight of ﬁnancial advisors in ﬁve of its ten provinces. This increased oversight of mutual fund dealers reduced households’ use of ﬁnancial advice and their mutual fund holdings. In lieu of mutual funds, households increased their cash holdings. The results are consistent with a decline in delegated investing caused by a negative shock to the supply of advice. The estimates suggest that having a ﬁnancial advisor is important in facilitating stock market participation. Investments and advisory channels not aﬀected by the regulation—direct equity and bond holdings and advisors aﬃliated with banks— show no eﬀects, reducing concerns about confounding economic and ﬁnancial market changes.
Post-combustion Capture Plants - Concept and Plant Integration

Amine-based post-combustion processes are discussed as one option for Carbon Capture and Storage (CCS), which can reduce the specific CO 2 emissions to less than 100g/kWh el . As a result, Hitachi Power Europe (HPE) has designed and constructed a mobile pilot plant to test the feasibility of this process at different sites of their co-operation partner Electrabel/GDF Suez and E.ON. HPE is using different amine-based solvents to develop strategies aiming at cost optimisation.
GIS-Based Approach for Municipal Renewable Energy Planning to Support Post-Earthquake Revitalization: A Japanese Case Study

Following a regional-level study conducted in Fukushima Prefecture, Japan (Wang et al., 2014 [ 1 ]), this paper presents an approach for municipal renewable energy planning and its experimental application in a Japanese municipality using a Geographic Information System (GIS). The proposed approach is comprised of local issue identification, renewable energy potential evaluation and visualization, site comparison and scenario analysis. GIS was used to analyze and visualize solar, wind and biomass (forest and agriculture residue) potential within Kawamata Town, Fukushima, Japan. According to local conditions, all potential sites were coded and then compared based on different criteria, such as solar radiation, wind speed, slope and land uses, among others. In the scenario analysis section, two scenarios, “renewable energy prioritized” and ”evacuation area prioritized”, were adopted and compared. The scenarios are altered in terms of placement and the number of renewable energy facilities inside and outside evacuation areas within the town. The results generated through the proposed approach can provide information on local potentials of renewable energy resources, as well as renewable energy development alternatives at the municipal level. They can be used in the interactive dialogue for the municipal renewable energy planning process, to help to fulfill the municipality’s post-earthquake energy developmental vision.
EXAMINING THE USE OF TRANSFORMATIVE PEDAGOGY IN TEACHING OF CIVIC EDUCATION IN SELECTED SECONDARY SCHOOLS IN NCHELENGE DISTRICT, ZAMBIA

This study examined the use of transformative pedagogy in Civic Education in selected secondary schools in Nchelenge district of Luapula Province focusing on teacher competency. The study was purely qualitative in which phenomenological research design was adopted. During data collection, individual interviews and focus group discussions were used as data collection instruments. Data was collected from three secondary schools in which a homogenous purposive sampling technique was employed to select 6 civic education teachers and 3 heads of Departments (HODs) for social sciences. When selecting 30 pupils that were taking Civic Education, convenient sampling techniques were employed. Individual interviews were conducted with teachers and HODs while focused group discussions were administered with pupils. All interviews were transcribed and were coded for themes. Analysis of the findings established that participants failed to link transformative pedagogy to civic education. Analysis showed that teachers were not competent enough in the use of transformative pedagogy implying that the practical part was left out during teaching and learning processes. In other words, hands-on was poor. The study established that despite being familiar with active methods that are learner-centred approaches, teachers still use traditional methods that fail to transform learners into socially analytical people instead widens the gap between school and community. The researcher recommends that reflective and objective teaching has to be implemented and emphasized, simply put the need for a change of teaching role from a teachingcantered to leaner-cantered’ active learning approach. Secondly, there is a need to develop teacher assessment capacity in a competency-based approach for transformative pedagogy.
M ETA - LEARNING AS L EARNING THE M ETA : A V IDEOGAME -T HEORETIC P ERSPECTIVE ON L EARNING TO L EARN

The field of meta-learning involves, not the training of a model for a particular task, but using training on a variety of related tasks to develop a model that transfers readily and generally to any of a set of similar tasks from some distribution. Similarly, top players of competitive videogames are required to contend with frequent updates or patches to their game of choice, and are expected to adapt to the changes quickly and without a significant decrease in skill. By considering each patch of a competitive game to be a separate task, drawn from a distribution describing versions thereof, the adaptation of players to new patches becomes analogous to the adaptation of models to new tasks. This paper seeks to describe the process of players adapting to updates of their games in the language of metalearning, and then to analyze that process to inspire future modifications to the meta-learning paradigm.
GCNSplit: bounding the state of streaming graph partitioning

This paper introduces GCNSplit, a streaming graph partitioning framework capable of handling unbounded streams with bounded state requirements. We frame partitioning as a classification problem and we employ an unsupervised model whose loss function minimizes edge-cuts. GCNSplit leverages an inductive graph convolutional network (GCN) to embed graph characteristics into a low-dimensional space and assign edges to partitions in an online manner. We evaluate GCNSplit with real-world graph datasets of various sizes and domains. Our results demonstrate that GCNSplit provides high-throughput, top-quality partitioning, and successfully leverages data parallelism. It achieves a throughput of 430K edges/s on a real-world graph of 1.6B edges using a bounded 147KB-sized model, contrary to the state-of-the-art HDRF algorithm that requires > 116GB in-memory state. With a well-balanced normalized load of 1.01, GCNSplit achieves a replication factor on par with HDRF, showcasing high partitioning quality while storing three orders of magnitude smaller partitioning state. Owing to the power of GCNs, we show that GCNSplit can generalize to entirely unseen graphs while outperforming the state-of-the-art stream partitioners in some cases.
Indexes for Efficient Search

In this picture, y∗ is outside Y1 (the magenta cell), thus yIVF ̸= y∗. There is no improvement in memory usage since we still need to store all target vectors (plus additional memory to store the codebook and the inverted file structure). However, runtime has improved to O(NIVFD) where NIVF is the expected size of the constrained search space. For instance, assuming uniform cluster sizes, IVF is KP times faster.
Homomorphic Encryption in Healthcare

Data security is often thought of as dealing with the secure transmission of information from point A to point B. We have discussed in class how encryption can be used to prevent adversaries from intercepting, understanding, and misusing the information we are trying to send. In these cases, we are comfortable with the recipient of the message being able to decrypt and use the original data or message we are trying to send. However, there are many cases where instead we would like to keep the underlying information private while still allowing the recipient to use the data for their own purposes. Take for example private healthcare data. Patient healthcare data is often highly confidential information that should not be disclosed to any third-party lest it be used in malicious ways. To protect against this, healthcare providers will often use an encryption scheme known as homomorphic encryption. This specific form of encryption allows certain computations to be performed on the encrypted data to yield new data such that any computation performed on the original decrypted data/message would have resulted in the same result as the decrypted result of the encrypted computation. More specifically, given an encryption function E, a decryption function D, some arbitrary (not necessarily arbitrary but we will talk about this later) calculation C, a private key k, and some message m:
There’s Always a Bigger Fish: A Clarifying Analysis of a Machine-Learning-Assisted Side-Channel Attack

Machine learning has made it possible to mount powerful attacks through side channels that are otherwise challenging to exploit. However, due to the black-box nature of machine learning models, these attacks can be difficult to interpret correctly. Models that simply find correlations cannot be used to analyze the various sources of information leakage behind an attack. This article highlights the limitations of relying on machine learning for side-channel attacks without completing a comprehensive security analysis. We show that a state-of-the-art website-fingerprinting attack powered by machine learning was only partially analyzed. Its authors were misled into believing their attack exploited a cache-based side channel when it actually exploited an interrupt-based side channel. We demonstrate this through a comprehensive analysis, in which we run controlled experiments to rule out alternative hypotheses about the attack’s primary source of leakage, and ultimately instrument the attack’s code to prove our hypothesis.
Review and analysis of the happy city situation and Identification its determinants in small urban spaces (Case: Ravansar Township)

Providing and promoting the level of happiness and vitality of citizens is one of the most important pillars of quality of life in urban spaces Which in recent decades, has attracted increasing attention from experts and urban planners and Has been raised as one of the most important goals of governments and the United Nations. The purpose of this quantitative and applied study has been done using a combination of descriptive-analytical and exploratory methods, is a study and analyze the situation of Shad city and identify its determinants in small urban spaces Which has been done as a case study in Ravansar city which is part of Kermanshah province. The statistical population of the study includes 7122 heads of households and among them with Using sample size calculation methods, 364 people were estimated as a statistical sample and the samples were selected completely randomly. The main research tool for collecting field data is a researcher-made questionnaire whose validity and reliability have been confirmed. The results showed that among the three indicators of a happy city (Social, physical and economic indicators) The average of social index with the value of 3.124 was significantly higher than the average of base (number 3) and The average of two physical and economic indicators respectively with values of 2.608 and 2.587, Have been significantly lower than the baseline mean. The results of the survey showed the general condition of the city happy the average calculated with the value of 2.788 Has been significantly below average. Regarding the determinants of a happy city, the results showed Due to the four identified key factors, 61.956% of the variance of the dependent variable has been explained. These factors and the percentage of variance of their specific value in order of importance are: Physical stability (21.041), Socio-cultural sustainability (16.4), Economic Stability (13.151) and Environmental sustainability (11.356). The identified factors seem to be a subset of the macro factor "integrated and efficient urban management" and Promoting this factor, as the main source of most policies and planning, can have a significant impact on the realization of a happy city in the study area.
Heterogeneous Supervised Topic Models

Abstract Researchers in the social sciences are often interested in the relationship between text and an outcome of interest, where the goal is to both uncover latent patterns in the text and predict outcomes for unseen texts. To this end, this paper develops the heterogeneous supervised topic model (HSTM), a probabilistic approach to text analysis and prediction. HSTMs posit a joint model of text and outcomes to find heterogeneous patterns that help with both text analysis and prediction. The main benefit of HSTMs is that they capture heterogeneity in the relationship between text and the outcome across latent topics. To fit HSTMs, we develop a variational inference algorithm based on the auto-encoding variational Bayes framework. We study the performance of HSTMs on eight datasets and find that they consistently outperform related methods, including fine-tuned black-box models. Finally, we apply HSTMs to analyze news articles labeled with pro- or anti-tone. We find evidence of differing language used to signal a pro- and anti-tone.